Task-based guarantee scheme generation method, system, equipment and medium

Abstract

The invention discloses a task-based guarantee scheme generation method, system and device and a medium, and the method comprises the steps: obtaining task information inputted by a user, and calling a trained task analysis large model to extract element information of each resource demand type from the task information; importing element information according to resource demand types, and calling the demand prediction model to form a demand prediction report; based on the demand prediction report, the current guarantee resource state and the feasible transportation route are integrated, the influence of distance, time and cost factors is comprehensively considered, a guarantee resource scheduling model is established and solved, and a guarantee resource scheduling scheme is generated and converged to form a guarantee plan; and simulating personnel, resources and transport capacity, carrying out deduction verification, evaluating the guarantee plan from four evaluation elements including a task completion rate, a guarantee coverage rate, guarantee resource consumption and guarantee cost, setting weights for the evaluation elements by adopting an analytic hierarchy process, and selecting the guarantee plan with the highest score. The method can assist in quickly and efficiently generating the guarantee scheme.

Description

Technical Field

[0001] The present invention relates to the field of mission assurance technology, and in particular to a mission-based assurance plan generation method, system, device and medium. Background Art

[0002] The guarantee activities are closely centered around the mission. Existing achievements are often carried out for independent guarantee demand sets, sorting various guarantee demands, generating guarantee plans in sequence, calculating guarantee effectiveness, and guiding guarantee plan generation according to the principle of maximizing effectiveness. In the prior art, there are methods for designing project guarantee plans based on combinatorial optimization strategies and dependency-exclusion networks. However, large-scale tasks lack a systematic and systematic guarantee plan formulation process. Plans are mostly formulated and generated by experts in various fields based on empirical data. Since the experience of relevant personnel in various fields is relatively independent, the guarantee plan formulation process is highly subjective and has a long completion cycle. At the same time, due to the lack of effective evaluation methods for the plans, the guarantee effects of plans in various fields on the mission cannot be judged. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a task-based security solution generation method, system, device and medium. Rapid construction from task to security solution is achieved. The user directly inputs the task information, and the unified model established by the system takes into account the differences in domain data, assists in the rapid and efficient generation of security solutions, relying on the task requirement decomposition model, security requirement prediction model, security resource scheduling model and security solution evaluation model provided by the system.

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

[0005] A task-based guarantee scheme generation method comprises the following steps:

[0006] Obtain the task information input by the user, and call the trained task analysis model to extract the element information of each resource requirement type from the task information;

[0007] Import factor information by resource demand type, call the demand forecast model, compare and analyze the resource demand type and total amount with the existing mission area support resources, and form a demand forecast report;

[0008] Based on the demand forecast report, the current security resource status and feasible transportation routes are comprehensively considered, and the impact of distance, time and cost factors are comprehensively considered to establish a security resource scheduling model, solve the security resource scheduling model, and generate a security resource scheduling plan;

[0009] The support resource dispatch plans for various types of materials are gathered together to form a support plan; personnel, resources and transportation capacity are simulated and verified through deductions; the support plan is evaluated based on four evaluation factors: task completion rate, support coverage rate, support resource consumption and support cost; the hierarchical analysis method is used to set weights for the evaluation factors, and the support plan with the highest score is selected.

[0010] To optimize the above technical solutions, the specific measures taken also include:

[0011] Furthermore, the training process of the task analysis large model is as follows:

[0012] Based on the domain knowledge base, we build a 100 billion token-level industry data set to expand the model vocabulary. We use the industry data set to train the general semantic big model to obtain the industry basic model. We use historical task-related data to fine-tune the industry basic model in a supervised manner to obtain the task analysis big model.

[0013] The types of resource requirements include: raw supplies, staple and non-staple foods, compressed foods, convenience foods and fuels. The extracted element information includes: mission members and number, stages, areas, routes, accompanying items, resource-related mobility rate, damage rate, supply strength and supply days.

