A prevention and control resource scheduling method based on a group dynamic deduction mechanism

Abstract

This invention discloses a medical prevention and control resource scheduling method based on a population dynamic deduction mechanism, relating to the field of medical prevention and control resource scheduling. The problem it addresses is that, in areas with high infectiousness outbreaks, resources need to be rationally allocated to each node on a daily basis, while satisfying resource balance constraints and node resource demand constraints, thereby minimizing the amount of prevention and control resources used, the duration of prevention and control efforts, and the number of infections. Regarding this problem, this invention proposes a method based on a population dynamic deduction mechanism, encompassing the following aspects: by integrating the continuous improvement mechanism of population dynamic deduction and matrix sequence encoding of resources, the characteristics and model of population dynamic deduction are abstracted. Based on this model, this invention proposes an algorithm to handle the medical prevention and control resource scheduling problem. This algorithm design includes individual self-deduction and collaborative deduction based on population dynamic deduction, continuous improvement processing, and matrix sequence encoding.

Description

Technical Field

[0001] This invention relates to the field of medical prevention and control resource allocation, and specifically to a prevention and control resource allocation method based on a population dynamic evolution mechanism. Background Technology

[0002] The problem of allocating resources for epidemic prevention and control is one of the major challenges facing today's healthcare systems, involving how to efficiently allocate limited medical resources to meet the needs of patients. The epidemic prevention and control resource allocation problem is an NP-hard (nondeterministic polynomial-time) problem, and methods for solving such problems are mainly divided into approximation algorithms and heuristic algorithms.

[0003] Approximation algorithms are designed for certain computational problems, especially those where finding an exact solution is difficult. The main goal of these algorithms is to find a feasible solution close to the optimal solution within a reasonable time. Approximation algorithms are commonly used to solve combinatorial optimization problems, such as the Traveling Salesman Problem, the Knapsack Problem, and the Graph Coloring Problem. These problems are characterized by a huge solution space, making exhaustive search virtually impossible.

[0004] Heuristic algorithms do not guarantee finding a near-optimal solution, but rather seek a "good enough" solution by leveraging the specific properties and structure of the problem. These algorithms rely on rules of thumb and heuristic strategies, aiming to explore the solution space through intelligent search. When faced with complex problems, heuristic algorithms can quickly converge to an acceptable solution. Summary of the Invention

[0005] This invention provides a method for scheduling medical prevention and control resources based on a group dynamic deduction mechanism. The method includes a continuous improvement mechanism for group dynamic deduction, individual self-deduction and collaborative deduction, and matrix sequence encoding.

[0006] To address the aforementioned technical problems, this invention provides a method for scheduling medical prevention and control resources based on a population dynamic deduction mechanism. This method includes the following steps:

[0007] Step 1: Define the dynamic spatiotemporal contact network of the contacts, assuming each individual is a node, and determine the activity space of each node as it changes over time;

[0008] Step 2: Matrix sequence encoding; the nodes in the dynamic spatiotemporal contact network and the prevention and control materials are the two dimensions of the matrix, and the allocation quantity of materials is the element value in the matrix sequence encoding. The entire sequence is arranged sequentially according to the prevention and control cycle T.

[0009] Step 3: Read geographic information grid and prevention and control resource information;

[0010] Step 4: Set the population size to P, and generate a population P containing |P| individuals based on the initial solution. Each individual in the population corresponds to a matrix sequence encoding.

[0011] Step 5: Calculate the objective functions for the minimum amount of prevention and control resources used, the minimum duration of prevention and control, and the minimum number of infections for each individual;

[0012] Step 6: Set the iteration number i = 1 and begin the iteration;

[0013] Step 7: Perform individual self-deduction operation: randomly select a segment of the matrix sequence encoding corresponding to the individual for update, and execute the process α times;

[0014] Step 8: Perform individual cooperative deduction operation: randomly select two individuals, and swap a segment of the matrix sequence encoding corresponding to the two individuals. The process is executed β times.

