Emergency equipment dispatching scheme generation method and system based on improved myxomycete algorithm

Abstract

The invention provides an emergency equipment dispatching scheme generation method and system based on an improved myxomycete algorithm, and relates to the technical field of emergency management.The method comprises the steps that firstly, an emergency equipment dispatching model is constructed based on emergency equipment dispatching influence factors and emergency equipment dispatching loss coefficients, and the emergency equipment dispatching model is constructed; the emergency equipment dispatching model is used for generating the number of emergency equipment provided by the supply point for the demand guarantee point and a corresponding transportation mode; then acquiring emergency dispatching task information, wherein the emergency dispatching task information comprises demand guarantee information and supply point information; and according to the emergency allocation task information, solving the emergency equipment allocation model by using an improved mucus algorithm, and outputting an emergency equipment allocation scheme. Through the method, the technical problems of low efficiency, low accuracy and low diversity in the solving process of an existing myxomycete algorithm are solved, the emergency equipment dispatching efficiency and accuracy are improved, and the technical effects of assisting in generating a dispatching scheme are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and in particular to a method and system for generating an emergency equipment transportation plan based on an improved slime mold algorithm. Background Art

[0002] Emergency equipment refers to various types of professional emergency equipment or instruments that are equipped to meet the needs of emergency response operations such as disaster investigation, personnel search and rescue, emergency communications, emergency power supply, and logistics support. At the emergency rescue site of large-scale disasters such as earthquakes and floods, the demand for equipment is huge, far exceeding the reserve capacity of a single local department. Therefore, timely transfer of emergency equipment from other areas has become the key to ensuring the smooth progress of rescue work. How to quickly and accurately generate emergency equipment transportation plans is a key issue in emergency management of emergencies, and it is the premise and guarantee for completing the task of emergency equipment transportation.

[0003] In recent years, the problem of large-scale emergency equipment dispatch (especially emergency equipment dispatch in disaster relief operations) has attracted great attention. Various emergency equipment transportation scenarios have been considered, and some emergency equipment transportation plan generation methods have been proposed one after another, such as: based on the experience of experts in the field, it takes a lot of manpower costs to form an expert group and manually formulate plans, and collect information, analyze the situation and formulate plans within a limited time; considering only the road transportation mode, by establishing a mathematical model of emergency equipment transportation, an intelligent optimization algorithm based on the priority strategy is designed to solve the nonlinear multi-objective mixed integer programming problem, and different emergency equipment transportation plans are generated according to different objective functions.

[0004] The current methods for generating emergency equipment transportation plans have the following problems: (1) Decisions are made based on subjective experience, which results in large computational workload, low efficiency, and low accuracy; (2) The emergency equipment transportation modeling only considers road transportation, which is a single mode of transportation; (3) When using the slime mold algorithm to solve the mathematical model of emergency equipment transportation, the initial positions of the population are randomly generated, which results in low diversity and easy to fall into local extreme values. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for generating an emergency equipment dispatching plan based on an improved slime mold algorithm, so as to alleviate the technical problems of low efficiency, low accuracy and low diversity existing in the prior art.

[0006] In the first aspect, an embodiment of the present invention provides a method for generating an emergency equipment transportation plan based on an improved slime mold algorithm, including: constructing an emergency equipment transportation model based on emergency equipment transportation influencing factors and emergency equipment transportation loss coefficients; the above-mentioned emergency equipment transportation model is used to generate the number of emergency equipment provided by the supply point to the demand guarantee point and the corresponding transportation method; obtaining emergency transportation task information; the above-mentioned emergency transportation task information includes: demand guarantee information and supply point information; according to the above-mentioned emergency transportation task information, using the improved slime mold algorithm to solve the above-mentioned emergency equipment transportation model, and outputting an emergency equipment transportation plan; the emergency equipment transportation plan includes the transportation subtasks of each supply point that constitute the above-mentioned emergency transportation task.

[0007] In some optional implementations, the above method further includes: calculating the loss coefficient of the transportation process of each mode of transportation through historical emergency equipment transportation data to generate an emergency equipment transportation loss coefficient.

[0008] In some optional implementations, the above-mentioned emergency equipment transportation influencing factors include: transportation time, transportation cost, transportation force, transportation mode, and transportation distance; based on the emergency equipment transportation influencing factors and the emergency equipment transportation loss coefficient, an emergency equipment transportation model is constructed, including: determining decision variables based on the emergency equipment transportation influencing factors and the emergency equipment transportation loss coefficient; the above-mentioned decision variables are used to represent the number of emergency equipment transported by different transportation methods; constraining the above-mentioned decision variables according to the emergency transportation task information, and determining the total transportation time function and the total transportation cost function; based on the above-mentioned total transportation time function and the above-mentioned total transportation cost function, an emergency equipment transportation model is constructed.

[0009] In some optional implementations, the above-mentioned demand assurance information includes: the types and quantities of emergency equipment to be transported, and the demand assurance locations; the above-mentioned supply point information includes: the types and quantities of emergency equipment at the supply points, and the transportation distances of various modes of transportation; the above-mentioned method also includes: based on the above-mentioned demand assurance information and the above-mentioned supply point information, judging whether the regional supply and demand match.

