Method and system for dispatching rescue teams under multi-point emergency events considering historical cooperation

Abstract

The present invention provides a method and system for dispatching rescue teams under multi-point emergencies considering historical collaboration, belonging to the technical field of emergency management. It obtains the demands of disaster-stricken points, integrates rescue team resources, establishes a set of disaster-stricken points and various rescue resource sets of each rescue point involved, and updates the post-disaster road rescue passage time; calculates the collaboration score of the rescue team dispatch plan according to external and internal collaboration information; establishes an objective function with the shortest arrival time, as much as possible to meet the medical needs of disaster-stricken points, and as much as possible to ensure that all rescue forces arrive simultaneously, and establishes constraint conditions according to the actual situation; designs and solves the NSGA-II algorithm considering collaboration information to obtain the rescue team dispatch plan. The present invention can obtain a rescue team deployment plan with better collaboration effect under the condition of ensuring the optimal goal in the dispatch of emergency rescue teams.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and particularly to a rescue team deployment method and system for multi-point emergencies considering historical collaboration. Background Art

[0002] The occurrence of large-scale geological disasters usually causes disasters at multiple construction sites in the railway construction section, seriously threatening the safety of construction workers and affecting the project progress. The emergency rescue team deployment method involving multiple subjects still needs to be improved. After a disaster occurs, during the emergency rescue process, the emergency resources at each rescue point are limited, and the rescue of one disaster-stricken point requires the collaboration of multiple rescue subjects. A rescue team dispatch plan that can simultaneously meet the shortest arrival time of all parties, meet the medical needs of the disaster-stricken point as much as possible, and ensure that the rescue forces of all parties arrive at one disaster-stricken point simultaneously can minimize personnel and property losses. At the same time, considering the historical collaboration information between different rescue teams during dispatch can achieve more efficient and close cooperation in an emergency rescue scenario and make the best use of rescue force resources. Summary of the Invention

[0003] The purpose of the present invention is to provide a rescue team deployment method and system for multi-point emergencies considering historical collaboration to solve at least one of the technical problems in the above background art.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] On the one hand, the present invention provides a rescue team deployment method for multi-point emergencies considering historical collaboration, including:

[0006] Obtain the requirements of the disaster-stricken points, integrate the rescue team resources, establish a set of disaster-stricken points and a set of various rescue resources at each rescue point involved, and update the post-disaster road rescue passage time;

[0007] Calculate the collaboration score of the rescue team dispatch plan according to external and internal collaboration information;

[0008] Establish an objective function with the shortest arrival time, meet the medical needs of the disaster-stricken point as much as possible, and ensure that the rescue forces of all parties arrive simultaneously, and establish constraint conditions according to the actual situation;

[0009] Design an NSGA-II algorithm considering collaboration information for solution to obtain the rescue team dispatch plan.

[0010] Further, a set of disaster-affected point demand, a set of various rescue resources, and an update of travel time are established, including: establishing a set of disaster-affected points and a set of disaster-affected point demand; wherein the set of disaster-affected point demand includes a set of disaster-affected point equipment rescue team demand, a set of disaster-affected point professional tunnel rescue team demand, a set of disaster-affected point fire brigade demand, a set of disaster-affected point doctor demand, a set of disaster-affected point nurse demand, and a set of disaster-affected point volunteer demand; a set of rescue points and rescue team numbers is established, including a set of rescue centers, a set of hospitals, a set of county governments, a set of professional tunnel rescue teams, a set of fire centers, a set of rescue centers with equipment rescue teams, a set of professional tunnel rescue teams, a set of fire centers with fire brigades, a set of hospitals with doctors, a set of hospitals with nurses, a set of hospitals with ambulances, and the total number of vehicles that can be dispatched by the county government where the hospital is located; a set of rescue team dispatch plans is established, including: rescue teams include rescue teams equipped with rescue equipment, professional tunnel rescue teams, fire brigades, doctors, and nurses; road travel time is updated according to the disaster situation, including: constructing a set of travel time required to reach the disaster-affected point from the departure point when no accident occurs t A0 , including the time required for three shortest paths from the starting point to a disaster-stricken point.

[0011] Furthermore, based on external and internal collaborative information, the collaborative score of the rescue team dispatch plan is calculated, including: establishing an external historical collaborative information set, constructing a historical drill number set of the rescue center and the hospital; obtaining the joint dispatch information of the rescue center and the hospital, constructing the rescue center dispatch decision variables, constructing the hospital dispatch decision variables, and constructing the joint dispatch information set of the rescue center and the hospital; calculating the external collaborative score, constructing the external collaborative information set, and calculating the external collaborative score; obtaining the joint dispatch information of doctors and nurses within the hospital, and constructing the joint dispatch decision variables of doctors and nurses within the hospital; calculating the internal collaborative score; calculating the collaborative score.

[0012] Furthermore, an objective function is established to minimize the arrival time, meet the medical needs of the disaster site as much as possible, and ensure that the rescue forces of all parties arrive at the same time as much as possible, and constraint conditions are established according to the actual situation; including:

[0013] Establish the shortest arrival time objective function; calculate the time required for various rescue teams to reach the disaster site;

[0014] Establish the objective function of the degree of unsatisfied medical needs at the minimum disaster site:

[0015]

[0016] Among them, F2 represents the second objective function, that is, the degree of unsatisfied medical needs at the minimum disaster site. Indicates b w Find the variance, Represents the variance of c w Find the variance; b w Represents the degree of dissatisfaction of the doctors dispatched to a certain disaster area, c w Represents the degree of dissatisfaction of the nurses dispatched to a certain disaster area;

[0017] Establish the objective function of the minimum time difference for the minimum arrival time of all rescue forces:

[0018]

[0019]

[0020]

[0021] Among them, F3 represents the third objective function, that is, the minimum time difference for the minimum arrival time of all rescue forces; Represents the variance of A, where A includes the time required for the rescue team to reach the disaster area, the time required for doctors, nurses, volunteers, and fire teams to reach the disaster area, and the time required for professional tunnel rescue teams to reach the disaster area, Represents the time required for doctors and nurses to reach the disaster area;

[0022] Establish constraints, including the number of rescue teams constraint, the number of dispatchable vehicles constraint, the number of medical staff constraint, and at least one rescue team at each disaster area.