[0015] Step 9: Provide new individuals based on the continuous improvement mechanism of dynamic deduction of the population; calculate the objective function of each individual, sort the individuals according to the results, eliminate the last individual, introduce new individuals, and repeat the process a set number of times;

[0016] The introduction of new individuals includes individual self-deductive operation and / or individual collaborative deductive operation and / or group dynamic deductive operation; the group dynamic deductive operation specifically involves randomly selecting T individuals, selecting segments from these T individuals, ensuring that the segments selected by each individual do not overlap in the sequence, and then combining the T segments to form a new individual;

[0017] Step 10: i++, restart execution from step 7;

[0018] Step 11: If the iteration number i = τ, where τ represents the maximum number of iterations, then stop the calculation; otherwise, go to step 7.

[0019] Step 12: Use the final resource allocation plan to schedule medical prevention and control resources.

[0020] Furthermore, the geographic information grid includes the contact time and closeness between nodes, and the prevention and control resource information includes the total number of resources, the maximum number of resources that each node can be allocated, and the total number of nodes.

[0021] Furthermore, the specific process for generating the initial solution is as follows:

[0022] First, input the node set, consumable material set, occupied material set, and prevention and control cycle set, with the prevention and control cycle being in days for a single cycle. Then, for the nodes in the current cycle, calculate the quantity of consumable materials and occupied materials to be allocated. If all nodes have recovered, there is no need to allocate materials anymore; otherwise, allocate random types and quantities of epidemic prevention materials for each node and increase the duration of prevention and control. Finally, return the epidemic prevention resource allocation result as the initial solution.

[0023] Furthermore, the specific steps of the individual self-deduction operation are as follows:

[0024] Step 7.1: Randomly select an individual p from the population Ρ.

[0025] Step 7.2: Randomly select the nth row and mth column in the matrix sequence encoding of individual p; 0 < n < total number of prevention and control resources, 0 < m < total number of nodes.

[0026] Step 7.3: Recalculate the quantity of consumable materials and occupied materials to be allocated for the segment composed of m * n.

[0027] Step 7.4: Replace the original segment of m * n with the newly generated segment of m * n to form a new individual p'.

[0028] Step 7.5: Calculate the objective function of the new individual p'. If the objective function of p' can dominate the scores of some individuals in the population P, then replace the worst one of these individuals with p'; otherwise, repeat the self-deduction operation until a new individual p' that is better than some individuals in the population P is found, and replace the worst one of these individuals with p' or reach the upper limit of the execution times.

[0029] Step 7.6: Repeat steps 7.1 - 7.5 for α times.

[0030] Furthermore, the specific steps of the individual collaborative deduction operation are as follows:

[0031] Step 8.1: Randomly select two individuals q1 and q2 from Ρ.

[0032] Step 8.2: Randomly select the number of rows a (0 < a < total number of prevention and control resources) and the number of columns b (0 < b < total number of nodes) in the matrix sequence encoding.

[0033] Step 8.3: Exchange the segments composed of a * b in the matrix sequence encodings of q1 and q2 to form two new individuals q1' and q2'.

[0034] Step 8.4: Calculate the objective functions of the new individuals q1' and q2'. If the objective functions of q1' and q2' can dominate the scores of some individuals in the population P, then replace the worst individual among these individuals with q1' and q2' respectively. Otherwise, repeat the co-deduction operation until a new individual q' that is better than some individuals in the population P is found, and q' replaces the worst individual among these individuals, or the maximum number of executions is reached.

[0035] Step 8.5: Repeat steps 8.1-8.3 β times.

[0036] Furthermore, step 9 is detailed as follows:

[0037] Step 9.1: Let r = i*θ / (α+β), where θ is a parameter that is manually adjusted. If r is an integer and the number of individuals in the population is greater than the initial number P, then execute steps 9.2 to 9.10, that is, remove the worst individual and add a new individual.

[0038] Step 9.2: Calculate the objective function for each individual in population P, and sort each individual from high to low according to the quality of the objective function;

[0039] Step 9.3: Remove r individuals from the end of the sorted population set P;

[0040] Step 9.4: Generate r / 2 new individuals using individual self-deduction and individual cooperative deduction, and add them to the population P;

[0041] Calculate the objective function and rank each individual from highest to lowest according to the quality of the objective function; Step 9.6: Perform population dynamics deduction on population P;

[0042] Step 9.7: Starting from the first individual in the sorted population set P, select T individuals in sequence;

[0043] Step 9.8: For the T individuals in the dynamic deduction of the population, select a subsequence from the matrix sequence encoding of each individual in order, that is, the first individual takes the 1 / T subsequence, the second individual takes the 2 / T subsequence, and so on, the λth individual takes the λ / T subsequence, and merge the T subsequences to form a new individual t;

[0044] Step 9.9: Repeat step 9.8 r / 2 times;

[0045] Step 9.10: Add the newly generated r / 2 individuals to the population P.

