CN118281388A

CN118281388A - Intelligent rapid discharge control method and related device for waste batteries

Info

Publication number: CN118281388A
Application number: CN202410674808.1A
Authority: CN
Inventors: 郑伟鹏; 丁柏栋; 傅婷婷; 易小宇
Original assignee: Shenzhen Jiecheng Nickel Cobalt New Energy Technology Co ltd
Current assignee: Shenzhen Jiecheng Nickel Cobalt New Energy Technology Co ltd
Priority date: 2024-05-29
Filing date: 2024-05-29
Publication date: 2024-07-02
Anticipated expiration: 2044-05-29
Also published as: CN118281388B

Abstract

The invention discloses an intelligent rapid discharge control method and a related device for waste batteries, and relates to the technical field of battery discharge, wherein the method comprises the following steps: acquiring battery state parameters of the waste batteries based on a battery equivalent circuit model so as to classify all the waste batteries; performing discharge state impact analysis based on real-time environmental data; constructing an initial discharge control scheme based on battery state parameters and a discharge state influence analysis result, and performing simulation analysis on the initial discharge control scheme to optimize the scheme; the battery discharging equipment performs discharging treatment based on an optimized discharging control scheme; in the process, acquiring real-time temperature of a corresponding area, and judging whether the temperature difference between the real-time temperature and the preset early warning temperature is smaller than or equal to a preset threshold value; if yes, determining the pressure adjustment amount and the heat dissipation medium incoming speed, and performing heat dissipation treatment in the area. The invention improves the discharge efficiency and the discharge quality of the waste batteries, and enables the discharge state of the waste batteries to achieve more ideal effect.

Description

Intelligent rapid discharge control method and related device for waste batteries

Technical Field

The invention mainly relates to the technical field of battery discharge, in particular to an intelligent rapid discharge control method and a related device for waste batteries.

Background

Along with the rapid development of new energy technology, the number of the waste batteries is also rapidly increased, so that the waste batteries need to be reasonably and effectively recycled and disassembled, and in order to ensure the safety of recycling and disassembling the waste batteries, the waste batteries need to be subjected to discharge treatment. In the traditional waste battery discharging method, under the condition that the state parameters of the waste batteries are not different is not considered, the waste batteries are not classified, and then the waste batteries are directly placed in discharging equipment to be discharged, so that the batteries are insufficiently discharged, and the discharging efficiency of part of the batteries is affected. In addition, the traditional battery discharging method is generally based on a fixed discharging scheme, meanwhile, the influence of environmental data on battery discharging is not considered in the discharging scheme, the environmental data not only affects the discharge capacity of the battery, but also affects the discharging efficiency of the battery, and the discharging efficiency and the discharging quality of the waste battery cannot be effectively improved due to the adoption of the fixed discharging scheme and neglecting the influence of the environmental data on the battery discharging. Meanwhile, heat is generated by current flowing in the battery discharging process, so that the safety of battery discharging is ensured, when the generated heat is large, the temperature is required to be reduced, the pressure is usually relieved and the temperature is reduced in the general temperature reducing process, and the whole battery discharging is performed by restarting the equipment after the temperature is reduced to a safe range, so that the battery discharging efficiency is definitely greatly influenced, and if the temperature is reduced in a local area, the problem of how to ensure the pressure adjustment quantity of the determined local area and the reliability of the medium-entering speed is needed to be solved.

Disclosure of Invention

The invention aims to overcome the defects of the prior art, and provides an intelligent rapid discharge control method and a related device for waste batteries, which improve the discharge efficiency and the discharge quality of the waste batteries and enable the discharge state of the waste batteries to achieve more ideal effects.

In order to solve the technical problems, the invention provides an intelligent rapid discharge control method of waste batteries, which comprises the following steps:

Establishing a battery equivalent circuit model, acquiring battery state parameters corresponding to a plurality of waste batteries based on the battery equivalent circuit model, and classifying all the waste batteries based on the battery state parameters to acquire a plurality of waste batteries of each class;

Acquiring real-time environment data, and carrying out discharge state influence analysis on a plurality of waste batteries of each class based on the real-time environment data to obtain a corresponding discharge state influence analysis result;

Constructing an initial discharge control scheme corresponding to a plurality of waste batteries of each category based on corresponding battery state parameters and discharge state influence analysis results, performing simulation analysis on the initial discharge control scheme to obtain simulation analysis results, and optimizing the initial discharge control scheme based on the simulation analysis results to obtain an optimized discharge control scheme corresponding to the plurality of waste batteries of each category;

Placing a plurality of waste batteries of each category in corresponding positions of battery discharging equipment, wherein the battery discharging equipment performs discharging treatment of the waste batteries based on the optimized discharging control scheme;

In the discharging treatment process of the waste batteries, based on a plurality of monitoring sensors arranged at preset positions of the battery discharging equipment, acquiring real-time temperatures of corresponding areas, calculating temperature differences between the real-time temperatures of the corresponding areas and preset early-warning temperatures, and judging whether the temperature differences of the corresponding areas are smaller than or equal to preset thresholds;

When the temperature difference of the corresponding area is judged to be smaller than or equal to a preset threshold value, determining the pressure adjustment quantity of the corresponding area based on the temperature difference, constructing a flow velocity-temperature association mapping matrix, determining the heat dissipation medium incoming speed based on the flow velocity-temperature association mapping matrix and combining the pressure adjustment quantity, and performing heat dissipation treatment of the waste batteries in the corresponding area based on the heat dissipation medium incoming speed and the pressure adjustment quantity.

