CN116047313B - Quality detection and maintenance method and system for lithium battery energy storage box - Google Patents
Quality detection and maintenance method and system for lithium battery energy storage box Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention discloses a quality detection and maintenance method and system for a lithium battery energy storage box, and relates to the field of data processing, wherein the method comprises the following steps: inputting the temperature change stability parameter and the highest temperature parameter into an abnormality detection model to obtain a first judgment result and a second judgment result; when the first judging result and/or the second judging result are yes, calculating to obtain a first abnormality degree parameter and/or a second abnormality degree parameter; inputting the first abnormality degree parameter and/or the second abnormality degree parameter into a maintenance model of the lithium battery energy storage box, obtaining a maintenance scheme, and maintaining the lithium battery energy storage box. The invention solves the technical problems of insufficient detection accuracy of the charging quality of the lithium battery energy storage box and poor maintenance effect of the lithium battery energy storage box in the prior art, and achieves the technical effects of improving the detection accuracy of the charging quality of the lithium battery energy storage box and improving the maintenance quality of the lithium battery energy storage box.
Description
Technical Field
The invention relates to the field of data processing, in particular to a quality detection and maintenance method and system for a lithium battery energy storage box.
Background
With the rapid development of new energy technology, the demand of lithium battery energy storage systems is increasing, and the wide application of lithium battery energy storage boxes is promoted. The quality detection and maintenance have important influence on the normal operation of the lithium battery energy storage box. How to timely and effectively detect and maintain quality of the lithium battery energy storage box is widely paid attention to. In the prior art, the technical problems of insufficient charging quality detection accuracy of the lithium battery energy storage box and poor maintenance effect of the lithium battery energy storage box exist.
Disclosure of Invention
The application provides a quality detection and maintenance method and system for a lithium battery energy storage box. The technical problems that in the prior art, the charging quality detection accuracy of the lithium battery energy storage box is insufficient, and the maintenance effect of the lithium battery energy storage box is poor are solved. The accuracy of the charge quality detection of the lithium battery energy storage box is improved, the maintenance quality of the lithium battery energy storage box is improved, and the technical effect of powerful guarantee is provided for the normal operation of the lithium battery energy storage box.
In view of the above, the present application provides a quality detection and maintenance method and system for a lithium battery energy storage box.
In a first aspect, the present application provides a quality detection and maintenance method for a lithium battery energy storage tank, wherein the method is applied to a quality detection and maintenance system for a lithium battery energy storage tank, the method comprising: charging the lithium battery energy storage box by adopting a preset charging method; in the charging process, collecting the temperature of the lithium battery energy storage box in a plurality of time windows to obtain a temperature parameter set; calculating to obtain a temperature change stability parameter and a highest temperature parameter based on the temperature parameter set; inputting the temperature change stability parameter and the maximum temperature parameter into a temperature change abnormality detection unit and a maximum temperature abnormality detection unit in an abnormality detection model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal or not, and obtaining a first judgment result and a second judgment result; when the first judging result and/or the second judging result are yes, calculating and obtaining a first abnormality degree parameter and/or a second abnormality degree parameter of the current temperature change stability parameter and/or the highest temperature parameter based on charging temperature data in historical time; inputting the first abnormality degree parameter and/or the second abnormality degree parameter into a maintenance model of the lithium battery energy storage box to obtain a maintenance scheme, and maintaining the lithium battery energy storage box by adopting the maintenance scheme.
In a second aspect, the present application also provides a quality detection and maintenance system for a lithium battery energy storage box, wherein the system comprises: the charging module is used for charging the lithium battery energy storage box by adopting a preset charging method; the temperature parameter acquisition module is used for acquiring the temperature of the lithium battery energy storage box in a plurality of time windows in the charging process to obtain a temperature parameter set; the temperature parameter calculation module is used for calculating and obtaining a temperature change stability parameter and a highest temperature parameter based on the temperature parameter set; the abnormality judging module is used for inputting the temperature change stability parameter and the maximum temperature parameter into a temperature change abnormality detecting unit and a maximum temperature abnormality detecting unit in an abnormality detecting model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal or not, and obtaining a first judging result and a second judging result; the abnormal parameter obtaining module is used for calculating and obtaining a first abnormal degree parameter and/or a second abnormal degree parameter of the current temperature change stability parameter and/or the highest temperature parameter based on charging temperature data in historical time when the first judging result and/or the second judging result are yes; the maintenance module is used for inputting the first abnormal degree parameter and/or the second abnormal degree parameter into a maintenance model of the lithium battery energy storage box, obtaining a maintenance scheme and adopting the maintenance scheme to maintain the lithium battery energy storage box.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the quality detection and maintenance method for the lithium battery energy storage box when executing the executable instructions stored in the memory.
In a fourth aspect, the present application further provides a computer readable storage medium storing a computer program, which when executed by a processor, implements a quality detection and maintenance method for a lithium battery energy storage box provided by the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
charging the lithium battery energy storage box by a preset charging method; in the charging process, acquiring the temperature of an energy storage box of the lithium battery according to a plurality of time windows to obtain a temperature parameter set; calculating a temperature parameter set to obtain a temperature change stability parameter and a highest temperature parameter; inputting the temperature change stability parameter and the maximum temperature parameter into an abnormality detection model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal, and obtaining a first judgment result and a second judgment result; when the first judging result and/or the second judging result are yes, calculating and obtaining a first abnormality degree parameter and/or a second abnormality degree parameter based on the charging temperature data in the history time; inputting the first abnormality degree parameter and/or the second abnormality degree parameter into a maintenance model of the lithium battery energy storage box, obtaining a maintenance scheme, and maintaining the lithium battery energy storage box according to the maintenance scheme. The accuracy of the charge quality detection of the lithium battery energy storage box is improved, the maintenance quality of the lithium battery energy storage box is improved, and the technical effect of powerful guarantee is provided for the normal operation of the lithium battery energy storage box.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
FIG. 1 is a flow chart of a method for quality detection and maintenance of a lithium battery energy storage tank according to the present application;
FIG. 2 is a schematic flow chart of obtaining a temperature parameter set in a method for quality detection and maintenance of an energy storage tank of a lithium battery according to the present application;
FIG. 3 is a schematic diagram of a quality inspection and maintenance system for a lithium battery energy storage tank according to the present application;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the device comprises a charging module 11, a temperature parameter acquisition module 12, a temperature parameter calculation module 13, an abnormality judgment module 14, an abnormality parameter acquisition module 15, a maintenance module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
The application provides a quality detection and maintenance method and system for a lithium battery energy storage box. The technical problems that in the prior art, the charging quality detection accuracy of the lithium battery energy storage box is insufficient, and the maintenance effect of the lithium battery energy storage box is poor are solved. The accuracy of the charge quality detection of the lithium battery energy storage box is improved, the maintenance quality of the lithium battery energy storage box is improved, and the technical effect of powerful guarantee is provided for the normal operation of the lithium battery energy storage box.
