Application of embedded computer and improved genetic algorithm in the strategy of community of human destiny: the development of artificial intelligence in the context of Covid-19.

Through the COVID-19 epidemic in 2020, the society has deeply realized the inevitability and necessity of building a community that shares the future of mankind. In the face of severely complex international trends and domestic and international economic conditions, artificial intelligence plays an important auxiliary role in the regular prevention and management of COVID-19. In order to effectively correspond to the formalized extensional prevention and control theory, it is essential to use coordination models, rule systems, prevention and control mechanisms, and governance landscapes to build artificial intelligence corresponding systems. This article uses a basic genetic algorithm to realize the robot path plan. This mainly includes the establishment of environmental models, the discovery of chromosomes and the determination of coding methods, the selection and design of fitness functions, and related designs. This paper proposes a new adaptive adjustment mode based on the basic genetic algorithm, which improves the selection and mutation operation, and improves the optimization efficiency of the genetic algorithm. Building an artificial intelligence response system may face various technical risks and governance dilemmas. Only by improving the rule system of artificial intelligence, creating an epidemic prevention and control ecology, conserving the public spirit of the whole people, strengthening the governance of the source of crisis, and further improving the new momentum of economic and social development and public safety. The modernization of governance capabilities can better respond to the current complex situation.

Introduction

With the rapid development of computer science, sensor technology and other related fields, artificial intelligence technology is developing towards intelligence and diversification. With the development of artificial intelligence, the use of industrial fields has expanded, and the ability to interact with the external environment has also improved. The so-called path planning means that in a specific environment, according to the optimization index, while avoiding obstacles in the environment, the optimal or quasi-optimal path can be planned between the starting point and the target point. The shortest or shortest energy consumption path. In the face of a severely complex international pandemic and domestic and international economic situations, we must adopt a higher political stance, maintain a problem-oriented approach, and be mentally prepared for long-term changes in the external environment.

Normalizing epidemic prevention and control requires that epidemic prevention and control work be included in the daily work of social governance, from material storage, monitoring and early warning, disease diagnosis and treatment, crisis response, and aftermath treatment, and the epidemic management from a passive “impact-response” model to The prevention model changes, and the investigation and supervision of key links and key areas should be done before the epidemic has not occurred or spread, so as to control and eliminate the potential risks of major epidemic outbreaks in a timely manner. The extension of prevention and management, according to the classification, stratification and distribution of the treatment mechanism of infectious diseases, the need to establish and improve the main occurrence, on the basis of prevention and management, through the process of social governance, it becomes the usual crisis awareness and the ultimate Thinking. Carry out all staffing, use of materials, capital allocation, and project implementation, and form a huge social force against the epidemic in a short time.

Related work

In the early 1960s, Professor Holland of the University of Michigan and others conducted pioneer research on genetic algorithms. Singh et al. (2017) introduces the idea of biological evolution into actual engineering problems and optimizes them, thus becoming the research basis of genetic algorithms. In Li et al. (2018), an evolutionary programming scheme in finite state automatic design and manufacturing is proposed. Roy et al. (2017) applied this method to data diagnosis, image recognition, engineering and control system design, and achieved satisfactory results. Yu et al. (2017) believes that personal information cannot be used, but general methods cannot be used. In the text encoding and manipulation in the mid-1960s, instead of the text encoding in the late 1960s, Zhu and Pham (2018) emphasized the need to use the crossover operator as the main genetic factor operator. Ababneh and Bataineh (2008) Genetic algorithm is the iteration of generating new individuals from random, and the iteration algorithm is advocated. Stark and Paliwal (2008) turns an optimal individualization into the optimal individualization through optimized calculation method. Be the best solution or satisfactory solution. Buciu et al. (2003) proposed that before the genetic algorithm was proposed, there were many optimization algorithms to solve various optimization problems such as dynamic planning method and gradient method. Chen et al. (2016) believes that these algorithms are not only applicable to their own opinions and opinions, but also to their own opinions. Fuentes et al. (2017) proposed that genetic algorithm has been widely used in many fields for its simple idea, easy implementation and optimized performance. Karaboga (2009) proposes that in the past few decades, genetic algorithms have achieved excellent results in production scheduling, data mining, image processing, and function optimization, and have produced a very good effect on people's research results. In recent years, genetic algorithms have become more and more suitable for professional fields. Lucey et al. (2010) proposed that genetic algorithm is one of the solutions to complex multi-function programming problems, artificial life and other solutions.