Optionally, the establishing a battery equivalent circuit model, acquiring battery state parameters corresponding to a plurality of waste batteries based on the battery equivalent circuit model, includes:

Acquiring historical waste battery test information, establishing a plurality of initial equivalent circuit models based on the historical waste battery test information, and calculating target error information and calculation time information of each initial equivalent circuit model;

Calculating the matching degree corresponding to each initial equivalent circuit model based on the target error information and calculation time information of each initial equivalent circuit model, and taking the initial equivalent circuit model with the highest matching degree as a battery equivalent circuit model;

and acquiring current data and voltage data of each waste battery, and carrying out parameter identification by using the battery equivalent circuit model based on the current data and the voltage data of each waste battery to acquire battery state parameters corresponding to each waste battery.

Optionally, the performing discharge state influence analysis on the plurality of waste batteries based on the real-time environmental data to obtain a corresponding discharge state influence analysis result includes:

acquiring a discharge state influence record data set, and performing numerical processing on the discharge state influence record data set to acquire a numerical discharge state influence record data set;

Constructing a characteristic data matrix based on the numerical discharge state influence record data set, and acquiring a discharge waveform characteristic vector and an environment characteristic vector based on the characteristic data matrix;

Performing feature interaction based on an attention mechanism based on the discharge waveform feature vector and the environment feature vector to obtain a dependency relationship feature;

Generating a plurality of discharge state environmental impact factors based on the discharge waveform characteristic vector and the environmental characteristic vector, and evaluating the plurality of discharge state environmental impact factors to obtain a plurality of corresponding discharge state environmental impact characteristic values;

Performing association relation analysis on a plurality of discharge state environment influence factors based on the dependency relation characteristics to obtain factor association relation, and constructing a state analysis topology network by combining the plurality of discharge state environment influence characteristic values based on the factor association relation;

and carrying out discharge state influence analysis on a plurality of waste batteries of each class by utilizing the real-time environment data based on the state analysis topology network to obtain a corresponding discharge state influence analysis result.

Optionally, the constructing an initial discharge control scheme corresponding to the plurality of waste batteries of each category based on the corresponding battery state parameters and the discharge state influence analysis result includes:

Constructing corresponding control constraint conditions based on battery state parameters and discharge state influence analysis results corresponding to a plurality of waste batteries of each category;

Acquiring historical discharge control scheme data, generating corresponding learning strategy data by using a reinforcement learning algorithm based on battery state parameters and discharge state influence analysis results corresponding to a plurality of waste batteries of each category, and updating the historical discharge control scheme data based on the learning strategy data to obtain updated discharge strategy data corresponding to the plurality of waste batteries of each category;

and constructing an initial discharge control scheme corresponding to a plurality of waste batteries of each category based on the control constraint conditions and the updated discharge strategy data.

Optionally, the optimizing the initial discharge control scheme based on the simulation analysis result to obtain an optimized discharge control scheme includes:

Extracting simulation feedback data based on the simulation analysis result, and performing scheme feedback analysis on the simulation feedback data based on a preset evaluation dimension to obtain scheme feedback analysis data;

Performing deep feedback analysis on the simulation feedback data based on a deep learning algorithm to obtain deep feedback analysis data;

And determining scheme improvement point data based on the scheme feedback analysis data and the depth feedback analysis data, and optimizing the initial discharge control scheme based on the scheme improvement point data to obtain an optimized discharge control scheme.

Optionally, the placing the plurality of waste batteries of each category in the corresponding position of the battery discharging device includes:

and identifying the positive electrode position and the negative electrode position of each waste battery based on an image identification technology, and connecting a plurality of waste batteries of corresponding categories in corresponding positions of the battery discharging equipment based on the positive electrode position and the negative electrode position.

Optionally, the determining the pressure adjustment amount of the corresponding area based on the temperature difference, constructing a flow rate-temperature association mapping matrix, and determining the heat dissipation medium incoming speed based on the flow rate-temperature association mapping matrix and the pressure adjustment amount includes:

acquiring initial pressure adjustment quantity of a corresponding area by using a preset temperature-relation-pressure mapping table based on the temperature difference;

Generating a parameter time domain matrix based on the temperature difference and the initial pressure adjustment amount, and performing topology association analysis based on the parameter time domain matrix to obtain a topology association feature matrix;

Determining the pressure adjustment quantity of the corresponding area based on the topological association feature matrix and the initial pressure adjustment quantity;

acquiring a plurality of heat dissipation medium incoming rate values and corresponding reaction temperature values of historical preset time points, and generating a heat dissipation medium incoming rate time sequence vector and a reflection temperature time sequence vector based on the heat dissipation medium incoming rate values and the corresponding reaction temperature values;

Calculating a transfer matrix of the heat dissipation medium incoming rate time sequence vector relative to the reflected temperature time sequence vector, and constructing a flow velocity-temperature association mapping matrix based on the transfer matrix;

And carrying out parameter guidance based on the flow velocity-temperature association mapping matrix and the pressure adjustment quantity, and obtaining the heat dissipation medium incoming speed.