Example 1.
Referring to fig. 1, the present application provides a quality detection and maintenance method for a lithium battery energy storage box, wherein the method is applied to a quality detection and maintenance system for a lithium battery energy storage box, and the method specifically includes the following steps:
step S100: charging the lithium battery energy storage box by adopting a preset charging method;
step S200: in the charging process, collecting the temperature of the lithium battery energy storage box in a plurality of time windows to obtain a temperature parameter set;
further, as shown in fig. 2, step S200 of the present application further includes:
step S210: collecting the temperature of the lithium battery energy storage box at a plurality of positions of the lithium battery energy storage box in a plurality of time windows to obtain a plurality of time temperature parameter sets;
Step S220: calculating and obtaining average temperature parameters of the time windows based on the time temperature parameter sets;
step S230: the set of temperature parameters is obtained based on the average temperature parameter of the plurality of time windows.
Specifically, when the lithium battery energy storage box is charged according to a preset charging method, temperature parameter collection is performed on multiple positions of the lithium battery energy storage box based on multiple time windows, and multiple time temperature parameter sets are obtained. Further, average value calculation is performed on the plurality of time temperature parameter sets, average temperature parameters of the plurality of time windows are obtained, and the average temperature parameters of the plurality of time windows are added to the temperature parameter sets. The preset charging method comprises the steps of presetting a charging current parameter, a charging voltage parameter, a charging duration parameter and charging time point information of a determined lithium battery energy storage box. The plurality of time windows include a plurality of time point information determined by a preset. The time temperature parameter sets have corresponding relations with the positions of the lithium battery energy storage box. Each time temperature parameter set comprises a plurality of temperature parameter information corresponding to each position of the lithium battery energy storage box in a plurality of time windows. The average temperature parameter of each time window comprises an average value corresponding to a plurality of temperature parameter information in each time temperature parameter set. The set of temperature parameters includes an average temperature parameter for a plurality of time windows. The method has the advantages that when the lithium battery energy storage box is charged according to the preset charging method, the temperature of the lithium battery energy storage box is collected in a plurality of time windows, a reliable temperature parameter set is obtained, and therefore the accuracy of charging quality detection of the lithium battery energy storage box is improved.
Step S300: calculating to obtain a temperature change stability parameter and a highest temperature parameter based on the temperature parameter set;
further, step S300 of the present application further includes:
step S310: obtaining the maximum value in the temperature parameter set and obtaining the maximum temperature parameter;
step S320: calculating to obtain a plurality of temperature change parameters according to the temperature parameter set and the plurality of time windows;
step S330: and calculating and obtaining a temperature variation parameter variance as the temperature variation stability parameter according to the temperature variation parameters.
Specifically, maximum value screening is performed on average temperature parameters of a plurality of time windows in the temperature parameter set, and the maximum temperature parameter is obtained. And calculating the temperature change rate of the average temperature parameter of the time windows in the temperature parameter set based on the time windows to obtain a plurality of temperature change parameters. Further, variance calculation is performed on the plurality of temperature change parameters to obtain a temperature change parameter variance, and the temperature change parameter variance is output as a temperature change stability parameter. The maximum temperature parameter comprises a maximum value corresponding to an average temperature parameter of a plurality of time windows in the temperature parameter set. The plurality of temperature change parameters comprise temperature change rates of a plurality of time windows corresponding to the temperature parameter set. The temperature variation parameter variances comprise variances corresponding to a plurality of temperature variation parameters. The smaller the temperature variation parameter variance, the better the stability of the temperature parameter set. The temperature variation stability parameter includes a temperature variation parameter variance. The method achieves the technical effects of obtaining accurate temperature change stability parameters and maximum temperature parameters by calculating and analyzing the temperature parameter set, thereby improving the accuracy and reliability of detecting the abnormal charging of the lithium battery energy storage box.
Step S400: inputting the temperature change stability parameter and the maximum temperature parameter into a temperature change abnormality detection unit and a maximum temperature abnormality detection unit in an abnormality detection model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal or not, and obtaining a first judgment result and a second judgment result;
further, step S400 of the present application further includes:
step S410: the method comprises the steps of adopting a charging temperature change stability parameter in the history time of a lithium battery energy storage box to construct a temperature change abnormality detection unit, wherein the temperature change abnormality detection unit comprises a plurality of layers of first detection nodes, and the plurality of layers of first detection nodes comprise first output nodes;
further, step S410 of the present application further includes:
step S411: obtaining a plurality of sample temperature change stability parameters based on the charging temperature change stability parameters in the history time of the lithium battery energy storage box, wherein the plurality of sample temperature change stability parameters comprise a plurality of sample abnormal temperature change stability parameters;
step S412: calculating to obtain a first abnormal proportionality coefficient based on the plurality of sample temperature change stability parameters and a plurality of sample abnormal temperature change stability parameters;
Step S413: randomly selecting sample temperature change stability parameters from the plurality of sample temperature change stability parameters, constructing the multi-layer first detection nodes, classifying the input temperature change stability parameters by each layer of first detection nodes, and inputting the classified temperature change stability parameters into an upper layer of first detection nodes;
step S414: and setting and obtaining the first output nodes in the multi-layer first detection nodes according to the first abnormal proportion coefficient, and obtaining the temperature change abnormal detection unit, wherein the first output nodes and the first detection nodes below are used for dividing and outputting single data as abnormal data.
