Retired battery disassembly scheduling method based on reinforcement learning

Abstract

The invention relates to the technical field of resource regeneration, and discloses a retired battery disassembly scheduling method based on reinforcement learning. The method comprises the following steps: acquiring a machine state of a disassembling machine, constructing a graph matrix code based on battery disassembling process steps, constructing an MGCN unit for extracting connectivity and a topological structure between different parts of a battery based on a Conv2d k1 block and a Conv2d k3 block, and constructing a multi-graph convolutional network according to the MGCN unit; inputting the machine state and the graph matrix code into a multi-graph convolutional network to obtain a disassembly weight; and obtaining the optimized disassembly path according to the disassembly weight in combination with deep search, and completing disassembly of the retired battery according to the optimized disassembly path, thereby solving the problem of low efficiency when the existing models such as reinforcement learning are applied to battery disassembly scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of resource recycling, and in particular to a method for disassembling and scheduling retired batteries based on reinforcement learning. Background Art

[0002] With the rapid development of the global electric vehicle industry, the production and use of power batteries have surged, and the disassembly and recycling of retired batteries have become increasingly prominent. How to efficiently and safely disassemble retired batteries and maximize the recovery of valuable materials has become a technical problem that needs to be solved in the battery recycling industry.

[0003] At present, the power battery disassembly process is carried out by combining reinforcement learning and other models to optimize the battery disassembly path. However, the existing reinforcement learning and other models do not take into account the relevance of deep disassembly in the process of optimizing the disassembly path. The disassembly process is not smart or efficient enough, and intelligent disassembly cannot be effectively realized. There is still room for optimization.

[0004] When reinforcement learning models are applied to optimize disassembly scheduling, there are problems such as the inability of the incentive function design to effectively reflect the multi-stage and multi-interaction dynamics of the disassembly process, insufficient multi-scale feature extraction, and limitations of the optimization path and feedback mechanism. Summary of the invention

[0005] The present invention provides a retired battery disassembly scheduling method based on reinforcement learning to solve the problem of low efficiency of existing reinforcement learning and other models when applied to battery disassembly scheduling.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions: The present invention provides a method for disassembling and scheduling retired batteries based on reinforcement learning, comprising the following steps: Step 1: Obtain the machine state of the disassembly machine, construct a graph matrix encoding based on the battery disassembly process steps, construct an MGCN unit based on the Conv2d k1 block and the Conv2d k3 block to extract the connectivity and topology between different parts of the battery, and construct a multi-graph convolutional network based on the MGCN unit; Step 2: Input the machine state and graph matrix encoding into the multi-graph convolutional network to obtain the disassembly weight; Step 3: Obtain the optimized disassembly path based on the disassembly weight and deep search, and complete the disassembly of the retired batteries according to the optimized disassembly path.

[0007] Furthermore, the MGCN unit is a three-input single-output multi-source information fusion feature extraction unit, and the MGCN unit includes an input layer, a fusion extraction layer, a weighted layer, and an output layer; The input layer includes three Conv2d k1 blocks for extracting local features of three types of inputs respectively; The fusion extraction layer includes a fusion block and a Conv2d k3 block for extracting spatial relations; The weighted layer comprises a SE block; The output layer includes a Conv2d k1 block and an output block for performing linear transformation and dimensionality compression.

[0008] Further, the constructing a multi-graph convolutional network according to the MGCN unit includes: constructing an action module and a value module according to the MGCN unit, and constructing a multi-graph convolutional network based on the action module and the value module; The action module extracts multi-scale features based on machine state and graph matrix encoding to obtain disassembly weights; The value module obtains the multi-scale features extracted by the action module and optimizes the disassembly weights in combination with the incentive function.

[0009] Furthermore, the action module sequentially includes an MGCN unit for extracting machine state and graph matrix encoding features, a detail extraction Conv2d k3 block for further extracting surface and internal detail features of the machine state and graph matrix encoding, an MGCN unit for strengthening the machine state and graph matrix encoding features, a detail strengthening Conv2d k3 block for further strengthening the surface and internal detail features of the machine state and graph matrix encoding, and a Conv2d k1 block for compressing and integrating all strengthened features.

[0010] Furthermore, the value module is connected to the detail extraction Conv2d k3 block and the detail enhancement Conv2d k3 block of the action module respectively, and the acquired multi-scale features are integrated and evaluated.