[0023] Furthermore, the calculation process of the degree of dissatisfaction of doctors in a certain disaster area includes:

[0024] Construct the set b″ of the number of all rescue doctors received by the disaster area:

[0025]

[0026] Among them, b″ j Represents the number of all rescue doctors received by the j-th disaster area;

[0027] Construct the set b of the degree of dissatisfaction of doctors dispatched to a certain disaster area w :

[0028]

[0029] Among them, Represents the degree of dissatisfaction of doctors dispatched to the j-th disaster area;

[0030] The calculation process of the degree of dissatisfaction of nurses in a certain disaster area includes:

[0031] Construct the set c″ of the number of all rescue nurses received by the disaster area:

[0032]

[0033] Among them, c″ j represents the number of all rescue nurses received at the j-th disaster-stricken point;

[0034] Construct a set c of the dissatisfaction degree of nurses dispatched to a certain disaster-stricken area w :

[0035]

[0036] Among them, represents the dissatisfaction degree of nurses dispatched to the j-th disaster-stricken area;

[0037] Furthermore, design an NSGA-II algorithm considering collaborative information to solve, including:

[0038] Chromosome coding: Use real number coding to construct chromosomes representing solutions, and regard a variable matrix as a whole as a substring of the chromosome; Each chromosome consists of 6 substrings, which respectively represent the dispatching plan of medium-sized rescue teams, the dispatching plan of large-sized rescue teams, the dispatching plan of professional tunnel rescue teams, the dispatching plan of fire brigades, the dispatching plan of doctors, and the dispatching plan of nurses;

[0039] Determine the model parameters and algorithm parameters;

[0040] Initialize the population: The variables involved in the model are in matrix form. The chromosomes representing the solutions use real number coding. According to the constraint conditions, the demands of the disaster-stricken points in the model, and the principle of dispatching all rescue teams as much as possible, x_num variables are randomly generated with pool eligible solutions respectively to form the gene pool of each substring itself. For a chromosome, each of its substrings is randomly selected from its corresponding gene pool. Randomly generate pop chromosomes to form the parent population chromo. At this time, the generation number gen = 1; calculate the objective function values corresponding to the pop solutions; perform a fast non-dominated sorting on the initial parent population, and divide the population into different Pareto ranks pareto_rank; calculate the cooperation scores of each solution within each pareto_rank and sort them in descending order according to the cooperation scores; add the individuals with pareto_rank = 1 in the population at this time, their objective functions, pareto_rank, and cooperation scores to the external optimal solution set BEST_CHROMO; add the individual with the highest cooperation score among the individuals with pareto_rank = 1 in the current population to the historical optimal solution set RESULT; randomly select 2 individuals from the parent population chromo, which are parent_1 and parent_2 respectively, and the number of crossover and mutation cm_num = 1; perform crossover to obtain the offspring off_1′, off_2′; perform mutation to obtain the offspring off_1″, off_2″; the number of crossover and mutation cm_num = cm_hum + 1, and after calculating the objective function of the obtained off_1″ and off_2″, add them to the offspring population chromo_off;

[0041] Elite selection strategy: Combine the offspring population chromo_off with the parent population chromo to obtain chromo_combine; perform fast non-dominated sorting on chromo_combine to divide the population into different Pareto ranks pareto_rank; calculate the cooperation score of each solution within each pareto_rank and sort them in descending order of the cooperation score; add the optimal pop_choose individuals after non-dominated sorting and cooperation score sorting of chromo_combine to the new population chromo_new; randomly select pop_random individuals from chromo_combine and add them to chromo_new; randomly select pop_new variables from each of the x_num gene pools to form pop_new newly introduced individuals from the outside and add them to chromo_new. pop_choose + pop_random + pop_new = pop. At this time, a new population after selection and introduction is obtained; add the individuals with pareto_rank of 1 in the current population chromo_new, their objective functions, pareto_rank, and cooperation scores to the external optimal solution set BEST_CHROMO; add the individual with the highest cooperation score among the individuals with pareto_rank of 1 in the current population chromo_new to the historical optimal solution set RESULT; let chromo_new be the parent population of the next generation, that is, chromo = chromo_new, and the generation number gen = gen + 1.

[0042] In a second aspect, the present invention provides a rescue team deployment system under multi-point emergencies considering historical cooperation, including:

[0043] A first acquisition module for acquiring the demands of the disaster-stricken points and rescue resources;

[0044] A second cooperation information scoring module for calculating the cooperation score of the rescue team dispatch plan;

[0045] A third objective and constraint module for establishing an objective function of the shortest arrival time, as much as possible to meet the medical needs of the disaster-stricken points, and as much as possible to ensure that all rescue forces arrive at the same time, and establishing constraint conditions according to the actual situation;

[0046] A fourth solution module for designing an NSGA-II algorithm considering cooperation information to solve the rescue team dispatch plan.

[0047] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the rescue team deployment method under multi-point emergencies considering historical cooperation as described above is implemented.

[0048] Fourthly, the present invention provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the rescue team deployment method under multi-point emergencies considering historical collaboration as described above.

[0049] Fifthly, the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes the instructions for implementing the rescue team deployment method under multi-point emergencies considering historical collaboration as described above.

[0050] Advantages of the present invention: Considering the characteristics of limited emergency resources and urgent emergency rescue, a model is constructed by considering the historical collaboration information between collaboration entities, making better use of the cooperation advantages between different rescue teams and personnel, which is conducive to efficiently completing emergency rescue tasks; designing the NSGA-II algorithm for the target collaborative rescue team deployment model for solution, replacing the traditional crowding degree with the collaboration score as the elite selection operator, and obtaining a rescue team deployment plan with better collaboration effect while ensuring the optimal goal.

[0051] The advantages of the additional aspects of the present invention will be more clearly given in the following description part, or understood through the practice of the present invention. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0053] Figure 1 It is the flowchart of the NSGA-II algorithm considering collaboration information according to the embodiment of the present invention.

