Method for improving safety performance of lithium battery electrolyte by combining graphene technology

Abstract

The invention discloses a lithium battery electrolyte safety performance improving method combined with a graphene technology, and relates to the technical field of electrochemistry, and the method comprises the following steps: calculating a safety risk coefficient of electrolyte operation data; forming a critical value of the safety risk coefficient; when the safety risk coefficient is smaller than the critical value, no processing is carried out; when the safety risk coefficient is greater than a critical value, generating a repair scheme; based on historical data, analysis is carried out to obtain a promotion consumed time distribution condition; decomposing the performance boosting packet into local boosting packets; and the local lifting package is used for improving the performance of the electrolyte of the lithium battery. According to the method, the safety risk coefficient of the electrolyte operation data is calculated, the performance improvement package is decomposed into at least one local improvement package, the updating time point is set for the local improvement package, and the selection of repair and performance improvement is determined, so that the adopted mode is a relatively economical improvement repair mode, and mutual conflicts during parameter updating are avoided.

Description

Technical Field

[0001] The present invention relates to the field of electrochemical technology, and in particular to a method for improving the safety performance of lithium battery electrolyte in combination with graphene technology. Background Art

[0002] In recent years, the environment has been deteriorating, non-renewable resources have been increasingly depleted, and people's environmental awareness has become stronger and stronger, so new energy products have emerged. Among many new energy products, lithium-ion batteries are widely used. Lithium-ion batteries have the advantages of high operating voltage, large specific energy, long cycle life, good safety performance, low self-discharge, and fast charging. They are widely used in portable electrical devices such as mobile phones and laptops. At the same time, as an alternative energy source to petroleum, they will also be widely used in electric vehicles and hybrid vehicles. However, with the rapid development of science and technology, various scientific and technological products have increasingly stringent requirements on the energy density of lithium-ion batteries.

[0003] Electrolyte plays a key role in the normal operation of lithium batteries. However, the existing upgrade mode is usually carried out in an indiscriminate and disorderly manner. On the one hand, the cost control is relatively insufficient. On the other hand, when upgrading, it is easy to cause conflicts between the previous and next parameters due to the order of upgrading. Summary of the invention

[0004] In order to solve the above technical problems, a method for improving the safety performance of lithium battery electrolyte combined with graphene technology is provided. This technical solution solves the problem that the existing improvement mode proposed in the above background technology is usually carried out in an indiscriminate and disorderly manner. On the one hand, the cost control is relatively insufficient. On the other hand, when improving, it is easy to cause conflicts between the previous and subsequent parameter improvements due to the improvement order.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for improving the safety performance of lithium battery electrolyte combined with graphene technology, comprising:

[0007] Obtaining electrolyte operation data of at least one actual operation scenario of a lithium battery;

[0008] Calculate the safety risk factor of electrolyte operation data;

[0009] Calculate the basic existing data to form the critical value of the safety risk factor;

[0010] When the safety risk factor is less than the critical value, no action is taken;

[0011] When the safety risk factor is greater than the critical value, the electrolyte vulnerabilities and unstable factors are obtained based on the electrolyte operation data, and a repair plan is generated based on the vulnerabilities and unstable factors. The parameters of the repair plan are summarized to generate a performance improvement package;

[0012] Based on historical data, the analysis obtains the distribution of time spent on improvement;

[0013] Based on the distribution of the time spent on improvement, the performance improvement package is decomposed into at least one local improvement package, and an update time point is set for the local improvement package;

[0014] The performance of the electrolyte of the lithium battery is improved using a local improvement package. When at least one local improvement package of the performance improvement package has completed the improvement, the performance improvement of the electrolyte of the lithium battery is completed.