[0011] Furthermore, the value module includes an acquisition unit for acquiring multi-scale features, an integration unit for integrating the multi-scale features, and an evaluation unit for evaluating the integrated multi-scale features; The acquisition unit includes a first channel, a second channel and a Conv2d k1 block, the first channel and the second channel are each composed of two Conv2d k3 blocks, and are both connected to the action module and the integration module, and the Conv2d k1 block is arranged between the first Conv2d k3 block in the direction where the first channel, the second channel and the action module are connected; The integration unit includes 1 Conv2d k1 block; The evaluation unit includes 1 fully connected block and one MGCN unit.

[0012] Furthermore, the value module obtains the multi-scale features extracted by the action module and optimizes the disassembly weight in combination with the incentive function, including: the value module predicts the value of the disassembly weight output by the action module based on the multi-scale features, the incentive function performs feedback calculation in combination with the machine state, and uses the difference between the feedback calculation and the value prediction as the objective function to optimize the disassembly weight.

[0013] Furthermore, the machine status includes average completion time, process waiting time, delay penalty, assembly and disassembly time, and station processing capacity; The activation function is expressed by the following formula: ; in, represents the activation function, represents the average completion time, Indicates the process waiting time, represents the delay penalty, Indicates assembly and disassembly time, Indicates the processing capacity of the workstation. All represent weight coefficients; The average completion time is expressed by the following formula: ; in, Indicates The time required to complete the disassembly of a battery pack, Indicates the maximum allowed completion time; The process waiting time is expressed by the following formula: ; in, Indicates The waiting time for the disassembly process of each battery pack, Indicates the maximum allowed waiting time; The delay penalty is expressed by the following formula: ; in, Indicates the total delay time for disassembling the battery; The assembly and disassembly time is expressed by the following formula: ; in, Indicates the battery pack from The time required for disassembly on the machine, Indicates that the battery pack is delivered to The time required by the machine; The processing capacity of the workstation is expressed by the following formula: ; in, Indicates the maximum processing capacity of the workstation, that is, the time required to disassemble a battery pack. Indicates The battery pack is in The process is The time required for machine processing, Indicates the total number of battery packs to be disassembled.

[0014] Further, constructing a graph matrix code based on the battery disassembly process steps includes: constructing a disassembly path according to the battery disassembly process steps in combination with a first predetermined rule, using the numbers of the nodes of the disassembly path as rows and columns of the graph matrix code, and filling the rows and columns of the graph matrix code based on the node relationship of the disassembly path in combination with a second predetermined rule; The first predetermined rule includes: numbering the main processes and key nodes of the battery disassembly process steps, setting the nodes of the paths that need to meet multiple process conditions at the same time as AND nodes, and setting the path nodes of whether to disassemble the battery after performance testing as OR nodes; The second predetermined rule includes: if the node relationship is an AND node or a connection relationship, it is defined as 1; if the node relationship is an OR node, it is defined as 2.

[0015] Further, obtaining the optimized disassembly path according to the disassembly weight combined with the deep search includes: traversing the disassembly path under predetermined constraints to calculate the statistical value according to the disassembly weight combined with the deep search, and determining the optimized disassembly path based on the statistical value combined with the battery pack detection information; The predetermined constraints are based on the status of the machine and the battery, the process sequence, the single process, the assembly and disassembly time, and the detection station node setting; The statistical value is calculated by the following formula: ; in, Indicates traversal to Node, The first branch The statistics of nodes, Indicates that from The sum of all possible out-degrees triggered by the node, Indicates The first branch The in-degree of a node, Indicates The first branch The out-degree of a node, Indicates The first branch The in-degree of a node.

[0016] Beneficial effects: The present invention provides a method for disassembling and scheduling retired batteries based on reinforcement learning. The present application designs a multi-graph convolutional network, obtains battery information, interacts and learns with the environment, and implements the disassembly of the battery according to the disassembly situation, thereby realizing autonomous adjustment of the disassembly path and process flow, and realizing more efficient and flexible disassembly. By rationally designing the incentive function, we optimized resource allocation and scheduling, avoided resource waste and bottleneck effects during the disassembly process, and covered multiple key performance indicators, such as average completion time, process waiting time, total delay penalty, assembly and disassembly time, and inspection station processing capacity. This comprehensive incentive function design can effectively optimize multiple goals at the same time, improve overall disassembly efficiency, rather than just focusing on a single goal, and improve the overall efficiency and stability of the production line; By designing negative incentives for different indicators, unnecessary waiting time, delays, and unreasonable resource allocation can be reduced, thereby improving the efficiency and rationality of the disassembly process. These incentive functions can directly affect the scheduling system and drive the system toward the optimization goal.