[0010] In some optional implementations, based on the emergency transportation task information, the emergency equipment transportation model is solved using an improved slime mold algorithm to output an emergency equipment transportation plan, including: setting slime mold algorithm solution parameters; the slime mold algorithm solution parameters include: initializing the number of slime mold populations, the number of populations selected by slime mold population diversity, the number of iterations, the dimension, and the initialization population; selecting the slime mold population through the slime mold population diversity selection strategy, and calculating the fitness value of each slime mold population.

[0011] In some optional implementations, based on the above-mentioned emergency transportation task information, the above-mentioned emergency equipment transportation model is solved using an improved slime mold algorithm, and an emergency equipment transportation plan is output, which also includes: calculating the slime mold weight coefficient and slime mold parameters based on the above-mentioned slime mold algorithm solution parameters; updating individual positions based on pre-generated random numbers and randomly initialized probabilities; calculating the fitness value through the fitness function of the improved slime mold algorithm, and updating the global optimal solution.

[0012] In some optional implementations, based on the above-mentioned emergency transportation task information, the above-mentioned emergency equipment transportation model is solved using an improved slime mold algorithm to output an emergency equipment transportation plan, and it also includes: judging whether the end condition is met based on the above-mentioned number of iterations, if so, outputting the global optimal solution and fitness value; otherwise, repeating the steps in the above-mentioned optional implementation.

[0013] In the second aspect, an embodiment of the present invention provides an emergency equipment transportation plan generation system based on an improved slime mold algorithm, including: a model construction module, used to construct an emergency equipment transportation model based on emergency equipment transportation influencing factors and emergency equipment transportation loss coefficients; the above-mentioned emergency equipment transportation model is used to generate the number of emergency equipment provided by the supply point to the demand guarantee point and the corresponding transportation method; an information acquisition module, used to obtain emergency transportation task information; the above-mentioned emergency transportation task information includes: demand guarantee information and supply point information; an output module, used to solve the above-mentioned emergency equipment transportation model according to the above-mentioned emergency transportation task information using the improved slime mold algorithm, and output an emergency equipment transportation plan; the emergency equipment transportation plan includes the transportation subtasks of each supply point that constitute the above-mentioned emergency transportation task.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the steps of any one of the methods described in the first aspect are implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to execute any method described in the first aspect above.

[0016] The present invention provides a method and system for generating an emergency equipment transportation plan based on an improved slime mold algorithm, the method comprising: firstly, based on the influencing factors of emergency equipment transportation and the loss coefficient of emergency equipment transportation, constructing an emergency equipment transportation model, the emergency equipment transportation model is used to generate the number of emergency equipment provided by the supply point for the demand guarantee point and the corresponding transportation method; then obtaining emergency transportation task information, the emergency transportation task information includes: demand guarantee information and supply point information; then according to the emergency transportation task information, using the improved slime mold algorithm to solve the emergency equipment transportation model, and output the emergency equipment transportation plan. This method solves the technical problems of low efficiency, low accuracy and low diversity in the existing slime mold algorithm solution process, realizes the improvement of emergency equipment transportation efficiency and accuracy, and achieves the technical effect of assisting in generating transportation plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm provided in an embodiment of the present invention;

[0019] Figure 2 A flowchart of another method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm provided in an embodiment of the present application invention;

[0020] Figure 3 A comparison chart of the effects of the improved slime mold algorithm and the traditional slime mold algorithm provided in the embodiment of the present application invention;

[0021] Figure 4 A schematic diagram of the structure of an emergency equipment dispatching plan generation system based on an improved slime mold algorithm provided by an embodiment of the present invention;

[0022] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. Some embodiments of the present invention are described in detail below in conjunction with the drawings. The following embodiments and features in the embodiments can be combined with each other without conflict.

[0026] In recent years, the dispatch of large-scale emergency equipment, especially emergency equipment dispatch in disaster relief operations, has attracted great attention. Various emergency equipment dispatch scenarios have been considered, and some emergency equipment dispatch plan generation methods have been proposed. For example, based on the experience of experts in the field, it takes a lot of manpower to form an expert group and manually formulate a plan, and it takes a limited time to collect information, analyze the situation and formulate a plan; considering only the road transportation mode, by establishing a mathematical model of emergency equipment dispatch, an intelligent optimization algorithm based on priority strategy is designed to solve the nonlinear multi-objective mixed integer programming problem, and different emergency equipment dispatch plans are generated according to different objective functions. The current emergency equipment dispatch plan generation method has the following problems: (1) Relying on human subjective experience to make decisions, resulting in large computational workload, low efficiency and low accuracy; (2) Emergency equipment dispatch modeling only considers road transportation mode, and the transportation mode is single; (3) When using the slime mold algorithm to solve the mathematical model of emergency equipment dispatch, the initial position of the population is randomly generated, which has problems such as low diversity and easy to fall into local extreme values.