In addition, the invention also provides an intelligent rapid discharge control device of the waste batteries, which comprises:

Waste battery classification module: the method comprises the steps of establishing a battery equivalent circuit model, acquiring battery state parameters corresponding to a plurality of waste batteries based on the battery equivalent circuit model, and classifying all the waste batteries based on the battery state parameters to acquire a plurality of waste batteries of each class;

Discharge state impact analysis module: the method comprises the steps of acquiring real-time environment data, and carrying out discharge state influence analysis on a plurality of waste batteries of each class based on the real-time environment data to obtain corresponding discharge state influence analysis results;

the discharge control scheme construction module: the method comprises the steps of constructing an initial discharge control scheme corresponding to a plurality of waste batteries of each category based on corresponding battery state parameters and discharge state influence analysis results, performing simulation analysis on the initial discharge control scheme to obtain simulation analysis results, and optimizing the initial discharge control scheme based on the simulation analysis results to obtain an optimized discharge control scheme corresponding to the plurality of waste batteries of each category;

And a discharge processing module: the battery discharging equipment is used for carrying out discharging treatment on the waste batteries based on the optimized discharging control scheme;

And a temperature monitoring module: the method comprises the steps of acquiring real-time temperatures of corresponding areas based on a plurality of monitoring sensors arranged at preset positions of battery discharging equipment in the discharging treatment process of waste batteries, calculating temperature differences between the real-time temperatures of the corresponding areas and preset early-warning temperatures, and judging whether the temperature differences of the corresponding areas are smaller than or equal to preset thresholds;

And the heat radiation module comprises: and when the temperature difference of the corresponding area is less than or equal to the preset threshold value, determining the pressure adjustment quantity of the corresponding area based on the temperature difference, constructing a flow velocity-temperature association mapping matrix, determining the heat dissipation medium incoming speed based on the flow velocity-temperature association mapping matrix and combining the pressure adjustment quantity, and performing heat dissipation treatment of the waste batteries in the corresponding area based on the heat dissipation medium incoming speed and the pressure adjustment quantity.

In addition, the invention also provides electronic equipment, which comprises a processor and a memory, wherein the memory is used for storing instructions, and the processor is used for calling the instructions in the memory, so that the electronic equipment executes the intelligent rapid discharge control method of the waste battery.

In addition, the invention also provides a computer readable storage medium which stores computer instructions, and when the computer instructions run on the electronic equipment, the electronic equipment is enabled to execute the intelligent rapid discharge control method of the waste battery.

In the embodiment of the invention, the battery state parameters corresponding to a plurality of waste batteries are obtained through the battery equivalent circuit model to classify the waste batteries, so that the classification can be more accurately carried out, and the partial insufficient discharge of the waste batteries is avoided. The effect of the environmental data on the discharge state of the battery can be analyzed more accurately by utilizing the constructed state analysis topological network to analyze the discharge state effect of the plurality of waste batteries of each category through the real-time environmental data. The discharge state influence analysis result is added into the consideration of the discharge control scheme, and the discharge control scheme is optimized according to the simulation analysis result obtained by the simulation analysis of the initial discharge control scheme, so that the finally obtained optimized discharge control scheme can be better adapted to the actual condition of the discharge of the waste battery, the influence of environmental factors on the discharge of the waste battery is reduced, and the discharge efficiency and the discharge quality can be effectively improved. In the discharging treatment process of the waste battery, the real-time temperature of the corresponding area is acquired through the monitoring sensor combination, when the temperature reduction treatment is required, the pressure adjustment quantity is determined according to the temperature difference between the real-time temperature of the corresponding area and the preset early warning temperature, the heat dissipation medium incoming speed is determined based on the flow velocity-temperature association mapping matrix and the pressure adjustment quantity, the repeated integral pressure relief of the battery discharging equipment is avoided, the heat dissipation treatment of the local corresponding area is realized, meanwhile, the pressure adjustment quantity of the corresponding area is determined through the topology association characteristic matrix, the heat dissipation medium incoming speed is determined through the flow velocity-temperature association mapping matrix and the pressure adjustment quantity, the accuracy of the obtained pressure adjustment quantity and the heat dissipation medium incoming speed is higher, the discharging efficiency of the waste battery is greatly improved, and the discharging state of the waste battery achieves a more ideal effect.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of an intelligent rapid discharge control method of waste batteries in an embodiment of the invention;

FIG. 2 is a schematic structural diagram of an intelligent rapid discharge control device for waste batteries in an embodiment of the invention;

Fig. 3 is a schematic structural composition diagram of an electronic device in an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

Referring to fig. 1, fig. 1 is a flow chart of an intelligent rapid discharge control method for waste batteries according to an embodiment of the invention.

As shown in fig. 1, an intelligent rapid discharge control method for waste batteries includes:

s11: establishing a battery equivalent circuit model, acquiring battery state parameters corresponding to a plurality of waste batteries based on the battery equivalent circuit model, and classifying all the waste batteries based on the battery state parameters to acquire a plurality of waste batteries of each class;

In the implementation process of the invention, the establishing a battery equivalent circuit model, and obtaining battery state parameters corresponding to a plurality of waste batteries based on the battery equivalent circuit model comprises the following steps: acquiring historical waste battery test information, establishing a plurality of initial equivalent circuit models based on the historical waste battery test information, and calculating target error information and calculation time information of each initial equivalent circuit model; calculating the matching degree corresponding to each initial equivalent circuit model based on the target error information and calculation time information of each initial equivalent circuit model, and taking the initial equivalent circuit model with the highest matching degree as a battery equivalent circuit model; and acquiring current data and voltage data of each waste battery, and carrying out parameter identification by using the battery equivalent circuit model based on the current data and the voltage data of each waste battery to acquire battery state parameters corresponding to each waste battery.