[0046] The beneficial effects of this invention are as follows:

[0047] Compared to traditional population evolution calculation methods, this invention mainly improves the speed and quality of finite resource scheduling optimization algorithms.

[0048] In terms of speed: This invention significantly improves the algorithm's computational speed and increases the frequency of population iteration through innovative non-dominated solution set screening and sorting methods. Simultaneously, it introduces a dynamic population deduction mechanism that continuously introduces excellent individuals and eliminates inferior ones, effectively preventing the algorithm from prematurely converging to local optima, thereby enhancing the overall performance of the algorithm. Furthermore, compared to traditional multi-objective optimization algorithms such as NSGA-II, SPEA-II, and MOEA / D, this algorithm exhibits significantly lower time complexity.

[0049] Regarding quality: This invention designs a novel algorithm—the Population Dynamic Deductive Algorithm—incorporating individual self-deduction and collaborative deduction, a continuous improvement mechanism, and matrix sequence encoding. Employing a population dynamic deductive approach, the algorithm continuously optimizes and supplements control resources through a continuous improvement mechanism, enabling rapid iteration. It constantly discards lower-quality solutions and introduces high-quality gene fragments to provide the impetus for population deduction, maintaining population diversity and innovation. The resulting Parate solution set also demonstrates significant advantages. Attached Figure Description

[0050] Figure 1 This is a flowchart of the population deduction mechanism process of the method of the present invention.

[0051] Figure 2 This is a schematic diagram of the matrix sequence encoding method of the present invention.

[0052] Figure 3 This is a flowchart of the initial solution generation process of the method of the present invention.

[0053] Figure 4 This is a schematic diagram of the self-deduction mechanism in the group deduction mechanism of the present invention.

[0054] Figure 5 This is a schematic diagram of cooperative deduction in the group deduction mechanism of the method of the present invention.

[0055] Figure 6 The graph shows the IGD index of the algorithm of this invention compared with other algorithms.

[0056] Figure 7 The HV index line chart compares the algorithm of this invention with other algorithms. Detailed Implementation

[0057] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The following embodiments or drawings are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0058] The resource scheduling method for epidemic prevention and control based on a population dynamic deduction mechanism includes the following: The population dynamic deduction mechanism proposed in this invention requires the entire population to continuously perform self-deduction and cooperative deduction, and each deduction will generate different types of high-quality individuals in the population. In order to further accelerate the population convergence speed and obtain higher-quality individuals, a continuous improvement mechanism is also required. First, a portion of relatively inferior individuals in the population are eliminated, and then a matrix subsequence is extracted from each of the remaining high-quality individuals to form new individuals added to the population. This ensures that the encoding of high-performing individuals is extracted, so that the population continuously generates higher-quality individuals in the iteration.

[0059] Conventional multi-objective optimization algorithms such as NSGA-II and MOEA / D suffer from insufficient evolutionary momentum, slow convergence speed, and insufficient solution diversity. In contrast, the population dynamic evolutionary mechanism of this invention promptly eliminates solutions with insufficient potential and supplements them with new solutions containing superior gene fragments. Furthermore, a continuous improvement mechanism ensures population diversity, maximizing the retention of high-performing individuals while simultaneously introducing high-quality individuals to enrich the population's diversity. This fast and sensitive mechanism achieves rapid convergence.

