Lithium battery short circuit diagnosis detection method and system

Abstract

The invention belongs to the field of battery short circuit detection, relates to a data analysis technology, and aims to solve the problem that a short circuit detection result cannot be verified in combination with risk analysis in the prior art, in particular to a lithium battery short circuit diagnosis detection method and system. The diagnosis detection platform is in communication connection with a short circuit detection module, a risk assessment module, a factor checking module, an optimization analysis module and a storage module; the short-circuit detection module is used for performing short-circuit detection analysis on the lithium battery: generating a detection period, dividing the detection period into a plurality of detection time periods, marking the lithium battery as a detection object, and marking the detection time periods as short-circuit normal time periods or short-circuit abnormal time periods through a short-circuit coefficient DL; according to the invention, short-circuit detection analysis can be carried out on the lithium battery, the operation parameters of the lithium battery in each detection time period are collected and calculated to obtain the short-circuit coefficient, the short-circuit probability of the lithium battery is fed back through the short-circuit coefficient, and then differential marking is carried out on the detection time periods.

Description

Technical Field

[0001] The present invention belongs to the field of battery short circuit detection, relates to data analysis technology, and specifically provides a lithium battery short circuit diagnosis and detection method and system. Background Art

[0002] A lithium battery short circuit refers to direct contact between the positive and negative electrodes in the battery, which causes the electrolyte inside the battery to short-circuit, resulting in an instantaneous burst of current and a loss of control of the battery, leading to problems such as overheating and fire. A lithium battery short circuit is a serious safety issue that can easily lead to fires, explosions and other dangers.

[0003] The invention patent with announcement number CN110376530B discloses a battery internal short circuit detection device and method. During the detection process, the process data of each process will not be affected by the working conditions, and the method has a good detection effect. The method can detect the internal short circuit before thermal runaway occurs, thereby greatly reducing the harm caused by thermal runaway; however, the method cannot verify the short circuit detection results in combination with risk analysis, resulting in the accuracy of the short circuit detection results cannot be guaranteed, and it is impossible to optimize the lithium battery maintenance based on the analysis results of the short circuit influencing factors.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a lithium battery short circuit diagnosis and detection method and system, which is used to solve the problem that the prior art cannot verify the short circuit detection results in combination with risk analysis; The technical problem to be solved by the present invention is: how to provide a lithium battery short circuit diagnosis and detection method and system that can verify the short circuit detection result in combination with risk analysis.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A lithium battery short circuit diagnosis and detection system comprises a diagnosis and detection platform, wherein the diagnosis and detection platform is communicatively connected with a short circuit detection module, a risk assessment module, a factor investigation module, an optimization analysis module and a storage module; The short-circuit detection module is used to perform short-circuit detection and analysis on the lithium battery: generate a detection cycle and divide the detection cycle into several detection time periods, mark the lithium battery as a detection object, obtain the temperature difference data WC and the pressure difference data YC of the detection object at the end of the detection time period and perform numerical calculation to obtain the short-circuit coefficient DL; mark the detection time period as a normal short-circuit time period or an abnormal short-circuit time period according to the short-circuit coefficient DL; The risk assessment module is used to perform risk assessment and analysis on lithium batteries: the smoke concentration and spark discharge voltage of the operating environment of the detection object are collected through the smoke sensor and the spark sensor respectively, and the maximum values ​​of the smoke concentration and spark discharge voltage of the operating environment in the detection period are marked as smoke density data and spark data respectively, and the detection period is marked as a safe normal period or a safe abnormal period through the smoke density data and spark data; The factor investigation module is used to investigate and analyze the factors affecting the short circuit of the lithium battery; The optimization analysis module is used to perform short-circuit optimization analysis on lithium batteries: at the end of the detection cycle, the detection periods that are marked as both normal short-circuit periods and safety abnormal periods are marked as ignored periods, and the ratio of the number of ignored periods to the total number of detection periods is marked as ignored coefficients. The ignored coefficients are used to determine whether the detection link needs to be optimized.

