Multi-resource allocation method and device based on optimization algorithm

Abstract

The invention discloses a multi-resource allocation method and device based on an optimization algorithm, and relates to the technical field of resource allocation, and the method comprises the steps: obtaining basic data of a target region; the basic data comprises to-be-allocated resource data and task data of the target area; deploying an initial position of each task unit according to the basic data; according to the initial position of each task unit, constructing a task demand configuration optimization model by adopting an electric field theory, solving by adopting a sequential quadratic programming algorithm, and calculating to obtain the position of each task node and the position of a guarantee node; and resource allocation is carried out based on the position of each task node and the position of the guarantee node, and initial resources are allocated for each task node. According to the invention, the accuracy of node site selection and resource allocation can be improved and guaranteed, and the resource allocation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of resource allocation, and in particular to a multi-resource allocation method and device based on an optimization algorithm. Background Art

[0002] At present, for multi-resource allocation scenarios, the location of the security node needs to be determined before resource allocation. The location selection of the security node usually involves multiple objectives (such as cost, distance, time, coverage, etc.), but traditional methods often find it difficult to take all of these objectives into account at the same time. They usually determine the initial location by optimizing a single goal (such as the shortest path), thereby ignoring other key indicators and resulting in unbalanced allocation. When calculating the location of the security node, traditional methods usually use heuristic algorithms or simple geometric programming algorithms, but these algorithms may only focus on local optimal solutions and do not consider the impact of global demand and other factors, resulting in inaccurate and unreasonable security node locations, and failing to achieve the maximum coverage or shortest path effect of resources.

[0003] In practical applications, the location of security nodes is affected by many factors, such as terrain, transportation, and material demand. Traditional methods often lack flexibility and adaptability when dealing with such complex constraints, which can easily lead to the selected location being inoperable in the real environment. As the scale of the problem increases, the location selection of security nodes becomes more complex, and the computational efficiency of traditional methods becomes very low, especially when facing large-scale, multi-objective, and multi-constrained scenarios, it may take a long time to find a suitable location.

[0004] In summary, the current traditional methods for multi-resource allocation and its guarantee node location processing generally have the problems of low accuracy and low computational efficiency. Therefore, how to provide a more accurate and efficient multi-resource allocation method has become a technical problem to be solved in this field. Summary of the invention

[0005] The purpose of this application is to provide a multi-resource allocation method and device based on an optimization algorithm, which can improve the accuracy of node site selection and resource allocation and enhance the efficiency of resource allocation.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a multi-resource allocation method based on an optimization algorithm, and the multi-resource allocation method based on the optimization algorithm comprises the following steps:

[0008] Acquire basic data of the target area; the basic data includes resource data and task data to be allocated in the target area;

[0009] Deploy the initial positions of each task unit according to the basic data;

[0010] According to the initial positions of each task unit, construct an optimization model for task requirement configuration using the electric field theory, and solve it using the sequential quadratic programming algorithm to calculate the positions of each task node and the positions of support nodes;

[0011] Based on the positions of each task node and the positions of support nodes, perform resource allocation and equip each task node with initial resources.

[0012] Optionally, deploying the initial positions of each task unit according to the basic data specifically includes the following steps:

[0013] According to the basic data, use the map drawing toolkit to draw the initial positions of each task unit within the corresponding target area range on the map;

[0014] According to the task standard requirements, confirm the rationality of the interval distance between each task unit.

[0015] Optionally, constructing an optimization model for task requirement configuration using the electric field theory according to the initial positions of each task unit, and solving it using the sequential quadratic programming algorithm to calculate the positions of each task node and the positions of support nodes specifically includes the following steps:

[0016] According to the initial positions of each task unit, construct an optimization model for task requirement configuration based on the electric field theory, and use the task requirement configuration optimization model to determine the positions of support nodes;

[0017] According to the positions of the support nodes and the initial positions of each task unit, use the sequential quadratic programming algorithm to solve and calculate the positions of the task nodes.

[0018] Optionally, constructing an optimization model for task requirement configuration based on the electric field theory according to the initial positions of each task unit, and using the task requirement configuration optimization model to determine the positions of support nodes specifically includes the following steps:

[0019] Suppose there are n task units, and the position coordinates of the initial positions of each task unit are (lat_1, lng_1), (lat_2, lng_2), (lat_3, lng_3), (lat_4, lng_4), (lat_i, lng_i), ……, (lat_n, lng_n), and the corresponding equipment task requirements are Q1, Q2, Q3, Q4, Q i 、……、Q n , where, (lat_i, lng_i) and Q iLet the position coordinates be the initial positions of the \(i\)-th task unit and the corresponding equipment task demand. The position coordinates of the task force point \(P\) are \((best\_lat, best\_lng)\). Then, the work done by the task force point \(P\) to transport the task demand required by the task unit to the first task unit \((lat\_1, lng\_1)\) is expressed as:

