Matching decision-making method and system for storage and recombination linkage management and control of retired power battery

Abstract

The invention relates to a matching decision-making method and system for storage and recombination linkage management and control of retired power batteries, and the method comprises the steps: detecting at least one piece of recombination decision-making information to be executed from first recombination matching decision-making information after recombination correction information is received, and enabling one piece of recombination decision-making information to be associated with one first recombination order; combining a dynamic recombination order corresponding to the recombination correction information and all the first recombination orders into a second recombination order, and performing coding and population initialization on all the second recombination orders according to preset mixed integer programming to generate a plurality of candidate coding individuals; based on a preset catastrophe adaptive genetic algorithm, mixed integer programming and the fitness corresponding to the candidate coding individuals participating in each iteration, performing iteration of genetic evolution operation and population catastrophe operation on the plurality of corresponding candidate coding individuals until a plurality of intention coding individuals are generated; and obtaining a target coding individual from the plurality of intention coding individuals to obtain a decision result including the target coding individual.

Description

Technical Field

[0001] The present application relates to the technical field of secondary utilization of retired power batteries, in particular to a matching decision method and system for the linkage control of storage and recombination of retired power batteries. Background Art

[0002] Power batteries are widely used in fields such as electric vehicles. As the usage time goes by, their performance will gradually decay, and finally reach the retired state where they can no longer meet the vehicle's usage requirements. After retirement, the power batteries still have a certain remaining capacity and can be used in scenarios such as energy storage. In related technologies, the methods for recycling and reusing power batteries include disassembly and recycling and secondary utilization. Secondary utilization is becoming increasingly popular in the recycling of power batteries because it can extend the life cycle value of the batteries and achieve the full utilization of resources.

[0003] In the process of secondary utilization of retired power batteries, how to efficiently match and recombine the disassembled batteries is the key to ensuring their benefits. Recombining power batteries requires that each single battery or module battery has high consistency in parameters such as state of health (SOH), internal resistance, and capacity to ensure the performance stability and service life of the recombined battery pack.

[0004] In related technologies, since the raw materials of the disassembled batteries include battery modules and battery cells, there are significant differences in parameters among these materials, and they are mixed and stored in the warehouse. In addition, due to the complexity of the recombination matching method of secondary products, the warehouse management and the scheduling efficiency of the production workshop are under great pressure. At the same time, in related technologies, there are also difficulties in matching order demands with production, that is, it is impossible to efficiently and timely select battery raw materials and recombine them while meeting the battery consistency.

[0005] Aiming at the problem that the secondary utilization of retired power batteries in related technologies has low scheduling efficiency in warehouse management and production workshops due to the complexity of the recombination matching method, no effective solution has been proposed yet. Summary of the Invention

[0006] The embodiments of the present application provide a matching decision method and system for the linkage control of storage and recombination of retired power batteries to at least solve the problem that the secondary utilization of retired power batteries in related technologies has low scheduling efficiency in warehouse management and production workshops due to the complexity of the recombination matching method.

[0007] In a first aspect, an embodiment of the present application provides a matching decision method for the storage and recombination linkage control of retired power batteries, including: after receiving recombination correction information, detecting at least one recombination decision information to be executed from the first recombination matching decision information that has been decision-generated before the current time, where the recombination decision information is used to represent the matching decision of the recombined power batteries corresponding to the demand information of a production order, and one recombination decision information is associated with a first recombination order; combining the dynamic recombination orders corresponding to the recombination correction information with all the first recombination orders to form second recombination orders, and encoding and initializing the population of all the second recombination orders according to a preset mixed integer programming to generate a plurality of candidate coding individuals, where the mixed integer programming includes a plurality of objective sub-functions and a plurality of objective sub-constraint parameters, and the sub-coding corresponding to the candidate coding individual is generated by encoding according to the corresponding objective sub-function and the objective sub-constraint parameter, and is used to represent one recombination decision information; based on a preset catastrophic adaptive genetic algorithm, the mixed integer programming, and the fitness corresponding to the candidate coding individuals participating in each iteration, performing iterative genetic evolution operations and population catastrophe operations on the plurality of corresponding candidate coding individuals until a plurality of intention coding individuals are generated, where the fitness is determined according to the collaborative operation cost parameter associated with the objective sub-function corresponding to the sub-coding of the candidate coding individual; obtaining a target coding individual from the plurality of intention coding individuals to obtain a decision result including the target coding individual.

[0008] In a second aspect, an embodiment of the present application provides a service system, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the matching decision method for the storage and recombination linkage control of retired power batteries described in the first aspect.

[0009] Compared with the related art, the matching decision method and system for the storage and reorganization linkage control of retired power batteries provided by the embodiments of the present application, after receiving the reorganization correction information, detect at least one reorganization decision information to be executed from the first reorganization matching decision information that has been decided and generated before the current time, where the reorganization decision information is used to represent the matching decision of the reorganized power batteries corresponding to the demand information of a production order, and one reorganization decision information is associated with a first reorganization order; combine the dynamic reorganization order corresponding to the reorganization correction information with all the first reorganization orders to form a second reorganization order, and encode and initialize the population of all the second reorganization orders according to a preset mixed integer programming to generate multiple candidate coding individuals, where the mixed integer programming includes multiple objective sub-functions and multiple objective sub-constraint parameters, and the sub-coding corresponding to the candidate coding individual is generated by encoding according to the corresponding objective sub-function and the objective sub-constraint parameter, and is used to represent one reorganization decision information; based on a preset catastrophic adaptive genetic algorithm, the mixed integer programming, and the fitness corresponding to the candidate coding individuals participating in each iteration, perform iterative genetic evolution operations and population catastrophe operations on the multiple corresponding candidate coding individuals until multiple intention coding individuals are generated, where the fitness is determined according to the collaborative operation cost parameter associated with the objective sub-function corresponding to the sub-coding of the candidate coding individual; obtain the target coding individual from the multiple intention coding individuals to obtain the decision result including the target coding individual, which solves the problem that the cascade utilization of retired power batteries in the related art has low scheduling efficiency in warehouse management and production workshops due to the complexity of the reorganization matching method. Through the matching decision method of the present application, the cascade utilization enterprise can reduce the complexity of reorganization production, strengthen the effective management of the warehouse, improve the warehouse turnover rate, reduce the inventory cost, optimize the cascade utilization process of retired power batteries, and improve the operation efficiency of the enterprise, while ensuring the timely delivery of orders.

[0010] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 is the hardware structure block diagram of the terminal of the matching decision method for the storage and reorganization linkage control of retired power batteries according to the embodiments of the present application; Figure 2 is the flowchart of the matching decision method for the storage and reorganization linkage control of retired power batteries according to the embodiments of the present application; Figure 3 is a schematic diagram of a chromosome according to an embodiment of the present application; Figure 4 is a schematic diagram of a battery raw material combination method according to a preferred embodiment of the present application; Figure 5 is a structural block diagram of a matching decision-making device for storage and recombination linkage control of retired power batteries according to an embodiment of the present application. Detailed implementation manners

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described and explained below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without creative efforts belong to the scope of protection of the present application. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as insufficient disclosure of the content of the present application.

[0013] Referring to "embodiments" in the present application means that specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0014] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those with ordinary skills in the technical field to which this application belongs. The words such as "a", "an", "one", "the" and the like involved in this application do not indicate a limitation in quantity and may represent a singular or plural number. The terms "include", "comprise", "have" and any variations thereof involved in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The "multiple links" involved in this application refer to two or more links. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0015] Before describing the matching decision method of the embodiments of this application, the technical problems solved by this application and the background proposed by this application are described as follows:

[0016] The related technologies used in the embodiments of this application are described as follows:

[0017] The Genetic Algorithm (GA) is a heuristic search algorithm inspired by natural selection and the theory of evolution. Traditional genetic algorithms find optimal solutions to problems by mimicking the processes of gene mutation, crossover pairing, and survival of the fittest in the biological world. However, for certain types of problems, especially those with high-dimensional, non-linear, or multi-modal characteristics, standard genetic algorithms may face problems such as slow convergence speed and being easily trapped in local optima. Based on this, in related technologies, methods for improving genetic algorithms have begun to be explored, such as the Adaptive Genetic Algorithm.

[0018] The Adaptive Genetic Algorithm (AGA) dynamically adjusts parameters in the algorithm (such as mutation probability, crossover probability, etc.) to meet the requirements of different problem characteristics and evolution stages, thereby improving the robustness and optimization efficiency of the algorithm.

[0019] The novel Catastrophe Adaptive Genetic Algorithm (CAGA) further incorporates the idea of catastrophe theory on the basis of AGA. Catastrophe theory is a mathematical theory that studies the sudden changes in systems, which can help us understand the behavior of systems when approaching critical points. In the novel catastrophe adaptive genetic algorithm, by detecting the trend of the decline in population diversity or other signals indicating that the algorithm may fall into a local optimum, catastrophe operations (such as large-scale perturbations, re-initializing some individuals, etc.) are applied in a timely manner to break the existing pattern and promote the emergence of new excellent solutions. The novel catastrophe adaptive genetic algorithm is an advanced genetic algorithm that combines adaptive ability and catastrophe theory. Its main feature is that it can dynamically adjust the algorithm parameters according to the characteristics of the problem and the state of the algorithm evolution, and take radical measures when necessary to prevent premature convergence, thereby improving the efficiency and effectiveness of problem-solving.

[0020] Mixed Integer Programming (MIP) is an optimization problem that combines the characteristics of linear programming and integer programming. In a mixed integer programming problem, some decision variables are continuous (can take any real value), while some decision variables are discrete (can only take integer values).

[0021] The method embodiments provided in this embodiment can be executed on a terminal, a computer, or a similar computing device. Taking running on a terminal as an example, Figure 1 is the hardware structure block diagram of the terminal for the matching decision method of the storage and reorganization linkage control of retired power batteries in the embodiments of the present application. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the above terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above terminal. For example, the terminal may further include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.