[0027] Based on this, the embodiments of the present invention provide a method and system for generating an emergency equipment transportation plan based on an improved slime mold algorithm to solve the technical problems of low efficiency, low accuracy and low diversity in the existing slime mold algorithm solution process.

[0028] To facilitate understanding of this embodiment, firstly, a method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm disclosed in an embodiment of the present invention is described in detail. Figure 1 The flowchart of a method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm is shown. The method can be executed by an electronic device and mainly includes the following steps S110 to S130:

[0029] S110: Construct an emergency equipment transportation model based on the influencing factors of emergency equipment transportation and the emergency equipment transportation loss coefficient;

[0030] Among them, the emergency equipment transportation model is used to generate the quantity of emergency equipment provided by the supply point to the demand guarantee point and the corresponding transportation method.

[0031] In one embodiment, factors affecting the transportation of emergency equipment include: transportation time, transportation cost, transportation force, transportation method, and transportation distance.

[0032] Emergency equipment transportation usually occurs in disaster investigation, personnel search and rescue, emergency communications, emergency power supply, logistics support, etc. Emergency equipment transportation is usually difficult. In addition to the tight time requirements, the available transportation capacity in a short period of time is also limited. Therefore, transportation time and transportation capacity are important factors affecting the transportation of emergency equipment. However, in the process of emergency equipment transportation, factors such as transportation mode, transportation loss, transportation distance, and transportation cost also need to be considered.

[0033] In one embodiment, the method further includes: calculating the loss coefficient of the transportation process of each mode of transportation through historical emergency equipment transportation data to generate the emergency equipment transportation loss coefficient.

[0034] As a specific example, in the process of emergency equipment transportation, there is often a certain amount of loss. Different transportation modes (road, air, rail) correspond to different losses. The loss coefficient of each transportation mode is calculated through historical emergency equipment transportation data. That is:

[0035]

[0036] In the formula, α i represents the loss coefficient of the i-th mode of transportation; c i represents the number of times emergency equipment was transported by the i-th mode of transportation during a certain period of history; p i j represents the number of emergency equipment transported for the jth time via the i-th mode of transportation; q ij It indicates the loss of emergency equipment in the jth transportation via the i-th mode of transportation.

[0037] In one embodiment, the step of constructing an emergency equipment transportation model based on the emergency equipment transportation influencing factors and the emergency equipment transportation loss coefficient in S110 includes:

[0038] (S111) determining a decision variable based on the influencing factors of emergency equipment transportation and the emergency equipment transportation loss coefficient; the decision variable is used to represent the quantity of emergency equipment transported by different transportation modes;

[0039] (S112) constraining decision variables according to the emergency transportation task information to determine the total transportation time function and the total transportation cost function;

[0040] (S113) Constructing an emergency equipment dispatching model based on the total transportation time function and the total transportation cost function.

[0041] As a specific example, suppose a certain support point urgently needs to transport N types of equipment, and the number of equipment of the jth type that needs to be transported is There are M supply points, and the quantity of the jth type of equipment that the i-th supply point can provide is s ij (0 <i≤M,0<j≤N,s ij ≥0); the air, rail and road distances from the i-th supply point to the guarantee point are d 1i d 2i and d 3i , the loss coefficients of transportation processes such as aviation, railway and road are α 1 , α 2 and α 3 , and its aviation, railway and road transport forces (only considering the load, ignoring the volume of emergency equipment) are C 1i , C 2i and C 3i , the transport capacity required to transport a piece of equipment of type j is c j The average air, rail and road freight costs per kilometer for transporting an emergency equipment are p 1 、p 2 and p 3 .

[0042] The problem of generating emergency equipment transportation plans is to determine the quantity and transportation method of each type of equipment provided by each supply point, so as to complete the overall transportation task in the most efficient way. For each supply point i and equipment type j, three decision variables x need to be determined: 1ij 、x 2ij and x 3ij , respectively represent the quantity of this type of equipment transported from the supply point by air, rail and road. Obviously, these equipment quantities need to meet the following constraints:

[0043]

[0044] Assume that each supply point can choose one or more modes of transportation for transportation, and the selected modes of transportation have only one batch. Starting from the supply point, the average speeds of air, rail and road transportation are v 1 、v 2 and v 3 ; The transportation time of equipment transported by air, rail and road transportation for supply point i is t 1i ,t 2i and t 3i ,Right now:

[0045]

[0046]

[0047]

[0048]

[0049] The total transportation time T can be calculated by the following formula:

[0050]

[0051] The total cost of transportation P can be calculated by the following formula:

[0052]

[0053] In the formula, x kij It indicates the quantity of the j-th emergency equipment transported by the i-th support point using the k-th mode of transportation.

[0054] This embodiment aims at the problem of emergency equipment transportation. Time and cost are affected by factors such as transportation force, transportation mode, transportation loss, and transportation distance. Time and cost are taken as important factors affecting transportation, and an emergency equipment transportation model is constructed, namely:

[0055]

[0056] In the formula, w T 、w p are the influence weights of the total transportation time T and total cost P respectively; T and P are the total transportation time and total cost respectively.