Specifically, the embodiment of the application constructs the temperature change abnormality detection unit based on the idea of an isolated forest algorithm. And based on the charging of the lithium battery energy storage box in the historical time and the temperature data during charging, carrying out historical data query on the charging temperature change stability parameters of the lithium battery energy storage box, and obtaining a plurality of sample temperature change stability parameters. The plurality of sample temperature variation stability parameters include a plurality of sample abnormal temperature variation stability parameters and a plurality of sample normal temperature variation stability parameters, which can be obtained by a technician manually distinguishing the marks, for example, the marks corresponding to the larger temperature variation stability parameters are abnormal. And calculating the ratio of the data quantity of the abnormal temperature change stability parameters of the plurality of samples to obtain a first abnormal proportional coefficient. Further, random selection is carried out on normal temperature change stability parameters of a plurality of samples in the plurality of sample temperature change stability parameters, and a plurality of layers of first detection nodes are obtained. Then, a first output node is set in the plurality of layers of first detection nodes based on the first abnormal scaling coefficient, and a temperature change abnormality detection unit is obtained. Illustratively, when the first output node is set, the first abnormal scaling factor is 5%, and the multi-layer first detection node includes 100 layers of detection nodes, then a fifth layer detection node from bottom to top in the multi-layer first detection node is set as the first output node.
Wherein the historical time may be determined by an adaptive setting. The plurality of sample abnormal temperature change stability parameters comprise a plurality of historical abnormal temperature change stability parameters of the lithium battery energy storage box in historical time. The plurality of sample normal temperature change stability parameters comprise a plurality of historical normal temperature change stability parameters of the lithium battery energy storage box in historical time. The first abnormal scaling factor comprises a ratio between the data volume of the abnormal temperature change stability parameters of the plurality of samples and the data volume of the temperature change stability parameters of the plurality of samples. The multi-layer first detection node comprises a plurality of sample normal temperature change stability parameters in a plurality of sample temperature change stability parameters. And the first detection nodes at each layer divide the input temperature change stability parameters into two categories and input the divided parameters into the first detection nodes at the upper layer. The single data divided and output by the first output node and the first detection nodes below are abnormal data, the difference between the single data and other normal data is large, isolated data points are formed, and the single data are easily divided by less detection nodes and divided into single data. The temperature change abnormality detection unit comprises a plurality of layers of first detection nodes, wherein the first output nodes are arranged in the plurality of layers of first detection nodes. The method achieves the technical effects of constructing a reliable temperature change abnormality detection unit based on the thought of an isolated forest algorithm, thereby improving the reliability of abnormality judgment of temperature change stability parameters and improving the accuracy of the charge quality detection of the lithium battery energy storage box.
Step S420: the method comprises the steps of adopting a charging highest temperature parameter in the history time of a lithium battery energy storage box to construct a highest temperature abnormality detection unit, wherein the highest temperature abnormality detection unit comprises a plurality of layers of second detection nodes, and the plurality of layers of second detection nodes comprise second output nodes;
step S430: obtaining the abnormality detection model based on the temperature change abnormality detection unit and the highest temperature abnormality detection unit;
step S440: respectively inputting the temperature change stability parameter and the maximum temperature parameter into the temperature change abnormality detection unit and the maximum temperature abnormality detection unit in combination with the temperature change stability parameter and the maximum temperature parameter within the historical time;
step S450: judging whether the temperature change stability parameter is output as single data by the first output node and a first detection node below the first output node, and obtaining the first judgment result;
step S460: and judging whether the highest temperature parameter is output as single data by the second output node and the second detection nodes below the highest temperature parameter, and obtaining the second judging result.
Specifically, based on historical time, historical data query of the highest temperature parameters of charging is conducted on the lithium battery energy storage box, and a plurality of sample highest temperature parameters are obtained. The plurality of sample maximum temperature parameters comprise a plurality of historical normal maximum temperature parameters and a plurality of historical abnormal maximum temperature parameters. Also, based on the idea of an isolated forest algorithm, a maximum temperature abnormality detection unit is constructed using a plurality of sample maximum temperature parameters. The construction process of the highest temperature abnormality detection unit is the same as that of the temperature change abnormality detection unit, and for brevity of description, description thereof will not be repeated here. The highest temperature abnormality detection unit comprises a plurality of layers of second detection nodes, and the second output nodes are arranged in the plurality of layers of second detection nodes. The multi-layer second detection node comprises a plurality of historical normal maximum temperature parameters which are random in a plurality of sample maximum temperature parameters. And the second detection nodes of each layer divide the input highest temperature parameter into two categories and input the highest temperature parameter into the second detection nodes of the upper layer. The second output node and the following second detection nodes divide the single data output as abnormal data.
Further, an abnormality detection model is obtained based on the temperature change abnormality detection unit and the highest temperature abnormality detection unit. The temperature change stability parameter and the temperature change stability parameter in the history time are used as input information and are input into a temperature change abnormality detection unit, and the temperature change abnormality detection unit obtains a first judgment result by judging whether the temperature change stability parameter is output as single data by a first output node and a first detection node below. The maximum temperature parameter and the maximum temperature parameter in the history time are input as input information to the maximum temperature abnormality detection unit, and the maximum temperature abnormality detection unit obtains a second determination result by determining whether the maximum temperature parameter is output as single data by the second output node and the second detection nodes below.