[0022] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the matching decision method for the storage and reorganization linkage control of retired power batteries in the embodiments of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0023] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (abbreviated as RF) module, which is used to communicate with the Internet wirelessly.

[0024] This embodiment provides a matching decision method for the storage and reorganization linkage control of retired power batteries running on the above terminal. Figure 2 It is a flowchart of the matching decision method for the storage and reorganization linkage control of retired power batteries according to the embodiments of the present application, as Figure 2 shown, and this process includes the following steps:

[0025] Step S201, after receiving the reorganization correction information, detect at least one reorganization decision information to be executed from the first reorganization matching decision information that has been decision-generated before the current time, where the reorganization decision information is used to represent the matching decision of the reorganized power batteries corresponding to the demand information of a production order, and one reorganization decision information is associated with a first reorganization order.

[0026] In this embodiment, the execution entity of the matching decision method of the embodiments of the present application is a Digital Twin system deployed in a cascaded utilization enterprise. From the macro perspective of the manager of the cascaded utilization enterprise, it realizes the precise tracking and efficient allocation of battery raw material resources in the warehouse, ensuring that each retired power battery can be maximally utilized, reasonably allocating raw material resources, and completing more customer orders. In this embodiment, the decision result is a battery product reorganization matching plan made according to the order demand information, which does not include the system layout design of the warehouse and the picking path, but includes the material outbound plan, that is, the reorganization decision information of the reorganized battery that meets the order demand information is planned by the decision. The reorganization decision information includes the battery type, battery material type, battery component type (battery module, battery cell), and specification (quantity) used in the production of each order. At the same time, after the reorganization decision information is determined, it will also map or manage the start reorganization time of each order in each process of each reorganization workshop and the material complete outbound time in the warehouse. It can be understood that in this embodiment, the selection of the battery component type in the reorganization decision information is based on considering the storage cost, turnover cost, and safety maintenance cost of the warehouse, that is, the decision result is based on the coordination between the warehouse and the reorganization. The coordination between the warehouse and the reorganization in the embodiments of the present application means that when making a matching decision, it is necessary to consider the benefits of each reorganization workshop (for example, reorganization production time, reorganization production cost) and the penalty cost caused by order delay, and also consider the benefits of the warehouse (for example, the turnover rate of different battery raw materials, the storage cost of all raw materials). For example, when making a decision, if only using battery modules for reorganization is considered, although it can reduce the reorganization time and cost, it will cause a large backlog of battery cells in the warehouse, which is not conducive to the turnover of the warehouse and increases the safety maintenance cost of the warehouse for battery cells.

[0027] In this embodiment, before the current time, the restructuring workshop will carry out the restructuring production of retired power batteries according to all the restructuring decision information corresponding to the first restructuring matching decision information, including picking battery raw materials in the warehouse, temporarily storing the battery raw materials in the warehouse buffer area, transporting the buffered battery raw materials to the restructuring workshop, and transporting the batteries to the finished product warehouse after the restructuring workshop completes the cost restructuring production of the batteries; at the current moment, the execution entity detects the occurrence of dynamics (for example: an emergency order is inserted). At this time, it is necessary to adjust and re-plan the first restructuring matching decision information. It can be understood that at the current moment, there will be unfinished restructuring orders, that is, there are unexecuted restructuring decision information. When re-planning the corresponding restructuring matching decision information, it is necessary to first determine the restructuring order corresponding to the unexecuted restructuring decision information, that is, the first restructuring order (corresponding to the production order that has not been restructured and produced), and then combine the first restructuring order and the restructuring order newly added due to the occurrence of dynamics (that is, the dynamic restructuring order) into the restructuring order corresponding to the current situation, and then re-plan the restructuring matching decision information in combination with the current production capacity of the restructuring workshop and the warehouse; in this embodiment, the first restructuring matching decision information determines its corresponding content according to the attributes of the dynamics generated at the current moment. When the dynamics occur at the first decision, the first restructuring matching decision information and the restructuring decision information to be executed are empty; when the dynamics occur at a non-first decision, the first restructuring matching decision information is generated by the previous decision plan. For example: the first restructuring matching decision information includes, but is not limited to, the restructuring matching decision information generated by planning according to the first real-time collected all-element data according to the decision steps of this embodiment. At the current moment, there is corresponding restructuring decision information to be executed; for another example: when the dynamics occur, two matching decisions have been completed, and the first restructuring matching decision information is the restructuring matching decision information generated by the second matching decision.

[0028] Step S202: Combine the dynamic restructuring orders corresponding to the restructuring correction information with all the first restructuring orders to form a second restructuring order, and encode and initialize the population of all the second restructuring orders according to a preset mixed-integer programming to generate multiple candidate coding individuals. The mixed-integer programming includes multiple objective sub-functions and multiple objective sub-constraint parameters. The sub-coding corresponding to the candidate coding individual is encoded according to the corresponding objective sub-function and objective sub-constraint parameter and is used to represent a restructuring decision information.

[0029] In this embodiment, for all the second reorganization orders corresponding to the retired power batteries to be reorganized, before generating the reorganization decision information, it is necessary to convert the decision variables and target variables in the reorganization decision information corresponding to the second reorganization orders into corresponding mathematical models to describe the reorganization process of battery products. That is, using the target sub-function and target sub-constraint parameters corresponding to the preset mixed-integer programming, the reorganization of all the second reorganization orders is mathematized; in this embodiment, based on the production demand data corresponding to the collected second reorganization orders, a mathematical model is established to describe the reorganization process of battery products, including cost calculation, resource allocation, and reorganization production plan, with the goal of minimizing the overall operation cost of the warehouse and the reorganization workshop. At the same time, according to constraints such as order demand, material availability for shipment, and reorganization production capacity, a reorganization matching plan for battery products is formulated to ensure that the model can comprehensively reflect various target sub-constraint parameters and target sub-functions in actual operations; in the target sub-function of the mathematical model in this embodiment, costs in multiple dimensions are associated, for example: reorganization production cost, warehouse storage cost, delivery delay penalty cost, and the corresponding costs will be used as the objective function values of the corresponding target sub-functions and are used to calculate the corresponding fitness. At the same time, in this embodiment, inequalities representing relevant sub-constraint parameters are designed. For example, the various types of battery components used in all orders cannot exceed the inventory. For each order product, the combined capacity and energy of the selected modules and battery cells must be greater than or equal to the specified capacity and energy required by the product; in this embodiment, based on the production demand data corresponding to the collected second reorganization orders, establishing a mathematical model to describe the reorganization process of battery products belongs to the non-deterministic polynomial hard (NP-Hard) problem. Therefore, relevant genetic algorithms are used for solving, and then the relevant information corresponding to the decision on battery reorganization needs to be genetically encoded, that is, chromosome gene encoding and population initialization are performed according to the preset encoding form to form an initial population corresponding to collaborative decision matching after dynamic occurrence, that is, an initial population including multiple candidate coding individuals.

[0030] Step S203: Based on the preset catastrophic adaptive genetic algorithm, mixed-integer programming, and the fitness corresponding to the candidate coding individuals participating in each iteration, perform iterative genetic evolution operations and population catastrophe operations on multiple corresponding candidate coding individuals until multiple intention coding individuals are generated, where the fitness is determined according to the collaborative operation cost parameters associated with the target sub-function corresponding to the sub-coding of the candidate coding individual.

[0031] In this embodiment, after generating the corresponding initial population, the sub-coding bodies of the coded individuals in the initial population are coded according to mixed integer programming and belong to the NP-Hard problem, which requires the use of a genetic algorithm for solution. However, the traditional genetic algorithm is prone to falling into a local optimum. Therefore, an adaptive genetic algorithm with a catastrophe operation is adopted for solution to solve the problem that the traditional genetic algorithm is prone to falling into a local optimum when facing non-linear, multi-extreme value, and multi-variable problems. It can be understood that the dynamic selection and dynamic crossover and mutation performed by the adaptive genetic algorithm are well-known techniques to those skilled in the art and do not constitute a limitation to the clarity of the technical solution. At the same time, it is also feasible to trigger a catastrophe according to a preset catastrophe rule to guide the adaptive genetic algorithm to perform corresponding genetic evolution operation iterations. In this embodiment, according to the fitness of the population individuals in each iteration, it is determined whether to perform a catastrophe and whether the population individuals after iteration (corresponding to candidate coding bodies) meet the set requirements, that is, whether they are intended coding individuals. Through multiple catastrophe operations and genetic evolution operations, the fitness of the individuals in the corresponding population meets the set requirements, and then multiple intended coding bodies are obtained.

[0032] In this embodiment, by separately converting the target sub-functions corresponding to the warehousing subsystem and the recombination subsystem into corresponding fitness functions, the target sub-functions in the warehousing subsystem and the recombination subsystem are the target sub-functions of the mathematical model in this embodiment. The objective function values of multiple target sub-functions (for example: the recombination production cost, warehouse storage cost, and delivery delay penalty cost corresponding to the collaborative operation in the two stages of warehousing and recombination) will be used to calculate the fitness of the corresponding coded individuals. In this embodiment, the higher the fitness, the lower the corresponding cost, and the better the effect of the candidate coded individuals corresponding to the genetic evolution operation and the catastrophe operation iteration.

[0033] Step S204: Obtain a target coding individual from multiple intended coding individuals to obtain a decision result including the target coding individual.

[0034] In this embodiment, iterations of genetic evolution operations and population catastrophe operations are performed on multiple corresponding candidate coding individuals to generate intended coding individuals that meet the set requirements (the fitness of the corresponding candidate coding individuals is higher than the set fitness threshold). The target coding individual is selected from the intended coding individuals to obtain a decision result including the target coding individual. In this embodiment, although the decision result is the target coding individual, it is actually the recombination decision information corresponding to all the target sub-codings of the target coding individual, that is, the battery product recombination matching scheme made to meet the corresponding order demand information.