[0017] The multi-graph convolutional network enhances the ability to capture the complex relationships and interactions between different parts of the power battery by introducing the MGCN unit. Traditional methods may not be able to effectively integrate multi-scale information, but the present invention optimizes the multi-scale feature extraction path through the MGCN unit, which can accurately capture the different levels of details of the battery components during the disassembly process.

[0018] Through the SE module, the present invention can intelligently adjust the importance of the feature graph, so that the system can dynamically adjust the disassembly strategy, thereby improving the adaptability and decision-making accuracy of the system in complex environments.

[0019] A feedback mechanism was designed to enable the model to adjust its decisions based on real-time feedback during the disassembly process, improving the flexibility and efficiency of the disassembly process. This dynamic optimization method enables the disassembly process to respond to environmental changes and optimize the disassembly path, thereby improving the overall disassembly efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the network structure of the MGCN unit in the multi-graph convolutional network in an embodiment of the present invention; Figure 2 Schematic diagram of the network structure of a multi-graph convolutional network in an embodiment of the present invention, wherein A represents the obtained optimized disassembly path; Figure 3 A flow chart of the disassembly process steps of three different batteries in an embodiment of the present invention; Figure 4 A schematic diagram of a graph matrix encoding constructed in an embodiment of the present invention; Figure 5This is a disassembly process step flow chart of three different batteries in an embodiment of the present invention after optimization. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not indicate quantity restrictions, but indicate the existence of at least one. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0023] See also Figure 1-2 The embodiment of the present application provides a method for disassembling and scheduling retired batteries based on reinforcement learning, comprising: Step 1: Obtain the machine state of the disassembly machine, construct a graph matrix encoding based on the battery disassembly process steps, construct an MGCN unit based on the Conv2d k1 block and the Conv2d k3 block to extract the connectivity and topology between different parts of the battery, and construct a multi-graph convolutional network based on the MGCN unit; See also Figure 3-4 , constructing a graph matrix code based on the battery disassembly process steps includes: constructing a disassembly path according to the battery disassembly process steps combined with a first predetermined rule, and constructing a graph matrix code based on the disassembly path; The first predetermined rule includes: numbering the main processes and key nodes of the battery disassembly process steps, setting the nodes of the paths that need to meet multiple process conditions at the same time as AND nodes, and setting the path nodes of whether to disassemble the battery after performance testing as OR nodes. The construction of matrix coding more effectively expresses the OR nodes, improving the decision-making of node selection and the efficiency of the disassembly path.

[0024] Constructing a graph matrix code based on the disassembly path includes: using the numbers of the nodes of the disassembly path as rows and columns of the matrix code, and filling the rows and columns of the matrix code based on the node relationship of the disassembly path in combination with a second predetermined rule; The second predetermined rule includes: if the node relationship is an AND node or a connection relationship, it is defined as 1; if the node relationship is an OR node, it is defined as 2.

[0025] Among them, the MGCN unit is a multi-source information fusion feature extraction unit with three inputs and one output. The MGCN unit includes an input layer, a fusion extraction layer, a weighted layer, and an output layer; The input layer includes three Conv2d k1 blocks that extract local features of three types of inputs respectively; The fusion extraction layer includes the fusion block and the Conv2d k3 block that extracts the spatial relationship; The weighted layer includes SE blocks; The output layer includes a Conv2d k1 block and an output block for linear transformation and dimension compression.

[0026] Constructing a multi-graph convolutional network based on the MGCN unit specifically includes: Construct action modules and value modules according to the MGCN unit, and construct a multi-graph convolutional network based on the action modules and value modules; The action module extracts multi-scale features based on machine state and graph matrix encoding to obtain disassembly weights; The value module obtains the multi-scale features extracted by the action module and optimizes the disassembly weights in combination with the incentive function. That is, the value module predicts the value of the disassembly weights output by the action module based on the multi-scale features. The incentive function collects the machine state for feedback calculation, and uses the difference between the feedback calculation and the value prediction as the objective function to optimize the disassembly weights. Specifically, the action module includes, in sequence, an MGCN unit for extracting machine state and graph matrix encoding features, a detail extraction Conv2d k3 block for further extracting surface and internal detail features of machine state and graph matrix encoding, an MGCN unit for enhancing machine state and graph matrix encoding features, a detail enhancement Conv2d k3 block for further enhancing surface and internal detail features of machine state and graph matrix encoding, and a Conv2d k1 block for compressing and integrating all enhanced features.