[0060] This invention provides a method for scheduling medical prevention and control resources based on a population dynamic evolution mechanism. Figure 1 The flowchart of the population deduction mechanism process of the method of the present invention is as follows: Figure 1 As shown, the method includes:

[0061] Step 1: Define the dynamic spatiotemporal contact network of the contacts and determine the activity space of each node (i.e., individual) as it changes over time;

[0062] Step 2: Matrix Sequence Encoding; A matrix is ​​used to represent the nodes in the dynamic spatiotemporal contact network, with prevention and control materials serving as the two dimensions of the matrix. The quantity of materials allocated is used as the element value in the matrix sequence encoding. The entire sequence is arranged sequentially according to the prevention and control cycle T; for example... Figure 2 The diagram illustrates the matrix sequence encoding method of this invention. To ensure population diversity, the initial population of the population dynamic deductive algorithm is obtained through random generation. Correspondingly, in matrix sequence encoding, this means randomly generating element values. In the matrix sequence, the horizontal axis of each matrix represents the individual ID, the vertical axis represents the control resources, and the element value is the specific allocation number of each resource. The entire sequence is arranged sequentially according to the control cycle.

[0063] Step 3: Read the geographic information grid and prevention and control resource information. The geographic information grid includes the contact time and closeness between nodes, and the prevention and control resource information includes the total number of resources, the maximum number of resources that each node can be allocated, and the total number of nodes.

[0064] Step 4: Set the population size as P, and generate a population P containing |Ρ| individuals according to the initial solution generation operator. The encoding of each individual in the population corresponds to a matrix sequence encoding; as Figure 3 shown in the flowchart of the initial solution generation process of the method of the present invention. First, it is necessary to input the node set, the consumable material set, the occupied material set, and the prevention and control cycle set, and use days as the unit for a single prevention and control cycle. Then, for the nodes in the current cycle, calculate the quantity of consumable materials and occupied materials that need to be allocated. If all nodes have recovered, there is no need to allocate materials anymore. Otherwise, it is necessary to allocate random types and random quantities of epidemic prevention materials for each node and increase the prevention and control duration. Finally, return the epidemic prevention resource allocation result as the initial feasible solution.

[0065] Step 5: Calculate the objective functions of the minimum prevention and control resource usage, the minimum prevention and control duration, and the minimum number of infected people for each individual;

[0066] Step 6: Let i = 1;

[0067] Step 7: Implement the individual self-deduction operation: randomly select a certain segment of the matrix sequence encoding corresponding to the individual for update, and the process is executed α times;

[0068] Step 7.1: Randomly select an individual p from the population Ρ;

[0069] Step 7.2: Randomly select the nth row and the mth column in the matrix sequence encoding of the individual p; 0 < n < the total number of prevention and control resources, 0 < m < the total number of nodes;

[0070] Step 7.3: Recalculate the quantity of consumable materials and occupied materials that need to be allocated for the segment composed of m * n;

[0071] Step 7.4: Replace the original segment of m * n with the newly generated segment of m * n to form a new individual p';

[0072] Step 7.5: Calculate the objective function of the new individual p'. If the objective function of p' can dominate the scores of some individuals in the population P, then replace the worst one of these individuals with p'. Otherwise, repeat the self-deduction operation until a new individual p' that is better than some individuals in the population P is found, and replace the worst one of these individuals with p' or reach the execution times limit;

[0073] Step 7.6: Repeat Step 7 α times.

[0074] Figure 4This is the self-deduction schematic diagram in the group deduction mechanism of the method of the present invention. Through self-deduction of the individual matrix sequence coding, new features are introduced or existing features are enhanced. This operation can enable individuals to have more possibilities during the search process, thereby improving the global search ability of the algorithm. The self-deduction operation can be carried out according to a preset probability or can be adaptively adjusted according to the fitness of the individual in the population.

[0075] Step 8: Implement the individual collaborative deduction operation; randomly select two individuals, and exchange a certain segment of the corresponding matrix sequence coding of the two individuals, and the process is executed β times;

[0076] Step 8.1: Randomly select two individuals q1 and q2 from Ρ;

[0077] Step 8.2: Randomly select the number of rows a (0 < a < total number of prevention and control resources) and the number of columns b (0 < b < total number of nodes) in the matrix sequence coding;

[0078] Step 8.3: Exchange the segment composed of the a*b in the matrix sequence coding of q1 and q2 to form two new individuals q1' and q2';

[0079] Step 8.4: Calculate the objective functions of the new individuals q1' and q2'. If the objective functions of q1' and q2' can dominate the scores of some individuals in the group Ρ, then use q1' and q2' to replace the worst one of these individuals respectively. Otherwise, repeat the collaborative deduction operation until new individuals q' better than some individuals in the group Ρ are found, and use q' to replace the worst one of these individuals or reach the upper limit of the execution times;

[0080] Step 8.5: Repeat Step 8 for β times.