[0007] Furthermore, the process of acquiring the temperature difference data WC and the pressure difference data YC includes: acquiring the surface temperature value and the circuit voltage value of the detection object in real time during the detection period, marking the maximum value and the minimum value of the surface temperature value of the detection object during the detection period as the high temperature value and the low temperature value, respectively, marking the difference between the high temperature value and the temperature value as the temperature difference data WC of the detection object during the detection period, marking the maximum value and the minimum value of the circuit voltage value of the detection object during the detection period as the high pressure value and the low pressure value, respectively, and marking the difference between the high pressure value and the low pressure value as the pressure difference data YC of the detection object during the detection period.

[0008] Furthermore, the specific process of marking the detection period as a normal short-circuit period or an abnormal short-circuit period includes: obtaining the short-circuit threshold DLmax through the storage module, and comparing the short-circuit coefficient DL of the detection object in the detection period with the short-circuit threshold DLmax: if the short-circuit coefficient DL is less than the short-circuit threshold DLmax, it is determined that the detection object does not have a short-circuit feature in the detection period, and the corresponding detection period is marked as a normal short-circuit period; if the short-circuit coefficient DL is greater than or equal to the short-circuit threshold DLmax, it is determined that the detection object has a short-circuit feature in the detection period, and the corresponding detection period is marked as a normal short-circuit period, and an investigation and analysis signal is generated and sent to the factor investigation module through the diagnostic detection platform.

[0009] Furthermore, the specific process of marking the detection period as a safe and normal period or a safe and abnormal period includes: obtaining the smoke density threshold and the spark threshold through the storage module, and comparing the smoke density data and the spark data with the smoke density threshold and the spark threshold respectively: if the smoke density data is less than the smoke density threshold and the spark data is less than the spark threshold, it is determined that the detection object does not have risk characteristics during the detection period, and the corresponding detection period is marked as a safe and normal period; otherwise, it is determined that the detection object has risk characteristics during the detection period, and the corresponding detection period is marked as a safe and abnormal period, and a risk warning signal is generated and sent to the mobile phone terminal of the manager through the diagnostic detection platform.

[0010] Furthermore, the specific process of the factor investigation module investigating and analyzing the short-circuit influencing factors of the lithium battery includes: analyzing whether the detection object is subjected to mechanical squeezing during the short-circuit abnormal period: if so, marking the influencing factor of the short-circuit abnormal period as physical squeezing; if not, obtaining the maximum value of the operating environment temperature value of the detection object during the short-circuit abnormal period and marking it as the ambient temperature value, and comparing the ambient temperature value with the preset ambient temperature threshold: if the ambient temperature value is greater than or equal to the ambient temperature threshold, marking the influencing factor of the short-circuit abnormal period as external temperature; if not, marking the influencing factor of the short-circuit abnormal period as overcharge and overdischarge.

[0011] Furthermore, the specific process of determining whether the detection link needs to be optimized includes: comparing the neglect coefficient with the preset neglect threshold: if the neglect coefficient is greater than or equal to the neglect threshold, it is determined that the short-circuit detection link needs to be optimized, generating a detection optimization signal and sending the detection optimization signal to the mobile phone terminal of the administrator; if the neglect coefficient is less than the neglect threshold, it is determined that the short-circuit detection link does not need to be optimized, and an in-depth analysis is performed.

[0012] Furthermore, the specific process of deep analysis includes: marking the detection period that is marked as a short circuit abnormality period and a safety abnormality period as a deep period, obtaining the marking results of the influencing factors of the deep period, marking the influencing factors of the deep period as physical extrusion, external temperature, and the number of overcharge and discharge as extrusion data, external temperature data, and charging and discharging data, respectively, and performing numerical comparison on the extrusion data, external temperature data, and charging and discharging data: if the value of the extrusion data is the largest, a space optimization signal is generated and the space optimization signal is sent to the mobile phone terminal of the administrator; if the value of the external temperature data is the largest, an environment optimization signal is generated and the environment optimization signal is sent to the mobile phone terminal of the administrator; if the value of the charging and discharging data is the largest, a usage optimization signal is generated and the usage optimization signal is sent to the mobile phone terminal of the administrator.