[0054] Figure 2 It is the flowchart of the chromosome encoding method in the NSGA-II algorithm considering collaboration information according to the embodiment of the present invention.

[0055] Figure 3 It is the actual significance diagram of gene fragments within a substring according to the embodiment of the present invention.

[0056] Figure 4 It is the crossover process diagram in the NSGA-II algorithm considering collaboration information according to the embodiment of the present invention.

[0057] Figure 5 This is a diagram of the mutation process in the NSGA-II algorithm considering collaborative information according to an embodiment of the present invention.

[0058] Figure 6 A schematic diagram of the Pareto frontier of the external optimal solution set obtained according to an embodiment of the present invention.

[0059] Figure 7 It is a schematic diagram comparing the changes in the objective function value of the shortest arrival time in the successive optimal solutions described in the embodiment of the present invention and the changes in the objective function value of the shortest arrival time in the successive optimal solutions in the external optimal solution set.

[0060] Figure 8 It is a schematic diagram comparing the changes in the objective function values of satisfying the medical needs of disaster-stricken points as much as possible in the optimal solutions of all generations described in the embodiments of the present invention and the changes in the objective function values of satisfying the medical needs of disaster-stricken points as much as possible in the optimal solutions of all generations in the external optimal solution set.

[0061] Figure 9 This is a schematic diagram comparing the objective function value changes of ensuring that rescue forces from all parties arrive at the same time as much as possible in the optimal solutions of all generations described in the embodiments of the present invention and the objective function value changes of ensuring that rescue forces from all parties arrive at the same time as much as possible in the optimal solutions of all generations in the external optimal solution set.

[0062] Figure 10 It is a schematic diagram comparing the changes in the collaborative scores of the successive optimal solutions described in the embodiment of the present invention with the changes in the collaborative scores of the successive optimal solutions in the external optimal solution set.

[0063] Figure 11 It is a schematic diagram showing the comparison between the optimal solution obtained by the method of the embodiment of the present invention and the method using the traditional congestion degree as the selection operator.

[0064] Figure 12 This is a flow chart of a method for deploying rescue teams in multi-point emergencies taking into account historical collaboration according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0066] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0067] It should also be understood that terms such as those defined in a general dictionary should be understood as having a meaning consistent with their meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as such here.

[0068] Those skilled in the art of the present technology can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the", and "said" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0069] In the description of this specification, descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0070] To facilitate the understanding of the present invention, the following further explains the present invention with specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation to the embodiments of the present invention.

[0071] Those skilled in the art should understand that the drawings are only schematic diagrams of the embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0072] Embodiment 1

[0073] In this Embodiment 1, first, a rescue team deployment system under multi-point emergencies considering historical collaboration is provided, including: a first acquisition module for acquiring the demands of the disaster-stricken points and rescue resources; a second collaborative information scoring module for calculating the collaborative score of the rescue team dispatch plan; a third objective and constraint module for establishing an objective function with the shortest arrival time, as much as possible to meet the medical needs of the disaster-stricken points, and as much as possible to ensure that all rescue forces arrive simultaneously, and establishing constraint conditions according to the actual situation; a fourth solution module for designing an NSGA-II algorithm considering collaborative information to solve the rescue team dispatch plan.

[0074] In this Embodiment 1, using the above system, a rescue team deployment method under multi-point emergencies considering historical collaboration is realized, such as Figure 12As shown in the figure, it includes the following steps: Obtain the needs of the disaster-stricken points, integrate the existing rescue team resources, establish various rescue resource sets for each rescue point involved in the disaster-stricken points, construct a set of rescue team dispatch plans, and update the rescue passage time according to the damaged situation of the post-disaster roads; Calculate the collaboration score of the rescue team dispatch plan based on external and internal collaboration information; Establish an objective function with the shortest arrival time, as much as possible to meet the medical needs of the disaster-stricken points, and as much as possible to ensure that all rescue forces arrive at the same time, and establish constraint conditions according to the actual situation; Design an NSGA-II algorithm considering collaboration information for solution.

[0075] Establish various rescue resource sets and update the passage time, including:

[0076] Step 1. Establish the disaster-stricken point set: Disaster-stricken point set, where I j represents the jth disaster-stricken point, and n d is the total number of disaster-stricken points.

[0077] Step 2. Establish the disaster-stricken point demand set: Disaster-stricken point medium equipment rescue team demand set, where represents the number of rescue teams required for the jth disaster-stricken point to be equipped with medium rescue equipment; Disaster-stricken point large equipment rescue team demand set, where represents the number of rescue teams required for the jth disaster-stricken point to be equipped with large rescue equipment; Disaster-stricken point professional tunnel rescue team demand set, where represents the number of professional tunnel rescue teams required for the jth disaster-stricken point; Disaster-stricken point fire brigade demand set, where represents the number of fire brigades for the jth disaster-stricken point; Disaster-stricken point doctor demand set, where b j represents the number of doctors required for the jth disaster-stricken point; Disaster-stricken point nurse demand set, where c j represents the number of nurses required for the jth disaster-stricken point; Disaster-stricken point volunteer demand set, where d j represents the number of volunteers required for the jth disaster-stricken point.

[0078] Step 3. Establish the set of each rescue point and the number of rescue teams: Rescue center set, where J i represents the ith rescue center, and n r is the total number of rescue centers; Hospital set, where Y k represents the kth hospital, and n p is the total number of hospitals; The set of county governments, where Z v represents the v-th county government, and n t is the total number of county governments; The set of professional tunnel rescue teams, where P u represents the u-th professional tunnel rescue team, and n z is the total number of professional tunnel rescue teams; The set of fire centers, where F w represents the w-th fire center, and n f is the total number of fire centers; The set of the number of medium-equipment rescue teams in the rescue center, where represents the number of medium-equipment rescue teams in the i-th rescue center; The set of the number of large-equipment rescue teams in the rescue center, where represents the number of large-equipment rescue teams in the i-th rescue center; The set of the number of professional tunnel rescue teams, where represents the number of professional tunnel rescue teams at the location of the u-th professional tunnel rescue team, The set of the number of fire teams in the fire center, where represents the number of fire teams in the w-th fire center; The set of the number of doctors in the hospital, where represents the number of doctors in the k-th hospital; The set of the number of nurses in the hospital, where represents the number of nurses in the k-th hospital.

