Warehousing network planning method and logistics simulation system for energy enterprise materials

Abstract

The invention provides a storage network planning method and a logistics simulation system for energy enterprise materials, and relates to the field of energy enterprise material planning, user data is received through an interactive interface, and the user data is pre-processed and stored in a database; after a user triggers a solving instruction, a back end calls a solver to construct a mixed integer programming model, a warehouse network layout scheme is iteratively optimized based on a constraint satisfaction algorithm, and a result is stored back to a database; the support visualization module generates a multi-dimensional chart to display data before and after optimization, provides a data export function to output a portable document according to a preset format, integrates business rules, algorithm solution and visual interaction, supports dynamic optimization and rapid verification of an energy storage network, realizes dynamic collaborative optimization of storage site selection, inventory decision and transportation planning, and improves the efficiency of the energy storage network. And through introduction of multi-scene and multi-dimensional simulation analysis capability, an integrated optimization and simulation platform and a human-computer interaction friendly visual decision-making interface are constructed.

Description

Technical Field

[0001] The present invention relates to the field of energy enterprise material planning, and in particular to a storage network planning method and a logistics simulation system for energy enterprise materials. Background Art

[0002] With the trend of global energy transformation and continuous optimization of the supply chain, the production operation and material supply management of energy companies have put forward higher requirements for warehousing and logistics systems. The means of production of energy companies (such as power equipment, pipeline accessories, wind power components, solar panel components, etc.) are characterized by diverse categories, complex batches, large demand changes, wide supply radius, and high requirements for deployment response speed. Traditional warehousing and logistics planning mostly rely on experience-based decision-making and static planning, which is difficult to meet the requirements of efficient operation and rapid response of the current energy industry chain. In recent years, with the continuous development of Industry 4.0, digital twins, supply chain collaborative optimization, and intelligent decision-making analysis technology, more and more research and practice have begun to combine digital simulation, intelligent optimization algorithms and warehousing network planning, in order to achieve efficient resource allocation and layout optimization based on data-driven.

[0003] Most of the existing tools and methods for warehouse planning and logistics simulation focus on the following aspects:

[0004] 1. General logistics planning and warehouse location selection model:

[0005] Traditional warehouse location and layout optimization methods are often based on classical mathematical models (such as facility location problems and transportation allocation problems) and use linear programming and integer linear programming tools to obtain optimal solutions. These methods are suitable for general manufacturing or consumer goods logistics, but lack industry-customized models for the complex material types and multi-level supply chain networks of energy companies.

[0006] 2. Static and Separate Planning Tools:

[0007] In the existing technology, the planning of warehousing network and the formulation of transportation scheduling plan are often carried out separately, that is, the location strategy of warehousing center is determined independently first, and then the optimization of transportation route is considered separately. This separation makes it difficult to dynamically optimize the global supply chain.

[0008] (III) Single-level optimization and lack of integrated platform:

[0009] Existing technologies often focus on single optimization of the warehousing or transportation end, and lack a systematic approach that integrates site selection planning, inventory strategy, and dynamic simulation decisions into a single platform for overall optimization and evaluation.

[0010] 4. Lack of visualization and interactivity in the decision-making process:

[0011] The existing platforms used for warehousing and logistics simulation are mostly low-level modeling and solving tools, which lack the visualization and interactive functions of data, models, and results, making it difficult for decision makers to intuitively understand and quickly iterate design solutions.

[0012] With respect to the above-mentioned prior art, the shortcomings are mainly classified into the following aspects:

[0013] 1. Lack of a storage network planning model specifically tailored to the characteristics of energy enterprise materials:

[0014] Most existing technologies are based on general logistics planning models, which do not fully consider the characteristics of energy companies such as the diversity of material categories, inconsistent specifications and standards, varying lengths of usage cycles, and uncertain demand cycles. As a result, planning schemes are difficult to adapt to the unique business scenarios of energy companies, resulting in problems such as overly extensive warehousing layouts that are difficult to cope with actual demand fluctuations.

[0015] 2. Static planning and segmented optimization lead to insufficient global coordination:

[0016] Existing technologies often optimize warehouse location, inventory strategy and transportation scheduling independently, but lack dynamic connection and global coordination, which easily leads to the phenomenon that subsystem optimization does not represent the global optimum.

[0017] 3. Lack of an integrated platform to achieve multi-level dynamic optimization and verification:

[0018] It is difficult to find a system solution in the existing technology that integrates warehouse network optimization, supply chain collaborative decision-making, transportation route selection and other links into the same platform, and quickly verifies, iterates and optimizes them through simulation. This lack makes the decision-making process cumbersome, data communication is not smooth, the optimization cycle is long and the cost is high.