[0013] A lithium battery short circuit diagnosis and detection method comprises the following steps: Step 1: Perform short circuit detection and analysis on the lithium battery: generate a detection cycle and divide the detection cycle into several detection time periods, and mark the detection time periods as short circuit normal time periods or short circuit abnormal time periods; Step 2: Conduct risk assessment and analysis on lithium batteries: mark the detection period as a safe normal period or a safe abnormal period; Step 3: Investigate and analyze the factors affecting the short circuit of the lithium battery and mark the factors affecting the abnormal short circuit period as physical squeezing, external temperature or overcharge and discharge; Step 4: Perform short-circuit optimization analysis on the lithium battery: generate a detection optimization signal, a space optimization signal, an environment optimization signal or a usage optimization signal and send it to the manager's mobile terminal.

[0014] The present invention has the following beneficial effects: The short-circuit detection module can be used to perform short-circuit detection and analysis on lithium batteries. The operating parameters of lithium batteries in each detection period are collected and calculated in a periodic detection manner to obtain the short-circuit coefficient. The short-circuit probability of the lithium battery is fed back through the short-circuit coefficient, and the detection period is differentiated and marked to provide data support for the optimization analysis process. The risk assessment module can be used to conduct risk assessment and analysis on lithium batteries, and evaluate the operating safety of lithium batteries by combining the smoke concentration and spark discharge voltage of the operating environment, so as to give an alarm in time when the lithium battery has risk characteristics, avoiding safety accidents caused by lithium battery short circuit; The factor investigation module can be used to investigate and analyze the factors affecting the short circuit of lithium batteries. When the lithium battery has short circuit characteristics, the external factors causing the short circuit can be gradually investigated and analyzed to improve the efficiency of short circuit abnormality processing and provide data support for optimizing the analysis process. 4. The optimization analysis module can be used to perform short-circuit optimization analysis on lithium batteries. The short-circuit detection results within the detection period are compared with the safety assessment results. The neglect coefficient is generated based on the analysis results. The necessity of optimizing the detection link is evaluated through the neglect coefficient, thereby improving the accuracy of subsequent short-circuit detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 As shown, a lithium battery short circuit diagnosis and detection system includes a diagnosis and detection platform, which is communicatively connected to a short circuit detection module, a risk assessment module, a factor investigation module, an optimization analysis module and a storage module.

[0019] The short circuit detection module is used to perform short circuit detection and analysis on the lithium battery: generate a detection cycle and divide the detection cycle into several detection time periods, mark the lithium battery as the detection object, obtain the surface temperature value and circuit voltage value of the detection object in real time during the detection period, mark the maximum value and minimum value of the surface temperature value of the detection object during the detection period as high temperature value and low temperature value respectively, mark the difference between the high temperature value and the temperature value as the temperature difference data WC of the detection object during the detection period, mark the maximum value and minimum value of the circuit voltage value of the detection object during the detection period as high pressure value and low pressure value respectively, and mark the difference between the high pressure value and the low pressure value as the pressure difference data YC of the detection object during the detection period; obtain the short circuit coefficient DL of the detection object during the detection period by the formula DL=c1×WC+c2×YC, where c1 and c2 are both proportional coefficients, and the value-taking process of c1 and c2 includes: calling the normal temperature range and normal voltage range of the detection object, marking the difference between the maximum value and the minimum value of the normal temperature range as the temperature range value WF, and marking the difference between the maximum value and the minimum value of the normal voltage range as The voltage range value DF, the sum of the temperature range value and the voltage range value is marked as the range mark value FB, and the values ​​of c1 and c2 are obtained by the formulas c1=1-WF / FB and c2=1-DF / FB; the short-circuit threshold DLmax is obtained through the storage module, and the short-circuit coefficient DL of the detection object in the detection period is compared with the short-circuit threshold DLmax: if the short-circuit coefficient DL is less than the short-circuit threshold DLmax, it is determined that the detection object does not have a short-circuit feature in the detection period, and the corresponding detection period is marked as a short-circuit normal period; if the short-circuit coefficient DL is greater than or equal to the short-circuit threshold DLmax, it is determined that the detection object has a short-circuit feature in the detection period, and the corresponding detection period is marked as a short-circuit abnormal period, and an investigation and analysis signal is generated and sent to the factor investigation module through the diagnostic detection platform; the lithium battery operating parameters in each detection period are collected and calculated in a periodic detection manner to obtain the short-circuit coefficient, and the short-circuit probability of the lithium battery is fed back through the short-circuit coefficient, and then the detection period is differentially marked to provide data support for the optimization analysis process.