Specifically, historical waste battery test information is obtained in a local database, the historical waste battery test information is experimental data information obtained through past offline tests, the historical waste battery test information comprises an open-circuit voltage-state-of-charge curve and ohmic internal resistance, a plurality of initial equivalent circuit models are built through the historical waste battery test information, namely the values of parameters of all elements in the initial equivalent circuit models are initialized through the historical waste battery test information, and a plurality of initial equivalent circuit models with different orders are built according to the values of the parameters of all the elements. After each initial equivalent circuit model is determined, testing each initial equivalent circuit model in corresponding test software, recording related data in the test process, wherein the related data comprise the difference value between the calculation time and final result information and preset result information, and calculating the target error information and calculation time information of each initial equivalent circuit model according to the difference value between the calculation time and final result information and the preset result information. Calculating the matching degree corresponding to each initial equivalent circuit model based on the target error information and the calculation time information of each initial equivalent circuit model, wherein the calculation expression of the matching degree is as follows:

,

wherein R is the matching degree, N is the parameter value of the initial equivalent circuit model, E is the target error information, c is the time factor, T is the calculation time information, Is a correction coefficient. And comparing the matching degree of each initial equivalent circuit model, and taking the initial equivalent circuit model with the highest matching degree as a battery equivalent circuit model. Acquiring current data and voltage data of each waste battery, wherein the current data and the voltage data of each waste battery can be acquired through a current sensor and a voltage sensor; and carrying out parameter identification by utilizing the battery equivalent circuit model based on the current data and the voltage data of each waste battery, constructing a battery state equation by utilizing the battery equivalent circuit model, and calculating battery state parameters corresponding to each waste battery in real time by utilizing a forgetting factor recursive least square method according to the current data and the voltage data of each waste battery and the battery state equation, wherein the battery state parameters comprise a state of charge and an ohmic internal resistance value. And classifying the waste batteries by using a clustering method according to the battery state parameters of all the waste batteries, acquiring a clustering center through the battery state parameters of each waste battery, calculating the Euclidean distance between the waste batteries and the clustering center, classifying the waste batteries into the nearest clustering center until all the waste batteries are classified, and obtaining a plurality of waste batteries of each class.

S12: acquiring real-time environment data, and carrying out discharge state influence analysis on a plurality of waste batteries of each class based on the real-time environment data to obtain a corresponding discharge state influence analysis result;

In the implementation process of the invention, the method for analyzing the discharge state influence of the plurality of waste batteries of each class based on the real-time environment data to obtain the corresponding discharge state influence analysis result comprises the following steps: acquiring a discharge state influence record data set, and performing numerical processing on the discharge state influence record data set to acquire a numerical discharge state influence record data set; constructing a characteristic data matrix based on the numerical discharge state influence record data set, and acquiring a discharge waveform characteristic vector and an environment characteristic vector based on the characteristic data matrix; performing feature interaction based on an attention mechanism based on the discharge waveform feature vector and the environment feature vector to obtain a dependency relationship feature; generating a plurality of discharge state environmental impact factors based on the discharge waveform characteristic vector and the environmental characteristic vector, and evaluating the plurality of discharge state environmental impact factors to obtain a plurality of corresponding discharge state environmental impact characteristic values; performing association relation analysis on a plurality of discharge state environment influence factors based on the dependency relation characteristics to obtain factor association relation, and constructing a state analysis topology network by combining the plurality of discharge state environment influence characteristic values based on the factor association relation; and carrying out discharge state influence analysis on a plurality of waste batteries of each class by utilizing the real-time environment data based on the state analysis topology network to obtain a corresponding discharge state influence analysis result.

Specifically, a discharge state influence record data set is obtained in a database, wherein the discharge state influence record data set is influence change record information of the environmental temperature, humidity and pressure data on the discharge time information and the discharge efficiency information of the battery. And before data analysis is performed, converting non-numerical data in the data set into numerical data so that subsequent analysis processing, namely numerical processing, can convert category type data in the discharge state influence record data set into continuous integer codes by adopting label codes, and further realize numerical conversion in the data set to obtain the numerical discharge state influence record data set. Constructing a characteristic data matrix based on the numerical discharge state influence record data set, acquiring a characteristic data set through the numerical discharge state influence record data set by adopting a characteristic extraction algorithm, performing decentralization processing on each characteristic data in the characteristic data set to acquire a plurality of feature data after decentralization processing, calculating a feature average value of the feature data after decentralization processing, constructing a new characteristic data set according to the feature average value, calculating a covariance matrix according to the new characteristic data set, calculating a feature value and a feature vector of the covariance matrix, constructing a characteristic data matrix according to the feature value and the feature vector of the covariance matrix, acquiring a discharge waveform feature vector and an environment feature vector based on the feature data matrix, the elements of the characteristic data matrix comprise discharge characteristic data and environment characteristic data, and the discharge waveform characteristic vector and the environment characteristic vector can be obtained through element composition analysis of the characteristic data matrix. And carrying out feature interaction based on an attention mechanism based on the discharge waveform feature vector and the environment feature vector, wherein the feature interaction based on the attention mechanism can automatically adjust the weight of the feature according to the importance of different features, so that the interaction feature vector can more accurately reflect the interaction effect of discharge performance and environment, and the discharge performance-environment interaction feature vector, namely the dependency feature is obtained. And generating a plurality of discharge state environment influence factors based on the discharge waveform characteristic vector and the environment characteristic vector, wherein the discharge state environment influence factors comprise a temperature influence factor, a humidity influence factor and the like. And evaluating the plurality of discharge state environment influence factors, wherein each discharge state environment influence factor is evaluated, and the discharge state environment influence factors have corresponding battery discharge state influence evaluation coefficients, so that the corresponding plurality of discharge state environment influence characteristic values can be obtained through the discharge state influence evaluation coefficients. And carrying out association relation analysis on a plurality of discharge state environment influence factors based on the dependency relation characteristics, using the plurality of discharge state environment influence factors as nodes, constructing a dependency relation matrix according to the dependency relation characteristics, generating a directed graph according to the dependency relation matrix, and analyzing the dependency hierarchical relation among the nodes according to the directed graph to obtain the factor association relation. And constructing a state analysis topology network based on the factor association relation and combining the plurality of discharge state environment influence characteristic values, connecting the plurality of discharge state environment influence factors according to the factor association relation, determining the connection strength among the factors according to the plurality of discharge state environment influence characteristic values, and obtaining the state analysis topology network after the connection of the plurality of discharge state environment influence factors is completed. The real-time environment data is acquired through the environment sensor combination, the real-time environment data comprises environment temperature, environment humidity, environment pressure and the like, and the battery discharges and exchanges heat with the environment, so that the environment temperature data can influence the battery discharge state, and the environment humidity and the environment pressure can influence the discharge capacity and the discharge efficiency of the battery. And (3) carrying out discharge state influence analysis on a plurality of waste batteries of each class by utilizing the real-time environment data based on the state analysis topology network, inputting the real-time environment data into the state analysis topology network, traversing a plurality of discharge state environment influence factors in the state analysis topology network, matching corresponding nodes, and acquiring influence change information at the corresponding nodes to obtain a corresponding discharge state influence analysis result.