[0014] Furthermore, the demand forecasting model is specifically:

[0015] J={J1,J2,J3,J4,J5}

[0016] In the formula, J represents the total demand for replenishment resources; J1 represents the demand for raw supplies; J2 represents the demand for staple and non-staple food products; J3 represents the demand for compressed food; J4 represents the demand for convenience food; J5 represents the demand for oil;

[0017] J m ={X1,...X i ,...X n}, m represents the supply resource demand calculation category, and its value is an integer from 1 to 5, X i is the demand for the i-th type of product supply, and its expression is: X i = <C i ,N i ,U i ,Q i >, C i is the catalog code for the i-th type of supply resources, N i is the name of the i-th type of supply, U i is the measurement unit of supply resources for the i-th category, Q i The specific quantity of resources required to replenish the i-th category; Q i The calculation expression is: Q i=G i ×P×D×(1+α)×(1+β),G i It represents the supply standard of the i-th type of supply resources; α represents the mobility rate, β represents the damage rate, P represents the supply strength, and D represents the supply days. The information of mobility rate, damage rate, supply strength and supply days is obtained by the task analysis model.

[0018] Furthermore, the security resource scheduling model includes a raw material supply resource scheduling model, a staple and non-staple food resource scheduling model, a compressed food resource scheduling model, a convenient food resource scheduling model and an oil resource scheduling model; solving each resource scheduling model to obtain the shortest distance, shortest time and minimum cost security resource scheduling plan;

[0019] The production supply resource scheduling model is specifically as follows:

[0020]

[0021] Among them, Z1 represents the total cost of scheduling production and supply resources, represents the unit cost of transporting the k1th type of supply from the e1th supply station to the fth member, represents the volume of the k1th type of life supply transported from the e1th life supply station to the fth member, is the stock of the k1th type of life supply at the e1th life supply station, is the demand for the k1th type of life supply of the fth member, E1 is the total number of life supply stations, K1 is the total number of life supply categories, and F is the total number of members. Constraint (1) requires that the transportation task assigned to each life supply station cannot exceed the storage capacity of the life supply station for life supplies; constraint (2) requires that the allocated life supply volume can meet the needs of each member.

[0022] Furthermore, the main and non-staple food resource scheduling model is specifically as follows:

[0023]

[0024] In the formula, Z2 represents the total cost of dispatching staple and non-staple food resources. represents the unit cost of transporting the k2th main and non-staple food from the e2th main and non-staple food station to the fth member, represents the transportation volume of the k2th main and non-staple food from the e2th main and non-staple food station to the fth member, is the stock of the k2th staple and non-staple food at the e2th staple and non-staple food station, is the demand for the k2th staple and non-staple food of the fth member, E2 is the total number of staple and non-staple food stations, K2 is the total number of staple and non-staple food categories, and F is the total number of members. Constraint (3) requires that the transportation tasks assigned to each staple and non-staple food station cannot exceed the storage capacity of the staple and non-staple food station for staple and non-staple food; Constraint (4) requires that the amount of staple and non-staple food assigned can meet the needs of each member;

[0025] The compressed food resource scheduling model is specifically as follows:

[0026]

[0027] Where Z3 represents the total cost of compressed food resource scheduling, represents the unit cost of transporting the k3th compressed food from the e3th compressed food station to the fth member, represents the volume of the k3th type of compressed food transported from the e3th compressed food station to the fth member, is the stock of the k3th type of compressed food at the e3th compressed food station, is the demand for the k3th type of compressed food of the fth member, E3 is the total number of compressed food stations, K3 is the total number of compressed food categories, F is the total number of members, and constraint (5) requires that the transportation task assigned to each compressed food station cannot exceed the storage capacity of the compressed food station for compressed food; constraint (6) requires that the amount of compressed food assigned can meet the needs of each member.

[0028] Furthermore, the instant food resource scheduling model is specifically as follows:

[0029]

[0030] Where Z4 represents the total cost of convenient food resource scheduling, represents the unit cost of transporting the k4th convenience food from the e4th convenience food station to the fth member, represents the volume of the k4th convenience food shipped from the e4th convenience food station to the fth member, is the stock of the k4th convenience food at the e4th convenience food station, is the demand for the k4th convenience food of the fth member, E4 is the total number of convenience food stations, K4 is the total number of convenience food categories, F is the total number of members, and constraint (7) requires that the transportation task assigned to each convenience food station cannot exceed the storage capacity of the convenience food at the convenience food station; constraint (8) requires that the amount of convenience food assigned can meet the needs of each member;

[0031] The oil resource scheduling model is specifically as follows:

[0032]

[0033] Among them, Z5 represents the total cost of oil resource scheduling, represents the unit cost of transporting the k5th type of oil from the e5th oil depot to the fth member, represents the volume of the k5th type of oil transported from the e5th oil depot to the fth member, is the inventory of type k5 oil in the e5th oil depot, is the demand for the k5th type of oil of the fth member, E5 is the total number of oil depots, K5 is the total number of oil categories, and F is the total number of members. Constraint (9) requires that the transportation task allocated to each oil depot cannot exceed the storage capacity of the oil depot for oil; constraint (10) requires that the allocated oil quantity can meet the needs of each member.