[0019] 4. Lack of visual, interactive and scalable decision support interface:

[0020] After providing planning results, existing technical means often present them in the form of tables and static reports, making it difficult for decision makers to intuitively understand the complex supply chain structure, material flow, and dynamic changes in time and space. At the same time, the lack of a friendly interactive interface for rapid iteration of solutions, parameter adjustment, and instant reporting is not conducive to the decision-making team's use in multi-party communication, rapid trial and error, and instant response.

[0021] Therefore, there is a need for a storage network planning method and a logistics simulation system for energy enterprise materials. Summary of the invention

[0022] The technical problem to be solved by the present invention is to overcome the defects of the prior art. The present invention proposes a warehousing network planning method and a logistics simulation system for energy enterprise materials, aiming to solve the problems of lack of a warehousing network planning model specifically for the characteristics of energy enterprise materials, insufficient global coordination caused by static planning and segmented optimization, lack of an integrated platform to achieve multi-level dynamic optimization and verification, and lack of a visual, interactive and scalable decision support interface.

[0023] In order to solve the above technical problems, the technical solution adopted by the present invention is: a storage network planning method for energy enterprise materials, comprising the following steps:

[0024] S1: receiving user input data through the interactive interface, the data import module calls the data preprocessing module through the communication interface, converts and cleans the data format, and stores it in the database; in response to the user's solution instruction, the back-end processor calls the solver through the communication interface to perform optimization calculation;

[0025] S2: The solver obtains model parameters from the database, builds a mixed integer programming model based on the business rules of the energy enterprise, uses the constraint satisfaction problem solving algorithm to perform iterative optimization, searches for the optimal warehouse network layout solution, and stores the solution results back to the database;

[0026] S3: In response to the user's result viewing operation through the human-computer interaction interface, the back-end processor calls the visualization processing module, obtains input and optimization result data from the database based on preset rules, generates a multi-dimensional visualization chart and pushes it to the user interface, and in response to the user's data export instruction, extracts the data through the data integration module, generates a portable document in a preset format and transmits it to the user terminal.

[0027] Furthermore, the specific steps of S1 are as follows:

[0028] S11: The user uploads input data through the interactive interface. After the data import module receives the data file, it calls the data preprocessing module through the HTTP interface;

[0029] S12: The data preprocessing module converts the input data into the format required by the optimization model, completes the missing data, and finally stores the results in the database;

[0030] S13: The user starts model solving through the interactive interface, and the backend processor service calls the solver through the API.

[0031] Furthermore, the steps of constructing the mixed integer programming model in S2 are as follows:

[0032] S21: Establish a multi-level warehouse network structure model;

[0033] S22: Establish decision variables;

[0034] S23: Establish constraints;

[0035] S24: Set the solution target.

[0036] Furthermore, the decision variables are established as follows:

[0037] The decision variables are established as follows: including warehouse selection variables and flow variables;

[0038] : Whether the i-th level warehouse is located;

[0039] : Whether the jth secondary warehouse is located;

[0040] : The logistics volume from supplier h to production site k;

[0041] :First-level warehouse candidate set;

[0042] : Secondary warehouse candidate set;

[0043] :Supplier Index;

[0044] :Production site index.

[0045] Furthermore, the objective function formula established by the decision variables is:

[0046]

[0047] in:

[0048] :First-level warehouse fixed costs

[0049] :Secondary warehouse fixed costs

[0050] : Unit transportation cost from supplier h to production site k.

[0051] Furthermore, the decision variables are established by introducing the transport distance factor to establish a dual-objective model. The specific formula is as follows:

[0052]

[0053] : The weight coefficient of cost and distance ( ).

[0054] : Transportation distance from supplier h to production site k.

[0055] Furthermore, the constraints include but are not limited to:

[0056] Warehouse quantity constraints:

[0057]

[0058] : Minimum quantity of first-level warehouse;

[0059] : Maximum number of first-level warehouses;

[0060] Indicates whether the i-th level warehouse is enabled ;

[0061] Transport balance constraints:

[0062]

[0063] represents the logistics volume from supplier h to primary warehouse i;

[0064] represents the logistics volume from primary warehouse i to secondary warehouse j;

[0065] represents the logistics volume from the first-level warehouse i to the production site k. For any first-level warehouse ;

[0066] Storage capacity utilization constraint:

[0067]

[0068] : Unit material storage volume;

[0069] : Warehouse capacity of first-level warehouse i;

[0070] : Storage capacity utilization rate of primary warehouse i;

[0071] : Material type collection, for any first-level warehouse .