[0015] Preferably, the calculation of the safety risk factor of the electrolyte operation data comprises the following steps:

[0016] Obtaining the interval length of the frequency fluctuation range used for current transmission in actual operation of the lithium battery as the fluctuation length;

[0017] Obtain the average output delay time when the lithium battery is running, and obtain the average value of the abnormal current output ratio when the lithium battery is running as the average output error value;

[0018] Obtain the occurrence frequency of lithium battery operation interruption as the interruption frequency, obtain the average duration of lithium battery operation interruption as the interruption time, multiply the interruption frequency by the interruption time to obtain the interruption evaluation value;

[0019] Obtaining the average value of the sudden current increment during the operation of the lithium battery as the abnormal current value, wherein the sudden current increment is the difference between the current actually output by the lithium battery and the current output by the lithium battery during normal operation;

[0020] Using AHP and expert scoring, the weights of fluctuation length, average network delay time, average transmission error value, interruption evaluation value and abnormal current value are formed;

[0021] Use the safety risk formula to calculate the safety risk factor of the electrolyte operation data;

[0022] The security risk formula is as follows:

[0023] A=αB+βC+γD+δE+εF

[0024] Among them, A is the safety risk coefficient, α, β, γ, δ and ε are the weights of the fluctuation length, average network delay time, average transmission error value, interruption assessment value and abnormal current value, respectively, and B, C, D, E and F are the fluctuation length, average network delay time, average transmission error value, interruption assessment value and abnormal current value, respectively.

[0025] Preferably, the calculation based on the existing data to form a critical value of the safety risk factor includes the following steps:

[0026] Based on the historical data, obtaining at least one historical electrolyte operation data, wherein the historical electrolyte operation data is the electrolyte operation data that has occurred;

[0027] Under the condition of historical electrolyte operation data, the set number of abnormal occurrence positions in the actual operation of the lithium battery is obtained as the characteristic value;

[0028] Based on big data, the average cost of repair at the location where the anomaly occurs is obtained and multiplied by the characteristic value to calculate the cost of the location where the anomaly occurs;

[0029] Based on the historical electrolyte operation data, the historical vulnerabilities and historical unstable factors of the actual operation of the lithium battery are obtained to generate a historical improvement plan;

[0030] Obtain the workload of operators required to generate the parameters of historical improvement plans, and estimate the cost of improvement based on market conditions and operator workload;

[0031] The historical electrolyte operation data whose lifting cost is less than the cost of the abnormality occurrence location is used as characteristic electrolyte operation data;

[0032] The safety risk coefficient of the characteristic electrolyte operation data is calculated, and the minimum value of the safety risk coefficient of at least one characteristic electrolyte operation data is used as the critical value of the safety risk coefficient.

[0033] Preferably, the method of obtaining the leakage and instability factors of the electrolyte based on the electrolyte operation data comprises the following steps:

[0034] Based on historical data, an abnormality database is established, wherein the abnormality database includes vulnerabilities and unstable factors that have occurred, and solutions corresponding to the vulnerabilities and unstable factors, wherein the solutions all include graphene technology;

[0035] Forming at least one identification feature for the vulnerabilities and unstable factors in the anomaly database, and pairing the identification feature with the vulnerabilities or unstable factors;

[0036] Performing feature recognition on the electrolyte operation data to obtain at least one suspected feature;

[0037] The loopholes or unstable factors corresponding to the identification features consistent with the suspected features are obtained as the loopholes and unstable factors of the electrolyte.

[0038] Preferably, generating a repair solution based on vulnerabilities and unstable factors includes the following steps:

[0039] The solutions corresponding to the vulnerabilities and unstable factors of the electrolyte are called in the abnormal database as repair solutions.

[0040] Preferably, the step of analyzing the distribution of the time spent on improvement based on historical data comprises the following steps:

[0041] The time consumed for using the local enhancement package to enhance the performance of the lithium battery electrolyte is obtained as the enhancement time consumed;

[0042] At least one local lifting package is paired and summarized with the lifting consumption time to obtain the lifting consumption time distribution.