[0027] The value module is connected to the detail extraction Conv2d k3 block and detail enhancement Conv2d k3 block of the action module respectively, and the acquired multi-scale features are integrated and evaluated; The value module includes an acquisition unit for acquiring multi-scale features, an integration unit for integrating the multi-scale features, and an evaluation unit for evaluating the integrated multi-scale features, wherein the acquisition unit is connected to the detail extraction Conv2dk3 block and the detail enhancement Conv2d k3 block of the action module; The acquisition unit includes a first channel, a second channel and a Conv2d k1 block. The first channel and the second channel are both composed of two Conv2d k3 blocks, and are both connected to the action module and the integration module. The Conv2d k1 block is arranged between the first Conv2d k3 block in the direction where the first channel, the second channel and the action module are connected; The integration unit includes 1 Conv2d k1 block; The evaluation unit consists of 1 fully connected block and one MGCN unit.

[0028] The value module predicts the value of the disassembly weight output by the action module based on multi-scale features. The incentive function calculates feedback based on the machine state, and uses the difference between the feedback calculation and the value prediction as the objective function to optimize the disassembly weight. Machine status includes average completion time, process waiting time, delay penalty, assembly and disassembly time, and station processing capacity; The activation function is expressed by the following formula: ; in, represents the activation function, represents the average completion time, Indicates the process waiting time, represents the delay penalty, Indicates assembly and disassembly time, Indicates the processing capacity of the workstation. All represent weight coefficients. In this embodiment, the weight coefficients of various parameters are all 0.2; The average completion time is expressed by the following formula: ; in, Indicates The time required to complete the disassembly of a battery pack, Indicates the maximum allowed completion time; The process waiting time is expressed by the following formula: ; in, Indicates The waiting time for the disassembly process of each battery pack, Indicates the maximum allowed waiting time; The delay penalty is expressed by the following formula: ; in, Indicates the total delay time for disassembling the battery; The assembly and disassembly time is expressed by the following formula: ; in, Indicates the battery pack from The time required for disassembly on the machine, Indicates that the battery pack is delivered to The time required by the machine; The processing capacity of the workstation is expressed by the following formula: ; in, Indicates the maximum processing capacity of the workstation, that is, the time required to disassemble a battery pack. Indicates The battery pack is in The process is The time required for machine processing, Indicates the total number of battery packs to be disassembled.

[0029] Step 2: Input the machine state and graph matrix encoding into the multi-graph convolutional network to obtain the disassembly weight; Step 3: Obtain the optimized disassembly path based on the disassembly weight and deep search, and complete the disassembly of the retired batteries according to the optimized disassembly path.

[0030] For details, see Figure 5 According to the disassembly weight combined with deep search, the disassembly path is traversed under predetermined constraints to calculate the statistical value, and the optimized disassembly path is determined based on the statistical value combined with the battery pack detection information, wherein the battery information includes module information and cell information, and the battery pack detection information includes the quality of the modules and cells, that is, the optimized disassembly path is determined based on the statistical value combined with the modules and cells disassembled from the battery pack.

[0031] The predefined constraints are based on the status of the machine and battery, the process sequence, the single process, the assembly and disassembly time, and the detection station node settings; The state constraints of the machine and battery are expressed by the following formula: ; in, Indicates that all machines and batteries are at the time It can be used at any time and is not bound to any machine; The process sequence constraint is expressed by the following formula: ; in, Indicates The battery pack is in The time required for each process; Indicates The battery pack is in The time required for each process; Indicates The battery pack is in The process is The time required for machine processing; Indicates the battery pack from The time required to disassemble the machine, Indicates that the battery pack is delivered to The time required on the machine, this formula indicates that the battery process is performed in sequence; The single process constraint is expressed by the following formula: ; in, Indicates The total number of processes for a battery pack, Indicates the number of machines in the workshop; Indicated in Moment, Battery Pack The process is Machine processing, , otherwise 0; This formula indicates that the battery can only perform one process at any time; ; This formula indicates that each machine can only handle one process at any time; The assembly and disassembly time constraints are expressed by the following formula: ; in, exist Moment, Battery Pack The process is Machine processing, , otherwise 0; This formula indicates that each battery must consider the time constraint during assembly and disassembly; The inspection station node constraints are expressed by the following formula: ; in, Represents the total number of retired batteries, It represents the processing capacity of the inspection station, that is, the number of batteries that can be inspected per unit time; this formula means that the module and cell of each battery are the process bottleneck at the inspection station, and its processing capacity needs to be given priority.