[0081] Figure 5 This is the collaborative deduction schematic diagram in the group deduction mechanism of the method of the present invention. By combining the excellent features of two different individuals, new individuals with higher fitness are generated. The collaborative deduction operation can increase the diversity in the population and make it easier for the algorithm to jump out of the local optimal solution during the search process.

[0082] Step 9: Provide new individuals based on the continuous improvement mechanism of group dynamic deduction; calculate the objective function of each individual, sort the individuals according to the results, eliminate the last individual, introduce new individuals, and repeat for a set number of times;

[0083] The introduction of new individuals includes individual self-deduction operation or / and individual collaborative deduction operation or / and group dynamic deduction; the specific group dynamic deduction operation is to randomly select T individuals, select segments for these T individuals, and the sequences where the segments selected by each individual are not overlapping. After obtaining T segments, they are combined to form new individuals;

[0084] Step 9.1: Let r = i*θ / (α+β), where θ is a parameter that is manually adjusted. If r is an integer and the number of individuals in the population is greater than the initial number P, then execute steps 9.2 to 9.10, that is, remove the worst individual and add a new individual.

[0085] Step 9.2: Calculate the objective function for each individual in population P, and sort each individual from high to low according to the quality of the objective function;

[0086] Step 9.3: Remove r individuals from the end of the sorted population set P;

[0087] Step 9.4: Generate r / 2 new individuals using individual self-deduction and individual cooperative deduction, and add them to the population P;

[0088] Step 9.5: Repeat step 9.2;

[0089] Step 9.6: Perform population dynamics deduction on population P;

[0090] Step 9.7: Starting from the sorted population set P, select T individuals in order;

[0091] Step 9.8: For the T individuals in the dynamic deduction of the population, select a subsequence from the matrix sequence encoding of each individual in order, that is, the first individual takes the 1 / T subsequence, the second individual takes the 2 / T subsequence, and so on, the λth individual takes the λ / T subsequence, and merge the T subsequences to form a new individual t;

[0092] Step 9.9: Repeat step 9.8 r / 2 times;

[0093] Step 9.10: Add the newly generated r / 2 individuals to the population P;

[0094] Step 10: i++, restart execution from step 7;

[0095] Step 11: If the number of iterations i = τ, where τ represents the maximum deductive algebra, then stop the calculation; otherwise, go to step 7.

[0096] Step 12: Use the final resource allocation plan to schedule medical prevention and control resources.

[0097] Figure 6 , Figure 7Line graphs comparing the IGD and HV metrics of the proposed algorithm with other algorithms are shown, illustrating the changes in IGD and HV metrics of the solution sets for five algorithms: SDA-DTEM (the proposed method), NSGA-II, MOEA / D, FP-GA, and SPEA-II, over the number of iterations. IGD and HV are two performance metrics that evaluate algorithms by comparing solution set coverage. IGD evaluates the solution set by comparing the distance between the solution set and the Pareto front. The average distance from each Pareto front reference solution to the nearest solution in the solution set is calculated, thus evaluating not only the convergence but also the diversity of the solution set. HV is the hypervolume of the union of the regions enclosed by the solution set and the reference point in the target space. The reference point is generally chosen as the point furthest from the Pareto front, i.e., the point composed of the range values ​​of each target. The wider the coverage of the solution set, the better the convergence and diversity. During the algorithm iteration process, every 200 iterations, all non-dominated solution sets obtained by the algorithms are extracted for metric analysis. Overall, for the IGD metric, the SDA-DTEM and NSGA-II algorithms show smoother and more stable performance compared to other algorithms, but the SDA-DTEM algorithm outperforms the NSGA-II algorithm before 500 iterations. For the HV metric, the method provided by this invention is clearly more stable.

[0098] Table 1 shows the C-metric values ​​between the method of this invention and other reference algorithms.