[0035] Through the above steps S201 to S204, after receiving the reorganization correction information, at least one reorganization decision information to be executed is detected from the first reorganization matching decision information that has been decided and generated before the current time. Among them, the reorganization decision information is used to represent the matching decision of the reorganized power battery corresponding to a production order demand information, and one reorganization decision information is associated with a first reorganization order; the dynamic reorganization order corresponding to the reorganization correction information is combined with all the first reorganization orders to form a second reorganization order, and according to the preset mixed integer programming, all the second reorganization orders are encoded and the population is initialized to generate multiple candidate coding individuals. The mixed integer programming includes multiple objective sub-functions and multiple objective sub-constraint parameters. The sub-coding corresponding to the candidate coding individual is generated by encoding according to the corresponding objective sub-function and objective sub-constraint parameters, and is used to represent a reorganization decision information; based on the preset catastrophic adaptive genetic algorithm, mixed integer programming, and the fitness corresponding to the candidate coding individuals participating in each iteration, iterative genetic evolution operations and population catastrophe operations are performed on the multiple corresponding candidate coding individuals until multiple intention coding individuals are generated. Among them, the fitness is determined according to the collaborative operation cost parameter associated with the objective sub-function corresponding to the sub-coding of the candidate coding individual; the target coding individual is obtained from the multiple intention coding individuals to obtain the decision result including the target coding individual, solving the problem that the cascade utilization of retired power batteries in the related technology has low scheduling efficiency in warehouse management and production workshops due to the complexity of the reorganization matching method. Through the matching decision method of the present application, the cascade utilization enterprise reduces the complexity of reorganization production, strengthens the effective management of the warehouse, improves the warehouse turnover rate, reduces the inventory cost, optimizes the cascade utilization process of retired power batteries, improves the operation efficiency of the enterprise, and at the same time ensures the timely delivery of orders.

[0036] It should be noted that to solve the decision-making problem of the coordination between the warehousing and restructuring of retired power batteries (corresponding to the restructuring matching in remanufacturing), it is necessary to perform real-time perception and dynamic interaction on the multi-scale data generated in each operation link of warehousing and restructuring. In the embodiments of this application, the Digital Twin (DT) image is extended to a DT system that meets the dynamic decision-making requirements of the "warehousing and restructuring" two-stage system of the retired power battery remanufacturing system, and a dynamic matching decision-making information architecture for the "warehousing and restructuring" two-stage coordination based on digital twin is constructed. The full-element data of the physical entity is obtained through the physical object warehouse of the DT information architecture (including order information, such as: order arrival time, delivery time, quantity, required specifications, price; specification information, inventory quantity, storage time, storage cost of various types of battery raw materials in the warehouse, real-time production capacity of the production line in the restructuring workshop, battery material matching time, order production online time; unit processing time and cost of battery modules and battery cells in different processes obtained based on the experience of workshop workers). Using DT-related technologies, the physical execution units of the production logistics process of the remanufacturing system are modeled and instantiated in the virtual image layer of the DT system to be mapped to virtual execution units that can reflect the real-time operating state of the system, that is, the physical entity is converted into a virtual image in the form of a model (corresponding mathematical function); finally, the corresponding data is received through the decision control layer of the DT system, and by comprehensively considering the coordination between the material outbound plan of the warehouse and the production plan of the restructuring workshop, real-time decision-making is made on the matching plan between the retired power battery raw materials and the battery product demand, guiding on-site operations, and realizing the intelligent control with the lowest operating cost of the entire remanufacturing production logistics system, that is, by embedding the models and algorithms mentioned below, real-time decision-making can be made on the matching plan between the retired power battery raw materials and the battery product demand, and the decision result can guide the on-site real-time operations; in this embodiment, digital twin technology is used to solve the problem of information non-sharing between the restructuring workshop and the warehouse, help the restructuring workshop formulate a reasonable order matching plan in combination with the inventory status of various types of battery raw materials in the warehouse, its own resources, and the order delivery date, and issue a material requisition plan to the warehouse, and while meeting the order delivery date, optimize the warehouse turnover rate and the restructuring production cost at the same time; in this embodiment, by establishing a mixed integer programming, a quantitative optimization of the matching decision between the battery raw materials and the battery product demand considering the "warehousing - restructuring" two-stage coordination with the goal of minimizing the overall operating cost is established, considering the respective constraints in the scenarios of multi-zone battery raw material matching outbound and multi-category product restructuring workshop multi-line production, and solving the challenges faced in systematic modeling of the matching decision problem of the battery raw materials and the battery product demand considering the coordination of the "multi-zone battery raw material warehouse material outbound plan - multi-category battery product restructuring workshop production plan" in the remanufacturing production logistics operation system composed of the "warehousing - restructuring" two-stageIn this embodiment, the mixed integer programming belongs to the NP-Hard problem and it is difficult to obtain an exact solution. Therefore, a genetic algorithm is used for solving. However, the traditional genetic algorithm is prone to falling into a local optimum. In this embodiment, an adaptive genetic algorithm with a catastrophe operation is used for solving. It can be understood and needs to be understood that the new catastrophe adaptive genetic algorithm is a genetic algorithm that combines a catastrophe operation and an adaptive adjustment mechanism, aiming to solve the problem that the traditional genetic algorithm is prone to falling into a local optimum solution when facing non-linear, multi-extremum, and multi-variable problems; in this embodiment, the corresponding mathematical model and algorithm are both nested in the decision control layer of the digital twin system. The decision control layer receives dynamic simulation data, completes the solution by matching the decision model, and issues the result to the physical object layer to guide the coordinated operation of the warehouse and the reorganized production workshop, realizing the intelligent control with the lowest cost of the entire remanufacturing "warehousing-reorganization" operation system.;

[0037] It should be further noted that the matching decision method for the coordination of warehousing and reorganization provided in the embodiment of the present application is used for the matching decision of the battery raw materials and battery product requirements for the two-stage coordination of warehousing and reorganization, so as to help the reorganized workshop of the retired power battery cascade utilization enterprise formulate a reasonable material requirements plan (MRP). On the premise of ensuring the timely completion of orders, it can not only reduce the complexity of reorganized production, but also strengthen the effective management of the warehouse, improve the warehouse turnover rate, and reduce the inventory cost, thereby optimizing the retired power battery cascade utilization process and enhancing the operation efficiency of the enterprise.

[0038] In some of these embodiments, the reorganization decision information includes the reorganization start time. Detect at least one reorganization decision information to be executed from the first reorganization matching decision information that has been decision-generated before the current time through the following steps:

[0039] Step 21, obtain all the reorganization decision information corresponding to the first reorganization matching decision information, and determine the reorganization start time corresponding to each reorganization decision information, where the first reorganization matching decision information is generated by performing genetic evolution operations and population catastrophe operations on the historical coding body based on the corresponding fitness. The historical coding body is generated by encoding and initializing the population according to the mixed integer programming based on all the first reorganization orders received before the current time.

[0040] In this embodiment, during the operation of the reorganization order that matches the production order demand information according to the reorganization decision information corresponding to the current decision result, when dynamics occur in a certain link (for example, an emergency insertion of a reorganization order), the reorganization decision information that has been executed before the occurrence of the dynamics (corresponding to dynamic interference) and the associated first reorganization order continue to be executed. At the moment when the dynamics occur, the DT system will adjust the decision result according to the current production capacity corresponding to the current warehousing subsystem and the reorganization workshop subsystem respectively, for the reorganization orders corresponding to the unexecuted reorganization decision information (corresponding to the to-be-executed reorganization decision information) and the reorganization order added by the dynamic interference. That is, it is necessary to detect the to-be-executed reorganization decision information from the first reorganization matching decision information that has been decision-generated before the current time. Specifically, it is assumed that the planned first reorganization matching decision information corresponds to reorganization orders 1, 2, 3, 4, 5, 6, and the corresponding production order is 3, 5, 6, 2, 1, 4. When executing reorganization order 6, dynamics occur (for example, there is an emergency inserted order 7). At this time, reorganization orders 3, 5, 6 are executed according to the existing reorganization decision information, and the to-be-executed reorganization orders 7, 2, 1, 4 are re-planned, and the detected to-be-executed reorganization orders are reorganization orders 2, 1, 4.

[0041] It can be understood that in this embodiment, the first reorganization matching decision information determines its corresponding content according to the attributes of the dynamics generated at the current moment. When the dynamics occur at the first decision, the first reorganization matching decision information and the to-be-executed reorganization decision information are empty; when the dynamics occur at a non-first decision, the first reorganization matching decision information is generated by the previous decision plan. For example, the first reorganization matching decision information includes, but is not limited to, the reorganization matching decision information generated by planning according to the decision steps of this embodiment based on the full-element data collected in real time for the first time. At the current moment, there is corresponding to-be-executed reorganization decision information; for another example, when the dynamics occur, two matching decisions have been completed, and the first reorganization matching decision information is the reorganization matching decision information generated by the second matching decision.

[0042] Step 22: Among all the reorganization decision information, select the reorganization decision information whose reorganization online time is later than the current time to obtain at least one to-be-executed reorganization decision information.

[0043] In this embodiment, when the DT system establishes a mathematical model to describe the reorganization process of retired power batteries according to the corresponding production demand data, the time element corresponding to the production demand data will be considered to at least meet the timely delivery of orders. In this embodiment, the production demand data obtained by the DT system is all-element data obtained based on the physical object layer of the DT system, including order information, warehousing information, and reorganization workshop information. Among them, the order information includes the arrival time, delivery time, quantity, required specifications, and price of the order; the warehousing information includes the specification information, inventory quantity, storage time, and storage cost of various types of battery raw materials in the warehouse; the reorganization workshop information includes the real-time production capacity of the production line in the reorganization workshop, the time for the battery materials to be complete, the time for the order to be put into production, and the unit processing time and cost of battery modules and battery cells in different processes. It can be understood that the order production start time in the reorganization workshop information is a factor affecting the execution status of the reorganization order. Therefore, by determining the order production start time, that is, the reorganization start time, it is possible to determine whether the corresponding reorganization order is in an executed state, thereby detecting the reorganization decision information to be executed, determining the unexecuted reorganization production order, and further realizing the adjustment of the reorganization order corresponding to the unexecuted reorganization decision information (corresponding to the reorganization decision information to be executed) and the reorganization order added by dynamic interference according to the current production capacity corresponding to the current warehousing subsystem and the reorganization workshop subsystem respectively.