[0057] Therefore, the optimization goal of the emergency equipment transportation problem is to minimize the comprehensive impact, namely:

[0058]

[0059] S120: Acquire emergency transport task information; the emergency transport task information includes: demand guarantee information and supply point information;

[0060] In one embodiment, the demand guarantee information includes: the types and quantities of emergency equipment to be transported, and the locations where the demand guarantee is required; the supply point information includes: the types and quantities of emergency equipment at the supply point, and the transportation distances of various modes of transportation. In other words, obtaining the emergency transportation task information includes: first inputting the types and quantities of emergency equipment to be transported and the locations where the demand guarantee is required into the emergency equipment transportation model; then obtaining the types and quantities of emergency equipment at the supply point, that is, reading the types and quantities of emergency equipment that each supply point can supply from the database table.

[0061] In one embodiment, the method further includes: judging whether regional supply and demand match based on demand guarantee information and supply point information. That is, whether the variety and quantity of emergency equipment that can be provided by supply points within the regional scope meet the variety and quantity of emergency equipment to be transported. If all supply points in the region lack the types of emergency equipment to be transported, or the quantity of each type of emergency equipment to be transported is greater than the quantity of such emergency equipment that can be provided by all supply points in the region, then the regional supply point supply and demand do not match, and cannot meet the variety and quantity of emergency equipment to be transported, that is, the transport plan cannot be output, the output is empty, and the process ends. On the contrary, the regional supply point supply and demand match, and the process continues to execute.

[0062] S130: solving the emergency equipment transportation model using the improved slime mold algorithm according to the emergency transportation task information, and outputting the emergency equipment transportation plan;

[0063] The emergency equipment transportation plan includes transportation subtasks of each supply point that constitute the emergency transportation task. The transportation subtasks include: the quantity of each type of emergency equipment provided by the supply point and the corresponding transportation method.

[0064] The optimal transportation model of emergency equipment is solved by using an improved slime mold algorithm. According to the obtained model parameters, an improved slime mold algorithm is used to solve the optimal transportation model of emergency equipment. The model parameters are obtained by reading the database table, including: the types and quantities of emergency equipment provided by each supply point; the transportation method and corresponding capacity of each supply point (ignoring the volume carried by the transportation tool, only considering the load); the transportation distance of each supply point to the support point by various modes of transportation (road, air, rail); air, rail and road freight per kilometer. As a specific example, the database table can be composed of tables of multiple supply point information, and the supply point information can include: supply point emergency equipment reserve information, supply point capacity information, supply point and support point distance information, transportation method information, etc. Tables 1 to 4 show a specific form of database table:

[0065] Table 1: Emergency equipment reserve information at supply points

[0066] Supply point number Name of emergency equipment Number of emergency equipment Transport capacity required for unit emergency equipment (kg) B1 Emergency lighting equipment 500 100 B1 Communication equipment 300 50 B1 Environmental monitoring equipment 50 200 B1 Emergency rescue tools 100 25 … … … …

[0067] Table 2: Capacity Information of Supply Points

[0068] Supply point number Air transport capacity (kg) Railway transport capacity (kg) Road transport capacity (kg) B1 3000 40000 5000 B2 3000 30000 3000 … … … … B500 1000 50000 2000

[0069] Table 3: Distance Information between Supply Points and Guarantee Points

[0070]

[0071] Table 4: Transportation Mode Information

[0072] serial number Transportation Average freight per unit (yuan) Average speed (km / h) Y1 Air Transport 360 800 Y2 Rail transport 50 250 Y3 Road transport 1 80

[0073] In one embodiment, the step of solving the emergency equipment transportation model by using the improved slime mold algorithm according to the emergency transportation task information in S130 and outputting the emergency equipment transportation plan includes:

[0074] (S131) Set the slime mold algorithm solution parameters; the slime mold algorithm solution parameters include: initializing the slime mold population size, the population size selected for slime mold population diversity, the number of iterations T, the dimension D, and initializing the population X * (0);

[0075] (S132) Select the slime mold population through the slime mold population diversity selection strategy and calculate the fitness value of each slime mold population; that is: through the slime mold population diversity selection strategy, select the N z least similar slime mold populations in the slime mold population to form X(0), and calculate the fitness value of each slime mold population.

[0076] In one embodiment, solving the emergency equipment transportation model by using the improved slime mold algorithm according to the emergency transportation task information and outputting the emergency equipment transportation plan further includes:

[0077] (S133) Calculate the slime mold weight coefficient W and slime mold parameters b, c based on the slime mold algorithm solution parameters;

[0078] (S134) Update the individual position based on the pre-generated random number and the randomly initialized probability;

[0079] Among them, to update the individual position, generate a random number r 1 , and judge the size of the random number r 1 and the parameter z. If r 1 < z, update the individual position according to the corresponding formula; otherwise, update the parameters p i , v b , v c , r 2 , judge the size of r 2 and the parameter p i and update the individual position according to the corresponding formula.