S13: constructing an initial discharge control scheme corresponding to a plurality of waste batteries of each category based on corresponding battery state parameters and discharge state influence analysis results, performing simulation analysis on the initial discharge control scheme to obtain simulation analysis results, and optimizing the initial discharge control scheme based on the simulation analysis results to obtain an optimized discharge control scheme corresponding to the plurality of waste batteries of each category;

In the implementation process of the invention, the construction of the initial discharge control scheme corresponding to the plurality of waste batteries of each category based on the corresponding battery state parameters and the discharge state influence analysis results comprises the following steps: constructing corresponding control constraint conditions based on battery state parameters and discharge state influence analysis results corresponding to a plurality of waste batteries of each category; acquiring historical discharge control scheme data, generating corresponding learning strategy data by using a reinforcement learning algorithm based on battery state parameters and discharge state influence analysis results corresponding to a plurality of waste batteries of each category, and updating the historical discharge control scheme data based on the learning strategy data to obtain updated discharge strategy data corresponding to the plurality of waste batteries of each category; and constructing an initial discharge control scheme corresponding to a plurality of waste batteries of each category based on the control constraint conditions and the updated discharge strategy data.

Further, the optimizing the initial discharge control scheme based on the simulation analysis result to obtain an optimized discharge control scheme includes: extracting simulation feedback data based on the simulation analysis result, and performing scheme feedback analysis on the simulation feedback data based on a preset evaluation dimension to obtain scheme feedback analysis data; performing deep feedback analysis on the simulation feedback data based on a deep learning algorithm to obtain deep feedback analysis data; and determining scheme improvement point data based on the scheme feedback analysis data and the depth feedback analysis data, and optimizing the initial discharge control scheme based on the scheme improvement point data to obtain an optimized discharge control scheme.

Specifically, corresponding control constraint conditions are constructed based on battery state parameters and discharge state influence analysis results corresponding to a plurality of waste batteries of each category, the state parameters of the waste batteries influence discharge performance, and meanwhile, the discharge efficiency and the discharge depth of the waste batteries are influenced by environmental temperature, humidity and pressure, so that the control constraint conditions comprise a contact area interval of the buried conductive particles of the waste batteries and a pressure interval applied to discharge of the waste batteries. obtaining historical discharge control scheme data through a local database, generating corresponding learning strategy data by using a reinforcement learning algorithm based on battery state parameters and discharge state influence analysis results corresponding to a plurality of waste batteries of each category, modifying self strategy data by using the generated data by the reinforcement learning algorithm, generating new data by interaction with the environment, selecting corresponding value functions according to the battery state parameters and discharge state influence analysis results corresponding to the plurality of waste batteries of each category, calculating approximation functions by using a Belman equation according to the corresponding value functions, performing iterative update on the approximation functions, performing data interaction based on the approximation functions after iterative update, Corresponding learning strategy data is obtained. and updating the historical discharge control scheme data based on the learning strategy data, namely, interacting the learning strategy data with the historical discharge scheme data to obtain updated discharge strategy data corresponding to a plurality of waste batteries of each category. And constructing an initial discharge control scheme corresponding to the plurality of waste batteries of each category based on the control constraint conditions and the updated discharge strategy data, introducing the control constraint conditions into a simulation environment, and executing iteration of updating the discharge strategy data under the simulation environment in which the control constraint conditions are introduced until the preset iteration times are reached, wherein scheme execution data can be obtained after each iteration, and the scheme execution data is added into the next iteration to obtain the initial discharge control scheme corresponding to the plurality of waste batteries of each category. And performing simulation analysis on the initial discharge control schemes, inputting each initial discharge scheme into a simulation test platform, wherein a related simulation discharge circuit is arranged in the simulation test platform, performing simulation on the initial discharge control scheme in the simulation test platform to obtain a plurality of simulation data, and performing analysis and comparison on the plurality of simulation data to obtain a simulation analysis result. And extracting simulation feedback data based on the simulation analysis result, wherein the simulation feedback data is discharge depth data and discharge efficiency data obtained after an initial discharge control scheme is tested in a simulation test platform. And carrying out scheme feedback analysis on the simulation feedback data based on a preset evaluation dimension, wherein the preset evaluation dimension is set by an expert rule, carrying out evaluation value calculation on the discharge depth data and the discharge efficiency data in the simulation feedback data according to the preset evaluation dimension, and carrying out scheme feedback analysis according to the calculated evaluation value to obtain scheme feedback analysis data. and carrying out deep feedback analysis on the simulation feedback data based on a deep learning algorithm, and analyzing the efficiency and the depth information of the waste battery discharge in the time sequence by adopting a recurrent neural network to obtain deep feedback analysis data. And determining scheme improvement point data based on the scheme feedback analysis data and the depth feedback analysis data, such as excessively fast temperature rise when the waste batteries are discharged, excessively high applied pressure or excessively high ambient humidity determined by excessively high ambient temperature and improper contact area of the waste batteries embedded into conductive particles, optimizing the initial discharge control scheme based on the scheme improvement point data, adjusting control parameters in the initial discharge control scheme according to the scheme improvement point data, generating scheme improvement data, and performing scheme optimization by iteratively learning and utilizing the scheme improvement data to obtain an optimized discharge control scheme, wherein the optimized discharge control scheme comprises the contact area of the waste batteries embedded into the conductive particles of each category, the applied pressure of the waste batteries of each category and the like.