[0044] In some of these embodiments, based on a preset catastrophic adaptive genetic algorithm, mixed integer programming, and the fitness corresponding to each candidate coding individual participating in each iteration, iterative genetic evolution operations and population catastrophe operations are performed on multiple corresponding candidate coding individuals, which are realized through the following steps:

[0045] Step 31, according to the objective sub-function corresponding to the mixed integer programming, calculate the collaborative operation cost parameter for the sub-coding corresponding to the candidate coding individual participating in the current iteration, and determine the fitness corresponding to each candidate coding individual in the current iteration according to the collaborative operation cost parameters corresponding to all sub-codings. Among them, the collaborative operation cost parameter is calculated based on the warehousing cost and the reorganization production cost, and the warehousing cost and the reorganization production cost are calculated according to the corresponding objective sub-function.

[0046] In this embodiment, a sub-code encoded based on the catastrophe adaptive genetic algorithm corresponds to a recombination decision information, that is, corresponds to a matching recombination plan for retired power batteries of a recombination production order. A matching recombination plan involves a warehousing subsystem (warehouse) and a recombination subsystem (recombination workshop). During the process of solving using the catastrophe adaptive genetic algorithm, the objective functions of the warehousing subsystem and the recombination subsystem are transformed into corresponding fitness functions, that is, the corresponding warehousing costs and recombination production costs are calculated through the target sub-functions corresponding to the warehousing subsystem and the recombination subsystem respectively, so as to determine the target cost corresponding to the recombination decision information of the plan, that is, to determine the collaborative operation cost parameters corresponding to each sub-code, and to determine the fitness of the candidate code individual according to the collaborative operation cost parameters corresponding to all the sub-codes of a candidate code individual.

[0047] In this embodiment, the target sub-functions corresponding to the warehousing subsystem and the recombination subsystem are respectively transformed into corresponding fitness functions. The target sub-functions corresponding to the warehousing subsystem and the recombination subsystem are the target sub-functions of the mathematical model in this embodiment. The objective function values of multiple target sub-functions (for example: the recombination production cost, warehouse storage cost, and delivery delay penalty cost corresponding to the collaborative operation in two stages of warehousing and recombination) are used to calculate the fitness of the corresponding code individual; in this embodiment, the process of encoding and population iteration for a code individual and the corresponding sub-code is an update of the decision on the recombination decision information corresponding to the recombination order, and the decision on the recombination decision information includes outputting decision variables and decision target values, where the decision variables include the number of modules and battery cells of a certain type (such as ternary lithium or lithium iron phosphate, etc.) of battery used in each recombination order of recombination production, the start recombination time of each process of each recombination order in each recombination workshop, and the time for the complete set of materials to be shipped out of the warehouse; the decision target values include the recombination completion time of each recombination order in each recombination workshop, the total recombination cost, the order delay time, the order delay cost; and the storage cost of each type of battery component in the warehouse with different unit storage costs affected by the storage time.

[0048] In this embodiment, when initializing the model, the following settings are made for the mathematical model to be used: 1. The demand of a customer order is for the same type of recombination battery product; 2. One customer order is a production batch and cannot be split; 3. The battery product categories in each recombination workshop are different, and each recombination workshop only produces one type of battery product; 4. The number of recombination lines in each recombination workshop is different; 5. The processes of all recombination lines in the same recombination workshop are the same.

[0049] In this embodiment, the mathematical model and parameter symbol definitions involved are as follows: Let \(i\) represent the production order number, where \(i\in\{1,2,3,\cdots,n\}\), and all battery products of production order \(i\) belong to one production batch; \(G(i)\) represents the number of reorganized batteries required for the \(i\)-th production order; \(Ah\) i represents the capacity requirement of the reorganized batteries required for the \(i\)-th production order; \(E\) i represents the energy requirement of the battery products required for the \(i\)-th production order; Let \(f\) represent the reconfiguration workshop number, where \(f\in\{1,2,\cdots,F\}\), \(l\) f represents the reconfiguration line number in reconfiguration workshop \(f\), \(l\) f \(\in\{1,2,\cdots,L\}\); Let \(j\) f represent the process number of reconfiguration workshop \(f\), \(j\) f \(\in\{1,2,\cdots,J\}\); Let \(t\) i,j represent the processing time of the battery products of the \(i\)-th production order in process \(j\) f ; \(C\) i,j represent the unit production cost of the battery products of the \(i\)-th production order processed in process \(j\) f ; \(T\) i represent the expected exchange time of the battery products of the \(i\)-th production order, represent the actual exchange time of the battery products of the \(i\)-th production order; represent the unit delay penalty cost of the \(i\)-th production order in the reconfiguration workshop; \(\omega\) i，lf is a variable of 0 or 1, indicating whether the battery products of the \(i\)-th production order are produced on the reconfiguration line \(l\) in reconfiguration workshop \(f\) f ; if so, \(\omega\) i，lf = 1, otherwise 0; Let \(m\) j,f represent the machine number in the \(j\) f -th process, \(m\) j,f \(\in\{1,2,\cdots,M\}\); is a variable of 0 or 1, indicating that the \(m\)-th machine in the \(j\)-th process is the immediate predecessor operation of the \(i\)-th production order for the \(i\)-th f ; \(\beta\) j,f ; Let \(\beta\) be a variable of 0 or 1, indicating whether the battery products of the \(i\)-th production order have passed through the \(j\)-th ’ process; \(R\) i,j represent the upper production capacity limit of the \(l\)-th reconfiguration line; f ; \(Q\) lf represent the time when the battery raw materials required for the \(i\)-th production order are fully stocked and shipped out; f ; represent the starting time of the battery products of the \(i\)-th production order in the reconfiguration workshop; represent the overstock unit penalty cost of the line side warehouse in the \(f\)-th reconfiguration workshop; represent the unit temporary storage cost of the line side warehouse in the \(f\)-th reconfiguration workshop; \(Q\) ; f,carepresents the upper limit of the capacity of the in-line warehouse of the f-th reorganization workshop at time t; p represents the batch number of the battery raw material warehousing, p ∈ {1, 2, …, P}; represents the warehousing time of the p-th batch of battery raw materials; represents the outbound time of the p-th batch of battery raw materials; represents the number of battery modules in the p-th batch of battery raw materials; represents the number of battery cells in the p-th batch of battery raw materials; is a variable of 0 or 1, indicating whether the battery raw materials required for the battery products of the i-th production order are stored in the cargo lane a of the buffer zone. If so, = 1, otherwise, = 0; represents the actual outbound quantity of the s-type battery modules required for the i-th production order; represents the actual outbound quantity of the s-type battery single cells required for the i-th production order; represents the unit processing time of the j f -th process during battery module assembly; represents the unit processing time of the j f -th process during battery cell assembly; v represents the storage location number, v ∈ {1, 2, ..., V}; a represents the cargo lane number, a = 1, 2, ..., N; s represents the battery model in the warehouse, s ∈ {1, 2, ..., S}; ah s,m represents the capacity of the s-type battery module m; ah s,c represents the capacity of the s-type battery cell c; e s,m represents the energy of the s-type battery module m; e s,c represents the energy of the s-type battery cell c; q s,m represents the existing inventory of the s-type battery module m in the warehouse; q s,c represents the existing inventory of the s-type battery cell c in the warehouse; represents the number of s-type battery modules m required for the battery products of the i-th production order; represents the number of s-type battery cells c required for the battery products of the i-th production order; represents the fixed storage cost of the battery module m; represents the fixed storage cost of the battery cell c; represents the unit time storage cost of the battery module m; represents the unit time storage cost of the battery cell c; represents the loss cost of the battery module m; represents the loss cost of the battery cell c; represents the initial value of the s-type battery module m; represents the initial value of the s-type battery cell c; Represents the initial performance of the S-type battery module m; Represents the initial performance of the S-type battery cell c; p s,m Represents the performance of the S-type battery module m at time t; p s,c Represents the performance of the S-type battery cell c at time t; f(t) m Represents the attenuation function of the battery module m, f(t) c Represents the attenuation function of the battery cell c; A variable that is 0 or 1, indicating whether the S-type battery module m is stored in the v-th storage location. If so, = 1, otherwise = 0; A variable that is 0 or 1, indicating whether the S-type battery cell c is stored in the v-th storage location. If so, = 1, otherwise = 0; α represents the safe outbound ratio; Represents the picking end time of the battery module m required for the i-th production order; Represents the picking end time of the battery cell c required for the i-th production order; σ represents the quantity ratio of battery cells and battery modules of the same model.

[0050] In this embodiment, the objective function corresponding to calculating the fitness of each coded individual is to minimize the total cost of warehousing and reorganization collaboration. Let Z be the total system cost, C ware Represents the warehousing cost, C recom Represents the reorganization cost, and minZ = C ware + C recom .

[0051] In this embodiment, the warehousing cost C direc Is the sum of the direct cost C direct And the indirect cost C undire , where The warehousing direct cost C direct = C fix + C var， C fix Represents the fixed cost of storing the battery module and the battery cell. The fixed cost C fix Is mainly related to equipment depreciation and is a fixed value; The variable cost per unit time C var Is mainly related to the storage time. There are batches for the raw materials entering the warehouse. Each batch of battery raw materials entering the warehouse includes a certain number of battery modules and battery cells. The variable cost per unit time .

[0052] The warehousing indirect cost C undire Is mainly the loss cost of placing the battery raw materials. Among them, C undire = + ; Indirect warehousing cost C undire including the loss cost of battery module placement and the loss cost of battery cell placement , = , = .