Minsky and Papert (1969) believes that genetic algorithm is a potential solution set problem that must be solved for the group, and the group is coded into a specific number of individuals according to genetic factors. Nie et al. (2017) believes that the dwarf traits of pea and other personal characteristics are external manifestations (symptoms) determined by the combination of genetic factors present in the chromosomes. Peng et al. (2016) believes that the realization of a fully intelligent mobile robot is a human dream. Current research shows that it is impossible to develop mobile robots with complete autonomy in unknown or complex dynamic environments. However, with the development of science and technology, robots can increasingly replace humans such as military, agriculture, and industry. Soni et al. (2018) believes that the path planning of mobile robots is one of the important technologies for robot applications. Through high-quality path planning, it can greatly save the robot's operating time and machine wear during assembly, welding, and emergency rescue operations. Therefore, the effect of path planning is closely related to the quality of tasks that the robot can perform. More and more scholars and experts continue to devote themselves to the investigation and research of new path planning methods. Ranjan et al. (2019) believes that genetic algorithm is a parallel global search algorithm based on Darwin's natural law of survival of the fittest, by simulating genetic operations in nature. Tai and Chung (2007) proposed that genetic algorithm can perform multi-point parallel calculation in the parameter space at the same time, so it is very likely to converge to the global best value. On the other hand, Walecki (2017) proposes that there is no need for differential possibility and continuity in the exploration space, and there are disadvantages such as too low local optimization ability. Therefore, the Yang (2010) proposes that through further research and improvement of genetic algorithm, and combining it with other algorithms, better results can be achieved through path planning.

The development foundation of intelligent robot based on embedded computer and improved genetic algorithm

Basic principle of genetic algorithm

Genetic algorithm (GA) is a random repeat and evolutionary search method. The natural law and genetic principle of biology are introduced into the retrieval process. The idea of survival of the suitable person is to eliminate the inappropriate factors in the repeated process of the algorithm. The original idea of random information exchange of solution knowledge can effectively improve the retrieval speed of rhythm.

Coding design of genetic algorithm

Coding is the solution of optimization problem of coding problem. The relationship between coding space of genetic algorithm and solution space of optimization problem is established. This largely depends on the nature of the optimization problem. Moreover, it has a great influence on the genetic operation of each process. Here, several general coding methods are introduced. (1) The binary code of binary codec uses binary 0–1 and string to seek the solution of optimization problem. Then, the corresponding genetic factor operation is performed on the string. Gene manipulation is accomplished by decoding. The form may be the result of the solution space solution reducing the algorithm gain and then calculating the adaptive function value. (2) Decimal encoding is a solution to the problem using decimal strings 0–9. (3) Actual coding refers to the use of real numbers to represent problem solutions. Different from the above two codes, the actual code directly performs the relevant genetic factor operation on the problem-solving form. (4) Gray coding is also expressed in the form of converted binary code. From binary code to:

Initial population of genetic algorithm

In order to meet the needs of population genetic algorithm operation, it is necessary to prepare the initial population samples. The initial population consists of initial solutions of several problems. The larger the overall size is, the longer the evaluation time of the adaptability function is, and the greater the computational load of the algorithm is, which affects the efficiency of the algorithm.