[0034] Furthermore, the method also includes: after the simulation verification is completed, displaying the simulation task results, including the completion time of the simulation task, the task completion status of each support team, the task completion rate, the completion time and the support cost; displaying the simulation situation based on the map, and visually displaying the geographical information, personnel distribution and task area in the simulation process.

[0035] The present invention also proposes a task-based guarantee solution generation system, comprising:

[0036] Data collection module, used to obtain task information input by users;

[0037] The information extraction module is used to call the trained task analysis model to extract the element information of each resource requirement type from the task information;

[0038] The forecasting module is used to import factor information according to resource demand types, call the demand forecasting model, compare and analyze the resource demand types and total amounts with the existing mission area support resources, and form a demand forecasting report;

[0039] The solution generation module is used to establish a support resource scheduling model based on the demand forecast report, comprehensively consider the current support resource status and feasible transportation routes, and comprehensively consider the influence of distance, time and cost factors, solve the support resource scheduling model, and generate a support resource scheduling plan; the support resource scheduling plans of various materials are aggregated to form a support plan;

[0040] The plan selection module is used to simulate personnel, resources and transportation capacity and conduct deduction and verification. It evaluates the support plan based on four evaluation factors: task completion rate, support coverage rate, support resource consumption and support cost. The hierarchical analysis method is used to set weights for the evaluation factors and select the support plan with the highest score.

[0041] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the task-based guarantee solution generation method as described above is implemented.

[0042] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the task-based guarantee solution generation method as described above.

[0043] The beneficial effects of the present invention are:

[0044] (1) The present invention provides a task-oriented support solution generation system, which uses a semantic big model, a resource demand prediction model, a resource scheduling model, and a deduction and evaluation model in sequence to support users to automatically generate support solutions. This technology breaks down the support solution formulation process into four stages: task demand decomposition, resource demand prediction, support resource scheduling, and solution evaluation output. At the same time, the analysis process of each stage uses big data, theoretical derivation, expert experience and other technical abstract processing as a model algorithm to support automatic solution and efficient output.

[0045] (2) The present invention provides a large model for task analysis. The model is trained based on the industry large model, and through historical training data, with the help of supervised fine-tuning, it can achieve rapid learning and extraction of task analysis information. Users can directly import the current task documents, and the model can provide demand parameters in the fields of transportation, supply, fuel, etc., covering: task members and number, stage, area\route, accompanying items, etc. As the data grows, the model accuracy will be higher.

[0046] (3) The present invention provides a resource forecasting model to optimize the forecasting process of various resource requirements. Taking the replenishment demand as an example, the process and scheme of resource demand estimation are given. The model uses the information extracted from the task analysis model and combines the replenishment resource supply standard to realize automatic demand forecasting.

[0047] (4) The present invention provides a resource scheduling model to model the process of scheduling various types of resources. Taking the demand for oil as an example, the process and solution of resource scheduling analysis are given. The model solves the minimum value of distance, time and cost, and gives a resource scheduling strategy using the shortest distance, shortest time and minimum cost.

[0048] (5) The present invention provides a simulation evaluation model to simulate personnel, resources, and transportation entities, conduct simulation verification, and collect statistics on insufficient support personnel, insufficient transportation capacity, and insufficient resources during the simulation process. After the simulation is completed, the simulation plan is evaluated in terms of task completion rate, support coverage rate, support resource consumption, support cost, etc., and the hierarchical analysis method is used to set weights for the evaluation factors, select the plan with the highest score, and form a plan output to the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of the task-based guarantee solution generation method proposed by the present invention;

[0050] Figure 2 Generate a system block diagram for the task-based assurance solution proposed in this invention. DETAILED DESCRIPTION

[0051] The present invention will now be described in further detail with reference to the accompanying drawings.