[0020] The risk assessment module is used to perform risk assessment and analysis on lithium batteries: the smoke concentration and spark discharge voltage of the operating environment of the detection object are collected through smoke sensors and spark sensors respectively, and the maximum values ​​of the smoke concentration and spark discharge voltage in the operating environment during the detection period are marked as smoke density data and spark data respectively. The smoke density threshold and spark threshold are obtained through the storage module, and the smoke density data and spark data are compared with the smoke density threshold and spark threshold respectively: if the smoke density data is less than the smoke density threshold and the spark data is less than the spark threshold, it is determined that the detection object does not have risk characteristics during the detection period, and the corresponding detection period is marked as a safe and normal period; otherwise, it is determined that the detection object has risk characteristics during the detection period, and the corresponding detection period is marked as a safe and abnormal period, and a risk warning signal is generated and sent to the mobile phone terminal of the manager through the diagnostic detection platform; the operating safety of the lithium battery is evaluated in combination with the smoke concentration and spark discharge voltage of the operating environment, so as to timely alarm when the lithium battery has risk characteristics to avoid safety accidents caused by lithium battery short circuit.

[0021] The factor investigation module is used to investigate and analyze the factors affecting the short circuit of lithium batteries: analyze whether the detection object is subjected to mechanical squeezing during the short circuit abnormal period: if so, mark the influencing factors of the short circuit abnormal period as physical squeezing; if not, obtain the maximum value of the operating environment temperature value of the detection object during the short circuit abnormal period and mark it as the ambient temperature value, and compare the ambient temperature value with the preset ambient temperature threshold: if the ambient temperature value is greater than or equal to the ambient temperature threshold, mark the influencing factors of the short circuit abnormal period as external temperature; if not, mark the influencing factors of the short circuit abnormal period as overcharge and discharge; when the lithium battery has short circuit characteristics, the external factors causing the short circuit are gradually investigated and analyzed to improve the efficiency of short circuit abnormality processing and provide data support for optimizing the analysis process.

[0022] The optimization analysis module is used to perform short-circuit optimization analysis on lithium batteries: at the end of the detection cycle, the detection periods that are marked as both normal short-circuit periods and safety abnormality periods are marked as neglected periods, the ratio of the number of neglected periods to the total number of detection periods is marked as neglect coefficient, and the neglect coefficient is compared with the preset neglect threshold: if the neglect coefficient is greater than or equal to the neglect threshold, it is determined that the short-circuit detection link needs to be optimized, and a detection optimization signal is generated and sent to the administrator's mobile phone terminal; if the neglect coefficient is less than the neglect threshold, it is determined that the short-circuit detection link does not need to be optimized, and an in-depth analysis is performed: the detection periods that are marked as both abnormal short-circuit periods and safety abnormality periods are marked as deep-divided periods, the marking results of the influencing factors of the deep-divided periods are obtained, and the influencing factors of the deep-divided periods are marked. The times marked as physical extrusion, external temperature, and overcharge and discharge are marked as extrusion data, external temperature data, and charge and discharge data, respectively, and the extrusion data, external temperature data, and charge and discharge data are numerically compared: if the value of the extrusion data is the largest, a space optimization signal is generated and the space optimization signal is sent to the mobile terminal of the administrator; if the value of the external temperature data is the largest, an environment optimization signal is generated and the environment optimization signal is sent to the mobile terminal of the administrator; if the value of the charge and discharge data is the largest, a use optimization signal is generated and the use optimization signal is sent to the mobile terminal of the administrator; the short-circuit detection results within the detection period are compared and analyzed with the safety assessment results, and a neglect coefficient is generated according to the analysis results. The necessity of optimizing the detection link is evaluated through the neglect coefficient, thereby improving the accuracy of subsequent short-circuit detection results.