[0053] In this embodiment, the restructuring cost C recom is composed of the restructuring production cost C pro , the storage cost C of the in-line warehouse in the restructuring workshop sto , and the deferred penalty cost C delay . C recom =C pro +C sto +C delay , where the restructuring production cost ; the storage cost of the in-line warehouse in the restructuring workshop ; the deferred penalty cost .

[0054] In this embodiment, the relevant constraints of the mathematical model, that is, multiple objective sub-constraint parameters include: multi-zone warehouse storage and outbound constraints, and multi-category restructuring workshop multi-production line production constraints. Among them, the multi-zone warehouse storage and outbound constraints include: a battery module can only be stored in one storage location, and the constraint formula is: ; a battery cell can only be stored in one storage location, and the constraint formula is: ; the outbound quantity of battery modules cannot exceed the current inventory, and the constraint formula is: ; the outbound quantity of battery cells cannot exceed the current inventory, and the constraint formula is: ; the material availability time required for the production order is less than the start time of the restructuring production, and the constraint formula is: ; to avoid the actual outbound quantity required for the restructuring demand being greater than the actual demand due to accidents such as collisions of battery modules, the constraint formula is: ; to avoid the actual outbound quantity required for the restructuring demand being greater than the actual demand due to accidents such as collisions of battery cells, the constraint formula is: ; the buffer area can only store the battery modules / battery cells required for 2 production orders at a time, and the constraint formula is: ; the battery modules / battery cells of the same production order can only be stored in one buffer area, and the constraint formula is: ; the material availability time of the i-th production order is the maximum value of the picking time of the battery modules / battery cells, and the constraint formula is: .

[0055] The production constraints for multiple product lines in the multi-category reorganization workshop include: The combined capacity of the battery modules and battery cells selected for each production order must be greater than or equal to the specified capacity required for the reorganized battery product. The constraint formula is: ; The combined energy of the battery modules and battery cells selected for each production order must be greater than or equal to the specified energy required for the reorganized battery product. The constraint formula is: ; Each production order product can only be processed and produced on one reorganization line. The constraint formula is: ; The start time of the production order on the reorganization line in the reorganization workshop should be equal to the material readiness time minus the plant transportation time. The constraint formula is: ; The completion time of the production order is equal to the start time plus the processing time of each process. The constraint formula is: ; The processing time of the i-th production order in the j-th f process. The constraint formula is: ; Indicator function: ; Capacity upper limit constraint. The number of products produced by each reorganization production line cannot exceed the maximum production capacity. The constraint formula is: ; The capacity constraint of the line-side warehouse in each reorganization workshop. The delivery time T of the raw materials required for the production in this reorganization workshop needs to comprehensively consider the capacity of the line-side warehouse at time T. The constraint formula is: .

[0056] Step 32: According to the fitness and the preset selection operation, select a preset number of alternative coding individuals from all the candidate coding individuals in the current iteration. Among them, the selection operation includes one of the following: roulette wheel selection, selection based on the elite retention strategy, and tournament selection method. The selection probability corresponding to the selection operation is determined based on the fitness of the corresponding candidate coding individual.

[0057] In this embodiment, according to the fitness value of the candidate coding individual, one of the roulette wheel selection, elite retention strategy, or tournament selection method is adopted to select some individuals to be crossed (corresponding to the preset number of alternative coding individuals) from the parent population (corresponding to all the candidate coding individuals in the current iteration). The probability of selecting an individual with a high fitness is relatively high. It can be understood that the minimum collaborative operation cost corresponds to a high fitness in the embodiments of the present application; in this embodiment, the elite retention strategy is preferably adopted to retain the top 5% of the excellent individuals to implement the selection operation.

[0058] Step 33, based on the corresponding adaptive crossover probability and adaptive mutation probability, perform crossover operation and mutation operation on all candidate coding individuals in turn to generate multiple first coding individuals, wherein the candidate coding individuals corresponding to the current genetic evolution operation iteration include the first coding individuals, the adaptive crossover probability is determined based on the crossover probability function corresponding to the catastrophe adaptive genetic algorithm, and the adaptive mutation probability is determined based on the mutation probability function corresponding to the catastrophe adaptive genetic algorithm.

[0059] In this embodiment, a crossover operation is first performed on a preset number of selected candidate coding individuals based on an adaptively adjusted crossover probability to generate new individuals; in this embodiment, the crossover probability is adaptively and dynamically adjusted according to the fitness value and evolutionary stage of the population. In the early stage of evolution, a higher crossover probability helps to maintain the diversity of the population, that is, a higher crossover rate is used for individuals with poor fitness to increase the scope of exploration; in the late stage of evolution, a lower crossover probability is conducive to local optimization, that is, a lower crossover rate is used for individuals with higher fitness to protect excellent genes; in this embodiment, after crossover generates new individuals, a mutation operation is performed on the genes of the new individuals based on the adaptively adjusted mutation probability to generate multiple first coding individuals. In this embodiment, the mutation probability can also be adaptively adjusted according to the evolutionary characteristics of the population and the fitness value of the individual. In the early stage of evolution, In the early stage, a higher mutation probability is helpful to explore the entire search space, that is, a higher mutation rate is adopted for individuals with poor fitness to increase the exploration range; in the late stage of evolution, a lower mutation probability is helpful to concentrate on local search to find the optimal solution, that is, individuals with higher fitness adopt a lower mutation rate to protect excellent genes; it can be understood that as the evolution proceeds, the algorithm gradually approaches the optimal solution. At this time, the crossover probability and mutation probability should be appropriately reduced to protect the obtained excellent genotypes from being destroyed, while allowing subtle searches to improve the quality of the solution. Therefore, in this embodiment, the crossover probability function and the mutation probability function are set to monotonically decreasing functions. For the minimization problem, the smaller the individual fitness value, the smaller the current crossover probability and mutation probability should be. In this embodiment, the set crossover probability function and mutation probability function are used to adaptively adjust the crossover probability and mutation probability.

[0060] In some of the embodiments, after generating a plurality of first coding individuals, the following steps are further performed:

[0061] Step 41, selecting the candidate coding individual with the best fitness from all the candidate coding individuals generated by the previous genetic evolution iteration, and obtaining the first best individual corresponding to the previous genetic evolution iteration.

[0062] Step 42: Select the first-coded individual with the optimal fitness from multiple first-coded individuals to obtain the second-optimal individual corresponding to the current genetic evolution iteration, and determine whether the fitness of the second-optimal individual is higher than that of the first-optimal individual.

[0063] In this embodiment, the optimal individual in the initial population (corresponding to all candidate coded individuals generated in the previous genetic evolution iteration) (corresponding to the first-optimal individual) is saved as the first local optimal individual; after several genetic operations on the population, whether the fitness of the second-optimal individual is higher than that of the first-optimal individual is used as the judgment condition for determining whether a better individual is generated and whether to perform a catastrophe operation. If the catastrophe determination condition is met, the catastrophe operation is performed.

[0064] Step 43: According to the judgment result, select the target optimal individual corresponding to the current genetic evolution iteration from the second-optimal individual and the first-optimal individual, and perform a catastrophe operation on all multiple first-coded individuals in a preset catastrophe manner to generate multiple second-coded individuals, where the candidate coded individuals corresponding to the current catastrophe operation iteration include the second-coded individuals.

[0065] In some alternative embodiments, performing a catastrophe operation on all multiple first-coded individuals in a preset catastrophe manner to generate multiple second-coded individuals is implemented through the following steps:

[0066] Step 431: In the case where it is determined that the fitness of the second-optimal individual is higher than that of the first-optimal individual, select a preset number of first-coded individuals in descending order of fitness to obtain elite coded individuals, and initialize all elite coded individuals based on mixed-integer programming to generate corresponding multiple second-coded individuals.

[0067] In this embodiment, if the optimal individual in the current population (corresponding to the second-optimal individual) is better than the previously updated local optimal individual (corresponding to the first-optimal individual), then update the current local optimal individual and perform a type of catastrophe operation, that is, select a preset number of first-coded individuals in descending order of fitness to obtain elite coded individuals, and initialize all elite coded individuals based on mixed-integer programming to generate corresponding multiple second-coded individuals; in this embodiment, it is preferably to initialize each individual in the elite class of the current population (corresponding to the elite coded individuals) one by one according to a preset rule.

[0068] Step 432, when it is determined that the fitness of the second optimal individual is not higher than that of the first optimal individual, select the first encoded individuals with the set cataclysm scale number in ascending order of fitness to obtain the eliminated encoded individuals. After removing all the eliminated encoded individuals from the multiple first encoded individuals, add multiple randomly generated new encoded individuals to the remaining first encoded individuals to obtain the corresponding multiple second encoded individuals, where the number of new encoded individuals is equal to the set cataclysm scale number.

[0069] In this embodiment, if the optimal individual in the current population (corresponding to the second optimal individual) is inferior to the local optimal individual saved in the previous cataclysm (corresponding to the first optimal individual), it is considered that the search direction of the current population has deviated, and a type-II cataclysm operation is performed, that is, eliminate M individuals with poor fitness in the population, randomly generate M new individuals and add them to the current population to improve the diversity of the population; the current cataclysm scale M is not constant, but decreases with the increase of the number of iterations to ensure the stability of the algorithm in the later stage. In this embodiment, the following formula is used to calculate the current cataclysm scale M: M is the current cataclysm scale, is the preset cataclysm scale, λ is the control parameter, is the current number of iterations, is the maximum number of iterations.

[0070] In some embodiments, after obtaining the corresponding multiple second encoded individuals, the following steps are further implemented:

[0071] Step 51, determine whether the current iteration satisfies the preset iteration termination condition after the current cataclysm operation iteration, where the iteration termination condition includes at least one of the following: the fitness difference between the second encoded individuals corresponding to the current cataclysm operation iteration and the previous cataclysm operation iteration is less than the population fitness change threshold, and the number of the current iteration exceeds the preset iteration number threshold.