[0079] Step 4. Establish other rescue resource sets:

[0080] The set of the number of ambulances in the hospital, where represents the number of ambulances in the k-th hospital; The total number of vehicles that can be dispatched by the county government where the hospital is located, where represents the number of vehicles that can be dispatched by the county government where the k-th hospital is located;

[0081] Step 5. Establish a rescue team dispatch plan set:

[0082] The rescue teams include rescue teams equipped with large rescue equipment, rescue teams equipped with medium rescue equipment, professional tunnel rescue teams, fire teams, doctors, and nurses;

[0083] a′ M : The actual dispatch plan for medium-rescue-equipment rescue teams:

[0084]

[0085] Among them, represents the number of rescue teams equipped with medium-sized rescue equipment dispatched from the \(i\)-th rescue center to the \(j\)-th disaster-stricken point in the actual dispatch plan;

[0086] a' L : Actual dispatch plan for rescue teams equipped with large-scale rescue equipment:

[0087]

[0088] Among them, represents the number of rescue teams equipped with large-scale rescue equipment dispatched from the \(i\)-th rescue center to the \(j\)-th disaster-stricken point in the actual dispatch plan;

[0089] a' P : Actual dispatch plan for professional tunnel rescue equipment rescue teams:

[0090]

[0091] Among them, represents the number of professional tunnel rescue teams dispatched from the location of the \(u\)-th professional tunnel rescue team to the \(j\)-th disaster-stricken point in the actual dispatch plan;

[0092] a' F : Actual dispatch plan for fire teams:

[0093]

[0094] Among them, represents the number of fire teams dispatched from the \(w\)-th fire center to the \(j\)-th disaster-stricken point in the actual dispatch plan;

[0095] b': Actual dispatch plan for doctors:

[0096]

[0097] Among them, b′ kj represents the number of doctors dispatched from the \(k\)-th hospital to the \(j\)-th disaster-stricken point in the actual dispatch plan;

[0098] c': Actual dispatch plan for nurses:

[0099]

[0100] Among them, c′ kj represents the number of nurses dispatched from the \(k\)-th hospital to the \(j\)-th disaster-stricken point in the actual dispatch plan;

[0101] Step 6. Update the road travel time according to the disaster situation:

[0102] Construct a set \(t\) of travel times required to reach the disaster-stricken point from the departure place when no accident occursA0 , including the time required for the three shortest paths from the starting point to a certain disaster-stricken point:

[0103]

[0104] Among them, represents the time required for the three shortest paths from the a-th location to the j-th disaster-stricken point via the arrival point, A represents different locations. When A = 1, it represents the rescue center; when A = 2, it represents the hospital; when A = 3, it represents the county government; when A = 4, it represents the fire center;

[0105] After the accident, the path is damaged and the passing time is t' A , t' A There are the following four cases:

[0106]

[0107] Among them, λ represents the degree of damage, which is represented by the distance d from the damage center:

[0108]

[0109] Among them, l is the influence coefficient of the road damage degree on the transportation time, and t 修 is the time required to repair the road;

[0110] Obtain the passing time set t' to reach the disaster-stricken point considering the damaged path conditions A0 as follows:

[0111]

[0112] Thus, establish the shortest time set t' to reach the disaster-stricken point A :

[0113]

[0114] Among them, represents the shortest time from the a-th rescue point to the j-th disaster-stricken point considering the damaged path conditions.

[0115] Calculate the cooperation score of the rescue team dispatch plan, including:

[0116] Step 1. Establish the external historical cooperation information set:

[0117] Construct the historical drill times set E of the rescue center and the hospital:

[0118]

[0119] Among them, E ikDenote the number of historical drills between the \(i\)-th rescue center and the \(k\)-th hospital;

[0120] Step 2. Obtain the co - dispatch information between the rescue center and the hospital:

[0121] Construct the dispatch decision variable \(\alpha\) of the rescue center r :

[0122]

[0123] where, is the decision variable representing the \(i\)-th rescue center dispatching a rescue team to the \(j\)-th disaster - affected point;

[0124] Construct the dispatch decision variable of the hospital:

[0125]

[0126] where, is the decision variable representing the \(k\)-th hospital dispatching doctors or nurses to the \(j\)-th disaster - affected point;

[0127] Construct the set of co - dispatch information between the rescue center and the hospital:

[0128]

[0129] where, represents the co - dispatch information between the \(i\)-th rescue center and the \(k\)-th hospital, and num2() represents calculating the number of the digit 2 in the matrix; \(\alpha\) r (i) represents the \(i\)-th row of \(\alpha\) r i.e., the dispatch information of the \(i\)-th rescue center to all disaster - affected points; \(\alpha\) pw (k) represents the \(k\)-th row of \(\alpha\) pw i.e., the dispatch information of the \(k\)-th hospital to all disaster - affected points;

[0130] Step 3. Calculate the external collaboration score:

[0131] Construct the set of external collaboration information \(H\):

[0132]

[0133] where, \(H\) ik represents the collaboration information between the \(i\)-th rescue center and the \(k\)-th hospital;

[0134] Calculate the external collaboration score score w :

[0135]

[0136] Step 4. Obtain the co - dispatch information of doctors and nurses within the hospital:

[0137] Construct the decision variable α for the joint dispatch of doctors and nurses within the hospital pn :

[0138]

[0139] Wherein, represents the decision variable for the k-th hospital to jointly dispatch doctors and nurses to the j-th disaster-stricken point, that is, the information on the joint dispatch of doctors and nurses;

[0140] Step 5. Calculate the internal collaboration score:

[0141] Calculate the internal collaboration score score n :

[0142]

[0143] Step 6. Calculate the collaboration score:

[0144] score = ω1 * score w + ω2 * score n

[0145] Wherein, score represents the total collaboration score of the rescue personnel dispatch this time, ω1 represents the weight of external collaboration information; ω2 represents the weight of internal collaboration information.