[0099]

[0100]

[0101] Table 1 shows the C-metric solution set coverage of the swarm dynamic deductive algorithm and five other algorithms. The C-metric is an indicator for comparing the quality of two solution sets; it calculates the proportion of solutions in one solution set that are dominated by at least one solution in the other solution set, hence it is also called solution set coverage. Simulated test cases are numbered ExMy-z, where x is the total number of people affected by the epidemic, y is the number of days of epidemic prevention, and z is the index of the simulated test cases of the same size. The value of x can be any positive integer, and the value of y can be 7, 14, or 21. Each combination of x and y has ten test cases, meaning the value of z can be 1-10. For example, E50M7-1 represents a test case with a total number of 50 people affected by the epidemic and 7 days of prevention. In each test case, the swarm dynamic deductive algorithm's C-metric value is significantly better than nearly half of the other algorithms, with many C-metric values ​​equal to 1, indicating that every solution in the solution set obtained by the other algorithms is dominated by at least one solution in the solution set obtained by the swarm dynamic deductive algorithm. Conversely, nearly half of the C-metric values ​​of the other algorithms are significantly close to zero compared to the swarm dynamic deductive algorithm, and some are equal to zero. Clearly, the swarm dynamic deductive algorithm dominates the other four algorithms by a much higher percentage than the other four algorithms dominate the swarm dynamic deductive algorithm. The results indicate that the swarm dynamic deductive algorithm outperforms the other four algorithms in terms of convergence.

[0102] Table 2 shows the IGD and HV values ​​of the method of this invention and other reference algorithms.

[0103]

[0104]

[0105] Table 2 shows the IGD and HV values ​​of the method of this invention and other reference algorithms on different use cases. The simulated use cases are the same as those in Table 1. The IGD and HV values ​​of the Population Dynamic Deduction Algorithm are optimal in some use cases. Overall, NSGA-II's index values ​​are second best, although it performs very well in the IGD index. Next, it occasionally shows the best performance in certain use cases. Compared to other algorithms, FP-GA performs the worst in both the IGD and HV indices.

[0106] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A method for scheduling medical prevention and control resources based on a population dynamic evolution mechanism, characterized in that, Include the following steps: Step 1: Define the dynamic spatio-temporal contact network of contacts. Set each individual as a node and determine the activity space of each node that changes over time. Step 2: Matrix sequence encoding. The nodes in the dynamic spatio-temporal contact network and the prevention and control materials are used as the two dimensions of the matrix, and the allocated quantity of materials is used as the element value in the matrix sequence encoding. The entire sequence is arranged in sequence according to the prevention and control period T. Step 3: Read the geographical information grid and prevention and control resource information. Step 4: Set the population size to P, and generate a population P containing |Ρ| individuals based on the initial solution. Each individual in the population corresponds to a matrix sequence encoding. Step 5: Calculate the objective functions of the minimum prevention and control resource usage, the minimum prevention and control duration, and the minimum number of infected people for each individual. Step 6: Let the iteration number i = 1 and start the iteration. Step 7: Implement the individual self-deduction operation: Randomly select a certain segment of the matrix sequence encoding corresponding to an individual for update, and the process is executed α times. Step 8: Implement the individual collaborative deduction operation: Randomly select two individuals, and exchange a certain segment of the matrix sequence encoding corresponding to the two individuals, and the process is executed β times. Step 9: Provide new individuals based on the continuous improvement mechanism of population dynamic deduction; calculate the objective function of each individual, sort the individuals according to the results, eliminate the last individual, introduce new individuals, and repeat the execution for a set number of times. The introduction of new individuals includes the individual self-deduction operation or / and the individual collaborative deduction operation or / and the population dynamic deduction; the specific population dynamic deduction operation is to randomly select T individuals, select segments for these T individuals, and the sequences where the segments selected by each individual are not overlapping. After obtaining T segments, combine them to form a new individual. Step 10: i++, and start from Step 7 again. Step 11: If the iteration number i = τ, where τ represents the maximum number of deductions, then stop the calculation; otherwise, go to Step 7. Step 12: Adopt the final resource allocation plan for medical prevention and control resource scheduling.

2. The method for scheduling medical prevention and control resources based on a population dynamic evolution mechanism according to claim 1, characterized in that, The geographical information grid includes the contact time and closeness between nodes, and the prevention and control resource information includes the total number of resources, the maximum number of resources that can be allocated to each node, and the total number of nodes.