[0032] The statistical value is calculated by the following formula: ; in, Indicates traversal to Node, The first branch The statistics of nodes, Indicates that from The sum of all possible out-degrees triggered by the node, Indicates The first branch The in-degree of a node, Indicates The first branch The out-degree of a node, Indicates The first branch The in-degree of a node.

[0033] See also Figure 3 , for the node 6 of battery 1, the out-degree of node 6 is -3. There are a total of Nodes, when When , the branch statistics are nodes 9 and 10, where the sum of the in-degree and out-degree of node 9 is equal to 0, and the in-degree of node 10 is 1. At this time, the statistical value is -2. When , the branch statistics are nodes 9, 10, 11 and 15, among which the sum of the in-degree and out-degree of nodes 9, 10 and 11 is equal to 0, and the in-degree of 15 is 3, so the statistical value is 0 at this time.

[0034] Finally, in this embodiment, a multi-graph convolutional network constructed based on a retired battery disassembly scheduling method based on reinforcement learning of the present invention is used together with manual scheduling, genetic algorithm and particle swarm optimization algorithm to make decisions on the disassembly path. Please see Table 1 for details.

[0035] Table 1: Average completion time, process waiting time, delay penalty and overall goal

[0036] As shown in Table 1, the multi-graph convolutional network is superior to the traditional manual scheduling, genetic algorithm and particle swarm optimization algorithm in the scheduling of retired battery disassembly. In terms of average completion time, process waiting time, delay penalty and other indicators, the multi-graph convolutional network performs best, especially reducing the average completion time to 3287 seconds, the process waiting time to 482 seconds, and the delay penalty to 65 seconds, and the comprehensive total target value reaches 3834 seconds. Each value is the lowest among all methods, which clearly shows that the method of the present invention has excellent scheduling optimization capabilities.

[0037] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for disassembling and scheduling retired batteries based on reinforcement learning, characterized in that: The steps include: Step 1: Obtain the machine state of the disassembly machine, construct a graph matrix encoding based on the battery disassembly process steps, construct an MGCN unit based on the Conv2d k1 block and the Conv2d k3 block to extract the connectivity and topology between different parts of the battery, and construct a multi-graph convolutional network based on the MGCN unit; Step 2: Input the machine state and graph matrix encoding into the multi-graph convolutional network to obtain the disassembly weight; Step 3: Obtain the optimized disassembly path based on the disassembly weight and deep search, and complete the disassembly of the retired batteries according to the optimized disassembly path.

2. The method for dismantling retired batteries based on reinforcement learning according to claim 1 is characterized in that: The MGCN unit is a multi-source information fusion feature extraction unit with three inputs and one output, and the MGCN unit includes an input layer, a fusion extraction layer, a weighted layer and an output layer; The input layer includes three Conv2d k1 blocks for extracting local features of three types of inputs respectively; The fusion extraction layer includes a fusion block and a Conv2d k3 block for extracting spatial relations; The weighted layer comprises a SE block; The output layer includes a Conv2d k1 block and an output block for performing linear transformation and dimensionality compression.

3. The method for dismantling and scheduling retired batteries based on reinforcement learning according to claim 2 is characterized in that: The constructing of a multi-graph convolutional network according to the MGCN unit includes: constructing an action module and a value module according to the MGCN unit, and constructing a multi-graph convolutional network based on the action module and the value module; The action module extracts multi-scale features based on machine state and graph matrix encoding to obtain disassembly weights; The value module obtains the multi-scale features extracted by the action module and optimizes the disassembly weights in combination with the incentive function.

4. The method for dismantling and scheduling retired batteries based on reinforcement learning according to claim 3 is characterized in that: The action module sequentially includes an MGCN unit for extracting machine state and graph matrix encoding features, a detail extraction Conv2d k3 block for further extracting machine state and graph matrix encoding surface and internal detail features, an MGCN unit for strengthening machine state and graph matrix encoding features, a detail strengthening Conv2d k3 block for further strengthening machine state and graph matrix encoding surface and internal detail features, and a Conv2d k1 block for compressing and integrating all strengthened features.