[0072] Furthermore, the constraint satisfaction problem solving algorithm is CP-SAT solution, and the CP-SAT solution process is as follows:

[0073] Problem initialization: Generate a mixed integer programming model from input data, including the objective function, variable definitions, and constraints;

[0074] Search and solve: Use the CP-SAT core solving engine to quickly traverse the possible solution space and find the solution that meets the constraints and optimizes the optimization goal;

[0075] Verification and output of solutions: After finding the optimal solution, verify the solution and output the specific warehouse location and logistics volume allocation plan.

[0076] Furthermore, the specific steps of S3 are as follows:

[0077] S31: The user enters the result page through the interactive interface, and the backend processor service calls the built-in visualization module;

[0078] S32: The visualization module reads input and result data from the database, organizes the data into multiple chart styles according to a predetermined processing logic, and returns the charts to the user interface;

[0079] S33: The user starts data export through the interactive interface, and the backend processor service calls the built-in data integration module;

[0080] S34: The data integration module reads input and result data from the database, converts the data into a preset format, generates an Excel file, and finally transmits the generated file to the user.

[0081] Furthermore, a logistics simulation system for energy enterprise materials is proposed, which is used to implement a storage network planning method for energy enterprise materials, including:

[0082] User interface layer: provides a visual operation interface, supports user input, viewing and data export functions, including data import module, model solution module, result display module and data export module;

[0083] Data processing layer: mainly the engine of the decision-making system, including data preprocessing module, solver, visualization module, and data integration module;

[0084] Data storage layer: Use a relational database to store user and system data.

[0085] Compared with the prior art, the beneficial effects of the present invention include:

[0086] 1. Construction of a storage network planning method for energy enterprises’ unique material characteristics:

[0087] A mathematical model and optimization method will be established that takes into account the material diversity, demand uncertainty, inventory cycle and standard specificity of energy enterprises to better suit the actual environment and conditions of the energy industry chain.

[0088] 2. Realize dynamic collaborative optimization of warehouse site selection, inventory decision-making, and transportation planning:

[0089] It will break the static and segmented optimization limitations of existing technologies, and incorporate warehouse network layout, inventory strategy and transportation scheduling plans into a unified dynamic model for linkage optimization to ensure the optimization of global resource allocation and dynamic response capabilities.

[0090] 3. Introduction of multi-scenario and multi-dimensional simulation analysis capabilities:

[0091] It will integrate simulation capabilities of multiple scenarios and multiple parameter changes, allowing decision makers to conduct predictive evaluation, solution design and decision optimization for different scenarios in a virtual environment.

[0092] 4. Build an integrated optimization and simulation platform:

[0093] Through the construction of a software platform, we will achieve close integration of data, models, optimization algorithms and simulation engines, and provide enterprises with comprehensive decision support tools that can be quickly iterated and instantly verified, greatly shortening the optimization decision cycle and improving the scientificity and accuracy of decisions.

[0094] 5. Human-computer interaction friendly visual decision interface:

[0095] Through the method and system of the present invention, decision makers will have an intuitive visual interface, can interactively adjust parameters, observe dynamic changes in the logistics network, and quickly compare the pros and cons of different planning schemes, thereby achieving more efficient communication and a more agile decision-making process. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] The disclosure of the present invention is described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. In the accompanying drawings, the same reference numerals are used to refer to the same components. Among them:

[0097] Figure 1 A flowchart of a method proposed according to an embodiment of the present invention is schematically shown;

[0098] Figure 2 A system logic block diagram according to an embodiment of the present invention is schematically shown;

[0099] Figure 3 A system deployment architecture block diagram proposed according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0100] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.

[0101] According to one embodiment of the present invention, Figure 1-Figure 2 It is shown that the storage network planning method for energy enterprise materials includes the following steps:

[0102] Step 1: The user uploads input data through the interactive interface. After the data import module receives the data file, it calls the data preprocessing module through the HTTP interface.

[0103] Specifically, the input data includes but is not limited to the following:

[0104] System parameters: This data table is used to set the parameters required for model operation. Including: upper and lower limits of quantity by warehouse category; rates and service levels for second and third-leg transportation; upper and lower limits of storage capacity utilization and throughput by warehouse category and storage method; upper and lower limits of cost by category, optimization objectives, etc.