[0043] Preferably, decomposing the performance improvement package into at least one local improvement package and setting an update time point for the local improvement package comprises the following steps:

[0044] At least one operating function during the operation of the electrolyte, where the operating function is the function achieved during normal operation at the location where the abnormality occurred;

[0045] Obtaining the battery operating parameters before the electrolyte is improved, and decomposing the battery operating parameters into at least one local operating parameter based on the operating function, each of the local operating parameters realizing an operating function;

[0046] The performance improvement package is decomposed into at least one local improvement package, and the parameters generated by each local improvement package realize an operation function;

[0047] Pairing a local enhancement package that realizes the same operation function with a local operation parameter, and during enhancement, replacing the corresponding local operation parameter at the abnormality occurrence location according to the parameter in the local enhancement package, and the order of enhancement is according to the order of processing at least one abnormality occurrence location in the historical data;

[0048] Taking at least one of the local enhancement packages as a target local enhancement package;

[0049] The local lifting package that is lifted before the target local lifting package in sequence is used as a characteristic local lifting package;

[0050] Accumulate the time consumed by at least one characteristic local improvement package to obtain the update time point of the target local improvement package;

[0051] When the target local improvement package traverses at least one local improvement package, the setting of all update time points is completed.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] By calculating the safety risk coefficient of the electrolyte operation data, decomposing the performance improvement package into at least one local improvement package, and setting an update time point for the local improvement package, the choice of repair and performance improvement can be determined for the current vulnerabilities in the electrolyte according to the actual performance conditions, so that the adopted method is a more economical improvement and repair mode. At the same time, when performing the improvement, a gradual improvement method is used, and the improvement is performed in the order determined in the historical data to avoid errors in the improvement order, which causes the improved parameters to be affected by the later improved parameters, thereby avoiding conflicts when the parameters are updated. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a process for improving safety performance of lithium battery electrolyte in combination with graphene technology of the present invention;

[0055] Figure 2 A schematic diagram of a flow chart for calculating the safety risk factor of electrolyte operation data according to the present invention;

[0056] Figure 3 A schematic diagram of a process for calculating the critical value of the safety risk factor based on the basic existing data of the present invention;

[0057] Figure 4 A schematic diagram of a process for obtaining electrolyte leaks and unstable factors based on electrolyte operation data of the present invention;

[0058] Figure 5 A schematic diagram of a flow chart of analyzing and obtaining the distribution of time consumption for improvement based on historical data in the present invention;

[0059] Figure 6 The present invention is a flow chart of decomposing a performance improvement package into at least one local improvement package and setting an update time point for the local improvement package. DETAILED DESCRIPTION

[0060] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0061] Reference Figure 1 As shown, a method for improving the safety performance of lithium battery electrolyte combined with graphene technology includes:

[0062] Obtaining electrolyte operation data of at least one actual operation scenario of a lithium battery;

[0063] Calculate the safety risk factor of electrolyte operation data;

[0064] Calculate the basic existing data to form the critical value of the safety risk factor;

[0065] When the safety risk factor is less than the critical value, no action is taken;

[0066] When the safety risk factor is greater than the critical value, the electrolyte vulnerabilities and unstable factors are obtained based on the electrolyte operation data, and a repair plan is generated based on the vulnerabilities and unstable factors. The parameters of the repair plan are summarized to generate a performance improvement package;

[0067] Based on historical data, the analysis obtains the distribution of time spent on improvement;

[0068] Based on the distribution of the time spent on improvement, the performance improvement package is decomposed into at least one local improvement package, and an update time point is set for the local improvement package;

[0069] The performance of the electrolyte of the lithium battery is improved using a local improvement package. When at least one local improvement package of the performance improvement package has completed the improvement, the performance improvement of the electrolyte of the lithium battery is completed.

[0070] In this solution, in order for the electrolyte to focus on dealing with risks during use, it is determined based on the acquired data whether the parameters used by the electrolyte can ensure the normal operation of the lithium battery. According to the degree of abnormal operation, the improvement method is selected, and the critical value is used as the dividing point for judgment. Among them, the critical value is the basis for judging the selection of the update method. When the safety risk factor does not exceed the critical value, it is more cost-effective to only use maintenance in case of failure. When the safety risk factor is greater than the critical value, the improvement cost is less than the cost of the abnormal location. Therefore, the repair is completed by parameter improvement. At the same time, since various substances need to be added when adjusting the electrolyte parameters, when there is no requirement on the order, the subsequently added substances may interfere with the adjusted parameters. Therefore, a reasonable order setting is required to ensure that the interfering substances are added to the electrolyte first and react with the electrolyte to change the performance of the electrolyte. At the same time, since the substance will not react with subsequent substances after the change, parameter conflicts can be avoided.