[0020]

[0021] Among them, \(F\) represents the work done by the task force point \(P\) to transport the task demand required by the task unit to the first task unit;

[0022] When each point in the task demand area is used as the task detachment configuration position, the work done to complete the specific task demand becomes the task demand potential energy of the high point in the equipment task field, denoted by \(E(P)\). Then, when there is only one task unit in the task demand area, the task demand potential energy from the task force point \(P\) to any configuration position is expressed as:

[0023]

[0024] Among them, \(E(P)\) represents the task demand potential energy from the task force point \(P\) to any configuration position, \(F(P→Q1)\) represents the work done by the task force point \(P\) corresponding to the first task unit, \(K1\) is the road detour coefficient from the task force point \(P\) to the first task unit, and \(Q1\) is the equipment task demand corresponding to the first task unit;

[0025] For multiple task units, a composite task field is formed in the task demand area and satisfies the superposition principle, expressed as:

[0026]

[0027] Among them, \(F(P→Q n ) represents the work done by the task force point \(P\) corresponding to the \(n\)-th task unit, \(K i is the road detour coefficient from the task force point \(P\) to the \(i\)-th task unit, and \(Q i is the equipment task demand corresponding to the \(i\)-th task unit;

[0028] From this, an optimization model for the task demand configuration of the equipment task force can be obtained. Select the position with the lowest potential energy in the task demand area, that is, \(minE(P)\), to minimize the potential energy, expressed as:

[0029]

[0030] Among them, \(x\) and \(y\) are the position coordinates of the position with the lowest potential energy;

[0031] Then there is:

[0032]

[0033] Among them, d i is the distance from the task force point P to the i-th task unit,

[0034] Finally, the position coordinates of the guarantee node are obtained, expressed as:

[0035]

[0036] Among them, cen_lat and cen_lng are the position coordinates of the guarantee node.

[0037] Optionally, according to the position of the guarantee node and the initial positions of the respective task units, a sequential quadratic programming algorithm is used for solution to calculate the positions of the task nodes, specifically including the following steps:

[0038] According to the position of the guarantee node and the initial positions of the respective task units, the objective function and the constraint relationship are respectively determined based on the electric field strategy; the objective function refers to the functional relationship with the goal of minimizing the optimization cost, and the constraint relationship refers to the functional relationship that constrains the distances between the respective task nodes and the distances from the task nodes to the guarantee node;

[0039] Based on the objective function and the constraint relationship, a sequential quadratic programming algorithm is used for optimization solution to obtain the position coordinates of the respective task nodes.

[0040] Optionally, the expression of the objective function is:

[0041]

[0042] Among them, cen_lat and cen_lng are the position coordinates of the guarantee node, and lat_i and lng_i are the position coordinates of the i-th task node;

[0043] The expression of the constraint relationship is:

[0044]

[0045] Among them, lat_j and lng_j are the position coordinates of the j-th task node.

[0046] Optionally, based on the positions of the respective task nodes and the position of the guarantee node, resource allocation is performed to allocate initial resources to the respective task nodes, specifically including the following steps:

[0047] Based on the positions of the respective task nodes and the positions of the support nodes, initial resources are allocated to each task node according to the resource requirements, required resource types, resource consumption, number of people, urgency of actions, and priority of resource allocation of each task node.

[0048] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the multi-resource allocation method based on an optimization algorithm described in the first aspect.

[0049] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the multi-resource allocation method based on an optimization algorithm described in the first aspect.

[0050] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the multi-resource allocation method based on an optimization algorithm described in the first aspect.

[0051] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0052] The present application provides a multi-resource allocation method and device based on an optimization algorithm. First, the initial positions of each task unit are deployed, and then according to the initial positions of each task unit, an optimization model for task requirement configuration is constructed using the electric field theory, and the sequential quadratic programming algorithm is used for solution to calculate the positions of each task node and the positions of the support nodes. After determining the positions of the task nodes and the support nodes, initial resources are allocated to the task nodes. Based on the electric field theory, by establishing an optimization model for task requirement configuration and using the sequential quadratic programming algorithm for solution, the positions of the task nodes and the support nodes can be determined, the rapid and accurate site selection of the support nodes and the task nodes can be realized, the accuracy of support node site selection and resource allocation can be improved, and the efficiency of resource allocation can be enhanced. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0054] Figure 1 It is an application environment diagram of a multi-resource allocation method based on an optimization algorithm in an embodiment of the present application.

[0055] Figure 2 Schematic flowchart of a multi-resource allocation method based on an optimization algorithm provided by an embodiment of the present application.