[0105] Warehouse basic data: This data lists the basic information, operating costs, area, number of shelves and other related information of all candidate warehouses, providing data for warehouse site selection and layout optimization;

[0106] Supplier basic data: basic information of suppliers, including supplier-related codes, names, addresses, longitude and latitude, providing a data basis for supplier selection and material supply route planning;

[0107] Production site basic data: basic information of the production site, including the code, name, address, longitude and latitude related to the production site, providing a data basis for production site selection and material supply route planning; Material basic data: This worksheet is used to record detailed information on materials, covering basic information such as material number, name, classification, storage area, stocking mode, etc. It is the basic data source for material management and provides data support for inventory management and material transportation;

[0108] Warehouse Radiation Relationship: This table is mainly used to display the radiation relationship between the upper and lower warehouses, and whether the relationship must be retained in the results. It is mainly used to clarify the flow direction and distribution path of materials in the second-stage transportation, and provide a basis for transportation route planning and warehouse layout optimization;

[0109] Production site radiation relationship: This table is mainly used to display the radiation relationship between the warehouse and the production site, and whether the relationship must be retained in the results. It is mainly used to clarify the flow direction and distribution path of materials in three-way transportation, and provide a basis for transportation route planning and warehouse layout optimization;

[0110] Supplier Radiation Relationship: This table is mainly used to display the radiation relationship between suppliers and warehouses, and the input of whether the relationship is retained. It is mainly used to clarify the flow direction and distribution path of materials in a transport, and provide a basis for transport route planning and warehouse layout optimization;

[0111] Material supply and demand information: This worksheet is used to record the supply and demand of materials, including the demand quantity, demand time, supplier, production site and other information, reflecting the production site's demand plan for different materials.

[0112] Step 2: The data preprocessing module converts the input data into the format required by the optimization model, completes the missing data, and finally stores the results in the database;

[0113] Step 3: The user starts the model solving through the interactive interface, and the backend processor service calls the solver through the API.

[0114] Step 4: The solver reads the model data from the database, calls the built-in solver to perform optimization calculations, and stores the results in the database after the calculations are completed.

[0115] Step 4 The specific steps are as follows:

[0116] 1. Model construction: Construct a mixed integer programming model based on the actual business needs and input data of energy enterprises.

[0117] Furthermore, the steps of the mixed integer programming model are as follows:

[0118] 11. Establish a multi-level warehouse network structure model and introduce instances of sites, links, materials, etc.

[0119] 12. Establish decision variables, including warehouse selection variables (1 means the warehouse is enabled, 0 means the warehouse is not enabled), flow variables (the amount of materials transported on each link, an integer), etc.

[0120] The decision variables are established as follows: including continuous variables (such as transportation volume) and discrete variables (such as warehouse location decisions).

[0121] : Whether the i-th level warehouse is located;

[0122] : Whether the jth secondary warehouse is located;

[0123] The primary warehouse and the secondary warehouse are warehouse location decisions and also warehouse selection variables;

[0124] : The logistics volume from supplier h to production site k;

[0125] in:

[0126] : First-level warehouse candidate set;

[0127] : Secondary warehouse candidate set;

[0128] : supplier index;

[0129] : Production site index;

[0130] The mathematical formula of the objective function based on the above variables is:

[0131]

[0132] in:

[0133] : Fixed cost of primary warehouse;

[0134] : Fixed cost of secondary warehouse;

[0135] : Unit transportation cost from supplier h to production site k.

[0136] Furthermore, on top of the above cost-based objective function, an indicator reflecting the transport distance is added. Specifically, the overall transport ton-kilometers are added to the objective function, a dual-objective model is established, and then the optimization goal of the final solution is determined by adjusting the following weight coefficients;

[0137] Incorporating the transport distance into the objective function to establish a dual-objective model:

[0138]

[0139] : The weight coefficient of cost and distance ( );

[0140] : Transportation distance from supplier h to production site k.

[0141] 13. Establish constraints, including warehouse capacity constraints, inventory capacity constraints, throughput constraints, etc.