[0146] Establish an objective function for the shortest arrival time, as much as possible to meet the medical needs of the disaster-stricken points, and as much as possible to ensure that all rescue forces arrive simultaneously, and establish constraint conditions, including:

[0147] Step 1. Establish an objective function for the shortest arrival time:

[0148]

[0149] Wherein, F1 represents the first objective function, that is, the shortest arrival time; t i represents the arrival time of various rescue teams, and t1 - t5 respectively represent the sum of the arrival times of the rescue teams equipped with large-scale rescue equipment, the rescue teams equipped with medium-scale rescue equipment, doctors and nurses, volunteers, fire teams, and professional tunnel rescue teams to the disaster-stricken points;

[0150] Step 2. Calculate the time required for various rescue teams to reach the disaster-stricken points:

[0151]

[0152]

[0153]

[0154] Among them, \(t_1\) represents the sum of the time required for the rescue teams equipped with large-scale rescue equipment and the rescue teams equipped with medium-scale rescue equipment to reach the disaster area; represents the shortest time required from the \(i\)-th rescue center to the \(j\)-th disaster area under the condition of damaged roads; represents the decision variable of whether the \(i\)-th rescue center dispatches a rescue team with large-scale equipment to the \(j\)-th disaster area; represents the decision variable of whether the \(i\)-th rescue center dispatches a rescue team with large-scale equipment to the \(j\)-th disaster area.

[0155]

[0156]

[0157]

[0158]

[0159]

[0160] \(t_2\) represents the sum of the time required for doctors and nurses to reach the disaster area; represents the shortest time required from the \(k\)-th hospital to the \(j\)-th disaster area under the condition of damaged roads; \(\beta\) kj is the decision variable of whether the \(k\)-th hospital dispatches doctors to the \(j\)-th disaster area; \(\gamma\) kj is the decision variable of whether the \(k\)-th hospital dispatches nurses to the \(j\)-th disaster area; represents the time required to dispatch vehicles to the \(k\)-th hospital; \(\delta\) kj is the decision variable of whether to dispatch vehicles from nearby to the \(k\)-th hospital to the \(j\)-th disaster area.

[0161]

[0162]

[0163]

[0164] Among them, \(t_3\) represents the sum of the time required for volunteers to reach the disaster area; \(t''_3(j)\) represents the total time required for volunteers to be dispatched from different counties to the \(j\)-th disaster area; represents the total time required for volunteers to be dispatched from the \(v\)-th county to the \(j\)-th disaster area; represents the shortest time required for volunteers to reach the \(j\)-th disaster area from the \(v\)-th county government; represents the assembly time of volunteers in the \(v\)-th county considering the disaster situation; It represents the volunteer assembly time of the v-th county when no accident occurs; ζ(v) is the disaster-affected impact coefficient of the v-th county, indicating the degree of disaster-affected impact of the v-th county.

[0165]

[0166]

[0167] Among them, t4 represents the sum of the time required for the fire brigade to reach the disaster-affected points; It represents the shortest time from the w-th fire center to the j-th disaster-affected point considering the damaged path condition; It represents the decision variable of whether the w-th fire center dispatches a professional tunnel rescue team to the j-th disaster-affected location.

[0168]

[0169]

[0170] Among them, t5 represents the sum of the time required for the professional tunnel rescue team to reach the disaster-affected points; It represents the shortest time from the location of the u-th professional tunnel rescue team to the j-th disaster-affected point considering the damaged path condition; It represents the decision variable of whether the location of the u-th professional tunnel rescue team dispatches a professional tunnel rescue team to the j-th disaster-affected location;

[0171] Step 3. Establish the objective function of the minimum degree of unmet medical needs at the disaster-affected points:

[0172]

[0173] Among them, F2 represents the second objective function, that is, the minimum degree of unmet medical needs at the disaster-affected points, It represents taking the variance of b w and It represents taking the variance of c w ; b w represents the degree of unmet needs of the doctors dispatched to a certain disaster-affected area, and c w represents the degree of unmet needs of the nurses dispatched to a certain disaster-affected area;

[0174] The calculation process of the degree of unmet needs of doctors in a certain disaster-affected area includes:

[0175] Construct the set b″ of the number of all rescue doctors received at the disaster-affected point:

[0176]

[0177] Among them, b″ j represents the number of all rescue doctors received at the j-th disaster-affected point;

[0178] Construct the set \(b\) of the dissatisfaction degrees of the doctors dispatched to a certain disaster area w :

[0179]

[0180] where represents the dissatisfaction degree of the doctors dispatched to the \(j\)-th disaster area;

[0181] The calculation process of the dissatisfaction degree of nurses in a certain disaster area includes:

[0182] Construct the set \(c''\) of the total number of rescue nurses received at the disaster site:

[0183]

[0184] where \(c''\) j represents the total number of rescue nurses received at the \(j\)-th disaster site;

[0185] Construct the set \(c\) of the dissatisfaction degrees of the nurses dispatched to a certain disaster area w :

[0186]

[0187] where represents the dissatisfaction degree of the nurses dispatched to the \(j\)-th disaster area;

[0188] Step 4. Establish the objective function for the minimum time difference of the minimum arrival of all rescue forces:

[0189]

[0190]

[0191]

[0192] where \(F_3\) represents the third objective function, that is, the minimum time difference of the minimum arrival of all rescue forces; represents the variance of \(A\), and \(A\) includes the time required for the rescue team to reach the disaster site, the time required for doctors and nurses to reach the disaster site, the time required for volunteers to reach the disaster site, the time required for the fire brigade to reach the disaster site, and the time required for the professional tunnel rescue team to reach the disaster site, represents the time required for doctors and nurses to reach the disaster site.