[0056] Figure 3 Schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0057] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0058] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0059] The multi-resource allocation method based on an optimization algorithm provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed in the cloud or on other servers. The terminal 102 can send the basic data of the target area to the server 104. After receiving the basic data of the target area, for the basic data of the target area, the server 104 deploys the initial positions of each task unit according to the basic data; according to the initial positions of each task unit, according to the initial positions of each task unit, constructs a task requirement configuration optimization model using the electric field theory, and uses the sequential quadratic programming algorithm to solve it, calculates the positions of each task node and the positions of support nodes; performs resource allocation based on the positions of each task node and the positions of support nodes, and allocates initial resources to each task node. The server 104 can feedback the obtained resource allocation result to the terminal 102. In addition, in some embodiments, the multi-resource allocation method based on an optimization algorithm can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly perform position deployment and resource allocation for the basic data of the target area, or the server 104 can obtain the basic data of the target area from the data storage system and perform position deployment and resource allocation for the basic data of the target area.

[0060] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0061] In an exemplary embodiment, as Figure 2 shown, a multi-resource allocation method based on an optimization algorithm is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or can be jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in

[0062] Step S1: Obtain the basic data of the target area. Among them, the basic data includes data such as the resource data to be allocated and task data in the target area. The task data includes data such as tasks to be executed and task standard requirements. The task standard requirements include the execution specification standards for deploying task units.

[0063] Step S2: Deploy the initial positions of each task unit according to the basic data.

[0064] In this embodiment, step S2 deploys the initial positions of each task unit according to the basic data, and specifically includes the following steps:

[0065] Step S21: According to the basic data, use a map drawing toolkit to draw the initial positions of each task unit within the corresponding target area range on the map.

[0066] Step S22: Confirm the rationality of the interval distance between each task unit according to the task standard requirements.

[0067] Step S3: According to the initial positions of each task unit, construct an optimization model for task requirement configuration using the electric field theory, and use the sequential quadratic programming algorithm to solve it, and calculate the positions of each task node and the positions of guarantee nodes.

[0068] The multi-resource allocation method based on an optimization algorithm proposed in this embodiment, where the optimization algorithm refers to constructing an optimization model for task requirement configuration based on the electric field theory to determine the positions of the guarantee nodes, and using the sequential quadratic programming algorithm to solve and calculate the positions of the task nodes. In addition, besides the sequential quadratic programming algorithm, a genetic immune algorithm can also be used for solving. The genetic immune algorithm is an optimization algorithm obtained by combining the genetic algorithm and the immune algorithm. This embodiment preferably uses the sequential quadratic programming algorithm for solving.

[0069] In this embodiment, a task unit is the overall task that needs to allocate resources, a task node is a node under the task unit that needs to refine the resource allocation and execute the task, and a guarantee node is a node that provides resource guarantee. The purpose of this embodiment is to determine the positions of the task unit, task nodes, and guarantee nodes and complete a reasonable resource allocation.

[0070] In this embodiment, in step S3, according to the initial positions of the respective task units, an optimization model for task requirement configuration is constructed based on the electric field theory, and the sequential quadratic programming algorithm is used for solving to calculate the positions of the respective task nodes and the guarantee nodes, which specifically includes the following steps:

[0071] Step S31: According to the initial positions of the respective task units, construct an optimization model for task requirement configuration based on the electric field theory, and use the task requirement configuration optimization model to determine the positions of the guarantee nodes;

[0072] Step S32: According to the positions of the guarantee nodes and the initial positions of the respective task units, use the sequential quadratic programming algorithm to solve and calculate the positions of the task nodes.

[0073] In this embodiment, step S31 constructs an optimization model for task requirement configuration based on the electric field theory according to the initial positions of the respective task units, and uses the task requirement configuration optimization model to determine the positions of the guarantee nodes, which specifically includes the following steps:

[0074] Step S311: Assume there are n task units, and the position coordinates of the initial positions of the respective task units are (lat_1, lng_1), (lat_2, lng_2), (lat_3, lng_3), (lat_4, lng_4), (lat_i, lng_i), ……, (lat_n, lng_n), and the corresponding equipment task demand quantities are Q1, Q2, Q3, Q4, Q i 、……、Q n , where, (lat_i, lng_i) and Q iThey are the position coordinates of the initial position of the i-th task unit and the corresponding equipment task demand. The position coordinates of the task force point P are (best_lat, best_lng). Then, the work done by the task force point P to transport the task demand required by the task unit to the first task unit (lat_1, lng_1) is expressed as:

[0075]

[0076] Among them, F represents the work done by the task force point P to transport the task demand required by the task unit to the first task unit.