Fitness function of genetic algorithm

The adaptive function is the standard to evaluate the quality of the solution obtained by genetic algorithm. That decision-making process needs to be combined with the problem. In general, expressions or other functions. The main basis of operation selection in the algorithm is the adaptive function value of each solution.

Selection operator of genetic algorithm

(1) Proportional selection method proportional selection method is a random sampling method based on adaptive function value. When the population size is h and the fitness value of zth individuals in the population is human, the probability of individual selection is as follows:

(2) Elite selection the basic idea of elite selection is to protect the individuals with the highest fitness function value in the population and directly replace the next generation without genetic factor operation. The best solution of one generation in the process of genetic factors can be directly inherited to the next generation without taking part in genetic factor operation, which is not damaged by crossover and mutation. (3) League selection method League selection method is similar to sports system. Usually, the size of the league is two. The selection method of the league is also based on the relationship between everyone's fitness. (4) The basic idea of expectation value method is as follows. First, we calculate the expected survival value of each person in the next generation population

Improvement of genetic algorithm

In addition, with the development of GA, people also choose some practical research methods. It can not only improve the search efficiency of GA, but also prevent the local optimal value from falling into. The formula is as follows:

Gray coding is to overcome the shortcomings of binary coding of Hamming cliff, through the transformation of binary coding. The conversion from binary code to Gray code is as follows:

Floating point coding is better than binary coding in many aspects, so it is a general coding method in the current use of genetic algorithm. Operators and operators are usually different. The two parent entities are a and B, respectively. For the random numbers between (0, 1), the new children generated after the crossover operation are a 'and b', respectively

Uniform variation is to randomly select a variation position in the parent individuals, assuming that it is the k-th one

Assuming that the uneven change is the parent, component v is used for the variation. Among them:

The specific expression is as follows:

Adaptive mutation defined the mutation temperature as follows:

The function expression is changed to:

There are linear rules and nonlinear rules for sequence selection. Simple sorting method:

The common nonlinear sorting methods are as follows:

The design of parameter r and the selection probability p obtained from it are as follows:

The definitions of adaptive crossover and mutation probability are as follows:

The improved genetic algorithm based on the above sorting selection is used to optimize the test function F6:

After 100 times of optimization, the results are shown in Table 1 (compared with general floating-point coding GA).

Table 1

Performance comparison between general floating point coding GA and improved GA

	Function	Threshold	Blocks	Average cutoff algebra	Maximum genetic algebra
Floating point encoding GA	F6	0.999	12	70.6	100
Sort GA			0	6.2	20

It can be seen from Table 1 that the improved genetic algorithm based on sorting selection can find all the best solutions within the set maximum genetic algebra, and has the strong ability to exceed the local extremum, and the convergence speed will be greatly accelerated.

Establishment of robot operating environment model

In this paper, the static environment of mobile robot is modeled by grid method. Because we know the environment, we know the number and location of obstacles. As SG, the robot workspace is set as 2D plane. The 2D plane on the upper left of SG adjusts the origin, the horizontal plane on the right adjusts the x-axis direction, and the vertical plane is set as the y-direction of the coordinate plane (Zeng et al. 2018). The mobile robot can move the maximum range of X and Y respectively in the horizontal direction and vertical direction XY. According to the obtained TLAB mode. Figure 1 shows a black obstacle grid and a white free grid.

Fig. 1

Environment model represented by grid method

In this paper, the grid is represented by the sequential method and the orthogonal coordinate method respectively. 1. Rectangular coordinate method. As shown in Fig. 1, the Cartesian coordinate system is established in SG space, and the grating width is unit length. The usual method. As shown in Fig. 2, each grid is numbered from 0 to 99 from left to right and 0 from top to bottom. Here, “0” represents the start and end points of the robot path.