[0080] (S135) Calculating the fitness value through the fitness function of the improved slime mold algorithm and updating the global optimal solution.

[0081] In one embodiment, according to the emergency transportation task information, the emergency equipment transportation model is solved by using the improved slime mold algorithm to output the emergency equipment transportation plan, and the following is also included:

[0082] Determine whether the end condition is met based on the number of iterations. If so, output the global optimal solution and fitness value; otherwise, repeat the above steps (S133) to (S135).

[0083] An improved slime mold algorithm proposed in an embodiment of the present invention has the following algorithm improvement ideas: Assuming that the number of slime mold population is N z , the dimension is D, and the upper bound of the search area is U b ,, the next term is L b , the slime mold algorithm uses a random method to initialize the population. The specific initialization formula is as follows:

[0084]

[0085]

[0086] Among them, X * (0) means The initial position of the slime mold, With N z are integers, and Much larger than N z ,Right now rand represents a random number between [0,1]. However, the use of random initialization to generate populations has problems such as low diversity and easy to fall into local extremes. Therefore, in the population selection, the present invention selects the most dissimilar N in the slime mold population through the slime mold population diversity selection strategy. z The comprehensive similarity of the i-th slime mold population S(X i (0)), the calculation formula is as follows:

[0087]

[0088]

[0089] In the formula,

[0090] By calculating the comprehensive similarity of the slime mold population, the comprehensive similarity set of the slime mold population is obtained. Sort the comprehensive similarity values ​​in the set from small to large, and select the first N with smaller values zThe corresponding slime mold populations form X(0), which is the most dissimilar N in the slime mold population. z This enriches the diversity of the initial slime mold population.

[0091]

[0092] The position of the i-th slime mold can be expressed as Its fitness function is expressed as f(X i (0)), i=1,2,…,N z . Then the positions X(t) and fitness f(X(t)) of the N slime molds at the current iteration t are as follows:

[0093]

[0094]

[0095] The position X of the i-th slime mold individual at iteration t+1 of the next generation i (t+1) is updated using the following formula:

[0096]

[0097] In the formula, r 1 and r 2 is a random number between [0,1]; z is the probability of random initialization; X b is the current optimal slime mold individual position; X A and X B are two randomly selected individual positions; W is the weight coefficient of the slime mold; p i represents the gap between the fitness of the i-th slime mold individual and the fitness of the current optimal slime mold individual; v b is a parameter randomly selected between values ​​[-b, b]; v c is a parameter randomly selected between the values ​​[-c, c]. i ,W is calculated as follows:

[0098]

[0099]

[0100] In the formula, T is the maximum number of iterations, and t is the current number of iterations;

[0101] p i =tanh|f(X i )-f(X b )|,i=1,2,…,N (25)

[0102] Where tanh represents the hyperbolic tangent function, f(Xb ) represents the best fitness obtained in the current iteration.

[0103]

[0104] Where r is a random number between 0 and 1; f(X w ) represents the worst fitness value; f(X b ) represents the best fitness; SortIndex(i) represents the i-th slime mold individual after the fitness value of the current iteration is sorted from small to large, and SortIndex represents the fitness value sequence, sorted from small to large.

[0105] The method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm proposed in the embodiment of the present invention integrates factors such as transportation time, transportation force, transportation mode, transportation loss, transportation distance and transportation cost into the constraints of the emergency equipment dispatching model, constructs an emergency equipment dispatching model, and changes the previous mode of only considering a single transportation mode, making the generation of emergency equipment dispatching plans richer; an improved slime mold algorithm is proposed and applied to the emergency equipment dispatching model solving process, which improves the diversity of the initial positions of the population, solves the problem of falling into local extreme values ​​when solving the traditional slime mold algorithm, avoids the subjective cognitive constraints and limitations of manually formulated plans, and thus improves the efficiency of generating emergency equipment dispatching plans. In order to illustrate the effect of the present invention, combined with Figure 2 As shown, this embodiment 1 describes in detail the generation of the optimal transportation plan for emergency equipment.

[0106] Embodiment 1:

[0107] S1 inputs the types and quantities of emergency equipment to be transported and the required support locations. If the types and quantities of emergency equipment to be transported to support point A are emergency lighting equipment Station, communication equipment Environmental monitoring equipment tower;

[0108] S2 obtains the types and quantities of emergency equipment at the supply point, that is, reads the types and quantities of emergency equipment that each supply point can supply from the database table, as shown below:

[0109] Supply point B1 has emergency lighting equipment 11 =500 units, communication equipment 12 =300 sets, environmental monitoring equipments 13 = 50 units; supply point B2 has emergency lighting equipment 21 =200 units, communication equipment 22 =50 sets, environmental monitoring equipment 23 = 100 units; supply point B3 has emergency lighting equipment 31= 1000 units, communication equipment 32 =100 sets, environmental monitoring equipment 33 =200 units.