[0142] Constraints are used to limit the feasibility of solutions. Constraints for the characteristics of energy industry storage logistics support multiple constraints. Here are a few of them:

[0143] Warehouse quantity constraints:

[0144]

[0145] : Minimum quantity of first-level warehouse;

[0146] : Maximum number of first-level warehouses;

[0147] Indicates whether the i-th level warehouse is enabled ;

[0148] Transport balance constraints:

[0149]

[0150] represents the logistics volume from supplier h to primary warehouse i;

[0151] represents the logistics volume from primary warehouse i to secondary warehouse j;

[0152] represents the logistics volume from the first-level warehouse i to the production site k. For any first-level warehouse ;

[0153] Storage capacity utilization constraint:

[0154]

[0155] : Unit material storage volume;

[0156] : Warehouse capacity of first-level warehouse i;

[0157] : Storage capacity utilization rate of primary warehouse i;

[0158] : Material type collection, for any first-level warehouse .

[0159] 14. Set solution goals: Set the priority order of cost goals, distance goals, goal violations, etc. based on user selection and system logic.

[0160] 2. Solution process: Use the CP-SAT method to solve and search for the optimal warehouse network layout through iterative optimization.

[0161] Call a commercial or open source solver to solve the model established above. It is recommended to use the open source CpSolver and its default underlying solver, but it is allowed to replace it with other solvers to obtain higher solving efficiency and accuracy.

[0162] When calling the solver, you can set an upper limit on the solution time to control the number of iterations and the degree of convergence.

[0163] The solver uses the CP-SAT algorithm to solve. The working principle of CP-SAT solution is:

[0164] Constraint modeling: converting the constraints of the optimization problem (such as logistics balance, storage capacity limit) into Boolean logic constraints;

[0165] Variable decomposition and encoding: convert integer variables into Boolean variables and use logical formulas to express the value range and relationship of variables;

[0166] Search strategy: Use techniques such as Branch and Bound, Pruning, and Conflict Learning to efficiently explore the search space.

[0167] The CP-SAT solution process is as follows:

[0168] Problem initialization: Generate a mixed integer programming model from input data, including the objective function, variable definitions, and constraints;

[0169] Search and solve: Use the CP-SAT core solving engine to quickly traverse the possible solution space and find the solution that meets the constraints and optimizes the optimization goal;

[0170] Verification and output of solutions: After finding the optimal solution, verify the solution and output the specific warehouse location and logistics volume allocation plan;

[0171] After the solution results come out, necessary post-processing is performed on the results.

[0172] 3. Simulation process: The above solution results select the enabled warehouse sites and determine the radiation relationship between warehouses. During the simulation, the material flow direction of each month will be simulated on this network structure;

[0173] Based on the simulation results, key indicators such as inventory, storage capacity utilization, throughput, and total cost are estimated;

[0174] If the input parameters include multiple options, the key indicators of these options should also be compared.

[0175] Step 5: The user enters the result page through the interactive interface, and the backend processor service calls the built-in visualization module.

[0176] Step 6: The visualization module reads input and result data from the database, organizes the data into multiple chart styles according to the predetermined processing logic, and returns the charts to the user interface.

[0177] Step 7: The user starts data export through the interactive interface, and the backend processor service calls the built-in data integration module.

[0178] Step 8: The data integration module reads the input and result data from the database, converts the data into a preset format, generates an Excel file, and finally transmits the generated file to the user.

[0179] Furthermore, a logistics simulation system for energy enterprise materials is proposed, including:

[0180] User interface layer: provides a visual operation interface to support users to input planning parameters, view planning results, etc. It includes major functional modules such as data import, model solution, result display, and data export;

[0181] Data processing layer: mainly includes the engine of the decision-making system, including major functional modules such as data preprocessing, solver, visualization, and data integration;

[0182] Data storage layer: Use a relational database to store user and system data, including input data, calculation results, solver intermediate states, etc.

[0183] It should be further explained that the system deployment architecture is based on the microservice architecture and is deployed on a network cluster. The cluster is managed by Kubernetes. Users use tools through browsers, and browser requests are sent to the front-end microservices through istio. The front-end microservices communicate with the back-end microservices through the HTTP protocol, and the back-end microservices are connected to the relational database RDBMS. In addition, when the algorithm solution calculation is started, the back-end microservice creates a new computing service pod through the Kubernetes interface, and the computing service can directly access the RDBMS.

[0184] The technical scope of the present invention is not limited to the contents in the above description. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of ​​the present invention, and these deformations and modifications should all fall within the protection scope of the present invention.