[0072] Step 52, when it is determined that the iteration termination condition is satisfied, use the multiple second encoded individuals as multiple intended encoded individuals.

[0073] Step 53, when it is determined that the iteration termination condition is not satisfied, perform iterative genetic evolution operations and population cataclysm operations on the multiple second encoded individuals until multiple intended encoded individuals are obtained.

[0074] In this embodiment, it is determined whether the algorithm terminates according to the preset termination conditions (such as the number of iterations, population fitness change). If the termination conditions are satisfied, the optimal solution is output (that is, multiple intended encoded individuals are output); if not, the next iteration is entered until the optimal solution is output.

[0075] In some of these embodiments, before generating a plurality of first encoded individuals, the following steps are further implemented:

[0076] Step 61: According to the fitness of the alternative encoded individuals, determine the average fitness and the minimum fitness corresponding to all the alternative encoded individuals, and determine whether the average fitness is less than the minimum fitness.

[0077] Step 62: In the case where it is determined that the average fitness is less than the minimum fitness, use the preset maximum crossover probability and maximum mutation probability as the corresponding adaptive crossover probability and adaptive mutation probability respectively.

[0078] Step 63: In the case where it is determined that the average fitness is greater than the minimum fitness, use the corresponding crossover probability function and mutation probability function to decrease the maximum crossover probability and the maximum mutation probability respectively, and use the first crossover probability and the first mutation probability obtained after the decrease as the corresponding adaptive crossover probability and adaptive mutation probability.

[0079] In this embodiment, the following crossover probability function and mutation probability function are used to adaptively adjust the crossover probability and the mutation probability. Specifically,

[0080] Crossover probability function

[0081] Mutation probability function

[0082] where respectively represent the lower bound and the upper bound of the crossover probability, f min represents the minimum fitness in the first encoded individuals corresponding to the previous genetic evolution operation iteration, f avg represents the average fitness of the alternative encoded individuals, represents the minimum fitness among the alternative encoded individuals, respectively represent the upper bound and the lower bound of the mutation probability, f m represents the fitness value of the mutant individual (the alternative encoded individual that has completed the current crossover operation); based on the characteristics of the sigmoid function ( ), the crossover probability function and the mutation probability function are monotonically decreasing functions, and the crossover probability and the mutation probability are monotonically decreasing.

[0083] In some of these embodiments, obtaining a target encoded individual from a plurality of intended encoded individuals to obtain a decision result including the target encoded individual is implemented through the following steps:

[0084] Step 71, determine the fitness corresponding to each intended coding individual.

[0085] Step 72, select the intended coding individual with the highest fitness from multiple intended coding individuals to obtain a target coding individual, and extract all sub-codings of the target coding individual to obtain target sub-codings.

[0086] Step 73, decode the recombination decision information corresponding to all target sub-codings, and use the recombination decision information corresponding to all target sub-codings as the decision result.

[0087] In some embodiments, according to a preset mixed integer programming, encode and initialize the population for all second recombination orders to generate multiple candidate coding individuals, which are implemented through the following steps:

[0088] Step 81, obtain the target sub-function and target sub-constraint parameters corresponding to the mixed integer programming, and obtain the production order information corresponding to each second recombination order, where the production order information includes the target information of the recombined power battery.

[0089] Step 82, match at least one battery combination matching information to each recombined power battery target information from the preset battery combination matching information, where one battery combination matching information is used to characterize the composition of the battery materials and battery components corresponding to a recombined power battery.

[0090] Step 83, use the target sub-function and target sub-constraint parameters to solve and optimize the battery combination matching information corresponding to each recombined power battery target information, generate the intended battery combination matching information corresponding to each recombined power battery information, and perform two-dimensional integer encoding on the intended battery combination matching information in a preset coding form to obtain sub-codings corresponding to the recombined power battery target information.

[0091] Step 84, after determining the collaborative operation cost parameters mapped by each sub-coding, integrate the sub-codings corresponding to all second recombination orders into a candidate coding individual, and perform population initialization based on one candidate coding individual obtained by encoding to generate multiple candidate coding individuals.

[0092] In some preferred embodiments, with reference to Figure 3 , the coding process of the coding individuals in the embodiments of the present application is described as follows:

[0093] According to the problem characteristics, each candidate coding individual is represented by a two-dimensional chromosome and all use the integer coding method. One candidate coding individual (with reference to Figure 3The vertical direction of the chromosome corresponding to P1 and P2 in it represents the order number Order, and the horizontal direction represents the number of battery types to be selected Type. Among them, the battery types are divided in turn according to the battery material type (for example: ternary lithium, lithium iron phosphate, lithium titanate), the battery component type (for example: battery module, battery cell), and the specifications (for example: voltage, capacity). In this embodiment, a certain row of the chromosome corresponds to a sub-code of a candidate coding individual; refer to Figure 3 , the ternary lithium battery material types include M1, C11, C12, the lithium iron phosphate battery material types include C2, and the lithium titanate battery materials include M3, C3. Considering Figure 3 the P1 coding individual (corresponding to a chromosome) described in, M1, C11, and C12 belong to the same material type, M1 and C11, C12 belong to different battery component types under the same material type, M1 is a battery module, and C11, C12 are battery cells with different specifications. Correspondingly, C2 belongs to the battery cells of the second material type, and M3, C3 belong to different battery component types of the third material type; it should be noted that for a production order, only battery components of the same material type can be used. For example, in Figure 3 the coding individual describing P2 in, for order 1 (#1), when there is data in the first three columns, the 4th - 6th columns will all be processed as 0. Similarly, for production order 2, when there is data in the 5th - 6th columns, the first 4 columns will all be processed as 0. Such a coding method encodes the chromosomes of the population and initializes the population, which can easily meet the constraint of only using battery components of the same material type and can be used more flexibly and effectively for subsequent processing.

[0094] In some alternative embodiments, the following steps are also performed:

[0095] Step 1: The execution entity receives a customer order and converts it into a production order through the enterprise's ERP system and issues it to each restructuring workshop. Among them, the customer order list is as shown in Table 1 below Table 1

[0096] In this embodiment, there are three types of restructuring workshops: (1) Home backup battery (1 - 10kwh) restructuring workshop: used for home emergency power supply or outdoor activities; (2) Residential energy storage battery (10 - 50kWh) restructuring workshop: mainly used in combination with rooftop solar photovoltaic panels to achieve self - sufficient energy supply and can also provide power support when the power grid is cut off; (3) Industrial and commercial energy storage battery (100 - 1000kwh) restructuring workshop: used in commercial buildings, data centers and other scenarios to optimize electricity costs and improve the flexibility of energy use.

[0097] Step 2: After receiving a production order, the restructuring workshop retrieves the inventory information from the Warehouse Management System (WMS) and independently decides on the types and quantities of battery raw materials required.

[0098] In this embodiment, each restructuring workshop has the ability to make independent decisions. The main goal of the decision-making is to reduce the restructuring cost on the premise of timely order delivery.

[0099] Step 3: After the warehouse receives the material requisition forms from each restructuring workshop, it picks goods in each storage area of the battery raw material warehouse. The picked battery raw materials are temporarily stored in the buffer area and then transported to each restructuring workshop by transport trolleys. The battery products completed by each restructuring workshop are also transported to the finished product warehouse waiting for outbound delivery.

[0100] In this embodiment, the warehouse conducts three-level zoning: First, it stores according to modules and battery cells; Second, the areas of each battery module and battery cell are further refined according to models. For example, the square lithium iron phosphate battery cell models of EVE Energy have LF304 and LF160, which are stored in separate zones; Finally, it continues to store in each area according to parameters such as 80% and 90% of the remaining capacity, etc.

[0101] Step 4: Based on the operation of the restructuring workshop, under the conditions of meeting the order requirements and delivery date, the restructuring workshop selects battery raw materials according to the fineness and cost of the products produced.

[0102] In this embodiment, there are various combinations of battery raw materials for battery products. A 51.2v - 200ah residential energy storage battery has multiple combination matching methods. For specific reference Figure 4 .

[0103] In this embodiment, the fineness is determined by the difference between the capacity of the battery pack actually reorganized and produced and the capacity required by the customer order, and the difference between the voltage of the battery pack actually reorganized and produced and the voltage required by the customer order; it can be understood that during the reorganization and production, the higher the fineness of selecting battery cell reorganization, the longer the time consumed and the higher the cost; although selecting battery modules sometimes cannot accurately meet the order requirements, it can greatly reduce the time and cost of reorganization; it should be noted that the decision-making in the reorganization workshop only focuses on the fineness and cost of the battery products produced in the reorganization workshop to select battery raw materials, but the matching plan will also have a certain impact on the battery raw material warehouse. First, the storage costs of battery modules and battery cells are different: the storage cost of battery cells is relatively high, and the equipment used for storage. Battery cells also need to be individually detected for each battery cell, requiring more detection equipment. Second, the storage of battery cells requires a certain interval. One pallet can store one battery module composed of 10 battery cells, but can only store 5 battery cells of the same model. Therefore, the number of warehouse positions occupied by battery modules and battery cells with the same power is different, resulting in a further increase in warehouse costs. Third, due to the special performance of the battery, when the battery is stored for too long, it will cause an accelerated decay of performance such as capacity, thus unable to bring economic benefits to the enterprise. Therefore, when the reorganization workshop selects battery raw materials for reorganization, it also needs to consider the status of the raw materials in the warehouse: the storage costs and storage times of different battery raw materials.

[0104] This embodiment also provides a matching decision-making device for the joint control of the storage and reorganization of retired power batteries. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0105] Figure 5 is a structural block diagram of a matching decision-making device for the joint control of the storage and reorganization of retired power batteries according to an embodiment of the present application. As Figure 5 shown, the device includes a detection module 51, an encoding module 52, a decision-making module 53, and a processing module 54, where

[0106] The detection module 51 is configured to, after receiving the reorganization correction information, detect at least one reorganization decision information to be executed from the first reorganization matching decision information that has been decided and generated before the current time, where the reorganization decision information is used to represent the matching decision of the reorganized power battery corresponding to a production order requirement information, and one reorganization decision information is associated with a first reorganization order.