Fig. 2

Correspondence between grid coordinates and serial numbers

In this figure, the coordinates corresponding to the grid and their sequence are mapped one by one. The Cartesian coordinates from ordinal to P are as follows:

Design of robot genetic algorithm path planning method

As shown in Fig. 3, in order to realize the realizable path of the mobile robot as an individual, when using orthogonal coordinates, such as {(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (2.5, 2.5), (3.5, 3.5), (5.5, 3.5), (6.5, 3.5), (7.5, 4.5), (7.5, 5.5), (8.5, 5.5), (8.5, 6.5), and (9.5, 7.5), (9.5, 8.5), (9.5, 9.5)}, which can be represented by grid:

Fig. 3

Route diagram

Because the coding length of sequence number method is short and the path individual is direct and clear, in this paper, in order to code the path individually, the usual method is used. This method is an initial path, no obstacle grid, but cannot guarantee the continuous path to achieve the goal, but the concept of discrete grid path is free. That is to say, from the starting point to the target point, from several random selection. Free mesh as node path (Wang et al. 2015). However, these grids may not be able to continuously connect the starting point and the target, but the flexibility of the algorithm is greatly improved. According to this idea, Fig. 3 shows a discontinuous free mesh path. The path code is [02434557688999].

The specific method of insertion operator is to fill in the free grid around the path break to realize the executable continuous path. Use the following formula to determine whether the sequence numbers of two adjacent grids are continuous (Fig. 4):

Fig. 4

Discontinuous white by grid path diagram

In the case of discontinuity, the average method is used to calculate the replacement mesh. The specific calculation is as follows:

In the path planning problem, the objective function is usually considered as the shortest path. In this paper, the individuals generated by initializing the population samples are all executable paths, so there is no need to set penalty functions for the non-executable paths to simplify the adaptive functions and speed up the implementation of the algorithm. The fitness function is set as follows:

Generate the non-executable path in basic genetic algorithm. Random variable is the most commonly used variable operation. However, even if several paths are feasible before the change operation, new change nodes will be generated in the obstacle grid after the change operation, as shown in Fig. 5, which may generate non executable paths. According to this, the optimization becomes much slower, and the number of subsequent operations will gradually increase.

Fig. 5

Schematic diagram of random variation operation

Research on these points has suddenly improved. One of the most common mutation operations is to investigate the feasibility of new mutant chromosomes. If that doesn’t work, the new stain will continue to mutate before producing an executable.

The new variable operation does not select the variable grid in the forward path direction, but determines the best path according to the matching value. Therefore, even if the variable grid is in the opposite direction, the selection of the variable grid with the best path can be ensured. As shown in Fig. 6, from the start point to the end point.

Fig. 6

The schematic diagram of mutation operation proposed in this paper

Refer to Fig. 6 as an example to explain the improved change process. Suppose each path, as shown by the solid red line in Fig. 1. 3.8 is [02477499], and the change position is randomly selected as grid number 47. The grid combination around 2.47 grid is {3637384648565758}, and grid number 3738 is obstacle grid. Therefore, in order to form a variable group, a = {364648565758} is excluded. 3. After judging the feasibility, select the feasibility change group B = {4648}. 4. Compare the compliance value of each executable path with that of the path before the variable. Finally, No.46 grid is selected as the variable grid to complete the variable operation. The red dotted line is used to represent the path after variable operation. Adaptive genetic algorithm uses the difference d between the best individual fitness value of the population and the average fitness value of each individual of the population to estimate the convergence of the population. The crossover probability and mutation probability are calculated as follows:

The calculation formula of standard deviation is as follows:

Intelligent algorithm has been widely used. The calculation method of probability formula is as follows:

The transmission probability p corresponding to the metropolitan standard is as follows:

The schematic diagram of improved genetic algorithm is shown in Fig. 7:

Fig. 7

Flow chart of improved genetic algorithm

Hardware design of mobile robot based on embedded computer

MCU based controller

MCU was used in the early industrial control field, MCU is also known as microcontroller. In order to minimize the computer system, MCU concentrates CPU and peripheral devices on a single chip. The microcontroller realizes the computer system integration chip. In other words, the microcontroller chip is a microcomputer, which can make the most of the advantages of the microcontroller under some more stringent control equipment requirements.