[0110] S3 determines whether the supply and demand of regional supply points match, that is, the emergency lighting equipment, communication equipment, and environmental monitoring equipment that can be provided by supply points B1, B2, and B3 within the area of ​​​​security point A meet the types and quantities of emergency equipment required by security point A, that is:

[0111] Emergency lighting equipment:

[0112] Communication equipment:

[0113] Environmental monitoring equipment:

[0114] S4 uses an improved slime mold algorithm to solve the optimal dispatch model of emergency equipment. According to the obtained model parameters, an improved slime mold algorithm is used to solve the optimal dispatch model of emergency equipment. The model parameters are obtained by reading the database table, that is, the supply point B1 has emergency lighting equipment s 11 =500 units, communication equipment 12 =300 sets, environmental monitoring equipments 13 = 50 units; supply point B2 has emergency lighting equipment 21 =200 units, communication equipment 22 =50 sets, environmental monitoring equipment 23 = 100 units; supply point B3 has emergency lighting equipment 31 = 1000 units, communication equipment 32 =100 sets, environmental monitoring equipment 33 = 200 units; the air, rail and road distances from supply point B1 to support point A are d 11 =480km,d 21 =535km and d 31 = 508km, the air, rail and road distances from supply point B2 to support point A are d 12 =325km,d 22 =400km and d 32 = 350km, the air, rail and road distances from supply point B3 to support point A are d 13 =450km,d 23 =580km and d 33 =526km; the loss coefficients of aviation, railway and highway transportation processes calculated by historical record data are α 1 =0.01%, α 2 =0.1% and α 3=0.5%; the transport capacity of aviation, railway and road (only considering the load, ignoring the volume of emergency equipment) owned by supply point B1 are C 11 =3000kg, C 12 =40000kg and C 13 = 5000kg, the transport capacity of air, rail and road (only considering the load, ignoring the volume of emergency equipment) of supply point B2 are C 21 =3000kg, C 22 =30000kg and C 23 = 3000kg, the transport capacity of air, rail and road (only considering the load, ignoring the volume of emergency equipment) of supply point B3 are C 31 =3000kg, C 32 =80000kg and C 33 =8000kg; the transport capacity required for emergency lighting equipment is c 1 = 100kg, the required transport capacity of the communication equipment is c 2 =50kg, the required transport capacity of environmental monitoring equipment is c 3 = 200kg; the average air, rail and road freight rates per ton per kilometer for transporting emergency equipment are p 1 360 yuan, p 2 50 yuan and p 3 is 1 yuan; the average speeds of air, rail and road transportation are v 1 =800km / h, v 2 =250km / h and v 3 =80km / h.

[0115] Substitute the above parameters into formulas (2) to (14) as the constraints of the emergency equipment dispatch model, namely:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] From the above calculation formula, we can know that the decision variable of the emergency equipment dispatch model is x 111 , x 112 , x 113 , x 121 , x 122 , x 123 , x 131 , x 132 , x 133 , x 211 , x 212 , x 213 , x 221 , x 222 , x 223 , x 231 , x 232 , x 233 , x 311 , x 312 , x 313 , x 321 , x 322 , x 323 , x 331 , x 332 , x 333 , r 11 , r 12 , r 13 , r 21 , r 22 , r 23 , r 31 , r 32 , r 33 etc., where: x 111 represents the number of emergency lighting equipment at support point B1 that chooses air transportation, x 111 ∈[0,500],x 111 is an integer, that is, x 111 The upper bound is 500 and the lower bound is 0; r 11 Indicates whether the support point B1 chooses air transportation, r 11 The value is 0 or 1, that is, r 11 The upper bound is 1 and the lower bound is 0; the values ​​of other decision variables can be known from the above parameters and will not be repeated.

[0128] Since T, P is affected by the above decision variables, namely:

[0129]

[0130]

[0131] Knowable It is a function of the above 36 decision variables. Therefore, f(T,P) can be transformed into a function of 36 decision variables as dependent variables, that is:

[0132]

[0133] The present invention converts f(x 111 ,…,x 333 ,r 11 ,…,r 33 ) is used as the fitness function of the proposed improved slime mold algorithm. In this embodiment, the total time and the total cost are considered equally important, so w T =w P =0.5;λ 1 =0.01,λ 2 =0.0001; where λ 1 and λ 2 It can be obtained through experience or other decimal values. The purpose of setting these two parameters is to slow down the trend of the exponential function, control the calculation results of the fitness function within a reasonable range, and save computing resources; parameter p k and d kj Obtained by reading the database table.