Wherein the abnormality detection model includes a temperature change abnormality detection unit and a highest temperature abnormality detection unit. The first judgment result comprises whether the temperature change stability parameter is/is output as single data by the first output node and the first detection nodes below. The second judging result comprises whether the highest temperature parameter is output as single data by the second output node or not by the second detection nodes below the highest temperature parameter. The method and the device achieve the technical effects that whether the temperature change stability parameter and the highest temperature parameter are abnormal or not is judged through the abnormality detection model, and an accurate first judgment result and an accurate second judgment result are obtained, so that the comprehensiveness of detecting the charging quality of the lithium battery energy storage box is improved.
Step S500: when the first judging result and/or the second judging result are yes, calculating and obtaining a first abnormality degree parameter and/or a second abnormality degree parameter of the current temperature change stability parameter and/or the highest temperature parameter based on charging temperature data in historical time;
further, step S500 of the present application further includes:
step S510: according to the charging temperature change stability parameter and the highest temperature parameter in the history time of the lithium battery energy storage box, calculating to obtain a history average charging temperature change stability parameter and a history average highest temperature parameter;
step S520: when the first judging result and/or the second judging result are yes, calculating a first deviation degree parameter and/or a second deviation degree parameter of the temperature change stability parameter and/or the highest temperature parameter and the historical average charging temperature change stability parameter and/or the historical average highest temperature parameter;
step S530: and taking the first deviation degree parameter and/or the second deviation degree parameter as the first abnormality degree parameter and/or the second abnormality degree parameter.
Specifically, based on historical time, data query is performed on the lithium battery energy storage box, and a plurality of historical charging temperature change stability parameters and a plurality of historical highest temperature data are obtained. And respectively carrying out average calculation on the plurality of historical charging temperature change stability parameters and the plurality of historical highest temperature data to obtain historical average charging temperature change stability parameters and historical average highest temperature parameters. Further, when the first judgment result and/or the second judgment result are yes, calculating a first deviation degree parameter and/or a second deviation degree parameter between the temperature change stability parameter and/or the highest temperature parameter and the historical average charging temperature change stability parameter and/or the historical average highest temperature parameter, and outputting the first deviation degree parameter and/or the second deviation degree parameter as the first abnormality degree parameter and/or the second abnormality degree parameter. The historical average charging temperature change stability parameters comprise average values corresponding to a plurality of historical charging temperature change stability parameters. The historical average highest temperature parameter comprises an average value corresponding to a plurality of historical highest temperature data. The first abnormality degree parameter includes a first deviation degree parameter. The second abnormality degree parameter includes a second deviation degree parameter.
In an exemplary embodiment, when the first deviation degree parameter and/or the second deviation degree parameter are obtained, a difference value is calculated between the temperature variation stability parameter and the historical average charging temperature variation stability parameter, a temperature variation stability standard difference value is obtained, and a ratio between the temperature variation stability standard difference value and the historical average charging temperature variation stability parameter is output as the first deviation degree parameter. And similarly, carrying out difference value calculation on the maximum temperature parameter and the historical average maximum temperature parameter to obtain a maximum temperature standard difference value, and outputting a second deviation degree parameter by the ratio of the maximum temperature standard difference value to the historical average maximum temperature parameter. The method and the device achieve the technical effects of adaptively calculating the temperature change stability parameter and/or the highest temperature parameter according to the first judging result and the second judging result to obtain the first abnormal degree parameter and/or the second abnormal degree parameter, thereby improving the reliability and the adaptation degree of maintaining the lithium battery energy storage box.
Step S600: inputting the first abnormality degree parameter and/or the second abnormality degree parameter into a maintenance model of the lithium battery energy storage box to obtain a maintenance scheme, and maintaining the lithium battery energy storage box by adopting the maintenance scheme.
Further, step S600 of the present application further includes:
step S610: acquiring a plurality of first abnormal degree parameters of samples, a plurality of second abnormal degree parameters of samples and a plurality of sample maintenance schemes based on operation data in the history time of the lithium battery energy storage box;
step S620: dividing according to the first abnormality degree parameters of the plurality of samples and the second abnormality degree parameters of the plurality of samples to obtain a plurality of first abnormality degree sections and a plurality of second abnormality degree sections;
step S630: constructing a first index attribute and a plurality of first index values according to the plurality of first abnormality degree intervals;
step S640: constructing a second index attribute and a plurality of second index values according to the plurality of second abnormality degree intervals;
step S650: constructing a plurality of data elements according to the plurality of sample maintenance schemes;
step S660: constructing a first maintenance unit, a second maintenance unit and a third maintenance unit based on the first index attribute, the plurality of first index values, the second index attribute, the plurality of second index values and the plurality of data elements, and obtaining a maintenance model of the lithium battery energy storage box;
step S670: and inputting the first abnormality degree parameter and/or the second abnormality degree parameter into the first maintenance unit, the second maintenance unit or the third maintenance unit to obtain the maintenance scheme.
Specifically, based on historical time, operation record data query and calculation are performed on the lithium battery energy storage box, and a plurality of sample first abnormality degree parameters, a plurality of sample second abnormality degree parameters and a plurality of sample maintenance schemes are obtained. Further, the first abnormality degree parameters of the plurality of samples are clustered, and the first abnormality degree parameters of the samples with similar sizes are added to the same first abnormality degree section to obtain a plurality of first abnormality degree sections. And similarly, clustering the second abnormality degree parameters of the plurality of samples, and adding the second abnormality degree parameters of the samples with similar sizes into the same second abnormality degree interval to obtain a plurality of second abnormality degree intervals. Wherein the plurality of sample first abnormality degree parameters includes a plurality of historical first abnormality degree parameters. The plurality of sample second abnormality degree parameters includes a plurality of historical second abnormality degree parameters. The plurality of sample maintenance schemes comprise a plurality of historical first maintenance schemes corresponding to a plurality of sample first abnormality degree parameters and a plurality of historical second maintenance schemes corresponding to a plurality of sample second abnormality degree parameters. Illustratively, the plurality of historical first maintenance scenarios include temperature change failure analysis, operational environment optimization, shutdown overhaul, etc. of the lithium battery energy storage tank. The plurality of historical second maintenance schemes comprise high-temperature early warning, high Wen Yinhuan investigation, scrapping treatment and the like for the lithium battery energy storage box. Each first abnormality degree interval comprises a plurality of sample first abnormality degree parameters with similar sizes. Each second abnormality degree interval comprises a plurality of sample second abnormality degree parameters with similar sizes.