[0052] Embodiment 1

[0053] The present invention proposes a task-based guarantee solution generation method, the overall process of the method is as follows: Figure 1 As shown, the following steps are included:

[0054] Obtain the task information input by the user, call the trained task analysis model to extract the element information of each resource requirement type from the task information; the training process of the task analysis model is as follows:

[0055] Based on the domain knowledge base, we build an industry data set worth hundreds of billions of tokens to expand the model vocabulary. We use the industry data set to train the general semantic big model to obtain the industry basic model. We use historical task-related data to perform supervised fine-tuning on the industry basic model to obtain the task analysis big model.

[0056] The types of resource requirements include: raw supplies, staple and non-staple foods, compressed foods, convenience foods and fuels. The extracted element information includes: mission members and number, stage, area, route, accompanying items, resource-related mobility rate, damage rate, supply strength and supply days.

[0057] Import element information by resource demand type, call the demand forecast model, compare and analyze the resource demand type and total amount with the existing mission area support resources, and form a demand forecast report; the demand forecast model is as follows:

[0058] J={J1,J2,J3,J4,J5}

[0059] In the formula, J represents the total demand for replenishment resources; J1 represents the demand for raw supplies; J2 represents the demand for staple and non-staple food products; J3 represents the demand for compressed food; J4 represents the demand for convenience food; J5 represents the demand for oil;

[0060] J m ={X1,...X i ,...X n}, m represents the supply resource demand calculation category, and its value is an integer from 1 to 5, X iis the demand for the i-th type of product supply, and its expression is: X i = <C i ,N i ,U i ,Q i >, C i is the catalog code for the i-th type of supply resources, N i is the name of the i-th type of supply, U i is the measurement unit of supply resources for the i-th category, Q i The specific quantity of resources required to replenish the i-th category; Q i The calculation expression is: Q i =G i ×P×D×(1+α)×(1+β),G i It represents the supply standard of the i-th type of supply resources; α represents the mobility rate, β represents the damage rate, P represents the supply strength, and D represents the supply days. The information of mobility rate, damage rate, supply strength and supply days is obtained by the task analysis model.

[0061] The input parameters of the demand forecasting model are shown in the following table:

[0062]

[0063]

[0064] The differences between different types of resources are reflected in: the number of resource demand categories, the number of resource catalogs, mobility rate, damage rate, supply strength, supply days, etc. According to the resource type, the relevant model parameters are designed to obtain the demand for various types of resources.

[0065] Based on the demand forecast report, the current security resource status and feasible transportation routes are comprehensively considered, and the influence of distance, time and cost factors are comprehensively considered to establish a security resource scheduling model, solve the security resource scheduling model, and generate a security resource scheduling plan; the security resource scheduling model includes a raw supply resource scheduling model, a staple food resource scheduling model, a compressed food resource scheduling model, a convenient food resource scheduling model and an oil resource scheduling model; solve each resource scheduling model to obtain the shortest distance, shortest time and minimum cost security resource scheduling plan. The unit cost of different resource types and member types is different, so the unit cost difference is different. The unit cost can be given by the task analysis model combined with historical data.

[0066] The specific model of life supply resource scheduling is as follows:

[0067]

[0068] Among them, Z1 represents the total cost of scheduling production and supply resources, represents the unit cost of transporting the k1th type of supply from the e1th supply station to the fth member, represents the volume of the k1th type of life supply transported from the e1th life supply station to the fth member, is the stock of the k1th type of life supply at the e1th life supply station, is the demand for the k1th type of life supply of the fth member, E1 is the total number of life supply stations, K1 is the total number of life supply categories, and F is the total number of members. Constraint (1) requires that the transportation task assigned to each life supply station cannot exceed the storage capacity of the life supply station for life supplies; constraint (2) requires that the allocated life supply volume can meet the needs of each member.

[0069] The specific model for the resource scheduling of staple and non-staple foods is as follows:

[0070]

[0071] In the formula, Z2 represents the total cost of dispatching staple and non-staple food resources. represents the unit cost of transporting the k2th main and non-staple food from the e2th main and non-staple food station to the fth member, represents the transportation volume of the k2th main and non-staple food from the e2th main and non-staple food station to the fth member, is the stock of the k2th staple and non-staple food at the e2th staple and non-staple food station, is the demand for the k2th staple and non-staple food of the fth member, E2 is the total number of staple and non-staple food stations, K2 is the total number of staple and non-staple food categories, and F is the total number of members. Constraint (3) requires that the transportation tasks allocated to each staple and non-staple food station cannot exceed the storage capacity of the staple and non-staple food station for staple and non-staple food; constraint (4) requires that the amount of staple and non-staple food allocated can meet the needs of each member.