[0134] The specific solution process of the emergency equipment transportation model is as follows:

[0135] S41 sets the parameters, the number of iterations of the improved slime mold algorithm T is 1000, and the population diversity of the slime mold is selected as the population size N z =100, initialize the slime mold population The dimension obtained from the number of decision variables is D = 36. Initialize the population X * (0), that is, X * (0) is a 2000×36 matrix vector;

[0136] S42 uses the slime mold population diversity selection strategy to calculate the comprehensive similarity of the slime mold population according to formula (17) to obtain the slime mold population comprehensive similarity set {S(X 1 (0)),S(X 2 (0)),…,S(X 2000 (0))}, sort the comprehensive similarity values ​​in the set from small to large, and select the first N with smaller values z = 100 corresponding slime mold populations form X(0), that is:

[0137]

[0138] S43 Calculate the slime mold weight coefficient W and parameters b, c, and obtain b = 0.785, c = 0.999 through formulas (23) - (26); if r is randomly taken as 0.3, then:

[0139]

[0140] S44 Update the individual position and generate a random number r 1 , and the randomly initialized probability z is taken as 0.03; judge the random number r 1 and the parameter z. If r 1 < z, update the individual position according to the corresponding formula; otherwise, update the parameters p i , v b , v c , r 2 , judge r 2 and the parameter p i and update the individual position X 1 (t + 1), X 2 (t + 1), …, X 100 (t + 1);

[0141] S45 Calculate the fitness value f(x 111 , …, x 333 , r 11 , …, r 33 ), and update the global optimal solution [x 111 , …, x 333 , r 11 , …, r 33 ;

[0142] S46 Judge whether the end condition is met, that is, whether the number of iterations is greater than T = 1000. If it is met, output the global optimal solution and the fitness value; otherwise, repeat steps S42 - S46. Solve the emergency equipment according to the improved slime mold algorithm to obtain the optimal solution [x 111 , x 112 , x 113 , x 121 , x 122 , x 123 , x 131 , x 132 , x 133 , x 211 , x 212 , x 213 , x 221 , x 222 , x 223 , x 231 , x 232 , x 233 , x 311 , x 312 , x313 , x 321 , x 322 , x 323 , x 331 , x 332 , x 333 , r 11 , r 12 , r 13 , r 21 , r 22 , r 23 , r 31 , r 32 , r 33 ]=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,300,50,200,50,100,1000,0,0,0,0,0,0,0,1,1,1].

[0143] S5 outputs the emergency equipment transportation plan, outputs the solution obtained in S4 as the result of the emergency equipment transportation plan, and ends the process.

[0144] Embodiment 2:

[0145] In order to better illustrate that the improved slime mold algorithm proposed in the present invention has more advantages, a comparison is made with the traditional slime mold algorithm based on Example 1, and the effect comparison is as follows: Figure 3 As shown, the vertical axis represents the fitness value, and the horizontal axis represents the number of algorithm iterations. The improved slime mold algorithm proposed in the present invention is more efficient in obtaining emergency equipment transportation plans than the traditional slime mold algorithm.

[0146] In the embodiment of the present invention, the diversity of the slime mold population is improved by improving the initialization method of the slime mold population, the comprehensive similarity of the slime mold population is calculated by using cosine similarity, and the slime mold population with a smaller comprehensive similarity is selected. In addition, the spatial distance between the slime mold populations can also be considered when initializing the slime mold population. The comprehensive similarity of the slime mold population is judged by the distance between the slime mold populations in the high-dimensional space, and the diversity effect of initializing the slime mold population in the present invention can also be achieved. In the embodiment of the present invention, the improved slime mold algorithm is used to solve the emergency equipment dispatching model, and other similar intelligent optimization algorithms can also replace the improved algorithm to solve the model, such as the sparrow search algorithm, the whale optimization algorithm, etc.

[0147] Compared with other methods, the method proposed in the embodiment of the present invention applies an improved slime mold algorithm to the process of solving the emergency equipment dispatch model, and realizes the generation of emergency equipment dispatch plans. When constructing the emergency equipment dispatch model, the coexistence of multiple modes of transportation is integrated into the model constraints, making the generation of emergency equipment dispatch plans more abundant; in addition, the method provided by the present invention reduces the subjective influence of human beings, solves the emergency equipment dispatch model through the improved slime mold algorithm, improves the diversity of the initial position of the group, solves the problem of falling into local extreme values ​​during the solution, reduces the solution operation time, and improves the efficiency of generating emergency equipment dispatch plans.

[0148] In addition, the embodiment of the present invention also provides an emergency equipment dispatching plan generation system based on the improved slime mold algorithm, see Figure 4 As shown, the system includes:

[0149] The model building module 310 is used to build an emergency equipment transportation model based on the influencing factors of emergency equipment transportation and the emergency equipment transportation loss coefficient; the emergency equipment transportation model is used to generate the number of emergency equipment provided by the supply point to the demand guarantee point and the corresponding transportation method;

[0150] The information acquisition module 320 is used to acquire emergency transport task information; the emergency transport task information includes: demand guarantee information and supply point information;

[0151] The output module 330 is used to solve the emergency equipment transportation model using the improved slime mold algorithm according to the emergency transportation task information, and output the emergency equipment transportation plan; the emergency equipment transportation plan includes the transportation subtasks of each supply point that constitute the emergency transportation task.