Controller based on embedded system

Compared with single chip microcomputer, embedded microprocessor is the core of embedded system. Generally, it has more powerful functions, faster computing speed and various interfaces than the traditional 8-bit single chip microcomputer (Peters et al. 2010). The multiflex2-pxa270 controller used in this white paper, as shown in Fig. 8, is a typical controller based on embedded system. This kind of robot controller has the following five characteristics: (1) the controller adopts multi task operating system, which can carry out multi task operation. (2) SD card storage, WiFi, Ethernet, USB and other advanced interface functions. (3) Compared with the general single-chip computer system, the controller based on embedded system has faster computing speed and can deal with highly complex tasks such as video codec and real-time sound processing. (4) As for power consumption, although it consumes more energy than single chip microcomputer, the power consumption is much lower than that of PC. (5) The real-time performance is usually lower than that of single chip computer system, but it is much higher than that of PC.

Fig. 8

Multiflex2-pxa270 controller

Muscle actuator of robot

As the action actuator of mobile robot, DC motor, stepping motor and steering device are usually used. Motor is the output device of robot control system. The output shaft of the motor is connected with the mobile device of the robot, and the motor rotates to drive the mobile mechanism to complete the action control of the robot.

Skeleton mechanical structure of robot

Robots can be classified into robots, bicycles, etc. According to the different structure of the control system, it can be divided into functional robot, action robot and two kinds of hybrid robot. According to different workspace, it can be divided into aerial UAV, underwater robot, land mobile robot and so on (Figs. 9, 10).

Fig. 9

Population evolution curve of basic genetic algorithm

Fig. 10

Population evolution curve of improved genetic algorithm

Simulation experiment and result analysis

This paper uses MATLAB to simulate. Three possibilities have been enabled and improved algorithms. Figure 1: comparison with the basic genetic algorithm. Figure 1, size: 10 * 10 grid start point: blue point end point: red point obstacle density: 22%. Compared with the basic genetic algorithm, the improved genetic algorithm and the basic genetic algorithm are compared between Figs. 1 and 2, and are used for 15 simulation rounds. The results are shown in Tables 2 and 3.

Table 2

15 simulation results of Map 1 improved genetic algorithm

Number of runs	Evolutionary algebra	Path length	Number of grids passed	Solving time (ms)
1	42	11.7727	5	768
2	36	11.7727	5	773
3	53	11.7727	5	802
4	47	11.9016	5	756
5	44	11.7727	5	739
6	42	11.7727	5	726
7	51	11.7727	5	836
8	45	11.7727	5	821
9	39	11.7727	5	769
10	41	11.7727	5	746
11	49	11.8651	5	805
12	46	11.7727	5	775
13	53	11.7727	5	761
14	46	11.7727	5	816
15	45	11.7727	5	732
	Average length	11.7850	Average solving time (ms)	775

Table 3

15 simulation results of Map 1 basic genetic algorithm

Number of runs	Evolutionary algebra	Path length	Number of grids passed	Solving time (ms)
1	86	12.4196	11	2685
2	93	12.5728	12	2873
3	87	12.4196	11	1980
4	96	12.4196	11	2386
5	79	12.4196	11	2794
6	88	12.5728	12	2286
7	99	12.4196	11	1680
8	91	12.9918	13	2969
9	85	12.4196	11	2651
10	80	12.5728	12	2565
11	76	12.4196	11	2731
12	97	12.4196	11	2856
13	92	12.9918	13	3218
14	83	12.4196	11	2693
15	87	12.4196	11	2901
	Average length	12.5265	Average solving time (ms)	2618

Comparing Tables 2 and 3, the improved genetic algorithm used to optimize the success rate is 86.7%, and the basic genetic algorithm used to optimize the success rate is only 66.7%. At the same time, the average time, population evolution algebra and optimal path length in the algorithm. In this article we suggest that the improved genetic algorithm is better than the basic genetic algorithm. Figures 11 and 12 show the optimization path and optimization plan.