Further, the first abnormality degree section is set as a first index attribute, the plurality of first abnormality degree sections are set as a plurality of first index values, and the plurality of history first maintenance schemes corresponding to the plurality of first abnormality degree sections are set as a plurality of first data elements. The first maintenance unit is obtained based on the first index attribute, the plurality of first index values, and the plurality of first data elements. Similarly, the second abnormality degree section is set as a second index attribute, the plurality of second abnormality degree sections are set as a plurality of second index values, and the plurality of history second maintenance schemes corresponding to the plurality of second abnormality degree sections are set as a plurality of second data elements. And obtaining a second maintenance unit based on the second index attribute, the plurality of second index values and the plurality of second data elements. Further, a third dimension unit is obtained based on the first index attribute, the plurality of first index values, the plurality of first data elements, the second index attribute, the plurality of second index values, and the plurality of second data elements. And obtaining a maintenance model of the lithium battery energy storage box based on the first maintenance unit, the second maintenance unit and the third maintenance unit.
And when the first judgment result is yes and the second judgment result is no, obtaining a first abnormality degree parameter. And taking the first abnormality degree parameter as input information, inputting the first abnormality degree parameter into a first maintenance unit of a lithium battery energy storage box maintenance model, obtaining a maintenance scheme, and maintaining the lithium battery energy storage box according to the maintenance scheme.
And if the first judgment result is negative, obtaining a second abnormal degree parameter, taking the second abnormal degree parameter as input information, inputting the second abnormal degree parameter into a second maintenance unit of the lithium battery energy storage box maintenance model, obtaining a maintenance scheme, and maintaining the lithium battery energy storage box according to the maintenance scheme.
And if the first judgment result is yes, acquiring a first abnormal degree parameter and a second abnormal degree parameter, taking the first abnormal degree parameter and the second abnormal degree parameter as input information, inputting the input information into a third maintenance unit of the lithium battery energy storage box maintenance model, acquiring a maintenance scheme, and maintaining the lithium battery energy storage box according to the maintenance scheme.
The first maintenance unit comprises a first index attribute, a plurality of first index values and a plurality of first data elements. The second maintenance unit comprises a second index attribute, a plurality of second index values and a plurality of second data elements. The third dimension protection unit comprises a first index attribute, a plurality of first index values, a plurality of first data elements, a second index attribute, a plurality of second index values and a plurality of second data elements. The plurality of data elements includes a plurality of first data elements, a plurality of second data elements. The lithium battery energy storage box maintenance model comprises a first maintenance unit, a second maintenance unit and a third maintenance unit.
The technical effects of carrying out adaptive analysis on the first abnormal degree parameter and/or the second abnormal degree parameter through the lithium battery energy storage box maintenance model, obtaining a reasonable and reliable maintenance scheme and improving the maintenance quality of the lithium battery energy storage box are achieved.
In summary, the quality detection and maintenance method for the lithium battery energy storage box provided by the application has the following technical effects:
1. charging the lithium battery energy storage box by a preset charging method; in the charging process, acquiring the temperature of an energy storage box of the lithium battery according to a plurality of time windows to obtain a temperature parameter set; calculating a temperature parameter set to obtain a temperature change stability parameter and a highest temperature parameter; inputting the temperature change stability parameter and the maximum temperature parameter into an abnormality detection model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal, and obtaining a first judgment result and a second judgment result; when the first judging result and/or the second judging result are yes, calculating and obtaining a first abnormality degree parameter and/or a second abnormality degree parameter based on the charging temperature data in the history time; inputting the first abnormality degree parameter and/or the second abnormality degree parameter into a maintenance model of the lithium battery energy storage box, obtaining a maintenance scheme, and maintaining the lithium battery energy storage box according to the maintenance scheme. The accuracy of the charge quality detection of the lithium battery energy storage box is improved, the maintenance quality of the lithium battery energy storage box is improved, and the technical effect of powerful guarantee is provided for the normal operation of the lithium battery energy storage box.
2. By calculating and analyzing the temperature parameter set, accurate temperature change stability parameters and maximum temperature parameters are obtained, so that the accuracy and reliability of detecting the abnormal charging of the lithium battery energy storage box are improved.
3. And according to the first judging result and the second judging result, adaptively calculating the temperature change stability parameter and/or the highest temperature parameter to obtain a first abnormality degree parameter and/or a second abnormality degree parameter, thereby improving the reliability and the adaptation degree of maintaining the lithium battery energy storage box.
Example 2.
Based on the same inventive concept as the quality detection and maintenance method for a lithium battery energy storage tank in the foregoing embodiment, the present invention also provides a quality detection and maintenance system for a lithium battery energy storage tank, referring to fig. 3, the system includes:
the charging module 11 is used for charging the lithium battery energy storage box by adopting a preset charging method;
the temperature parameter acquisition module 12 is used for acquiring the temperature of the lithium battery energy storage box in a plurality of time windows in the charging process to obtain a temperature parameter set;
a temperature parameter calculation module 13, wherein the temperature parameter calculation module 13 is configured to calculate and obtain a temperature variation stability parameter and a maximum temperature parameter based on the temperature parameter set;
The abnormality judging module 14 is configured to input the temperature change stability parameter and the maximum temperature parameter into a temperature change abnormality detecting unit and a maximum temperature abnormality detecting unit in an abnormality detecting model, and judge whether the temperature change stability parameter and the maximum temperature parameter are abnormal, so as to obtain a first judging result and a second judging result;
an abnormal parameter obtaining module 15, where the abnormal parameter obtaining module 15 is configured to calculate and obtain, when the first determination result and/or the second determination result are yes, a first abnormal degree parameter and/or a second abnormal degree parameter of the current temperature variation stability parameter and/or the current maximum temperature parameter based on charging temperature data in a history time;
the maintenance module 16 is configured to input the first abnormality degree parameter and/or the second abnormality degree parameter into a maintenance model of the lithium battery energy storage box, obtain a maintenance scheme, and use the maintenance scheme to maintain the lithium battery energy storage box.