3. The method for scheduling medical prevention and control resources based on a population dynamic evolution mechanism according to claim 1, characterized in that, The specific process of generating the initial solution is as follows: First, input the node set, consumable material set, occupancy material set, and prevention and control period set, and use days as the unit for a single prevention and control cycle. Then, for the nodes in the current cycle, calculate the quantity of consumable materials and occupancy materials that need to be allocated. If all nodes have recovered, there is no need to allocate materials anymore; otherwise, for each node, allocate random types and random quantities of epidemic prevention materials and increase the prevention and control duration. Finally, return the epidemic prevention resource allocation result as the initial solution.

4. The method for scheduling medical prevention and control resources based on a population dynamic evolution mechanism according to claim 1, characterized in that, The specific steps of the individual self-deduction operation are as follows: Step 7.1: Randomly select an individual p from the population Ρ. Step 7.2: Randomly select the nth row and mth column in the matrix sequence encoding of individual p; 0 < n < total number of prevention and control resources, 0 < m < total number of nodes. Step 7.3: Recalculate the quantity of consumable materials and occupancy materials that need to be allocated for the segment composed of m*n. Step 7.4: Replace the original m*n segment with the newly generated m*n segment to form a new individual p'. Step 7.5: Calculate the objective function of the new individual p'. If the objective function of p' can dominate the scores of some individuals in the population P, then replace the worst one of these individuals with p'. Otherwise, repeat the self-deduction operation until a new individual p' that is better than some individuals in the population P is found, and replace the worst one of these individuals with p' or reach the upper limit of the execution times. Step 7.6: Repeat Steps 7.1 - 7.5 for α times.

5. A method for scheduling medical prevention and control resources based on a population dynamic evolution mechanism according to claim 4, characterized in that, The specific steps of the individual collaborative deduction operation are as follows: Step 8.1: Randomly select two individuals q1 and q2 from Ρ. Step 8.2: Randomly select the number of rows a (0 < a < total number of prevention and control resources) and the number of columns b (0 < b < total number of nodes) in the matrix sequence encoding. Step 8.3: Exchange the segments composed of the a*b in the matrix sequence encodings of q1 and q2 to form two new individuals q1' and q2'. Step 8.4: Calculate the objective functions of the new individuals q1' and q2'. If the objective functions of q1' and q2' can dominate the scores of some individuals in the population P, then replace the worst one of these individuals with q1' and q2' respectively. Otherwise, repeat the collaborative deduction operation until new individuals q' that are better than some individuals in the population P are found, and replace the worst one of these individuals with q' or reach the upper limit of the execution times. Step 8.5: Repeat Steps 8.1 - 8.3 for β times.

6. A method for scheduling medical prevention and control resources based on a population dynamic deduction mechanism according to claim 5, characterized in that, The specific content of Step 9 is as follows: Step 9.1: Let r = i*θ / (α + β), where θ is a parameter adjusted manually. If r is an integer and the number of individuals in the population is greater than the initial number P, then execute Steps 9.2 to 9.10, that is, remove the worst individuals and supplement new individuals. Step 9.2: Calculate the objective function for each individual in the population Ρ, and sort each individual from high to low according to the quality of the objective function. Step 9.3: Remove r individuals from the end of the sorted population Ρ set. Step 9.4: Use the individual self-deduction operation and individual collaboration to generate r / 2 new individuals and add them to the population Ρ. Step 9.5: Calculate the objective function and sort each individual from high to low according to the quality of the objective function; Step 9.6: Perform group dynamic deduction on the population Ρ. Step 9.7: Starting from the first one in the sorted population Ρ set, select T individuals in sequence. Step 9.8: For the T individuals in the group dynamic deduction, select a subsequence in the matrix sequence encoding of each individual in order, that is, the first individual takes the 1 / Tth subsequence, the second individual takes the 2 / Tth subsequence, and so on, the λth individual takes the λ / Tth subsequence, and fuse the T subsequences to form a new individual t. Step 9.9: Repeat Step 9.8 for r / 2 times. Step 9.10: Add the newly generated r / 2 individuals to the population Ρ.

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