[0107] The encoding module 52, which is coupled to the detection module 51, is configured to combine the dynamic reorganization orders corresponding to the reorganization correction information with all the first reorganization orders into second reorganization orders, and encode and initialize the population for all the second reorganization orders according to a preset mixed-integer programming, so as to generate a plurality of candidate encoded individuals. The mixed-integer programming includes a plurality of objective sub-functions and a plurality of objective sub-constraint parameters. The sub-encoding corresponding to a candidate encoded individual is generated by encoding according to the corresponding objective sub-function and objective sub-constraint parameters, and is used to represent a reorganization decision information.

[0108] The decision-making module 53, which is coupled to the encoding module 53, is configured to perform iterative genetic evolution operations and population catastrophe operations on a plurality of corresponding candidate encoded individuals based on a preset catastrophic adaptive genetic algorithm, mixed-integer programming, and the fitness corresponding to the candidate encoded individuals participating in each iteration, until a plurality of intended encoded individuals are generated. The fitness is determined according to the collaborative operation cost parameters associated with the objective sub-function corresponding to the sub-encoding of the candidate encoded individual.

[0109] The processing module 54, which is coupled to the decision-making module 53, is configured to obtain a target encoded individual from a plurality of intended encoded individuals to obtain a decision result including the target encoded individual.

[0110] In some embodiments, the reorganization decision information includes a reorganization online time. The detection module 51 further includes:

[0111] The first acquisition unit is configured to acquire all the reorganization decision information corresponding to the first reorganization matching decision information and determine the reorganization online time corresponding to each reorganization decision information, where the first reorganization matching decision information is generated by performing iterative genetic evolution operations and population catastrophe operations on a historical encoded body based on the corresponding fitness. The historical encoded body is generated by encoding and initializing the population according to the mixed-integer programming for all the first reorganization orders received before the current time.

[0112] The selection unit, which is coupled to the first acquisition unit, is configured to select, from all the reorganization decision information, the reorganization decision information whose reorganization online time is later than the current time to obtain at least one reorganization decision information to be executed.

[0113] In some embodiments, the decision-making module 53 further includes:

[0114] A calculation unit is configured to calculate the collaborative operation cost parameter for the sub-codes corresponding to the candidate coding individuals participating in the current iteration according to the objective sub-function corresponding to the mixed-integer programming, and determine the fitness corresponding to each candidate coding individual in the current iteration according to the collaborative operation cost parameters corresponding to all sub-codes. The collaborative operation cost parameter is calculated based on the warehousing cost and the restructuring production cost, and the warehousing cost and the restructuring production cost are calculated according to the corresponding objective sub-function.

[0115] A selection unit is coupled to the calculation unit and is configured to select a preset number of alternative coding individuals from all candidate coding individuals in the current iteration according to the fitness and a preset selection operation. The selection operation includes one of the following: roulette wheel selection, selection based on the elitist retention strategy, and tournament selection method. The selection probability corresponding to the selection operation is determined based on the fitness of the corresponding candidate coding individual.

[0116] An operation unit is coupled to the selection unit and is configured to perform a crossover operation and a mutation operation on all alternative coding individuals in sequence based on the corresponding adaptive crossover probability and adaptive mutation probability to generate a plurality of first coding individuals. The candidate coding individuals corresponding to the current genetic evolution operation iteration include the first coding individuals. The adaptive crossover probability is determined based on the crossover probability function corresponding to the catastrophic adaptive genetic algorithm, and the adaptive mutation probability is determined based on the mutation probability function corresponding to the catastrophic adaptive genetic algorithm.

[0117] In some embodiments, after generating a plurality of first coding individuals, the matching decision device for storage and restructuring linkage control of retired power batteries is further configured to select the candidate coding individual with the optimal fitness from all candidate coding individuals generated in the previous genetic evolution iteration to obtain the first optimal individual corresponding to the previous genetic evolution iteration; select the first coding individual with the optimal fitness from the plurality of first coding individuals to obtain the second optimal individual corresponding to the current genetic evolution iteration, and determine whether the fitness of the second optimal individual is higher than that of the first optimal individual; according to the judgment result, select the target optimal individual corresponding to the current genetic evolution iteration from the second optimal individual and the first optimal individual, and perform a catastrophic operation on all the plurality of first coding individuals in a preset catastrophic manner to generate a plurality of second coding individuals. The candidate coding individuals corresponding to the current catastrophic operation iteration include the second coding individuals.

[0118] In some of these embodiments, the matching decision device for the storage and reorganization linkage control of retired power batteries is further configured to, when it is determined that the fitness of the second optimal individual is higher than that of the first optimal individual, select a preset number of first encoded individuals in descending order of fitness to obtain elite encoded individuals, and initialize all the elite encoded individuals based on mixed integer programming to generate corresponding second encoded individuals; when it is determined that the fitness of the second optimal individual is not higher than that of the first optimal individual, select a set number of first encoded individuals corresponding to the scale of the catastrophe in ascending order of fitness to obtain eliminated encoded individuals, and after removing all the eliminated encoded individuals from the multiple first encoded individuals, add a plurality of randomly generated new encoded individuals to the remaining first encoded individuals to obtain corresponding second encoded individuals, where the number of new encoded individuals is equal to the set number of the scale of the catastrophe.

[0119] In some of these embodiments, after obtaining the corresponding second encoded individuals, the matching decision device for the storage and reorganization linkage control of retired power batteries is further configured to determine whether the current iteration meets a preset iteration termination condition after the current catastrophe operation iteration, where the iteration termination condition includes at least one of the following: the difference in fitness between the second encoded individuals corresponding to the current catastrophe operation iteration and the previous catastrophe operation iteration is less than the population fitness change threshold, and the number of the current iteration exceeds the preset iteration number threshold; when it is determined that the iteration termination condition is met, use the multiple second encoded individuals as multiple intended encoded individuals; when it is determined that the iteration termination condition is not met, perform iterative genetic evolution operations and population catastrophe operations on the multiple second encoded individuals until multiple intended encoded individuals are obtained.

[0120] In some of these embodiments, before generating the multiple first encoded individuals, the matching decision device for the storage and reorganization linkage control of retired power batteries is further configured to determine the average fitness and the minimum fitness corresponding to all alternative encoded individuals according to the fitness of the alternative encoded individuals, and determine whether the average fitness is less than the minimum fitness; when it is determined that the average fitness is less than the minimum fitness, use the preset maximum crossover probability and maximum mutation probability as the corresponding adaptive crossover probability and adaptive mutation probability respectively; when it is determined that the average fitness is greater than the minimum fitness, use the corresponding crossover probability function and mutation probability function to decrease the maximum crossover probability and the maximum mutation probability respectively, and use the first crossover probability and the first mutation probability obtained after the decrease as the corresponding adaptive crossover probability and adaptive mutation probability respectively.

[0121] In some of these embodiments, the processing module 54 further includes:

[0122] A determination unit, configured to determine the fitness corresponding to each intended encoded individual.

[0123] An extraction unit, coupled to the determination unit, is configured to select the intention coding individual with the highest fitness from multiple intention coding individuals to obtain a target coding individual, and extract all sub-codings of the target coding individual to obtain target sub-codings.

[0124] A decoding unit, coupled to the extraction unit, is configured to decode the recombination decision information corresponding to all target sub-codings, and use the recombination decision information corresponding to all target sub-codings as a decision result.

[0125] In some embodiments, the decision module 53 further includes:

[0126] A second acquisition unit, configured to acquire the objective sub-function and objective sub-constraint parameters corresponding to the mixed-integer programming, and acquire the production order information corresponding to each second recombination order, where the production order information includes the target information of the recombined power battery.

[0127] A matching unit, coupled to the second acquisition unit, is configured to match at least one battery combination matching information to each recombined power battery target information from the preset battery combination matching information, where one battery combination matching information is used to represent the composition of the battery materials and battery components corresponding to a recombined power battery.

[0128] A generation unit, coupled to the matching unit, is configured to use the objective sub-function and objective sub-constraint parameters to solve and optimize the battery combination matching information corresponding to each recombined power battery target information, generate the intention battery combination matching information corresponding to each recombined power battery information, and perform two-dimensional integer coding on the intention battery combination matching information in a preset coding form to obtain sub-codings corresponding to the recombined power battery target information.

[0129] An encoding unit, coupled to the generation unit, is configured to, after determining the collaborative operation cost parameters mapped by each sub-coding, integrate the sub-codings corresponding to all second recombination orders into a candidate coding individual, and perform population initialization based on the candidate coding individual obtained by encoding to generate multiple candidate coding individuals.

[0130] This embodiment also provides a matching decision system for the linkage control of the storage and recombination of retired power batteries, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0131] Optionally, the above recombination configuration system may further include a transmission device and an input / output device, where the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0132] Optionally, in this embodiment, the above-mentioned processor may be configured to execute the following steps through a computer program:

[0133] S1. After receiving the recombination correction information, detect at least one recombination decision information to be executed from the first recombination matching decision information that has been decided and generated before the current time, where the recombination decision information is used to represent the matching decision of the recombined power battery corresponding to a production order demand information, and one recombination decision information is associated with a first recombination order.

[0134] S2. Combine the dynamic recombination order corresponding to the recombination correction information with all the first recombination orders into a second recombination order, and encode and initialize the population of all the second recombination orders according to a preset mixed-integer programming to generate multiple candidate coding individuals, where the mixed-integer programming includes multiple objective sub-functions and multiple objective sub-constraint parameters, and the sub-coding corresponding to the candidate coding individual is generated by encoding according to the corresponding objective sub-function and objective sub-constraint parameters, and is used to represent a recombination decision information.