[0072] The compressed food resource scheduling model is as follows:

[0073]

[0074] Where Z3 represents the total cost of compressed food resource scheduling, represents the unit cost of transporting the k3th compressed food from the e3th compressed food station to the fth member, represents the volume of the k3th type of compressed food transported from the e3th compressed food station to the fth member, is the stock of the k3th type of compressed food at the e3th compressed food station, is the demand for the k3th type of compressed food of the fth member, E3 is the total number of compressed food stations, K3 is the total number of compressed food categories, F is the total number of members, and constraint (5) requires that the transportation task assigned to each compressed food station cannot exceed the storage capacity of the compressed food station for compressed food; constraint (6) requires that the amount of compressed food assigned can meet the needs of each member.

[0075] The specific model of convenient food resource scheduling is as follows:

[0076]

[0077] Where Z4 represents the total cost of convenient food resource scheduling, represents the unit cost of transporting the k4th convenience food from the e4th convenience food station to the fth member, represents the volume of the k4th convenience food shipped from the e4th convenience food station to the fth member, is the stock of the k4th convenience food at the e4th convenience food station, is the demand for the k4th convenience food of the fth member, E4 is the total number of convenience food stations, K4 is the total number of convenience food categories, F is the total number of members, and constraint (7) requires that the transportation task assigned to each convenience food station cannot exceed the storage capacity of the convenience food at the convenience food station; constraint (8) requires that the amount of convenience food assigned can meet the needs of each member.

[0078] The specific fuel resource scheduling model is as follows:

[0079]

[0080] Among them, Z5 represents the total cost of oil resource scheduling, represents the unit cost of transporting the k5th type of oil from the e5th oil depot to the fth member, represents the volume of the k5th type of oil transported from the e5th oil depot to the fth member, is the inventory of type k5 oil in the e5th oil depot, is the demand for the k5th type of oil of the fth member, E5 is the total number of oil depots, K5 is the total number of oil categories, and F is the total number of members. Constraint (9) requires that the transportation task allocated to each oil depot cannot exceed the storage capacity of the oil depot for oil; constraint (10) requires that the allocated oil quantity can meet the needs of each member.

[0081] The support resource scheduling plans for various types of materials are gathered to form a support plan; personnel, resources and transportation capacity are simulated and verified, and the support plan is evaluated from the four evaluation factors of task completion rate, support coverage rate, support resource consumption and support cost. The hierarchical analysis method is used to set weights for the evaluation factors and select the support plan with the highest score. After the simulation verification is completed, the simulation task results are displayed, including the completion time of the simulation task, the task completion status of each support team, the task completion rate, the completion time and the support cost; the simulation situation is displayed based on the map, and the geographical information, personnel distribution and task area in the simulation process are visualized.

[0082] Embodiment 2

[0083] The present invention proposes a task-based assurance solution generation system corresponding to the method of embodiment 1, comprising:

[0084] Data collection module, used to obtain task information input by users;

[0085] The information extraction module is used to call the trained task analysis model to extract the element information of each resource requirement type from the task information;

[0086] The forecasting module is used to import factor information according to resource demand types, call the demand forecasting model, compare and analyze the resource demand types and total amounts with the existing mission area support resources, and form a demand forecasting report;

[0087] The solution generation module is used to establish a support resource scheduling model based on the demand forecast report, comprehensively consider the current support resource status and feasible transportation routes, and comprehensively consider the influence of distance, time and cost factors, solve the support resource scheduling model, and generate a support resource scheduling plan; the support resource scheduling plans of various materials are aggregated to form a support plan;

[0088] The plan selection module is used to simulate personnel, resources and transportation capacity and conduct deduction and verification. It evaluates the support plan based on four evaluation factors: task completion rate, support coverage rate, support resource consumption and support cost. The hierarchical analysis method is used to set weights for the evaluation factors and select the support plan with the highest score.