[0071] Reference Figure 2 As shown, calculating the safety risk factor of electrolyte operation data includes the following steps:

[0072] Obtaining the interval length of the frequency fluctuation range used for current transmission in actual operation of the lithium battery as the fluctuation length;

[0073] Obtain the average output delay time when the lithium battery is running, and obtain the average value of the abnormal current output ratio when the lithium battery is running as the average output error value;

[0074] Obtain the occurrence frequency of lithium battery operation interruption as the interruption frequency, obtain the average duration of lithium battery operation interruption as the interruption time, multiply the interruption frequency by the interruption time to obtain the interruption evaluation value;

[0075] Obtaining the average value of the sudden current increment during the operation of the lithium battery as the abnormal current value, wherein the sudden current increment is the difference between the current actually output by the lithium battery and the current output by the lithium battery during normal operation;

[0076] Using AHP and expert scoring, the weights of fluctuation length, average network delay time, average transmission error value, interruption evaluation value and abnormal current value are formed;

[0077] Use the safety risk formula to calculate the safety risk factor of the electrolyte operation data;

[0078] The security risk formula is as follows:

[0079] A=αB+βC+γD+δE+εF

[0080] Among them, A is the safety risk coefficient, α, β, γ, δ and ε are the weights of the fluctuation length, average network delay time, average transmission error value, interruption assessment value and abnormal current value, respectively, and B, C, D, E and F are the fluctuation length, average network delay time, average transmission error value, interruption assessment value and abnormal current value, respectively.

[0081] The safety risk factor is used to evaluate the actual operation of the lithium battery. When the parameters used for the electrolyte are not reasonable, the operating quality will decline, which will be reflected in the safety risk factor. When the safety risk factor is large, it means that the abnormality is large, which will cause the lithium battery to operate abnormally and require parameter improvement. When the safety risk factor is small, it means that the abnormality is small and repair is required.

[0082] Reference Figure 3 As shown, the basic existing data calculation to form the critical value of the safety risk factor includes the following steps:

[0083] Based on the historical data, obtaining at least one historical electrolyte operation data, wherein the historical electrolyte operation data is the electrolyte operation data that has occurred;

[0084] Under the condition of historical electrolyte operation data, the set number of abnormal occurrence positions in the actual operation of the lithium battery is obtained as the characteristic value;

[0085] Based on big data, the average cost of repair at the location where the anomaly occurs is obtained and multiplied by the characteristic value to calculate the cost of the location where the anomaly occurs;

[0086] Under the condition of historical electrolyte operation data, obtain the historical vulnerabilities and historical unstable factors of the actual operation of lithium batteries, and generate historical improvement plans;

[0087] Obtain the workload of operators required to generate the parameters of historical improvement plans, and estimate the cost of improvement based on market conditions and operator workload;

[0088] The historical electrolyte operation data whose lifting cost is less than the cost of the abnormality occurrence location is used as characteristic electrolyte operation data;

[0089] The safety risk coefficient of the characteristic electrolyte operation data is calculated, and the minimum value of the safety risk coefficient of at least one characteristic electrolyte operation data is used as the critical value of the safety risk coefficient.

[0090] The critical value is used to determine the update method, and its setting is based on the cost control of the update. The minimum value of the safety risk factor of at least one characteristic electrolyte operation data is used as the critical value of the safety risk factor. When the safety risk factor is greater than the critical value, the improvement cost is less than the cost of the abnormal location, so maintenance and repair are used. Conversely, when the safety risk factor does not exceed the critical value, the improvement cost does not exceed the cost of the abnormal location, so the electrolyte parameter adjustment method is used for repair.