S14: placing a plurality of waste batteries of each category in corresponding positions of battery discharging equipment, wherein the battery discharging equipment performs discharging treatment of the waste batteries based on the optimized discharging control scheme;

In the implementation process of the invention, the placing of the plurality of waste batteries of each category in the corresponding positions of the battery discharging equipment comprises the following steps: and identifying the positive electrode position and the negative electrode position of each waste battery based on an image identification technology, and connecting a plurality of waste batteries of corresponding categories in corresponding positions of the battery discharging equipment based on the positive electrode position and the negative electrode position.

Specifically, the target image of each waste battery is obtained through an image acquisition device, the target image of each waste battery is preprocessed, the preprocessing comprises noise reduction and image enhancement, a plurality of preprocessed target images are obtained, feature extraction is carried out on each preprocessed target image, corresponding target features are obtained, and then the positive electrode position and the negative electrode position of each waste battery are identified by utilizing the corresponding target features through a deep learning method. And connecting a plurality of waste batteries of corresponding types in corresponding positions of the battery discharging equipment through corresponding equipment, namely connecting the positive end and the negative end of each waste battery to the positive end and the negative end of the battery discharging equipment, wherein corresponding equipment can adopt a mechanical arm. After all the waste batteries are placed, the battery discharging equipment performs discharging treatment on the waste batteries based on an optimized discharging control scheme.

S15: in the discharging treatment process of the waste batteries, based on a plurality of monitoring sensors arranged at preset positions of the battery discharging equipment, acquiring real-time temperatures of corresponding areas, calculating temperature differences between the real-time temperatures of the corresponding areas and preset early-warning temperatures, and judging whether the temperature differences of the corresponding areas are smaller than or equal to preset thresholds;

In the specific implementation process of the invention, in the discharge treatment process of the waste batteries, the real-time temperature of the corresponding area is collected based on a plurality of monitoring sensors arranged at the preset position of the battery discharge equipment, and each monitoring sensor is correspondingly arranged in each area of the battery discharge equipment, so that each monitoring sensor can accurately collect the real-time temperature of the corresponding area, and the local area temperature data collection is realized. And calculating the temperature difference between the real-time temperature of the corresponding area and the preset early warning temperature, judging whether the temperature difference of the corresponding area is smaller than or equal to a preset threshold value, if the temperature difference of the corresponding area is larger than the preset threshold value, continuing to discharge the waste battery, and if the temperature difference of the corresponding area is smaller than or equal to the preset threshold value, carrying out heat dissipation treatment on the corresponding area.

S16: when the temperature difference of the corresponding area is judged to be smaller than or equal to a preset threshold value, determining the pressure adjustment quantity of the corresponding area based on the temperature difference, constructing a flow velocity-temperature association mapping matrix, determining the heat dissipation medium incoming speed based on the flow velocity-temperature association mapping matrix and combining the pressure adjustment quantity, and performing heat dissipation treatment of the waste batteries in the corresponding area based on the heat dissipation medium incoming speed and the pressure adjustment quantity.

In the implementation process of the invention, the step of determining the pressure adjustment quantity of the corresponding area based on the temperature difference, constructing a flow velocity-temperature association mapping matrix, and determining the heat dissipation medium incoming velocity based on the flow velocity-temperature association mapping matrix and combining the pressure adjustment quantity comprises the following steps: acquiring initial pressure adjustment quantity of a corresponding area by using a preset temperature-relation-pressure mapping table based on the temperature difference; generating a parameter time domain matrix based on the temperature difference and the initial pressure adjustment amount, and performing topology association analysis based on the parameter time domain matrix to obtain a topology association feature matrix; determining the pressure adjustment quantity of the corresponding area based on the topological association feature matrix and the initial pressure adjustment quantity; acquiring a plurality of heat dissipation medium incoming rate values and corresponding reaction temperature values of historical preset time points, and generating a heat dissipation medium incoming rate time sequence vector and a reflection temperature time sequence vector based on the heat dissipation medium incoming rate values and the corresponding reaction temperature values; calculating a transfer matrix of the heat dissipation medium incoming rate time sequence vector relative to the reflected temperature time sequence vector, and constructing a flow velocity-temperature association mapping matrix based on the transfer matrix; and carrying out parameter guidance based on the flow velocity-temperature association mapping matrix and the pressure adjustment quantity, and obtaining the heat dissipation medium incoming speed.