[0077] Step S312: When each point in the task demand area is used as the task detachment configuration position, the work done to complete the specific task demand becomes the task demand potential energy of the high point in the equipment task field, denoted as E(P). Then, when there is only one task unit in the task demand area, the task demand potential energy from the task force point P to any configuration position is expressed as:

[0078]

[0079] Among them, E(P) represents the task demand potential energy from the task force point P to any configuration position, F(P→Q1) represents the work done by the task force point P to the first task unit, K1 is the road detour coefficient from the task force point P to the first task unit, and Q1 is the equipment task demand corresponding to the first task unit.

[0080] Step S313: For multiple task units, a composite task field is formed in the task demand area and satisfies the superposition principle, expressed as:

[0081]

[0082] Among them, F(P→Q n ) represents the work done by the task force point P to the n-th task unit, K i is the road detour coefficient from the task force point P to the i-th task unit, and Q i is the equipment task demand corresponding to the i-th task unit.

[0083] From this, the task demand configuration optimization model of the equipment task force can be obtained. Select the position with the lowest potential energy in the task demand area, that is, minE(P), to minimize the potential energy, expressed as:

[0084]

[0085] Among them, x and y are the position coordinates of the position with the lowest potential energy.

[0086] Then there is:

[0087]

[0088] Among them, d i is the distance from the task force point P to the i-th task unit,

[0089] Finally, the position coordinates of the guarantee node are obtained, expressed as:

[0090]

[0091] Among them, cen_lat and cen_lng are the position coordinates of the guarantee node.

[0092] In this embodiment, step S32 is solved by using the sequential quadratic programming algorithm according to the position of the guarantee node and the initial positions of the respective task units, and the position of the task node is calculated, which specifically includes the following steps:

[0093] Step S321: Based on the position of the guarantee node and the initial positions of the respective task units, the objective function and the constraint relationship are respectively determined based on the electric field strategy; the objective function refers to the functional relationship with the goal of minimizing the optimization cost, and the constraint relationship refers to the functional relationship that constrains the distances between the respective task nodes and the distances between the task nodes and the guarantee node.

[0094] Among them, the expression of the objective function is:

[0095]

[0096] Among them, cen_lat and cen_lng are the position coordinates of the guarantee node, and lat_i and lng_i are the position coordinates of the i-th task node.

[0097] The expression of the constraint relationship is:

[0098]

[0099] Among them, lat_j and lng_j are the position coordinates of the j-th task node.

[0100] Step S322: Based on the objective function and the constraint relationship, the sequential quadratic programming algorithm is used for solving to obtain the position coordinates of the respective task nodes.

[0101] Step S4: Based on the positions of the respective task nodes and the position of the guarantee node, resource allocation is performed to allocate initial resources to the respective task nodes.

[0102] In this embodiment, step S4 performs resource allocation based on the positions of the respective task nodes and the support nodes, and allocates initial resources to each task node, which specifically includes the following steps:

[0103] Based on the positions of the respective task nodes and the support nodes, according to the resource requirements, required resource types, resource consumption, number of people, urgency of the action, and priority of dividing resources of each task node, initial resources are allocated to each task node.

[0104] To make the technical solution of this embodiment clearer, the following will take the form of an example to illustrate in detail the specific implementation process of this embodiment.

[0105] This embodiment proposes a multi-resource allocation method based on an optimization algorithm. First, the initial positions of the task units are deployed and made reasonable according to the task standard requirements. Then, the positions of the support nodes and task nodes are calculated based on the initial positions of the task units. Finally, initial resources are allocated according to the specific conditions of the task nodes.

[0106] In this embodiment, deploying the initial positions of the task units and making them reasonable with reference to the task standard requirements includes using a map drawing toolkit to draw the initial positions of the task units in the road network, mainly paying attention to the interval distance between the task units, such as setting the interval distance to 3 km, etc.

[0107] In this embodiment, calculating the positions of the task nodes based on the initial positions of the task units includes using a genetic immune algorithm to calculate the optimal initial positions of the task nodes with reference to the task standard requirements, or using a sequential quadratic programming method to solve the non-linear constraint problem to obtain the optimal initial positions of the task nodes, and then obtaining the positions of other task nodes according to the optimal initial positions with reference to the task standard requirements.

[0108] In this embodiment, performing initial resource allocation according to the specific conditions of the task nodes includes configuring the quantity and type of resources of the task nodes according to the number of people in the task nodes, the strength (combat power or priority) of the task units, etc.