[0089] The implementation methods of each module and module function in the system are completely consistent with the steps of the method in Example 1, so they will not be repeated here.

[0090] Embodiment 3

[0091] The present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the task-based guarantee solution generation method as described in Embodiment 1 is implemented.

[0092] Embodiment 4

[0093] The present invention provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the task-based guarantee solution generation method as described in the first embodiment.

[0094] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0095] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0096] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A task-based guarantee plan generation method, characterized in that: The following steps are involved: Obtain the task information input by the user, and call the trained task analysis model to extract the element information of each resource requirement type from the task information; Import factor information by resource demand type, call the demand forecast model, compare and analyze the resource demand type and total amount with the existing mission area support resources, and form a demand forecast report; Based on the demand forecast report, the current security resource status and feasible transportation routes are comprehensively considered, and the impact of distance, time and cost factors are comprehensively considered to establish a security resource scheduling model, solve the security resource scheduling model, and generate a security resource scheduling plan; The support resource dispatch plans for various types of materials are gathered together to form a support plan; personnel, resources and transportation capacity are simulated and verified through deductions; the support plan is evaluated based on four evaluation factors: task completion rate, support coverage rate, support resource consumption and support cost; the hierarchical analysis method is used to set weights for the evaluation factors, and the support plan with the highest score is selected.

2. The task-based guarantee solution generation method according to claim 1, characterized in that: The training process of the task analysis large model is as follows: Based on the domain knowledge base, we build a 100 billion token-level industry data set to expand the model vocabulary. We use the industry data set to train the general semantic big model to obtain the industry basic model. We use historical task-related data to fine-tune the industry basic model in a supervised manner to obtain the task analysis big model. The types of resource requirements include: raw supplies, staple and non-staple foods, compressed foods, convenience foods and fuels. The extracted element information includes: mission members and number, stages, areas, routes, accompanying items, resource-related mobility rate, damage rate, supply strength and supply days.

3. The task-based guarantee scheme generation method according to claim 1, characterized in that: The demand forecasting model is specifically: J={J1,J2,J3,J4,J5} In the formula, J represents the total demand for replenishment resources; J1 represents the demand for raw supplies; J2 represents the demand for staple and non-staple food products; J3 represents the demand for compressed food; J4 represents the demand for convenience food; J5 represents the demand for oil; J m ={X1,...X i ,...X n }, m represents the supply resource demand calculation category, and its value is an integer from 1 to 5, X i is the demand for the supply of the i-th category, and its expression is: X i =<C i ,N i ,U i , Q i >, C i is the catalog code for the i-th type of supply resources, N i is the name of the i-th type of supply, U i is the measurement unit of supply resources for the i-th category, Q i The specific quantity of resources required to replenish the i-th category; Q i The calculation expression is: Q i =G i ×P×D×(1+α)×(1+β),G i It represents the supply standard of the i-th type of supply resources; α represents the mobility rate, β represents the damage rate, P represents the supply strength, and D represents the supply days. The information of mobility rate, damage rate, supply strength and supply days is obtained by the task analysis model.

4. The task-based guarantee solution generation method according to claim 1, characterized in that: The security resource scheduling model includes a raw supply resource scheduling model, a staple and non-staple food resource scheduling model, a compressed food resource scheduling model, a convenient food resource scheduling model and an oil resource scheduling model; solving each resource scheduling model to obtain the shortest distance, shortest time and minimum cost security resource scheduling plan; The production supply resource scheduling model is specifically as follows: Among them, Z1 represents the total cost of scheduling production and supply resources, represents the unit cost of transporting the k1th type of supply from the e1th supply station to the fth member, represents the volume of the k1th type of life supply transported from the e1th life supply station to the fth member, is the stock of the k1th type of life supply at the e1th life supply station, is the demand for the k1th type of life supply of the fth member, E1 is the total number of life supply stations, K1 is the total number of life supply categories, and F is the total number of members. Constraint (1) requires that the transportation task assigned to each life supply station cannot exceed the storage capacity of the life supply station for life supplies; constraint (2) requires that the allocated life supply volume can meet the needs of each member.