Further, the system further comprises:
the time temperature parameter obtaining module is used for collecting the temperature of the lithium battery energy storage box at a plurality of positions of the lithium battery energy storage box in a plurality of time windows to obtain a plurality of time temperature parameter sets;
The average temperature parameter obtaining module is used for calculating and obtaining average temperature parameters of the plurality of time windows based on the plurality of time temperature parameter sets;
the temperature parameter set obtaining module is used for obtaining the temperature parameter set based on average temperature parameters of the plurality of time windows.
Further, the system further comprises:
the maximum temperature parameter obtaining module is used for obtaining the maximum value in the temperature parameter set and obtaining the maximum temperature parameter;
the temperature change parameter calculation module is used for calculating and obtaining a plurality of temperature change parameters according to the temperature parameter set and the plurality of time windows;
the temperature change stability parameter determining module is used for calculating and obtaining a temperature change parameter variance as the temperature change stability parameter according to the plurality of temperature change parameters.
Further, the system further comprises:
the first execution module is used for constructing the temperature change abnormality detection unit by adopting a charging temperature change stability parameter in the history time of the lithium battery energy storage box, wherein the temperature change abnormality detection unit comprises a plurality of layers of first detection nodes, and the plurality of layers of first detection nodes comprise first output nodes;
The second execution module is used for constructing the highest temperature abnormality detection unit by adopting a charging highest temperature parameter in the history time of the lithium battery energy storage box, wherein the highest temperature abnormality detection unit comprises a plurality of layers of second detection nodes, and the plurality of layers of second detection nodes comprise second output nodes;
the third execution module is used for obtaining the abnormality detection model based on the temperature change abnormality detection unit and the highest temperature abnormality detection unit;
the fourth execution module is used for respectively inputting the temperature change stability parameter and the maximum temperature parameter in the combination history time into the temperature change abnormality detection unit and the maximum temperature abnormality detection unit;
the first judgment result obtaining module is used for judging whether the temperature change stability parameter is output as single data by the first output node and the first detection nodes below to obtain the first judgment result;
and the second judgment result obtaining module is used for judging whether the highest temperature parameter is output as single data by the second output node and the second detection nodes below the highest temperature parameter to obtain the second judgment result.
Further, the system further comprises:
the sample parameter obtaining module is used for obtaining a plurality of sample temperature change stability parameters based on the charging temperature change stability parameters in the history time of the lithium battery energy storage box, wherein the plurality of sample temperature change stability parameters comprise a plurality of sample abnormal temperature change stability parameters;
the first abnormal proportional coefficient obtaining module is used for calculating and obtaining a first abnormal proportional coefficient based on the plurality of sample temperature change stability parameters and the plurality of sample abnormal temperature change stability parameters;
the fifth execution module is used for randomly selecting sample temperature change stability parameters from the plurality of sample temperature change stability parameters, constructing the multi-layer first detection nodes, dividing the input temperature change stability parameters into two categories by each layer of first detection nodes, and inputting the divided temperature change stability parameters into an upper layer of first detection nodes;
and the sixth execution module is used for setting and obtaining the first output node in the multi-layer first detection node according to the first abnormal proportion coefficient to obtain the temperature change abnormal detection unit, wherein the first output node and the first detection nodes below are used for dividing and outputting single data as abnormal data.
Further, the system further comprises:
the historical parameter calculation module is used for calculating and obtaining a historical average charging temperature change stability parameter and a historical average maximum temperature parameter according to the charging temperature change stability parameter and the maximum temperature parameter in the historical time of the lithium battery energy storage box;
the seventh execution module is used for calculating a first deviation degree parameter and/or a second deviation degree parameter of the temperature change stability parameter and/or the highest temperature parameter and the historical average charging temperature change stability parameter and/or the historical average highest temperature parameter when the first judgment result and/or the second judgment result are yes;
and the eighth execution module is used for taking the first deviation degree parameter and/or the second deviation degree parameter as the first abnormality degree parameter and/or the second abnormality degree parameter.
Further, the system further comprises:
the ninth execution module is used for acquiring a plurality of first abnormality degree parameters of samples, a plurality of second abnormality degree parameters of samples and a plurality of sample maintenance schemes based on operation data in the history time of the lithium battery energy storage box;
The abnormal degree interval obtaining module is used for dividing the first abnormal degree parameters of the samples and the second abnormal degree parameters of the samples to obtain a plurality of first abnormal degree intervals and a plurality of second abnormal degree intervals;
the first index construction module is used for constructing a first index attribute and a plurality of first index values according to the plurality of first abnormal degree intervals;
the second index construction module is used for constructing a second index attribute and a plurality of second index values according to the plurality of second abnormal degree intervals;
the data element construction module is used for constructing a plurality of data elements according to the plurality of sample maintenance schemes;
the tenth execution module is used for constructing a first maintenance unit, a second maintenance unit and a third maintenance unit based on the first index attribute, the plurality of first index values, the second index attribute, the plurality of second index values and the plurality of data elements, and obtaining the maintenance model of the lithium battery energy storage box;
the maintenance scheme obtaining module is used for inputting the first abnormality degree parameter and/or the second abnormality degree parameter into the first maintenance unit, the second maintenance unit or the third maintenance unit to obtain the maintenance scheme.