[0109] In this embodiment, by deploying the initial positions of the task units, it is judged whether the interval distance between two task units meets the task standard requirements and whether it is within the set rectangular range, so that the positions meet the task standard requirements and are reasonable. The positions of the task nodes are calculated based on the initial positions of the task units; finally, initial resource allocation is performed according to the specific conditions of the task nodes, which can deploy the task force at the task nodes and will, to a certain extent, affect the command decisions on resource and personnel scheduling, improve the accuracy of support node site selection and resource allocation, enhance the efficiency of resource allocation, and strengthen the ability to respond to emergencies, laying a solid foundation for the success of the task and being conducive to ensuring scientific layout, smooth start, effective control of the situation, and successful conclusion.

[0110] When this embodiment is specifically applied, it mainly includes the following content.

[0111] (1) The initial positions of the deployment task units.

[0112] In this embodiment, the dc-sdk toolkit is used to draw the initial positions of the task units within the specified target area on the map, including drawing the corresponding task units in the corresponding areas, and setting a certain interval distance between every two task units as much as possible to meet the requirements of the task standard.

[0113] (2) Construct an optimization model for task requirement configuration based on the electric field theory and initially obtain the positions of the guarantee nodes.

[0114] In this embodiment, assume that there are five task units with position coordinates (lat_1, lng_1), (lat_2, lng_2), (lat_3, lng_3), (lat_4, lng_4), and (lat_5, lng_5) respectively, the equipment task requirements are Q1, Q2, Q3, Q4, Q5, and the position coordinates of the task force point (optimal resource task node) P are (best_lat, best_lng). Then the work done by the task force point P to transport the task requirements required by the task units to the first task unit is The work done to complete specific task requirements when each point in the task requirement area is used as the configuration position of the task detachment is the task requirement potential energy of the high point in the equipment task field, which is represented by E(P). Then when there is only one task unit C in the task requirement area, the task requirement potential energy at any configuration position of the task force is:[[]]

[0115]

[0116] Among them, E(P) represents the task requirement potential energy from the task force point P to any configuration position, F(P→Q1) represents the work done by the task force point P to the first task unit correspondingly, and K1 is the road detour coefficient from the task force point P to the first task unit, which is determined according to the road conditions. Therefore, for multiple task units, a composite task field will be formed within the task requirement area and satisfy the superposition principle, that is:

[0117]

[0118] Among them, F(P→Q n ) represents the work done by the task force point P to the nth task unit correspondingly, K i is the road detour coefficient from the task force point P to the ith task unit. In this embodiment, K i = 1.

[0119] From this, an optimized model for the task requirement configuration of equipment mission power can be obtained. It is necessary to select the position with the lowest potential energy within the task requirement area, that is, minE(P). To minimize the potential energy, the following must hold:

[0120]

[0121] where x and y are the position coordinates of the position with the lowest potential energy.

[0122] Then there is:

[0123]

[0124] where d i is the distance from the mission power point P to the i-th mission unit.

[0125] Finally, the position coordinates of the safeguard node are obtained as:

[0126]

[0127] where cen_lat and cen_lng are the position coordinates of the safeguard node.

[0128] (3) According to the initial positions of the mission units determined in (1), use the genetic immune algorithm to calculate the positions of the mission nodes.

[0129] The immune algorithm is a heuristic optimization algorithm based on the biological immune system. It simulates some key concepts and mechanisms in the biological immune system and is usually used to solve optimization problems. Its basic principle draws on some key principles of the biological immune system, such as cloning, selection, competition, and memory. The process of the immune algorithm mainly consists of four parts: encoding of solutions, cloning operation, selection and competition, and memory mechanism. In the immune algorithm, candidate solutions are regarded as antibodies, and the solution space of the optimization problem is regarded as the antigen of the immune system. In the immune system, solutions are usually encoded through chromosomes or vectors so that the algorithm can operate and mutate them. The cloning operation in the immune algorithm simulates the cloning process in the biological immune system. In the cloning operation, excellent solutions are cloned into multiple copies and then adjusted according to their fitness. The selection and competition mechanism in the immune algorithm is used to screen and update candidate solutions to ensure that the algorithm can search in the direction of a better solution. The memory mechanism of the immune algorithm can be used to store and utilize the information obtained in the previous search process to accelerate the search and avoid falling into local optimal solutions.