[0091] Reference Figure 4 As shown, based on the electrolyte operation data, obtaining the electrolyte vulnerabilities and unstable factors includes the following steps:

[0092] Based on historical data, an exception database is established, wherein the exception database includes vulnerabilities and unstable factors that have occurred, as well as solutions corresponding to the vulnerabilities and unstable factors;

[0093] Forming at least one identification feature for the vulnerabilities and unstable factors in the anomaly database, and pairing the identification feature with the vulnerabilities or unstable factors;

[0094] Performing feature recognition on the electrolyte operation data to obtain at least one suspected feature;

[0095] The loopholes or unstable factors corresponding to the identification features consistent with the suspected features are obtained as the loopholes and unstable factors of the electrolyte.

[0096] Here, all solutions include graphene technology. First, through historical data, at least one preparatory solution can be obtained. The preparatory solution may or may not include graphene technology. Using text recognition, the preparatory solution including graphene technology is selected as the solution corresponding to the vulnerabilities and unstable factors.

[0097] Based on the vulnerabilities and instability factors, generating a fix includes the following steps:

[0098] The solutions corresponding to the vulnerabilities and unstable factors of the electrolyte are called in the abnormal database as repair solutions.

[0099] Reference Figure 5 As shown, based on historical data, analyzing the distribution of time consumption includes the following steps:

[0100] The time consumed for using the local enhancement package to enhance the performance of the lithium battery electrolyte is obtained as the enhancement time consumed;

[0101] At least one local lifting package is paired and summarized with the lifting consumption time to obtain the lifting consumption time distribution.

[0102] Reference Figure 6 As shown, decomposing the performance improvement package into at least one local improvement package and setting an update time point for the local improvement package includes the following steps:

[0103] At least one operating function during the operation of the electrolyte, where the operating function is the function achieved during normal operation at the location where the abnormality occurred;

[0104] Obtaining the battery operating parameters before the electrolyte is improved, and decomposing the battery operating parameters into at least one local operating parameter based on the operating function, each of the local operating parameters realizing an operating function;

[0105] The performance improvement package is decomposed into at least one local improvement package, and the parameters generated by each local improvement package realize an operation function;

[0106] Pairing a local enhancement package that realizes the same operation function with a local operation parameter, and during enhancement, replacing the corresponding local operation parameter at the abnormality occurrence location according to the parameter in the local enhancement package, and the order of enhancement is according to the order of processing at least one abnormality occurrence location in the historical data;

[0107] Taking at least one of the local enhancement packages as a target local enhancement package;

[0108] The local lifting package that is lifted before the target local lifting package in sequence is used as a characteristic local lifting package;

[0109] Accumulate the time consumed by at least one characteristic local improvement package to obtain the update time point of the target local improvement package;

[0110] When the target local improvement package traverses at least one local improvement package, the setting of all update time points is completed.

[0111] The local operating parameters of the battery operating parameters before the local upgrading package are independent of each other and correspond to different operating functions, so it is convenient to determine the upgrading sequence;

[0112] The order of processing at least one abnormal occurrence location in the historical data is obtained according to the actual situation after processing, that is, when the electrolyte parameters are adjusted according to this order, if the subsequent parameter adjustment will interfere with the adjusted parameters, the order will be corrected until this situation does not occur. Then, the order of processing at least one abnormal occurrence location is used as the order of parameter improvement in this scheme.