[0193] Step 5. Establish various constraint conditions:

[0194] Establish constraint conditions, including the number of rescue teams constraint, the available vehicles constraint, the number of medical staff constraint, and at least one rescue team at each disaster site;

[0195] Construct the constraint on the number of rescue teams:

[0196]

[0197]

[0198]

[0199]

[0200] Among them, represents the total number of large rescue equipment rescue teams and medium rescue equipment rescue teams actually dispatched by the i-th rescue center; represents the total number of professional tunnel rescue teams actually dispatched from the location where the u-th professional tunnel rescue team is located; represents the total number of fire teams actually dispatched by the w-th fire center;

[0201] Construct the constraint on dispatchable vehicles, including:

[0202] Construct the vehicle number matrix D to be dispatched:

[0203]

[0204] Among them, D kj represents the number of vehicles to be dispatched when the k-th hospital dispatches doctors or nurses to the j-th disaster-stricken point; represents the total number of doctors and nurses who need to take the vehicles dispatched by the government to the disaster-stricken areas, and 4 represents the approved number of passengers in a vehicle excluding the driver;

[0205] Construct the constraint on dispatchable vehicles:

[0206]

[0207] Among them, d k represents the total number of vehicles to be dispatched by a hospital;

[0208] Construct the constraint on the number of medical staff:

[0209]

[0210]

[0211] Among them, represents the total number of doctors dispatched by the k-th hospital; represents the total number of nurses dispatched by the k-th hospital;

[0212] Construct the constraint that each disaster-stricken point has at least one rescue team:

[0213]

[0214] It indicates that the actual number of medium-sized rescue teams received at each disaster-stricken point is at least 1.

[0215] The NSGA-II algorithm considering collaborative information is designed for solution, including:

[0216] Step 1: Chromosome coding

[0217] Use real number coding to construct chromosomes representing solutions, and regard a variable matrix as a whole as a substring of the chromosome. Each chromosome consists of 6 substrings, representing the dispatching plan of medium-sized rescue teams, large-sized rescue teams, professional tunnel rescue teams, fire brigades, doctors, and nurses respectively, as Figure 1 shown. Taking the dispatching plan of medium-sized rescue teams as an example, the actual meaning of the gene segments within its substring is as Figure 2 shown.

[0218] Step 2: Determine model parameters

[0219] Model parameters: According to the initial conditions involved in the established rescue team allocation model based on target collaboration, the following are: a M 、a L 、a P 、a F 、b, c, R M 、R L 、R P 、R F 、b0, c0, T’ c 、n c 、E, t 10 、t 20 、t 30 、t 40 、t g 、t d etc.

[0220] Algorithm parameters: population size pop, maximum number of iterations gen_max, gene pool size pool, crossover probability pc, mutation probability pm, number of variables x_num, number of objective functions f_num, number of optimal individuals retained in elite selection pop_choose, number of optimal individuals randomly selected pop_random, number of external individuals introduced pop_new.

[0221] Step 3: Initialize the population

[0222] In Step 3.1, the variables involved in the model are in matrix form. The chromosomes representing the solutions use real number encoding. According to the constraint conditions, the demands of the disaster-stricken points in the model, and the principle of dispatching all rescue teams as much as possible, x_num variables are randomly generated with pool eligible solutions respectively to form the gene pool of each substring itself.

[0223] In Step 3.2, for a chromosome, each of its substrings is randomly selected from its corresponding gene pool. Randomly generate pop chromosomes to form the parent population chromo. At this time, the generation number gen = 1.

[0224] Step 3.3 Calculate the objective function values corresponding to the pop solutions.

[0225] In Step 3.4, perform a fast non-dominated sorting on the initial parent population, and divide the population into different Pareto ranks pareto_rank.

[0226] In Step 3.5, calculate the cooperation scores of each solution within each pareto_rank, and sort them in descending order according to the cooperation scores.

[0227] In Step 3.6, add the individuals with pareto_rank = 1 in the population at this time, their objective functions, pareto_rank, and cooperation scores to the external optimal solution set BEST_CHROMO; add the individual with the highest cooperation score among the individuals with pareto_rank = 1 in the current population to the historical optimal solution set RESULT.

[0228] Step 4: Crossover and mutation to generate offspring

[0229] In Step 4.1, randomly select 2 individuals from the parent population chromo, namely parent_1 and parent_2, and the number of crossover and mutation times cm_num = 1.

[0230] In Step 4.2, the offspring off_1′ and off_2′ are obtained by crossover: randomly generate the current crossover probability pc_n. If pc_n ≥ pc, no crossover is performed, and the offspring off_1′ = parent_1, off_2′ = parent_2; if pc_n < pc, perform the crossover operation: randomly select the substring position crosspoint in this chromosome, and swap the two chromosomes parent_1 and parent_2 from crosspoint + 1 to the last substring position. The two chromosomes after crossover are the two offspring off_1′ and off_2′. The crossover process is as Figure 3 shown:

[0231] Step 4.3 Generate offspring off_1″ and off_2″ through mutation: Generate the current mutation probabilities pm_n1 and pm_n2 for off_1′ and off_2′ obtained in the previous step respectively. If pm_n1 ≥ pm, no mutation is performed, and the offspring off_1″ = off_1′, off_2″ = off_2′; if pm_n1 < pm, perform a mutation operation on off_1′: Randomly select a substring position mutatepoint in this chromosome, and randomly select a substring from the gene pool of this substring and exchange it with it to obtain the offspring off_1″; off_2′ is the same. The mutation process is as Figure 4 shown.

[0232] Step 4.4 Increment the crossover and mutation count cm_num = cm_num + 1, calculate the objective function for the obtained off_1″ and off_2″, and add them to the offspring population chromo_off.

[0233] Step 4.5 When cm_hum ≤ 0.5 * pop, repeat Step 4.1 - Step 4.3.

[0234] Step 5: Elite selection strategy

[0235] Step 5.1 Merge the offspring population chromo_off with the parent population chromo to obtain chromo_comb ine.

[0236] Step 5.2 Perform a fast non - dominated sorting on chromo_combine, and divide the population into different Pareto ranks pareto_rank.

[0237] Step 5.3 Calculate the cooperation score for each solution within each pareto_rank, and sort them in descending order according to the cooperation score.