[0023] Embodiment 2: Figure 2 As shown, a lithium battery short circuit diagnosis and detection method includes the following steps: Step 1: Perform short circuit detection and analysis on the lithium battery: Generate a detection cycle and divide the detection cycle into several detection time periods, mark the lithium battery as the detection object, obtain the pressure difference data YC and temperature difference data WC of the detection object in the detection period, and perform numerical calculation to obtain the short circuit coefficient DL, and mark the detection period as a normal short circuit period or an abnormal short circuit period according to the short circuit coefficient DL; Step 2: Conduct risk assessment and analysis on lithium batteries: Use smoke sensors and spark sensors to collect smoke concentration and spark discharge voltage in the operating environment of the detection object, and mark the detection period as a safe normal period or a safe abnormal period based on the smoke concentration and spark discharge voltage in the operating environment; Step 3: Investigate and analyze the factors affecting the short circuit of the lithium battery and mark the factors affecting the abnormal short circuit period as physical squeezing, external temperature or overcharge and discharge; Step 4: Perform short-circuit optimization analysis on the lithium battery: generate a detection optimization signal, a space optimization signal, an environment optimization signal or a usage optimization signal and send it to the manager's mobile terminal.

[0024] A lithium battery short circuit diagnosis and detection method and system, when working, generates a detection cycle and divides the detection cycle into several detection time periods, marks the lithium battery as the detection object, obtains the pressure difference data YC and the temperature difference data WC of the detection object in the detection period, and performs numerical calculation to obtain the short circuit coefficient DL, and marks the detection period as a normal short circuit period or an abnormal short circuit period through the short circuit coefficient DL; collects the smoke concentration and spark discharge voltage of the operating environment of the detection object through the smoke sensor and the electric spark sensor respectively, and marks the detection period as a safe normal period or a safe abnormal period through the smoke concentration and spark discharge voltage of the operating environment; investigates and analyzes the factors affecting the short circuit of the lithium battery and marks the factors affecting the abnormal short circuit period as physical extrusion, external temperature or overcharge and discharge; and performs short circuit optimization analysis on the lithium battery. Generate a detection optimization signal, a space optimization signal, an environment optimization signal or a use optimization signal and send it to the mobile phone terminal of the manager.

[0025] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0026] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0027] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A lithium battery short circuit diagnosis and detection system, characterized in that: It includes a diagnostic testing platform, which is communicatively connected with a short circuit detection module, a risk assessment module, a factor troubleshooting module, an optimization analysis module and a storage module; The short-circuit detection module is used to perform short-circuit detection and analysis on the lithium battery: generate a detection cycle and divide the detection cycle into several detection time periods, mark the lithium battery as a detection object, obtain the temperature difference data WC and the pressure difference data YC of the detection object at the end of the detection time period and perform numerical calculation to obtain the short-circuit coefficient DL; mark the detection time period as a normal short-circuit time period or an abnormal short-circuit time period according to the short-circuit coefficient DL; The risk assessment module is used to perform risk assessment and analysis on lithium batteries: the smoke concentration and spark discharge voltage of the operating environment of the detection object are collected through the smoke sensor and the spark sensor respectively, and the maximum values ​​of the smoke concentration and spark discharge voltage of the operating environment in the detection period are marked as smoke density data and spark data respectively, and the detection period is marked as a safe normal period or a safe abnormal period through the smoke density data and spark data; The factor investigation module is used to investigate and analyze the factors affecting the short circuit of the lithium battery; The optimization analysis module is used to perform short-circuit optimization analysis on lithium batteries: at the end of the detection cycle, the detection periods that are marked as both normal short-circuit periods and safety abnormal periods are marked as ignored periods, and the ratio of the number of ignored periods to the total number of detection periods is marked as ignored coefficients. The ignored coefficients are used to determine whether the detection link needs to be optimized.