Specifically, when it is determined that the temperature difference of the corresponding region is smaller than or equal to the preset threshold, the initial pressure adjustment amount of the corresponding region is obtained by using the preset temperature-relation-pressure mapping table based on the temperature difference, the relation between the corresponding temperature difference and the pressure value can be obtained through the preset temperature-relation-pressure mapping table, and the initial pressure adjustment amount of the corresponding region can be obtained, but only the pressure adjustment amount obtained through the mapping table has a certain error, so that certain adjustment is needed. And generating a parameter time domain matrix based on the temperature difference and the initial pressure adjustment quantity, arranging the temperature difference and the initial pressure adjustment quantity into a two-dimensional matrix, and carrying out matched filtering on the two-dimensional matrix to obtain the parameter time domain matrix. Performing topology association analysis based on the parameter time domain matrix, performing matrix segmentation on the parameter time domain matrix to obtain a plurality of sub-parameter time domain matrices, inputting the plurality of sub-parameter time domain matrices into an association feature extractor based on a convolutional neural network to perform feature mining to obtain association feature distribution information, calculating cosine similarity between each sub-parameter time domain matrix according to the association feature distribution information, and generating topology association feature matrices according to the association feature distribution information and the cosine similarity between each sub-parameter time domain matrix. And determining the pressure adjustment quantity of the corresponding area based on the topology association characteristic matrix and the initial pressure adjustment quantity, and determining the pressure adjustment quantity of the corresponding area by combining the initial pressure adjustment quantity and the corresponding characteristic distribution information in the topology association characteristic matrix, so that the pressure adjustment quantity is corrected, and the finally obtained pressure adjustment quantity can be more suitable for actual conditions. And acquiring a plurality of heat dissipation medium input speed values and corresponding reaction temperature values at historical preset time points from a database, and vectorizing based on the heat dissipation medium input speed values and the corresponding reaction temperature values to generate heat dissipation medium input speed time sequence vectors and reflected temperature time sequence vectors. Calculating a transfer matrix of the heat dissipation medium incoming rate time sequence vector relative to the reflected temperature time sequence vector, wherein the calculation expression of the transfer matrix is as follows:

,

Wherein Z is a transfer matrix, S is a heat dissipation medium incoming rate timing vector, M is a temperature reflecting timing vector, Is a correction coefficient. And constructing a flow velocity-temperature association mapping matrix based on the transfer matrix, wherein the transfer matrix can highlight the transfer probability distribution of the state, the mapping relation between the flow velocity and the temperature of the heat dissipation medium is established based on the transfer matrix, the conversion between the flow velocity and the temperature reduction state of the heat dissipation medium can be reflected through the transfer matrix, and the flow velocity-temperature association mapping matrix is established based on the mapping relation established by the transfer matrix. And carrying out parameter guidance based on the flow velocity-temperature association mapping matrix and the pressure adjustment quantity, determining the contact area of the waste battery and the heat dissipation medium through the pressure adjustment quantity, carrying out separation parameter guidance through the flow velocity-temperature association mapping matrix to obtain a flow velocity state matrix, and obtaining the heat dissipation medium input speed through the contact area of the flow velocity state matrix and the heat dissipation medium. And carrying out heat dissipation treatment on the waste batteries in the corresponding area based on the heat dissipation medium transmission speed and the pressure adjustment amount, carrying out proper pressure relief on the waste batteries in the corresponding area according to the pressure adjustment amount, and transmitting the heat dissipation medium in the corresponding area according to the heat dissipation medium transmission speed, so as to realize heat dissipation treatment of the local area. When the waste batteries are discharged, only monitoring whether the temperature of each area is abnormal or not is needed, if so, the corresponding area is subjected to heat dissipation treatment without integral pressure relief heat dissipation of all the waste batteries. And stopping heat dissipation until the recalculated temperature difference in the corresponding area is larger than a preset threshold value, continuously monitoring the residual capacity of the waste battery in the discharging treatment process of the waste battery, and stopping the discharging treatment of the waste battery when the residual capacity of the waste battery reaches the preset residual capacity threshold value.

Example two

Referring to fig. 2, fig. 2 is a schematic structural diagram of an intelligent rapid discharge control device for waste batteries according to an embodiment of the invention.