[0238] Step 5.4 Add the optimal pop_choose individuals after non - dominated sorting and cooperation score sorting of chromo_combine to the new population chromo_new; randomly select pop_random individuals from chromo_combine and add them to chromo_new; randomly select pop_new variables from x_num gene pools respectively to form pop_new newly introduced individuals from outside and add them to chromo new. pop_choose + pop_random + pop_new = pop. At this time, the newly selected and introduced population is obtained.

[0239] Step 5.5 Add the individuals with pareto_rank of 1 in the current population chromo_new and their objective function, pareto_rank, and synergy score to the external optimal solution set BEST_CHROMO; add the individuals with the highest synergy score among the individuals with pareto_rank of 1 in the current population chromo_new to the optimal solution set RESULT.

[0240] Step 5.6 Let cheomo_new be the parent population of the next generation, that is, cheomo = cheomo_new, and the generation number gen = gen + 1.

[0241] Step 5.7gen <gen_max时,重复Step 4-Step 5。

[0242] The algorithm flow is as follows Figure 5 shown.

[0243] Based on the actual example input, the optimal solutions of the external optimal solution set tend to converge after the maximum iteration number reaches 300. The Pareto frontier of the external optimal solution set is obtained as follows: Figure 6 As shown in the figure, the changes in the objective function values corresponding to the optimal solutions of all generations and the objective function values of the optimal solutions of all generations in the external optimal solution set are shown in the figure. Figure 7 , Figure 8 , Figure 9 , Figure 10 As shown; Under the same parameter settings, the comparison results are as follows: Figure 11 As shown. By comparison, compared with the traditional congestion selection operator, the two objective functions of meeting the medical needs of the disaster site as much as possible and ensuring that the rescue forces of all parties arrive at the same time as much as possible and the coordination score are improved by 46.4%, 3% and 9.3% respectively, which verifies the effectiveness of the NSGA-II algorithm considering coordination information described in this embodiment.

[0244] Example 2

[0245] This embodiment 2 provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the method for deploying rescue teams in multi-point emergencies considering historical coordination as described above is implemented.

[0246] Example 3

[0247] This embodiment 3 provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method for deploying rescue teams in multi-point emergencies taking into account historical coordination.

[0248] Example 4

[0249] Example 4 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes the instructions for implementing the rescue team deployment method under multi-point emergencies considering historical collaboration as described above.

[0250] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0251] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0252] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implements the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0253] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0254] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, they are not limitations on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts should be covered within the protection scope of the present invention.

Claims

1. A method for dispatching rescue teams under multi-point emergencies considering historical collaboration, characterized in that Including: Obtain the demands of disaster-stricken points, integrate the resources of rescue teams, establish sets of disaster-stricken points and various rescue resource sets of each rescue point involved, and update the post-disaster road rescue passage time; Calculate the collaboration score of the rescue team dispatch plan according to external and internal collaboration information, including: establish a set of external historical collaboration information, construct a set of historical drill times between the rescue center and the hospital; obtain the joint dispatch information between the rescue center and the hospital, construct the rescue center dispatch decision variable, construct the hospital dispatch decision variable, construct a set of joint dispatch information between the rescue center and the hospital; calculate the external collaboration score, construct a set of external collaboration information, calculate the external collaboration score; obtain the joint dispatch information between doctors and nurses within the hospital, construct the joint dispatch decision variable of doctors and nurses within the hospital; calculate the internal collaboration score; calculate the collaboration score; Establish an objective function that minimizes the arrival time, maximally meets the medical needs of disaster-stricken points, and maximally ensures the simultaneous arrival of all rescue forces, and establish constraint conditions according to the actual situation; Design and solve the NSGA-II algorithm considering collaboration information to obtain the rescue team dispatch plan.

2. The rescue team deployment method under multi-point emergency events considering historical collaboration according to claim 1, characterized in that Establish the demand set of disaster-stricken points, various rescue resource sets, and update the travel time, including: establish the set of disaster-stricken points, and establish the demand set of disaster-stricken points; among them, the demand set of disaster-stricken points includes the demand set of equipment rescue teams at disaster-stricken points, the demand set of professional tunnel rescue teams at disaster-stricken points, the demand set of fire teams at disaster-stricken points, the demand set of doctors at disaster-stricken points, the demand set of nurses at disaster-stricken points, and the demand set of volunteers at disaster-stricken points; establish the set of each rescue point and the number of rescue teams, including the rescue center set, the hospital set, the county government set, the professional tunnel rescue team set, the fire center set, the number of equipment rescue teams owned by the rescue center set, the number of professional tunnel rescue teams set, the number of fire teams owned by the fire center set, the number of doctors owned by the hospital set, the number of nurses owned by the hospital set, the number of ambulances owned by the hospital set, and the total number of vehicles that can be dispatched by the county government where the hospital is located; establish the set of rescue team dispatch plans, including: the rescue teams include rescue teams equipped with rescue equipment, professional tunnel rescue teams, fire teams, doctors, and nurses; update the road travel time according to the disaster situation, including: construct the set of travel times t required to reach the disaster-stricken point from the departure place when no accident occurs A0 , including the time required for the three shortest paths from the departure location through to a certain disaster-stricken point.

3. The method for dispatching rescue teams under multi-point emergencies considering historical collaboration as described in claim 1, characterized in that Establish an objective function that minimizes the arrival time, maximally meets the medical needs of disaster-stricken points, and maximally ensures the simultaneous arrival of all rescue forces, and establish constraint conditions according to the actual situation; including: Establish an objective function for the shortest arrival time; calculate the time required for various rescue teams to reach the disaster-stricken points; Establish an objective function for the minimum degree of unmet medical needs of disaster-stricken points: Among them, F2 represents the second objective function, that is, the degree of non - satisfaction of the medical needs at the least affected points. Denotes the variance of b w Calculated variance. Denotes the variance of c w Calculated variance; b w Represents the degree of non - satisfaction of doctors dispatched to a certain disaster - affected area, and c w Represents the degree of non - satisfaction of nurses dispatched to a certain disaster - affected area. Establish an objective function for the minimum difference in the minimum arrival times of all rescue forces: Among them, F3 represents the third objective function, that is, the minimum time difference for the minimum rescue forces of all parties to arrive; denotes the variance calculation for A, where A includes the time required for the rescue team to reach the disaster area, the time required for doctors and nurses to reach the disaster area, the time required for volunteers to reach the disaster area, the time required for the fire brigade to reach the disaster area, and the time required for the professional tunnel rescue team to reach the disaster area. represents the time required for doctors and nurses to reach the disaster area; Establish constraint conditions, including the number of rescue teams constraint, the constraint of available vehicles, the number of medical staff constraint, and at least one rescue team at each disaster-stricken point.