[0113] A lithium battery electrolyte safety performance improvement system combined with graphene technology, used to implement the above-mentioned lithium battery electrolyte safety performance improvement method combined with graphene technology, comprising:

[0114] A data acquisition module, wherein the data acquisition module acquires electrolyte operation data of at least one actual operation scenario of a lithium battery;

[0115] A risk calculation module, wherein the risk calculation module calculates a safety risk factor of electrolyte operation data;

[0116] A margin setting module, wherein the margin setting module basically calculates existing data to form a critical value of a safety risk factor;

[0117] A risk processing module, wherein when the safety risk coefficient is less than a preset value, the risk processing module does not perform any processing; when the safety risk coefficient is greater than a critical value, the risk processing module obtains the loopholes and unstable factors of the electrolyte based on the electrolyte operation data, generates a repair plan based on the loopholes and unstable factors, and summarizes the parameters of the repair plan to generate a performance improvement package;

[0118] A time acquisition module, which analyzes and obtains the distribution of time consumption based on historical data;

[0119] An update setting module, which decomposes the performance improvement package into at least one local improvement package based on the distribution of the time spent on improvement, and sets an update time point for the local improvement package;

[0120] An update module, wherein the update module uses a local improvement package to improve the performance of the electrolyte of the lithium battery. When at least one local improvement package of the performance improvement package has completed the improvement, the performance improvement of the electrolyte of the lithium battery is completed.

[0121] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned method for improving the safety performance of lithium battery electrolyte combined with graphene technology is executed.

[0122] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0123] To sum up, the advantages of the present invention are: by calculating the safety risk coefficient of the electrolyte operation data, the performance improvement package is decomposed into at least one local improvement package, and an update time point is set for the local improvement package. For the current vulnerabilities in the electrolyte, according to the actual performance conditions, the choice of repair and performance improvement can be determined, so that the adopted method is a more economical improvement and repair mode. At the same time, when performing the improvement, a gradual improvement method is used, and the improvement is performed in the order determined in the historical data, avoiding errors in the improvement order, which causes the improved parameters to be affected by the later improved parameters, thereby avoiding conflicts when the parameters are updated.

[0124] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for improving the safety performance of lithium battery electrolyte combined with graphene technology, characterized in that: include: Obtaining electrolyte operation data of at least one actual operation scenario of a lithium battery; Calculate the safety risk factor of electrolyte operation data; Calculate the basic existing data to form the critical value of the safety risk factor; When the safety risk factor is less than the critical value, no action is taken; When the safety risk factor is greater than the critical value, the electrolyte vulnerabilities and unstable factors are obtained based on the electrolyte operation data, and a repair plan is generated based on the vulnerabilities and unstable factors. The parameters of the repair plan are summarized to generate a performance improvement package; Based on historical data, the analysis obtains the distribution of time spent on improvement; Based on the distribution of the time spent on improvement, the performance improvement package is decomposed into at least one local improvement package, and an update time point is set for the local improvement package; The performance of the electrolyte of the lithium battery is improved using a local improvement package. When at least one local improvement package of the performance improvement package has completed the improvement, the performance improvement of the electrolyte of the lithium battery is completed.

2. The method for improving safety performance of lithium battery electrolyte combined with graphene technology according to claim 1, characterized in that: The calculation of the safety risk factor of the electrolyte operation data comprises the following steps: Obtaining the interval length of the frequency fluctuation range used for current transmission in actual operation of the lithium battery as the fluctuation length; Obtain the average output delay time when the lithium battery is running, and obtain the average value of the abnormal current output ratio when the lithium battery is running as the average output error value; Obtain the occurrence frequency of lithium battery operation interruption as the interruption frequency, obtain the average duration of lithium battery operation interruption as the interruption time, multiply the interruption frequency by the interruption time to obtain the interruption evaluation value; Obtaining the average value of the sudden current increment during the operation of the lithium battery as the abnormal current value, wherein the sudden current increment is the difference between the current actually output by the lithium battery and the current output by the lithium battery during normal operation; Using AHP and expert scoring, the weights of fluctuation length, average network delay time, average transmission error value, interruption evaluation value and abnormal current value are formed; Use the safety risk formula to calculate the safety risk factor of the electrolyte operation data; The security risk formula is as follows: A=αB+βC+γD+δE+εF Among them, A is the safety risk coefficient, α, β, γ, δ and ε are the weights of the fluctuation length, average network delay time, average transmission error value, interruption assessment value and abnormal current value, respectively, and B, C, D, E and F are the fluctuation length, average network delay time, average transmission error value, interruption assessment value and abnormal current value, respectively.