As shown in fig. 2, an intelligent rapid discharge control device for waste batteries, the device comprises:

Waste battery classification module 21: the method comprises the steps of establishing a battery equivalent circuit model, acquiring battery state parameters corresponding to a plurality of waste batteries based on the battery equivalent circuit model, and classifying all the waste batteries based on the battery state parameters to acquire a plurality of waste batteries of each class;

Discharge state impact analysis module 22: the method comprises the steps of acquiring real-time environment data, and carrying out discharge state influence analysis on a plurality of waste batteries of each class based on the real-time environment data to obtain corresponding discharge state influence analysis results;

Discharge control scheme construction module 23: the method comprises the steps of constructing an initial discharge control scheme corresponding to a plurality of waste batteries of each category based on corresponding battery state parameters and discharge state influence analysis results, performing simulation analysis on the initial discharge control scheme to obtain simulation analysis results, and optimizing the initial discharge control scheme based on the simulation analysis results to obtain an optimized discharge control scheme corresponding to the plurality of waste batteries of each category;

Discharge processing module 24: the battery discharging equipment is used for carrying out discharging treatment on the waste batteries based on the optimized discharging control scheme;

Temperature monitoring module 25: the method comprises the steps of acquiring real-time temperatures of corresponding areas based on a plurality of monitoring sensors arranged at preset positions of battery discharging equipment in the discharging treatment process of waste batteries, calculating temperature differences between the real-time temperatures of the corresponding areas and preset early-warning temperatures, and judging whether the temperature differences of the corresponding areas are smaller than or equal to preset thresholds;

Heat dissipation module 26: and when the temperature difference of the corresponding area is less than or equal to the preset threshold value, determining the pressure adjustment quantity of the corresponding area based on the temperature difference, constructing a flow velocity-temperature association mapping matrix, determining the heat dissipation medium incoming speed based on the flow velocity-temperature association mapping matrix and combining the pressure adjustment quantity, and performing heat dissipation treatment of the waste batteries in the corresponding area based on the heat dissipation medium incoming speed and the pressure adjustment quantity.

In the implementation process of the present invention, the specific embodiments of the apparatus item may refer to the embodiments of the method item described above, which are not described herein again.

The embodiment of the invention provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the program is executed by a processor to realize the intelligent rapid discharge control method of the waste battery in any one of the embodiments. The computer readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random AcceSS Memory, random access memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (ELECTRICALLY ERASABLE PROGRAMMABLEREAD-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a form readable by a device (e.g., computer, cell phone), and may be read-only memory, magnetic or optical disk, etc.

Example III

Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.

The embodiment of the invention also provides an electronic device comprising a memory 31, a processor 33 and a computer program 32 stored in the memory 31 and executable on the processor 33, as shown in fig. 3. Those skilled in the art will appreciate that the electronic device shown in fig. 3 does not constitute a limitation of all devices, and may include more or fewer components than shown, or may combine certain components. The memory 31 may be used to store a computer program 32 and functional modules, and the processor 33 runs the computer program 32 stored in the memory 31 to perform various functional applications of the device and data processing. The memory may be internal memory or external memory, or include both internal memory and external memory. The internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), flash memory, or random access memory. The external memory may include a hard disk, floppy disk, ZIP disk, U-disk, tape, etc. The Processor 33 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor, a single-chip microcomputer or the processor 33 may be any conventional processor or the like. The processors and memories disclosed herein include, but are not limited to, these types of processors and memories. The processors and memories disclosed herein are by way of example only and not by way of limitation.

As one embodiment, the electronic device includes: the one or more processors 33, the memory 31, and the one or more computer programs 32, wherein the one or more computer programs 32 are stored in the memory 31 and configured to be executed by the one or more processors 33, and the one or more computer programs 32 are configured to execute the intelligent rapid discharge control method for the waste battery in any of the foregoing embodiments, and the specific implementation process is referred to the foregoing embodiments and is not repeated herein.

In addition, the foregoing describes in detail the method for controlling intelligent rapid discharge of waste batteries and the related devices provided by the embodiments of the present invention, and specific examples should be adopted herein to illustrate the principles and embodiments of the present invention, where the foregoing examples are only for helping to understand the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. An intelligent rapid discharge control method for waste batteries is characterized by comprising the following steps:

2. The intelligent rapid discharging control method for waste batteries according to claim 1, wherein the establishing a battery equivalent circuit model, based on which battery state parameters corresponding to a plurality of waste batteries are obtained, comprises:

3. The intelligent rapid discharge control method of waste batteries according to claim 1, wherein the performing discharge state influence analysis on the plurality of waste batteries based on the real-time environmental data to obtain a corresponding discharge state influence analysis result comprises:

4. The intelligent rapid discharge control method of waste batteries according to claim 1, wherein the constructing an initial discharge control scheme corresponding to a plurality of waste batteries of each category based on the corresponding battery state parameters and the discharge state influence analysis results comprises:

5. The intelligent rapid discharge control method for waste batteries according to claim 1, wherein the optimizing the initial discharge control scheme based on the simulation analysis result to obtain an optimized discharge control scheme comprises:

6. The intelligent rapid discharge control method for waste batteries according to claim 1, wherein the placing of the plurality of waste batteries of each category in the corresponding position of the battery discharge device comprises:

7. The intelligent rapid discharge control method of waste batteries according to claim 1, wherein the determining the pressure adjustment amount of the corresponding area based on the temperature difference, constructing a flow rate-temperature association mapping matrix, and determining the heat dissipation medium incoming speed based on the flow rate-temperature association mapping matrix in combination with the pressure adjustment amount, comprises:

8. An intelligent rapid discharge control device for waste batteries, which is characterized by comprising:

9. An electronic device comprising a processor and a memory, wherein the memory is configured to store instructions, and the processor is configured to invoke the instructions in the memory, so that the electronic device performs the intelligent rapid discharge control method of the waste battery according to any one of claims 1 to 7.

10. A computer readable storage medium storing computer instructions that, when run on an electronic device, cause the electronic device to perform the intelligent rapid discharge control method of a waste battery according to any one of claims 1 to 7.