[0135] S3. Based on a preset catastrophic adaptive genetic algorithm, mixed-integer programming, and the fitness corresponding to the candidate coding individuals participating in each iteration, perform iterative genetic evolution operations and population catastrophe operations on multiple corresponding candidate coding individuals until multiple intention coding individuals are generated, where the fitness is determined according to the collaborative operation cost parameter associated with the objective sub-function corresponding to the sub-coding of the candidate coding individual.

[0136] S4. Obtain a target coding individual from multiple intention coding individuals to obtain a decision result including the target coding individual.

[0137] It should be noted that the specific examples in this embodiment may refer to the examples described in the above-mentioned embodiments and optional implementation manners, and will not be elaborated herein.

[0138] In addition, in combination with the above-mentioned matching decision method for the storage and recombination linkage control of retired power batteries in the embodiment, the embodiment of the present application can provide a storage medium to implement. A computer program is stored on the storage medium; when the computer program is executed by a processor, it implements any one of the above-mentioned matching decision methods for the storage and recombination linkage control of retired power batteries.

[0139] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, all possible combinations of the technical features in the above embodiments are not described. However, as long as these combinations of technical features do not conflict, they should be considered to be within the scope described in this specification.

[0140] The above embodiments merely illustrate several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A matching decision method for storage and reorganization linkage management of retired power batteries, characterized in that: include: After receiving the reorganization correction information, at least one reorganization decision information to be executed is detected from the first reorganization matching decision information that has been decided and generated before, wherein the reorganization decision information is used to represent the matching decision of the reorganized power battery corresponding to a production order demand information, and one reorganization decision information is associated with one first reorganization order; The dynamic reorganization order corresponding to the reorganization correction information is combined with all the first reorganization orders into a second reorganization order, and all the second reorganization orders are encoded and population initialized according to a preset mixed integer programming to generate a plurality of candidate coding individuals, wherein the mixed integer programming includes a plurality of target sub-functions and a plurality of target sub-constraint parameters, and the sub-code corresponding to the candidate coding individual is generated by encoding according to the corresponding target sub-function and the target sub-constraint parameter, and is used to represent one reorganization decision information; Based on the preset catastrophe adaptive genetic algorithm, the mixed integer programming and the fitness corresponding to the candidate coding individuals participating in each iteration, the genetic evolution operation and the population catastrophe operation are iterated on the multiple corresponding candidate coding individuals until multiple intention coding individuals are generated, wherein the fitness is determined according to the collaborative operation cost parameter associated with the target sub-function corresponding to the sub-coding corresponding to the candidate coding individual; A target coding individual is obtained from the plurality of intended coding individuals, and a decision result including the target coding individual is obtained.

2. The method according to claim 1, characterized in that The reorganization decision information includes the reorganization online time, and detecting at least one reorganization decision information to be executed from the first reorganization matching decision information that has been decided and generated before the current one, including: Acquire all the reorganization decision information corresponding to the first reorganization matching decision information, and determine the reorganization online time corresponding to each reorganization decision information, wherein the first reorganization matching decision information is generated by iterating a genetic evolution operation and a population catastrophe operation based on the corresponding fitness on a historical coding body, and the historical coding body is generated by encoding and population initialization according to the mixed integer programming based on all the first reorganization orders received before the current time; Among all the reorganization decision information, the reorganization decision information whose reorganization online time is later than the current time is selected to obtain at least one reorganization decision information to be executed.

3. The method according to claim 1, characterized in that Based on the preset catastrophe adaptive genetic algorithm, the mixed integer programming and the fitness corresponding to the candidate coding individuals participating in each iteration, iterating the genetic evolution operation and the population catastrophe operation on a plurality of corresponding candidate coding individuals, including: According to the target sub-function corresponding to the mixed integer programming, the collaborative operation cost parameter is calculated for the sub-codes corresponding to the candidate code individuals participating in the current iteration, and the fitness corresponding to each candidate code individual of the current iteration is determined according to the collaborative operation cost parameters corresponding to all the sub-codes, wherein the collaborative operation cost parameter is calculated according to the storage cost and the reorganization production cost, and the storage cost and the reorganization production cost are calculated according to the corresponding target sub-function; According to the fitness and a preset selection operation, a preset number of candidate coding individuals are selected from all the candidate coding individuals of the current iteration, wherein the selection operation includes one of the following: roulette wheel selection, selection based on elite retention strategy, and league selection method, and the selection probability corresponding to the selection operation is determined based on the fitness of the corresponding candidate coding individual; Based on the corresponding adaptive crossover probability and adaptive mutation probability, crossover operations and mutation operations are performed on all the candidate coding individuals in turn to generate multiple first coding individuals, wherein the candidate coding individuals corresponding to the current genetic evolution operation iteration include the first coding individuals, the adaptive crossover probability is determined based on the crossover probability function corresponding to the catastrophe adaptive genetic algorithm, and the adaptive mutation probability is determined based on the mutation probability function corresponding to the catastrophe adaptive genetic algorithm.

4. The method according to claim 3, characterized in that After generating a plurality of the first coding individuals, the method further comprises: Among all the candidate coding individuals generated by the previous genetic evolution iteration, the candidate coding individual with the best fitness is selected to obtain the first best individual corresponding to the previous genetic evolution iteration; Selecting the first coding individual with the best fitness from the multiple first coding individuals, obtaining the second best individual corresponding to the current genetic evolution iteration, and determining whether the fitness of the second best individual is higher than that of the first best individual; According to the judgment result, the target optimal individual corresponding to the current genetic evolution iteration is selected from the second optimal individual and the first optimal individual, and a catastrophe operation is performed on all the first coded individuals according to a preset catastrophe method to generate multiple second coded individuals, among which the candidate coded individuals corresponding to the current catastrophe operation iteration include the second coded individual.

5. The method according to claim 4, characterized in that Performing a disaster operation on all the first coding individuals according to a preset disaster mode to generate a plurality of second coding individuals includes: When it is determined that the fitness of the second best individual is higher than that of the first best individual, a preset number of the first coding individuals are selected in descending order of the fitness to obtain elite coding individuals, and all elite coding individuals are initialized based on the mixed integer programming to generate a corresponding plurality of the second coding individuals; When it is determined that the fitness of the second optimal individual is not higher than that of the first optimal individual, the first coded individuals of a set number of disaster scales are selected in order of the fitness from low to high to obtain eliminated coded individuals, and after removing all the eliminated coded individuals from the multiple first coded individuals, multiple randomly generated new coded individuals are added to the remaining first coded individuals to obtain corresponding multiple second coded individuals, wherein the number of the new coded individuals is equal to the set number of disaster scales.

6. The method according to claim 5, characterized in that After obtaining the corresponding plurality of the second coding individuals, the method further includes: After the catastrophe operation iteration, whether the iteration satisfies a preset iteration termination condition, wherein the iteration termination condition includes at least one of the following: when the fitness difference between the second coding individual corresponding to the catastrophe operation iteration and the previous catastrophe operation iteration is less than the population fitness change threshold, when the number of iterations exceeds the preset iteration number threshold; When it is determined that the iteration termination condition is satisfied, taking the plurality of the second coding individuals as the plurality of the intended coding individuals; When it is determined that the iteration termination condition is not satisfied, the genetic evolution operation and the population catastrophe operation are iterated on the plurality of the second coding individuals until the plurality of the intended coding individuals are obtained.

7. The method according to claim 3, characterized in that Before generating a plurality of first coding individuals, the method further includes: According to the fitness of the candidate coding individuals, determine the average fitness and the minimum fitness corresponding to all the candidate coding individuals, and judge whether the average fitness is less than the minimum fitness; When it is determined that the average fitness is less than the minimum fitness, the preset maximum crossover probability and the maximum mutation probability are used as the corresponding adaptive crossover probability and the adaptive mutation probability respectively; When it is determined that the average fitness is greater than the minimum fitness, the corresponding crossover probability function and the mutation probability function are used to reduce the maximum crossover probability and the maximum mutation probability respectively, and the first crossover probability and the first mutation probability distribution obtained after the reduction are used as the corresponding adaptive crossover probability and the adaptive mutation probability.

8. The method according to claim 1, characterized in that Obtaining a target coding individual from the plurality of intended coding individuals, and obtaining a decision result including the target coding individual, comprises: Determining the fitness corresponding to each of the intentional coding individuals; Selecting the intended coding individual with the highest fitness from the plurality of intended coding individuals to obtain the target coding individual, and extracting all the sub-codings of the target coding individual to obtain the target sub-coding; The reorganization decision information corresponding to all the target sub-codes is decoded, and the reorganization decision information corresponding to all the target sub-codes is used as the decision result.

9. The method according to claim 1, characterized in that: According to the preset mixed integer programming, all the second reorganization orders are encoded and the population is initialized to generate multiple candidate encoding individuals, including: Obtaining the objective sub-function and the objective sub-constraint parameter corresponding to the mixed integer programming, and obtaining production order information corresponding to each second reorganization order, wherein the production order information includes reorganization power battery target information; Matching at least one of the battery combination matching information to each of the recombined power battery target information from the preset battery combination matching information, wherein one of the battery combination matching information is used to characterize the composition of battery materials and battery components corresponding to a recombinant power battery; By using the target sub-function and the target sub-constraint parameter, the battery combination matching information corresponding to each of the reorganized power battery target information is solved and optimized, and the intended battery combination matching information corresponding to each of the reorganized power battery information is generated, and the intended battery combination matching information is two-dimensionally integer-encoded in a preset coding form to obtain the sub-code corresponding to the reorganized power battery target information; After determining the collaborative operation cost parameter mapped by each sub-code, the sub-codes corresponding to all the second reorganization orders are integrated into one candidate code individual, and a population is initialized based on one candidate code individual to generate multiple candidate code individuals.

10. A matching decision system for storage and reorganization linkage management of retired power batteries, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of the matching decision method for the linked management and control of storage and reorganization of retired power batteries as described in any one of claims 1 to 9.

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