The quality detection and maintenance system for the lithium battery energy storage box provided by the embodiment of the invention can execute the quality detection and maintenance method for the lithium battery energy storage box provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example 3.
Fig. 4 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 4, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 4, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to a method for quality detection and maintenance of a lithium battery energy storage box in an embodiment of the present invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e. implements a quality detection and maintenance method for lithium battery energy storage boxes as described above.
The application provides a quality detection and maintenance method for a lithium battery energy storage box, wherein the method is applied to a quality detection and maintenance system for the lithium battery energy storage box, and the method comprises the following steps: charging the lithium battery energy storage box by a preset charging method; in the charging process, acquiring the temperature of an energy storage box of the lithium battery according to a plurality of time windows to obtain a temperature parameter set; calculating a temperature parameter set to obtain a temperature change stability parameter and a highest temperature parameter; inputting the temperature change stability parameter and the maximum temperature parameter into an abnormality detection model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal, and obtaining a first judgment result and a second judgment result; when the first judging result and/or the second judging result are yes, calculating and obtaining a first abnormality degree parameter and/or a second abnormality degree parameter based on the charging temperature data in the history time; inputting the first abnormality degree parameter and/or the second abnormality degree parameter into a maintenance model of the lithium battery energy storage box, obtaining a maintenance scheme, and maintaining the lithium battery energy storage box according to the maintenance scheme. The technical problems that in the prior art, the charging quality detection accuracy of the lithium battery energy storage box is insufficient, and the maintenance effect of the lithium battery energy storage box is poor are solved. The accuracy of the charge quality detection of the lithium battery energy storage box is improved, the maintenance quality of the lithium battery energy storage box is improved, and the technical effect of powerful guarantee is provided for the normal operation of the lithium battery energy storage box.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. A quality inspection and maintenance method for a lithium battery energy storage box, the method comprising:
charging the lithium battery energy storage box by adopting a preset charging method;
in the charging process, collecting the temperature of the lithium battery energy storage box in a plurality of time windows to obtain a temperature parameter set;
calculating to obtain a temperature change stability parameter and a highest temperature parameter based on the temperature parameter set;
inputting the temperature change stability parameter and the maximum temperature parameter into a temperature change abnormality detection unit and a maximum temperature abnormality detection unit in an abnormality detection model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal or not, and obtaining a first judgment result and a second judgment result;
When the first judging result and/or the second judging result are yes, calculating and obtaining a first abnormality degree parameter and/or a second abnormality degree parameter of the current temperature change stability parameter and/or the highest temperature parameter based on charging temperature data in historical time;
inputting the first abnormal degree parameter and/or the second abnormal degree parameter into a maintenance model of the lithium battery energy storage box to obtain a maintenance scheme, and maintaining the lithium battery energy storage box by adopting the maintenance scheme;
when the first judgment result and/or the second judgment result are yes, calculating and obtaining a first abnormality degree parameter and/or a second abnormality degree parameter of the current temperature change stability parameter and/or the highest temperature parameter based on charging temperature data in historical time, wherein the method comprises the following steps:
according to the charging temperature change stability parameter and the highest temperature parameter in the history time of the lithium battery energy storage box, calculating to obtain a history average charging temperature change stability parameter and a history average highest temperature parameter;
when the first judging result and/or the second judging result are yes, calculating a first deviation degree parameter and/or a second deviation degree parameter of the temperature change stability parameter and/or the highest temperature parameter and the historical average charging temperature change stability parameter and/or the historical average highest temperature parameter;
The first deviation degree parameter and/or the second deviation degree parameter are/is used as the first abnormality degree parameter and/or the second abnormality degree parameter;
the first abnormality degree parameter and/or the second abnormality degree parameter are/is input into a maintenance model of the lithium battery energy storage box, and a maintenance scheme is obtained, including:
acquiring a plurality of first abnormal degree parameters of samples, a plurality of second abnormal degree parameters of samples and a plurality of sample maintenance schemes based on operation data in the history time of the lithium battery energy storage box;
dividing according to the first abnormality degree parameters of the plurality of samples and the second abnormality degree parameters of the plurality of samples to obtain a plurality of first abnormality degree sections and a plurality of second abnormality degree sections;
constructing a first index attribute and a plurality of first index values according to the plurality of first abnormality degree intervals;
constructing a second index attribute and a plurality of second index values according to the plurality of second abnormality degree intervals;
constructing a plurality of data elements according to the plurality of sample maintenance schemes;
constructing a first maintenance unit, a second maintenance unit and a third maintenance unit based on the first index attribute, the plurality of first index values, the second index attribute, the plurality of second index values and the plurality of data elements, and obtaining a maintenance model of the lithium battery energy storage box;
And inputting the first abnormality degree parameter and/or the second abnormality degree parameter into the first maintenance unit, the second maintenance unit or the third maintenance unit to obtain the maintenance scheme.
2. The method of claim 1, wherein during charging, the temperature of the lithium battery energy storage tank is collected over a plurality of time windows to obtain a set of temperature parameters, comprising:
collecting the temperature of the lithium battery energy storage box at a plurality of positions of the lithium battery energy storage box in a plurality of time windows to obtain a plurality of time temperature parameter sets;
calculating and obtaining average temperature parameters of the time windows based on the time temperature parameter sets;
the set of temperature parameters is obtained based on the average temperature parameter of the plurality of time windows.
3. The method of claim 1, wherein calculating a temperature change stability parameter and a maximum temperature parameter based on the set of temperature parameters comprises:
obtaining the maximum value in the temperature parameter set and obtaining the maximum temperature parameter;
calculating to obtain a plurality of temperature change parameters according to the temperature parameter set and the plurality of time windows;
And calculating and obtaining a temperature variation parameter variance as the temperature variation stability parameter according to the temperature variation parameters.