[0130] The genetic algorithm is a heuristic search and optimization algorithm that simulates the process of biological evolution in nature. Based on Darwin's theory of evolution and Mendel's genetic theory, it searches for the optimal solution in the solution space iteratively generation by generation by simulating mechanisms such as natural selection, crossover, and mutation in the process of biological evolution. Its main principle is to represent the solution of a problem in the form of chromosomes or genes. A chromosome is a representation of a solution, composed of multiple genes, and a gene is an element in a chromosome, representing a feature or parameter. Calculate the fitness of each individual, that is, the quality of the solution. The fitness function is usually determined by the objective function of the problem, which can be a minimization problem or a maximization problem. After calculation, select individuals with higher fitness from the current population as parents through a selection strategy to produce the next generation of the population. Selection strategies usually can use the roulette wheel method, tournament selection method, etc. The selected parent individuals generate new individuals through the crossover operation. The crossover operation simulates the mating process of organisms, and by exchanging some segments of chromosomes, excellent features are retained. Perform a mutation operation on the newly generated individuals, that is, randomly change some genes of the individuals to introduce new mutant individuals and increase the diversity of the population. Add the newly generated individuals to the next generation of the population and replace some parent individuals to form a new population. Repeat the selection, crossover, and mutation operations until the stopping condition is met, such as reaching the maximum number of iterations, convergence of the objective function, etc. The genetic algorithm has good parallel performance and is easy to be implemented in parallel. It can perform global search in the solution space to find the global optimal solution or a solution close to the optimal solution, and is used for various types of optimization problems, including continuous optimization, discrete optimization, combinatorial optimization, etc.

[0131] In this embodiment, a genetic immune algorithm is used for solving. First, the population is initialized. A set of initial position coordinates of task nodes is randomly generated as the initial population, and a certain number of positions are generated and added to the population according to the constraint conditions and specific situations. Secondly, fitness evaluation is carried out: for each individual, its fitness is calculated. The fitness can usually be defined as the total distance, that is, the sum of the distances from all task nodes to the task object. Then, selection operation is carried out: using selection operators such as roulette wheel method, tournament selection, etc., a certain number of individuals are selected from the current population as the parents of the next generation population, and combined with the immune algorithm, individuals with higher immunity are selected as part of the population. Individuals with higher immunity may show greater differences or diversities. Next, crossover operation is carried out: for the selected individuals, crossover operators such as single-point crossover, multi-point crossover, etc. are used for crossover operation to generate new individuals. Then, mutation operation is started: the newly generated individuals are subjected to mutation operation to increase the diversity of the population. The mutation operation can be to change some genes of randomly selected individuals, and the immune algorithm is used to mutate the cloned individuals to introduce new individuals and increase the diversity of the population. In addition, the population needs to be updated: the newly generated individuals are added to the next generation population to replace some parent individuals, and the immune algorithm is used to add the cloned and mutated individuals to the original population to form a new generation population. The above steps are comprehensively iteratively optimized: the selection, crossover, and mutation operations are repeated until the maximum number of iterations is reached or the termination condition is satisfied. Finally, the result is obtained: the fitness of the final population is calculated to evaluate the quality of the optimal solution. In addition, it can be optimized and improved according to requirements, mainly including optimizing and improving the algorithm parameters and operations according to the actual situation, such as the cloning factor, mutation rate, etc., to improve the performance and convergence speed of the algorithm.

[0132] In this embodiment, the positions of task nodes are calculated based on the initial positions of task units. When determining task nodes, two main ideas are adopted to determine the positions of task nodes. One is to first generate a list of candidate points according to the constraint conditions and objective function, and then find the position of the best point among them. Here, the electric field strategy method is mainly used to make non-linear constraints and the sequential quadratic programming algorithm is used for solving. The other is to directly find the position of the best point through an optimization algorithm and then adjust it according to the actual situation. Here, the genetic immune algorithm is used for solving.

[0133] This embodiment adopts the relevant concepts of electric fields, that is, the distribution of each task element within a certain area and the spatial distribution of the influence effects generated by each other during operation in the space-time dimension. In the task field model, both the guarantee node and the task unit are regarded as a point in space. Then, the task force and the task unit can be characterized as "charges". The task ability of the task force and the demand degree of the task unit can be abstracted as the charge quantity of the charge. The attribute value of the task force is positive, and the attribute value of the task unit is negative, corresponding to the task force supplying "energy" and the task unit absorbing "energy" respectively. The larger the charge quantity of the guarantee node and the task unit, the stronger the task ability of the task force and the higher the task demand of the task unit.

[0134] In this embodiment, the electric field strategy is adopted to calculate the position of the task node. First, the problem objective is clarified: to minimize the optimization cost, that is, to minimize the total distance between each logistics center and the demand point (the task node of the demand resource). The objective function is:

[0135]

[0136] The constraint relationship is:

[0137]

[0138] Among them, cen_lat and cen_lng are the position coordinates of the confirmed guarantee node, and lat_i and lng_i are the position coordinates of the i-th task node.