5. The method for dismantling and scheduling retired batteries based on reinforcement learning according to claim 4 is characterized in that: The value module is connected to the detail extraction Conv2d k3 block and the detail enhancement Conv2d k3 block of the action module respectively, and integrates and evaluates the acquired multi-scale features.

6. The method for dismantling and scheduling retired batteries based on reinforcement learning according to claim 5 is characterized in that: The value module includes an acquisition unit for acquiring multi-scale features, an integration unit for integrating the multi-scale features, and an evaluation unit for evaluating the integrated multi-scale features; The acquisition unit includes a first channel, a second channel and a Conv2d k1 block, the first channel and the second channel are each composed of two Conv2d k3 blocks, and are both connected to the action module and the integration module, and the Conv2d k1 block is arranged between the first Conv2d k3 block in the direction where the first channel, the second channel and the action module are connected; The integration unit includes 1 Conv2d k1 block; The evaluation unit includes 1 fully connected block and one MGCN unit.

7. The method for dismantling and scheduling retired batteries based on reinforcement learning according to any one of claims 3 to 6, characterized in that: The value module obtains the multi-scale features extracted by the action module and optimizes the disassembly weight in combination with the incentive function, including: the value module predicts the value of the disassembly weight output by the action module based on the multi-scale features, the incentive function performs feedback calculation in combination with the machine state, and uses the difference between the feedback calculation and the value prediction as the objective function to optimize the disassembly weight.

8. The method for dismantling and scheduling retired batteries based on reinforcement learning according to claim 7 is characterized in that: The machine status includes average completion time, process waiting time, delay penalty, assembly and disassembly time, and station processing capacity; The activation function is expressed by the following formula: ； in, represents the activation function, represents the average completion time, Indicates the process waiting time, represents the delay penalty, Indicates assembly and disassembly time, Indicates the processing capacity of the workstation. All represent weight coefficients; The average completion time is expressed by the following formula: ； in, Indicates The time required to complete the disassembly of a battery pack, Indicates the maximum allowed completion time; The process waiting time is expressed by the following formula: ； in, Indicates The waiting time for the disassembly process of each battery pack, Indicates the maximum allowed waiting time; The delay penalty is expressed by the following formula: ； in, Indicates the total delay time for disassembling the battery; The assembly and disassembly time is expressed by the following formula: ； in, Indicates the battery pack from The time required for disassembly on the machine, Indicates that the battery pack is delivered to The time required by the machine; The processing capacity of the workstation is expressed by the following formula: ； in, Indicates the maximum processing capacity of the workstation, that is, the time required to disassemble a battery pack. Indicates The battery pack is in The process is The time required for machine processing, Indicates the total number of battery packs to be disassembled.

9. The method for dismantling and scheduling retired batteries based on reinforcement learning according to any one of claims 1 to 6, characterized in that: Constructing a graph matrix code based on the battery disassembly process steps includes: constructing a disassembly path according to the battery disassembly process steps in combination with a first predetermined rule, using the numbers of the nodes of the disassembly path as rows and columns of the graph matrix code, and filling the rows and columns of the graph matrix code based on the node relationship of the disassembly path in combination with a second predetermined rule; The first predetermined rule includes: numbering the main processes and key nodes of the battery disassembly process steps, setting the nodes of the paths that need to meet multiple process conditions at the same time as AND nodes, and setting the path nodes of whether to disassemble the battery after performance testing as OR nodes; The second predetermined rule includes: if the node relationship is an AND node or a connection relationship, it is defined as 1; if the node relationship is an OR node, it is defined as 2.

10. The method for dismantling and scheduling retired batteries based on reinforcement learning according to claim 9, characterized in that: The obtaining of the optimized disassembly path according to the disassembly weight combined with the deep search comprises: traversing the disassembly path under predetermined constraints to calculate statistical values ​​according to the disassembly weight combined with the deep search, and determining the optimized disassembly path based on the statistical values ​​combined with the battery pack detection information; The predetermined constraints are based on the status of the machine and the battery, the process sequence, the single process, the assembly and disassembly time, and the detection station node setting; The statistical value is calculated by the following formula: ； in, Indicates traversal to At node time, The first branch The statistics of nodes, Indicates that from The sum of all possible out-degrees triggered by the node, Indicates The first branch The in-degree of a node, Indicates The first branch The out-degree of a node, Indicates The first branch The in-degree of a node.

Images