5. The task-based guarantee scheme generation method according to claim 4, characterized in that: The main and non-staple food resource scheduling model is specifically as follows: In the formula, Z2 represents the total cost of dispatching staple and non-staple food resources. represents the unit cost of transporting the k2th main and non-staple food from the e2th main and non-staple food station to the fth member, represents the transportation volume of the k2th main and non-staple food from the e2th main and non-staple food station to the fth member, is the stock of the k2th staple and non-staple food at the e2th staple and non-staple food station, is the demand for the k2th staple and non-staple food of the fth member, E2 is the total number of staple and non-staple food stations, K2 is the total number of staple and non-staple food categories, and F is the total number of members. Constraint (3) requires that the transportation tasks assigned to each staple and non-staple food station cannot exceed the storage capacity of the staple and non-staple food station for staple and non-staple food; Constraint (4) requires that the amount of staple and non-staple food assigned can meet the needs of each member; The compressed food resource scheduling model is specifically as follows: Where Z3 represents the total cost of compressed food resource scheduling, represents the unit cost of transporting the k3th compressed food from the e3th compressed food station to the fth member, represents the volume of the k3th type of compressed food transported from the e3th compressed food station to the fth member, is the stock of the k3th type of compressed food at the e3th compressed food station, is the demand for the k3th type of compressed food of the fth member, E3 is the total number of compressed food stations, K3 is the total number of compressed food categories, F is the total number of members, and constraint (5) requires that the transportation task assigned to each compressed food station cannot exceed the storage capacity of the compressed food station for compressed food; constraint (6) requires that the amount of compressed food assigned can meet the needs of each member.

6. The task-based guarantee scheme generation method according to claim 4, characterized in that: The instant food resource scheduling model is specifically as follows: Where Z4 represents the total cost of convenient food resource scheduling, represents the unit cost of transporting the k4th convenience food from the e4th convenience food station to the fth member, represents the volume of the k4th convenience food shipped from the e4th convenience food station to the fth member, is the stock of the k4th convenience food at the e4th convenience food station, is the demand for the k4th convenience food of the fth member, E4 is the total number of convenience food stations, K4 is the total number of convenience food categories, F is the total number of members, and constraint (7) requires that the transportation task assigned to each convenience food station cannot exceed the storage capacity of the convenience food at the convenience food station; constraint (8) requires that the amount of convenience food assigned can meet the needs of each member; The oil resource scheduling model is specifically as follows: Among them, Z5 represents the total cost of oil resource scheduling, represents the unit cost of transporting the k5th type of oil from the e5th oil depot to the fth member, represents the volume of the k5th type of oil transported from the e5th oil depot to the fth member, is the inventory of type k5 oil in the e5th oil depot, is the demand for the k5th type of oil of the fth member, E5 is the total number of oil depots, K5 is the total number of oil categories, and F is the total number of members. Constraint (9) requires that the transportation task allocated to each oil depot cannot exceed the storage capacity of the oil depot for oil; constraint (10) requires that the allocated oil quantity can meet the needs of each member.

7. The task-based guarantee scheme generation method according to claim 1, characterized in that: The method further includes: after the simulation verification is completed, displaying the simulation task results, including the completion time of the simulation task, the task completion status of each support team, the task completion rate, the completion time and the support cost; displaying the simulation situation based on a map, and visually displaying the geographic information, personnel distribution and task area in the simulation process.

8. A task-based support plan generation system, characterized in that: include: Data collection module, used to obtain task information input by users; The information extraction module is used to call the trained task analysis model to extract the element information of each resource requirement type from the task information; The forecasting module is used to import factor information according to resource demand types, call the demand forecasting model, compare and analyze the resource demand types and total amounts with the existing mission area support resources, and form a demand forecasting report; The solution generation module is used to establish a support resource scheduling model based on the demand forecast report, comprehensively consider the current support resource status and feasible transportation routes, and comprehensively consider the influence of distance, time and cost factors, solve the support resource scheduling model, and generate a support resource scheduling plan; the support resource scheduling plans of various materials are aggregated to form a support plan; The plan selection module is used to simulate personnel, resources and transportation capacity and conduct deduction and verification. It evaluates the support plan based on four evaluation factors: task completion rate, support coverage rate, support resource consumption and support cost. The hierarchical analysis method is used to set weights for the evaluation factors and select the support plan with the highest score.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the task-based guarantee solution generation method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the task-based guarantee solution generation method according to any one of claims 1 to 7.

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