3. The method for improving safety performance of lithium battery electrolyte combined with graphene technology according to claim 2, characterized in that: The basic existing data calculation to form a critical value of the safety risk factor includes the following steps: Based on the historical data, obtaining at least one historical electrolyte operation data, wherein the historical electrolyte operation data is the electrolyte operation data that has occurred; Under the condition of historical electrolyte operation data, the set number of abnormal occurrence positions in the actual operation of the lithium battery is obtained as the characteristic value; Based on big data, the average cost of repair at the location where the anomaly occurs is obtained and multiplied by the characteristic value to calculate the cost of the location where the anomaly occurs; Under the condition of historical electrolyte operation data, obtain the historical vulnerabilities and historical unstable factors of the actual operation of lithium batteries, and generate historical improvement plans; Obtain the workload of operators required to generate the parameters of historical improvement plans, and estimate the cost of improvement based on market conditions and operator workload; The historical electrolyte operation data whose lifting cost is less than the cost of the abnormality occurrence location is used as characteristic electrolyte operation data; The safety risk coefficient of the characteristic electrolyte operation data is calculated, and the minimum value of the safety risk coefficient of at least one characteristic electrolyte operation data is used as the critical value of the safety risk coefficient.

4. The method for improving safety performance of lithium battery electrolyte combined with graphene technology according to claim 5, characterized in that: The method of obtaining the electrolyte vulnerabilities and unstable factors based on the electrolyte operation data comprises the following steps: Based on historical data, an abnormality database is established, wherein the abnormality database includes vulnerabilities and unstable factors that have occurred, and solutions corresponding to the vulnerabilities and unstable factors, wherein the solutions all include graphene technology; Forming at least one identification feature for the vulnerabilities and unstable factors in the anomaly database, and pairing the identification feature with the vulnerabilities or unstable factors; Performing feature recognition on the electrolyte operation data to obtain at least one suspected feature; The loopholes or unstable factors corresponding to the identification features consistent with the suspected features are obtained as the loopholes and unstable factors of the electrolyte.

5. The method for improving safety performance of lithium battery electrolyte combined with graphene technology according to claim 6, characterized in that: The generation of a repair solution based on vulnerabilities and unstable factors includes the following steps: The solutions corresponding to the vulnerabilities and unstable factors of the electrolyte are called in the abnormal database as repair solutions.

6. The method for improving safety performance of lithium battery electrolyte combined with graphene technology according to claim 7, characterized in that: The analysis based on historical data to obtain the distribution of time consumption includes the following steps: The time consumed for using the local enhancement package to enhance the performance of the lithium battery electrolyte is obtained as the enhancement time consumed; At least one local lifting package is paired and summarized with the lifting consumption time to obtain the lifting consumption time distribution.

7. The method for improving safety performance of lithium battery electrolyte combined with graphene technology according to claim 8, characterized in that: Decomposing the performance improvement package into at least one local improvement package and setting an update time point for the local improvement package comprises the following steps: At least one operating function during the operation of the electrolyte, where the operating function is the function achieved during normal operation at the location where the abnormality occurred; Obtaining the battery operating parameters before the electrolyte is improved, and decomposing the battery operating parameters into at least one local operating parameter based on the operating function, each of the local operating parameters realizing an operating function; The performance improvement package is decomposed into at least one local improvement package, and the parameters generated by each local improvement package realize an operation function; Pairing a local enhancement package that realizes the same operation function with a local operation parameter, and during enhancement, replacing the corresponding local operation parameter at the abnormality occurrence location according to the parameter in the local enhancement package, and the order of enhancement is according to the order of processing at least one abnormality occurrence location in the historical data; Taking at least one of the local enhancement packages as a target local enhancement package; The local lifting package that is lifted before the target local lifting package in sequence is used as a characteristic local lifting package; Accumulate the time consumed by at least one characteristic local improvement package to obtain the update time point of the target local improvement package; When the target local improvement package traverses at least one local improvement package, the setting of all update time points is completed.

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