Claims

1. A storage network planning method for energy enterprise materials, characterized in that: The steps include: S1: receiving user input data through the interactive interface, the data import module calls the data preprocessing module through the communication interface, converts and cleans the data format, and stores it in the database; in response to the user's solution instruction, the back-end processor calls the solver through the communication interface to perform optimization calculation; S2: The solver obtains model parameters from the database, builds a mixed integer programming model based on the business rules of the energy enterprise, uses the constraint satisfaction problem solving algorithm to perform iterative optimization, searches for the optimal warehouse network layout solution, and stores the solution results back to the database; S3: In response to the user's result viewing operation through the human-computer interaction interface, the back-end processor calls the visualization processing module, obtains input and optimization result data from the database based on preset rules, generates a multi-dimensional visualization chart and pushes it to the user interface, and in response to the user's data export instruction, extracts the data through the data integration module, generates a portable document in a preset format and transmits it to the user terminal.

2. The method for warehousing network planning for energy enterprise materials according to claim 1 is characterized in that: The specific steps of S1 are as follows: S11: The user uploads input data through the interactive interface. After the data import module receives the data file, it calls the data preprocessing module through the HTTP interface; S12: The data preprocessing module converts the input data into the format required by the optimization model, completes the missing data, and finally stores the results in the database; S13: The user starts the model solving through the interactive interface, and the backend processor service calls the solver through the API.

3. The method for warehousing network planning for energy enterprise materials according to claim 1 is characterized in that: The steps of constructing the mixed integer programming model in S2 are as follows: S21: Establish a multi-level warehouse network structure model; S22: Establish decision variables; S23: Establish constraints; S24: Set the solution target.

4. The method for planning a storage network for energy enterprise materials according to claim 3 is characterized in that: The decision variables are established as follows: including warehouse selection variables and flow variables; : Whether the i-th level warehouse is located; : Whether the jth secondary warehouse is located; : The logistics volume from supplier h to production site k; :First-level warehouse candidate set; : Secondary warehouse candidate set; :Supplier Index; :Production site index.

5. The method for planning a storage network for energy enterprise materials according to claim 4 is characterized in that: The objective function formula established by the decision variables is: ； in: : Fixed cost of first-level warehouse; :Secondary warehouse fixed costs; : Unit transportation cost from supplier h to production site k.

6. The method for planning a storage network for energy enterprise materials according to claim 5, characterized in that: The decision variables are introduced into the transport distance factor to establish a dual-objective model. The specific formula is as follows: ； : The weight coefficient of cost and distance ( ); : Transportation distance from supplier h to production site k.

7. The method for planning a storage network for energy enterprise materials according to claim 5, characterized in that: The constraints include but are not limited to: Warehouse quantity constraints: ； : Minimum quantity of first-level warehouse; : Maximum number of first-level warehouses; Indicates whether the i-th level warehouse is enabled ; Transport balance constraints: ； represents the logistics volume from supplier h to primary warehouse i; represents the logistics volume from primary warehouse i to secondary warehouse j; represents the logistics volume from the first-level warehouse i to the production site k. For any first-level warehouse ; Storage capacity utilization constraint: ； : Unit material storage volume; : Warehouse capacity of first-level warehouse i; : Storage capacity utilization rate of primary warehouse i; : Material type collection, for any first-level warehouse .

8. The method for planning a storage network for energy enterprise materials according to claim 5, characterized in that: The constraint satisfaction problem solving algorithm is CP-SAT solution, and the CP-SAT solution process is as follows: Problem initialization: Generate a mixed integer programming model from input data, including the objective function, variable definitions, and constraints; Search and solve: Use the CP-SAT core solving engine to quickly traverse the possible solution space and find the solution that meets the constraints and optimizes the optimization goal; Verification and output of solutions: After finding the optimal solution, verify the solution and output the specific warehouse location and logistics volume allocation plan.

9. The method for warehousing network planning for energy enterprise materials according to claim 1 is characterized in that: The specific steps of S3 are as follows: S31: The user enters the result page through the interactive interface, and the backend processor service calls the built-in visualization module; S32: The visualization module reads input and result data from the database, organizes the data into multiple chart styles according to a predetermined processing logic, and returns the charts to the user interface; S33: The user starts data export through the interactive interface, and the backend processor service calls the built-in data integration module; S34: The data integration module reads input and result data from the database, converts the data into a preset format, generates an Excel file, and finally transmits the generated file to the user.

10. A logistics simulation system for energy enterprise materials, used to implement the storage network planning method for energy enterprise materials according to any one of claims 1 to 9, characterized in that: include: User interface layer: provides a visual operation interface, supports user input, viewing and data export functions, including data import module, model solution module, result display module and data export module; Data processing layer: mainly the engine of the decision-making system, including data preprocessing module, solver, visualization module, and data integration module; Data storage layer: Use relational database to store user and system data.

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