4. The method for dispatching rescue teams under multi-point emergencies considering historical collaboration according to claim 3, characterized in that The calculation process of the degree of unmet doctors in a certain disaster-stricken area includes: Construct a set b” of the number of all rescue doctors received by the disaster-stricken point: where b″ j ″ represents the number of all rescue doctors received by the j-th disaster-stricken point; Construct the set \(b\) of dissatisfaction levels of doctors dispatched to a certain disaster area w : Among them, represents the dissatisfaction level of the doctors dispatched to the j-th disaster area; The calculation process of the degree of unmet nurses in a certain disaster-stricken area includes: Construct a set c” of the number of all rescue nurses received by the disaster-stricken point: where c″ j ″ represents the number of all rescue nurses received by the j-th disaster-stricken point; Construct the dissatisfaction degree set \(c\) of the nurses dispatched to a certain disaster area w : Among them, represents the dissatisfaction level of the nurses dispatched to the j-th disaster area.

5. The method for dispatching rescue teams under multi-point emergencies considering historical collaboration according to claim 1, characterized in that, Design and solve the NSGA-II algorithm considering collaboration information, including: Chromosome encoding: Use real number encoding to construct chromosomes representing solutions, regard a variable matrix as a whole as a substring of the chromosome; each chromosome consists of 6 substrings, representing the dispatch plans of medium-sized rescue teams, large-sized rescue teams, professional tunnel rescue teams, fire teams, doctor dispatch plans, and nurse dispatch plans respectively; Determine the model parameters and algorithm parameters; Initialize the population: The variables involved in the model are in matrix form. The chromosomes representing the solutions use real number coding. According to the constraint conditions, the demands of the disaster-stricken points in the model, and the principle of dispatching all rescue teams as much as possible, x_num variables are randomly generated with pool eligible solutions respectively to form the gene pool for each substring. For a chromosome, each of its substrings is randomly selected from its corresponding gene pool. Randomly generate pop chromosomes to form the parent population chromo. At this time, the generation number gen = 1. Calculate the objective function values corresponding to the pop solutions. Perform fast non-dominated sorting on the initial parent population, and divide the population into different Pareto ranks pareto_rank. Calculate the cooperation scores of each solution within each pareto_rank and sort them from high to low according to the cooperation scores. Add the individuals with pareto_rank = 1 in the population at this time, their objective functions, pareto_rank, and cooperation scores to the external optimal solution set BEST_CHROMO. Add the individual with the highest cooperation score among the individuals with pareto_rank = 1 in the current population to the historical optimal solution set RESULT. Randomly select 2 individuals from the parent population chromo, namely parent_1 and parent_2, and the number of crossover and mutation times cm_num = 1. Perform crossover to obtain the offspring off_1′ and off_2′. Perform mutation to obtain the offspring off_1″ and off_2″. The number of crossover and mutation times cm_num = cm_num + 1. After calculating the objective function of the obtained off_1″ and off_2″, add them to the offspring population chromo_off. Elite selection strategy: Combine the offspring population chromo_off with the parent population chromo to obtain chromo_combine; perform fast non-dominated sorting on chromo_combine to divide the population into different Pareto ranks pareto_rank; calculate the cooperation score of each solution within each pareto_rank and sort them in descending order of the cooperation score; add the top pop_choose individuals of chromo_combine after non-dominated sorting and cooperation score sorting to the new population chromo_new; randomly select pop_random individuals from chromo_combine and add them to chromo_new; randomly select pop_new variables from each of the x_num gene pools to form pop_new newly introduced individuals from the outside and add them to chromo_new; pop_choose + pop_random + pop_new = pop; at this time, obtain the new population after selection and introduction; add the individuals with pareto_rank of 1 in the current population chromo_new, their objective functions, pareto_rank, and cooperation scores to the external optimal solution set BEST_CHROMO; add the individual with the highest cooperation score among the individuals with pareto_rank of 1 in the current population chromo_new to the historical optimal solution set RESULT; let chromo_new be the parent population of the next generation, i.e., chromo = chromo_new, and the generation number gen = gen + 1.

6. A rescue team deployment system under multi-point emergencies considering historical collaboration, characterized in that Including: The first acquisition module is used to acquire the demands of the disaster-stricken points and rescue resources. The second cooperation information scoring module is used to calculate the cooperation score of the rescue team dispatch plan; including: establishing an external historical cooperation information set, constructing a historical drill times set of the rescue center and the hospital; obtaining the joint dispatch information of the rescue center and the hospital, constructing a rescue center dispatch decision variable, constructing a hospital dispatch decision variable, constructing a joint dispatch information set of the rescue center and the hospital; calculating the external cooperation score, constructing an external cooperation information set, calculating the external cooperation score; obtaining the joint dispatch information of the doctors and nurses within the hospital, constructing the dispatch decision variables of the doctors and nurses within the hospital; calculating the internal cooperation score; calculating the cooperation score. The third objective and constraint module is used to establish an objective function for the shortest arrival time, as much as possible to meet the medical needs of the disaster-stricken points, and as much as possible to ensure that all rescue forces arrive at the same time, and establish constraint conditions according to the actual situation. The fourth solution module is used to design an NSGA-II algorithm considering cooperation information to solve the rescue team dispatch plan.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the rescue team deployment method for multi-point emergencies considering historical cooperation as described in any one of claims 1-5 is implemented.

8. A computer device, characterized in that, It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the rescue team deployment method under multi-point emergencies considering historical collaboration as described in any one of claims 1-5.

9. An electronic device, characterized in that, It includes: A processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes the instructions for implementing the rescue team deployment method under multi-point emergencies considering historical collaboration as described in any one of claims 1-5.

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