Fig. 11

Path planning of basic genetic algorithm

Fig. 12

The genetic algorithm path planning diagram in this paper

The inevitability of building a community of shared future for mankind from the perspective of covid19 epidemic prevention and control

The novel coronavirus pneumonia epidemic is rampant around the world

Covid-19 popcorn, which attacked the world in 2020, has become the most serious public health emergency faced by human society since the war, even in the past century. Bread and snacks of unknown origin have swept the world for less than half a year without human preparation. Major infectious diseases, natural disasters, climate change, and other security threats different from before continue to expand. Human existence and instability are important factors of common existence.

Unity and mutual assistance are in the common interests of all mankind

Only the unity and mutual assistance of all mankind can overcome the new epidemic situation. The root cause lies in the common interests of the survival and development of all mankind to prevent and control the new epidemic. On the contrary, the spread of the new epidemic has proved the extreme importance of survival and development for mankind. “Cooperation and cooperation are the most powerful weapons for the international community to overcome emergencies. Only by abiding by multilateralism, promoting international cooperation, and building communities that share the future of humanity can we overcome and end the fight. Human beings have rescued the new virus from human colonies. In the current battle, we have seriously summarized and profoundly reflected on disease resistance, especially the necessity of international public health resources, as well as the status quo of the delayed development of the world infectious disease defense mechanism, “sound long-term financing mechanism, global public health security monitoring and early warning, and joint response mechanism”, and established the resource reserve and resource composition system and its management mechanism He cooperates with the “threat mechanism” to eliminate potential threats and viruses.

The community of shared future for mankind is China's wisdom and China's plan for realizing sustainable development of mankind

For the current global epidemic, the Chinese government has pointed out the following root causes. Based on the principles of integration, equality and mutual respect and cooperation, mankind should overcome the current and future threats to human security and public health disaster development, protect regional and world public health security, establish a sound, efficient and sustainable world public health system and promote buildings in human health communities, which are also an important part of the community of human destiny. Now the facts have proved once again that without peaceful development, the two sides cannot have advantageous cooperation. If we cannot stop the spread of the new epidemic and the recession of the world economy, mankind will suffer more suffering and losses. The concept and practice of the community of common destiny for mankind is more significant than ever before, and it is also a broad road for human beings to realize sustainable development.

Application and development strategy of artificial intelligence under the background of covid19 normalization prevention and control

Strengthen the top-level design and improve the rule system

When the extension of prevention and control is normalized, AI response not only conforms to the technical ethics, efficiency standards and intelligence level of AI, but also conforms to the market mechanism, social mechanism and independent mechanism of prevention and control extension. The rules need to grasp the transformational relationship between crisis and opportunity, and to prevent and control the popularity of dynamic “variables” through the economic and social development, prevention and control methods, and the sharing and utilization of digital power among certain dialects. Specific definitions such as definition, power restriction and tolerance spirit are needed to prevent artificial intelligence from being positive in prevention, expansion and abuse control the responsibility of realizing the unity of disease resistance performance, technical rationality and value rationality begins with the public power. First, to improve the framework rules is to improve the basic rules related to artificial intelligence response measures and epidemic prevention and control, involving basic issues such as guiding ideology, basic principles, organizational framework, application fields and implementation mechanism. Second, to improve the technical rules is to improve the rules on how to design, apply and manage artificial intelligence, which can be divided into technical basic rules, technical support rules and technical application rules, from the review of artificial intelligence, the design review of intelligent prevention and control scheme, the requirements and allocation of technical equipment functions in the construction process, to the evaluation in the acceptance stage and the scientific management implementation in the application stage Process construction. Third, the purpose of improving the adjustment rules is to improve the security inspection rules, information protection rules, and artificial intelligence ethics rules by mediating the relationship between artificial intelligence and external environment, so as to prevent and avoid potential dangers and traps.