[0139] Then, the sequential quadratic programming algorithm is used for optimization and solution. Specifically, first, a point is initialized and a Lagrange multiplier is initialized, and the convergence threshold and the maximum number of iterations are set. Secondly, the Lagrangian function is defined: Among them, f(x) is the objective function, and δ i is the Lagrange multiplier, and g i (x) = 0 is the inequality constraint function. Then, the gradient and the Hessian matrix are calculated. The gradient at the current task node and the gradient of the constraint are calculated, and the Hessian matrix of the Lagrangian function is calculated. The Hessian matrix is defined as: Calculate the first-order partial derivative of the Lagrangian function Then continue to find the second-order partial derivative. Therefore, each element of the Hessian matrix can be expressed as Among them, H f (x) is the Hessian matrix of the objective function f(x), is the Hessian matrix of the constraint function g i (x). The first-order and second-order derivatives of the Lagrangian function constitute the KKT (Karush-Kuhn-Tucker) conditions, which are the core of solving the constrained optimization problem.

[0140] In this embodiment, the BFGS method can be used for approximate update to reduce the computational cost. The BFGS method is a quasi-Newton method. In each iteration, a sequential quadratic programming subproblem is solved to obtain the search direction of the current iteration point; then a line search is performed to determine an appropriate step size to ensure that the optimal solution is approximated in each step of the update; the KKT conditions are used to update the Lagrange multipliers in each iteration to ensure that the KKT conditions are satisfied in the final solution; finally, a convergence determination is made. If the convergence condition is satisfied, the final solution is returned.

[0141] (4) Based on the positions of the guarantee nodes obtained in (2) and the positions of the task nodes obtained in (3), initial resources are allocated.

[0142] In mission planning, configuring the initial resources of mission units and mission nodes is a key link to ensure the smooth progress of the mission, mainly including the configuration of aspects such as personnel, resources, and equipment. The purpose is to provide sufficient mission and support resources for each mission unit and mission node to cope with expected mission requirements. First, determine the resource requirement analysis, predict the required resource types for each mission node according to the objectives, and estimate the consumption and resource requirements based on historical data, experience, and parameters; secondly, prioritize the resources according to the urgency of the actions to ensure the priority allocation of critical resources; finally, calculate the resource requirements of each mission node and guarantee node. Clicking on configuring the initial resources in the software will perform the configuration according to the number of people at the mission nodes and guarantee nodes.

[0143] This embodiment proposes a multi-resource allocation method based on an optimization algorithm, which can establish a perfect resource configuration plan before the mission starts, optimize the selection, improve the accuracy of guarantee node location selection and resource allocation, enhance the efficiency of resource allocation, improve the ability to respond to emergencies, and lay a solid foundation for the success of the mission.

[0144] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store location deployment and resource allocation data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. The computer program, when executed by the processor, implements a multi-resource allocation method based on an optimization algorithm.

[0145] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0146] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0147] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0148] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0149] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0150] In this embodiment, a genetic immune algorithm is formed by combining the genetic algorithm and the immune algorithm. The genetic immune algorithm has the advantage of global optimization. Among them, the genetic algorithm continuously generates new candidate solutions through operations such as selection, crossover, and mutation, and the immune algorithm helps maintain population diversity through the memory and inhibition mechanisms of antibodies to prevent falling into local optimal solutions. The genetic immune algorithm after the combination can effectively solve the problem that traditional methods only focus on local optimality, thereby finding the position of the global optimal safeguard node. The genetic immune algorithm can handle multi-objective optimization problems. It can find the optimal solution that balances multiple objectives (such as distance, cost, demand coverage, etc.) according to the joint constraints of multiple objectives through a multi-objective evolutionary strategy, avoiding the limitations brought by single-objective optimization. The genetic immune algorithm performs excellently in dealing with complex environmental problems. Since the genetic algorithm is essentially based on a population search strategy, it can adaptively handle complex constraint conditions such as terrain and traffic. In addition, the introduction of the immune mechanism can effectively improve the adaptability of the genetic immune algorithm in a changing environment, thereby more accurately determining the position of the safeguard node. In addition, this embodiment adopts a sequential quadratic programming (SQP) method to calculate the initial safeguard, and the sequential quadratic programming algorithm can effectively handle nonlinear optimization problems with constraints. It solves the complex nonlinear problem by approximating it as a series of quadratic programming problems, which can significantly improve the calculation efficiency, especially for dealing with large-scale optimization problems.

[0151] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0152] Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A multi-resource allocation method based on an optimization algorithm, characterized in that: The multi-resource allocation method based on the optimization algorithm comprises: Acquire basic data of the target area; the basic data includes resource data and task data to be allocated in the target area; Deploy the initial positions of the various task units according to the basic data; According to the initial positions of the various task units, the electric field theory is used to construct a task requirement configuration optimization model, and the sequential quadratic programming algorithm is used to solve it, and the positions of the various task nodes and the guarantee nodes are calculated; Resources are allocated based on the positions of the various task nodes and the positions of the guarantee nodes, and initial resources are allocated to each task node.