[0152] The emergency equipment dispatching scheme generation system based on the improved slime mold algorithm provided in the embodiment of the present application can be specific hardware on the equipment or software or firmware installed on the equipment. The system provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. The emergency equipment dispatching scheme generation system based on the improved slime mold algorithm provided in the embodiment of the present application has the same technical features as the emergency equipment dispatching scheme generation method based on the improved slime mold algorithm provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0153] An embodiment of the present application further provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned embodiments.

[0154] Figure 5 A structural schematic diagram of an electronic device provided in an embodiment of the present application, the electronic device 400 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected via the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0155] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0156] The bus 42 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0157] Among them, the memory 41 is used to store programs, and the processor 40 executes the program after receiving the execution instruction. The method executed by the process definition device disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0158] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 40. The above processor 40 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0159] Corresponding to the above method, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the above method.

[0160] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0161] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0162] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0163] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0164] It should be noted that similar numbers and letters represent similar items in the accompanying drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating an emergency equipment transportation plan based on an improved slime mold algorithm, characterized in that: include: Based on the influencing factors of emergency equipment transportation and the loss coefficient of emergency equipment transportation, an emergency equipment transportation model is constructed; the emergency equipment transportation model is used to generate the number of emergency equipment provided by the supply point to the demand guarantee point and the corresponding transportation method; Obtain emergency transportation task information; The emergency transportation task information includes: demand guarantee information and supply point information; According to the emergency transportation task information, the emergency equipment transportation model is solved using an improved slime mold algorithm to output an emergency equipment transportation plan; the emergency equipment transportation plan includes transportation subtasks of various supply points that constitute the emergency transportation task.

2. The method for generating an emergency equipment transportation plan based on an improved slime mold algorithm according to claim 1, characterized in that: The method further comprises: The loss coefficient of each mode of transportation is calculated through historical emergency equipment transportation data to generate the emergency equipment transportation loss coefficient.

3. The method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm according to claim 2, characterized in that: The factors affecting the transportation of emergency equipment include: transportation time, transportation cost, transportation force, transportation method, and transportation distance; Based on the influencing factors of emergency equipment transportation and the loss coefficient of emergency equipment transportation, an emergency equipment transportation model is constructed, including: Determine a decision variable based on the influencing factors of emergency equipment transportation and the loss coefficient of emergency equipment transportation; the decision variable is used to represent the quantity of emergency equipment transported by different transportation modes; Constraining the decision variables according to the emergency transportation task information to determine the total transportation time function and the total transportation cost function; Based on the total transportation time function and the total transportation cost function, an emergency equipment dispatching model is constructed.

4. The method for generating an emergency equipment transportation plan based on an improved slime mold algorithm according to claim 1, characterized in that: The demand guarantee information includes: the types and quantity of emergency equipment to be transported, and the demand guarantee location; the supply point information includes: the types and quantity of emergency equipment at the supply point, and the transportation distance of various modes of transportation; the method also includes: Based on the demand guarantee information and the supply point information, determine whether regional supply and demand match.

5. The method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm according to claim 1, characterized in that: According to the emergency transportation task information, the emergency equipment transportation model is solved using the improved slime mold algorithm, and an emergency equipment transportation plan is output, including: Setting slime mold algorithm solution parameters; the slime mold algorithm solution parameters include: initialization of slime mold population size, slime mold population diversity selection population size, number of iterations, dimension, and initialization population; The slime mold population was selected through the slime mold population diversity selection strategy, and the fitness value of each slime mold population was calculated.

6. The method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm according to claim 5, characterized in that: According to the emergency transportation task information, the emergency equipment transportation model is solved by using an improved slime mold algorithm to output an emergency equipment transportation plan, and further includes: Calculating slime mold weight coefficients and slime mold parameters based on the slime mold algorithm solution parameters; Update individual positions based on pre-generated random numbers and randomly initialized probabilities; The fitness value is calculated through the fitness function of the improved slime mold algorithm and the global optimal solution is updated.

7. The method for generating an emergency equipment dispatching plan based on an improved slime mold algorithm according to claim 6, characterized in that: According to the emergency transportation task information, the emergency equipment transportation model is solved by using an improved slime mold algorithm to output an emergency equipment transportation plan, and further includes: Determine whether the termination condition is met according to the number of iterations, and if so, output the global optimal solution and fitness value; otherwise, repeat the steps in claim 6.

8. An emergency equipment dispatching plan generation system based on an improved slime mold algorithm, characterized in that: include: A model building module, used to build an emergency equipment transportation model based on the influencing factors of emergency equipment transportation and the emergency equipment transportation loss coefficient; the emergency equipment transportation model is used to generate the number of emergency equipment provided by the supply point to the demand guarantee point and the corresponding transportation method; Information acquisition module, used to obtain emergency transportation task information; The emergency transportation task information includes: demand guarantee information and supply point information; The output module is used to solve the emergency equipment transportation model using an improved slime mold algorithm according to the emergency transportation task information, and output an emergency equipment transportation plan; the emergency equipment transportation plan includes the transportation subtasks of each supply point that constitute the emergency transportation task.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to execute the method according to any one of claims 1 to 7.

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