Explore self-empowerment and create prevention and control ecology

The leading role of the government in promoting artificial intelligence applications and its process cannot be weakened, but special attention should be paid to the establishment of industry organizations, network operators, public consultation and cooperation and partnership. It is a network-based platform management system, which absorbs the infection of various code restrictions, important resources, main responsible persons, important data collection and other main fields. First, governments at all levels need to make efforts to promote open-door legislation, study, judge and feedback the epidemic situation and public opinion with the powerful information collection and screening ability of artificial intelligence, timely regulate, guide and correct the value preference and behavior of endangering public order by using the epidemic situation, so as to effectively create an open and transparent responsible government image. Second, through the power of technology, we should increase the weight of social governance such as social coordination and citizen participation, so as to make the responsibility and power of prevention and management more peaceful and decentralized. Through the information network platform, create the “e-government” management services of the actual unit and virtual unit, including paper, collective independent entity unit, namely community, basic unit division disease, and taking the masses as the core. The use of wechat group, QQ group, Weibo, e-government and public “platform”, the unique advantages of flexible and convenient power difference control and precision control, etc., to establish an internal mechanism of interest balance. Third, by integrating all kinds of big data such as transportation, telecommunications, people's livelihood and public opinion among Internet enterprises, government departments and levels, we can track common information, prevent the allocation of resources and power according to the principle of greatest common divisor, and create new infrastructure for the contract and separation between government and society. Fourth, through artificial intelligence, the government and society can create equal information rights, equal data rights, equal technology rights of prevention and management ecology, so that people can understand, assist and support the government's epidemic prevention and management measures. Moreover, in order to achieve this goal, it is even necessary to transfer the rights and establish the regression relationship between the government and the society.

Pay attention to technology to be good and cultivate public spirit

The first is to abide by technical ethics and build a self-discipline mechanism. Through moral education to enhance the awareness of social responsibility of intelligent prevention and control personnel, scientific set the threshold for intelligent prevention and control enterprises, and implement credit evaluation system for employees. Second, strengthen the construction of legal system and clarify the red line of governance. According to the classification of classified data, it is necessary to coordinate different levels of information platforms from the perspective of national security to ensure that relevant data are used for public security. It is necessary to set up a clear data forbidden zone to prevent illegal exercise of private rights and illegal performance of public rights. In addition to government purchase, it is strictly prohibited to trade data for epidemic prevention and control, so as to prevent personal privacy and public rights from being infringed, so as to provide sufficient social confidence for the orderly flow of data and the construction of digital prevention and control ecology. Third, break down information barriers and give full play to sharing advantages. Establish a data collaboration platform between the state and the local government under the emergency state, and promote the data fusion and association by the top-level design.

Adhere to bottom line thinking and strengthen source governance

In order to prevent and manage emergencies on a regular basis, it is necessary to give full play to the initiative and foresight of AI, and strengthen and solve the fundamental and fundamental problems that may occur in the research of emergency and progress mechanism and secondary disaster derivative rules. The main reason is the lack of knowledge about unknown virus and inadequate management means. The prevention and management system characterized by huge data, micro data and data processing holds the leading power of prevention and management.

Conclusion

The use of a new generation of artificial intelligence provides regular services for prevention and control extension and recovery of work and production, which is a practical application of artificial intelligence in grassroots social governance. In essence, this is a great change from “overall management” to “technical management”. Through the establishment of a sound system, scientific norms, effective and intelligent gabanance platform, promote scientific prevention and management, correct management. In the usual plot prevention and management, we need to balance the relationship between science and democracy, technology and governance, technology and politics, and expand and deepen the application of artificial intelligence on the track of rule of law.