4. The method according to claim 1, wherein inputting the temperature change stability parameter and the maximum temperature parameter into a temperature change abnormality detection unit and a maximum temperature abnormality detection unit within an abnormality detection model, determining whether abnormality occurs in the temperature change stability parameter and the maximum temperature parameter, comprises:
the method comprises the steps of adopting a charging temperature change stability parameter in the history time of a lithium battery energy storage box to construct a temperature change abnormality detection unit, wherein the temperature change abnormality detection unit comprises a plurality of layers of first detection nodes, and the plurality of layers of first detection nodes comprise first output nodes;
the method comprises the steps of adopting a charging highest temperature parameter in the history time of a lithium battery energy storage box to construct a highest temperature abnormality detection unit, wherein the highest temperature abnormality detection unit comprises a plurality of layers of second detection nodes, and the plurality of layers of second detection nodes comprise second output nodes;
obtaining the abnormality detection model based on the temperature change abnormality detection unit and the highest temperature abnormality detection unit;
Respectively inputting the temperature change stability parameter and the maximum temperature parameter into the temperature change abnormality detection unit and the maximum temperature abnormality detection unit in combination with the temperature change stability parameter and the maximum temperature parameter within the historical time;
judging whether the temperature change stability parameter is output as single data by the first output node and a first detection node below the first output node, and obtaining the first judgment result;
and judging whether the highest temperature parameter is output as single data by the second output node and the second detection nodes below the highest temperature parameter, and obtaining the second judging result.
5. The method of claim 4, wherein constructing the temperature variation anomaly detection unit using the charge temperature variation stability parameter over the lithium battery energy storage tank history time comprises:
obtaining a plurality of sample temperature change stability parameters based on the charging temperature change stability parameters in the history time of the lithium battery energy storage box, wherein the plurality of sample temperature change stability parameters comprise a plurality of sample abnormal temperature change stability parameters;
calculating to obtain a first abnormal proportionality coefficient based on the plurality of sample temperature change stability parameters and a plurality of sample abnormal temperature change stability parameters;
Randomly selecting sample temperature change stability parameters from the plurality of sample temperature change stability parameters, constructing the multi-layer first detection nodes, classifying the input temperature change stability parameters by each layer of first detection nodes, and inputting the classified temperature change stability parameters into an upper layer of first detection nodes;
and setting and obtaining the first output nodes in the multi-layer first detection nodes according to the first abnormal proportion coefficient, and obtaining the temperature change abnormal detection unit, wherein the first output nodes and the first detection nodes below are used for dividing and outputting single data as abnormal data.
6. A quality inspection and maintenance system for a lithium battery energy storage box, the system comprising:
the charging module is used for charging the lithium battery energy storage box by adopting a preset charging method;
the temperature parameter acquisition module is used for acquiring the temperature of the lithium battery energy storage box in a plurality of time windows in the charging process to obtain a temperature parameter set;
the temperature parameter calculation module is used for calculating and obtaining a temperature change stability parameter and a highest temperature parameter based on the temperature parameter set;
The abnormality judging module is used for inputting the temperature change stability parameter and the maximum temperature parameter into a temperature change abnormality detecting unit and a maximum temperature abnormality detecting unit in an abnormality detecting model, judging whether the temperature change stability parameter and the maximum temperature parameter are abnormal or not, and obtaining a first judging result and a second judging result;
the abnormal parameter obtaining module is used for calculating and obtaining a first abnormal degree parameter and/or a second abnormal degree parameter of the current temperature change stability parameter and/or the highest temperature parameter based on charging temperature data in historical time when the first judging result and/or the second judging result are yes;
the maintenance module is used for inputting the first abnormal degree parameter and/or the second abnormal degree parameter into a maintenance model of the lithium battery energy storage box to obtain a maintenance scheme, and the maintenance scheme is adopted to maintain the lithium battery energy storage box;
the historical parameter calculation module is used for calculating and obtaining a historical average charging temperature change stability parameter and a historical average maximum temperature parameter according to the charging temperature change stability parameter and the maximum temperature parameter in the historical time of the lithium battery energy storage box;
The seventh execution module is used for calculating a first deviation degree parameter and/or a second deviation degree parameter of the temperature change stability parameter and/or the highest temperature parameter and the historical average charging temperature change stability parameter and/or the historical average highest temperature parameter when the first judgment result and/or the second judgment result are yes;
an eighth execution module, configured to use the first deviation degree parameter and/or the second deviation degree parameter as the first abnormality degree parameter and/or the second abnormality degree parameter;
the ninth execution module is used for acquiring a plurality of first abnormality degree parameters of samples, a plurality of second abnormality degree parameters of samples and a plurality of sample maintenance schemes based on operation data in the history time of the lithium battery energy storage box;
the abnormal degree interval obtaining module is used for dividing the first abnormal degree parameters of the samples and the second abnormal degree parameters of the samples to obtain a plurality of first abnormal degree intervals and a plurality of second abnormal degree intervals;
the first index construction module is used for constructing a first index attribute and a plurality of first index values according to the plurality of first abnormal degree intervals;
The second index construction module is used for constructing a second index attribute and a plurality of second index values according to the plurality of second abnormal degree intervals;
the data element construction module is used for constructing a plurality of data elements according to the plurality of sample maintenance schemes;
the tenth execution module is used for constructing a first maintenance unit, a second maintenance unit and a third maintenance unit based on the first index attribute, the plurality of first index values, the second index attribute, the plurality of second index values and the plurality of data elements, and obtaining the maintenance model of the lithium battery energy storage box;
the maintenance scheme obtaining module is used for inputting the first abnormality degree parameter and/or the second abnormality degree parameter into the first maintenance unit, the second maintenance unit or the third maintenance unit to obtain the maintenance scheme.
7. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing a quality detection and maintenance method for a lithium battery energy storage tank according to any one of claims 1 to 5 when executing executable instructions stored in said memory.
8. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a quality detection and maintenance method for a lithium battery energy storage tank according to any one of claims 1 to 5.
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