2. The multi-resource allocation method based on optimization algorithm according to claim 1, characterized in that: According to the basic data, the initial positions of the various task units are deployed, including: Based on the basic data, using a map drawing toolkit to draw the initial position of each task unit within the corresponding target area in the map; According to the task standard requirements, confirm the rationality of the spacing between each task unit.

3. The multi-resource allocation method based on optimization algorithm according to claim 1, characterized in that: According to the initial positions of each task unit, the electric field theory is used to construct the task requirement configuration optimization model, and the sequential quadratic programming algorithm is used to solve it, and the positions of each task node and the guarantee node are calculated, which specifically includes: According to the initial positions of the various task units, a task requirement configuration optimization model is constructed based on electric field theory, and the positions of the guarantee nodes are determined using the task requirement configuration optimization model; According to the position of the guarantee node and the initial position of each task unit, a sequential quadratic programming algorithm is used to calculate and obtain the position of the task node.

4. The multi-resource allocation method based on optimization algorithm according to claim 3 is characterized in that: According to the initial positions of the various task units, a task requirement configuration optimization model is constructed based on electric field theory, and the positions of the guarantee nodes are determined using the task requirement configuration optimization model, specifically including: Assume that there are n task units, and the initial position coordinates of each task unit are lat_1,lng_1),lat_2,lng_2),(lat_3,lng_3),lat_4,lng_4),lat_i,lng_i),...,lat_n,lng_n), and the corresponding equipment task requirements are Q1,Q2,Q3,Q4,Q i ,……,Q n , where (lat_i,lng_i) and Q i are the position coordinates of the initial position of the i-th task unit and the corresponding equipment task demand, respectively. The position coordinates of the task force point P are (best_lat, best_lng). Then the work required by the task force point P to transport the task demand required by the task unit to the first task unit (lat_1, lng_1) is expressed as: Among them, F represents the work that the task force point P needs to do to transport the task requirements of the task unit to the first task unit; The work to be done to complete a specific task requirement when each point in the task requirement area is used as the task team configuration position is the task requirement potential energy of the high point in the equipment task field, represented by E(P). When there is only one task unit in the task requirement area, the task requirement potential energy from the task force point P to any configuration position is expressed as: Among them, E(P) represents the mission required potential energy from the mission force point P to any configuration position, F(P→Q1) represents the work done from the mission force point P to the first mission unit, K1 is the road detour coefficient from the mission force point P to the first mission unit, and Q1 is the equipment mission requirement corresponding to the first mission unit; For multiple task units, a composite task field is formed in the task requirement area, and the superposition principle is satisfied, which can be expressed as: Among them, F(P→Q n ) represents the work done from the task force point P to the nth task unit, K i is the road detour coefficient from the task force point P to the i-th task unit, Q i is the equipment task demand corresponding to the i-th task unit; Thus, the task requirement configuration optimization model of the equipment task force can be obtained. The position with the lowest potential energy, i.e., minE(P), is selected in the task requirement area to minimize the potential energy, which is expressed as: Among them, x and y are the position coordinates of the position with minimum potential energy; Then we have: Among them, d i is the distance from the task force point P to the i-th task unit, Finally, the position coordinates of the guarantee node are obtained, which is expressed as: Among them, cen_lat and cen_lng are the location coordinates of the security node.

5. The multi-resource allocation method based on optimization algorithm according to claim 4 is characterized in that: According to the position of the guarantee node and the initial position of each task unit, a sequential quadratic programming algorithm is used to calculate the position of the task node, which specifically includes: According to the position of the security node and the initial position of each task unit, the objective function and the constraint relationship are determined based on the electric field strategy; the objective function refers to a functional relationship with the goal of minimizing the optimization cost, and the constraint relationship refers to a functional relationship constraining the distance between each task node and the distance between the task node and the security node; Based on the objective function and the constraint relationship, a sequential quadratic programming algorithm is used to perform optimization and solve to obtain the position of the task node.

6. The multi-resource allocation method based on optimization algorithm according to claim 5, characterized in that: The expression of the objective function is: Among them, cen_lat and cen_lng are the position coordinates of the guarantee node, lat_i and lng_i are the position coordinates of the i-th task node; The expression of the constraint relationship is: Among them, lat_j and lng_j are the position coordinates of the j-th task node.

7. The multi-resource allocation method based on optimization algorithm according to claim 1, characterized in that: Based on the position of each task node and the position of the guarantee node, resources are allocated and initial resources are provided to each task node, specifically including: Based on the location of each task node and the location of the support node, initial resources are allocated to each task node according to the resource requirements of each task node, the required resource type, resource consumption, number of people, urgency of action and priority of resource division.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-resource allocation method based on the optimization algorithm described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-resource allocation method based on the optimization algorithm described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-resource allocation method based on the optimization algorithm described in any one of claims 1 to 7 is implemented.

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