2. A lithium battery short circuit diagnosis and detection system according to claim 1, characterized in that: The process of acquiring the temperature difference data WC and the pressure difference data YC includes: acquiring the surface temperature value and the circuit voltage value of the detection object in real time during the detection period, marking the maximum value and the minimum value of the surface temperature value of the detection object during the detection period as the high temperature value and the low temperature value, respectively, marking the difference between the high temperature value and the temperature value as the temperature difference data WC of the detection object during the detection period, marking the maximum value and the minimum value of the circuit voltage value of the detection object during the detection period as the high pressure value and the low pressure value, respectively, marking the difference between the high pressure value and the low pressure value as the pressure difference data YC of the detection object during the detection period.

3. A lithium battery short circuit diagnosis and detection system according to claim 2, characterized in that: The specific process of marking the detection period as a normal short-circuit period or an abnormal short-circuit period includes: obtaining the short-circuit threshold DLmax through the storage module, and comparing the short-circuit coefficient DL of the detection object in the detection period with the short-circuit threshold DLmax: if the short-circuit coefficient DL is less than the short-circuit threshold DLmax, it is determined that the detection object does not have a short-circuit feature in the detection period, and the corresponding detection period is marked as a normal short-circuit period; if the short-circuit coefficient DL is greater than or equal to the short-circuit threshold DLmax, it is determined that the detection object has a short-circuit feature in the detection period, and the corresponding detection period is marked as a normal short-circuit period, and an investigation and analysis signal is generated and sent to the factor investigation module through the diagnostic detection platform.

4. A lithium battery short circuit diagnosis and detection system according to claim 3, characterized in that: The specific process of marking the detection period as a safe normal period or a safe abnormal period includes: obtaining the smoke density threshold and the spark threshold through the storage module, and comparing the smoke density data and the spark data with the smoke density threshold and the spark threshold respectively: if the smoke density data is less than the smoke density threshold and the spark data is less than the spark threshold, it is determined that the detection object does not have risk characteristics during the detection period, and the corresponding detection period is marked as a safe normal period; otherwise, it is determined that the detection object has risk characteristics during the detection period, and the corresponding detection period is marked as a safe abnormal period, and a risk warning signal is generated and sent to the mobile terminal of the manager through the diagnostic detection platform.

5. A lithium battery short circuit diagnosis and detection system according to claim 4, characterized in that: The specific process of the factor investigation module to investigate and analyze the short-circuit influencing factors of the lithium battery includes: analyzing whether the detection object is subjected to mechanical squeezing during the short-circuit abnormal period: if so, the influencing factor of the short-circuit abnormal period is marked as physical squeezing; if not, the maximum value of the operating environment temperature value of the detection object during the short-circuit abnormal period is obtained and marked as the ambient temperature value, and the ambient temperature value is compared with the preset ambient temperature threshold: if the ambient temperature value is greater than or equal to the ambient temperature threshold, the influencing factor of the short-circuit abnormal period is marked as external temperature; if not, the influencing factor of the short-circuit abnormal period is marked as overcharge and overdischarge.

6. A lithium battery short circuit diagnosis and detection system according to claim 5, characterized in that: The specific process of determining whether the detection link needs to be optimized includes: comparing the neglect coefficient with the preset neglect threshold: if the neglect coefficient is greater than or equal to the neglect threshold, it is determined that the short-circuit detection link needs to be optimized, generating a detection optimization signal and sending the detection optimization signal to the mobile phone terminal of the manager; if the neglect coefficient is less than the neglect threshold, it is determined that the short-circuit detection link does not need to be optimized, and an in-depth analysis is performed.

7. A lithium battery short circuit diagnosis and detection system according to claim 6, characterized in that: The specific process of deep analysis includes: marking the detection period that is marked as a short circuit abnormality period and a safety abnormality period as a deep period, obtaining the marking results of the influencing factors of the deep period, marking the influencing factors of the deep period as physical extrusion, external temperature, and the number of overcharge and discharge as extrusion data, external temperature data, and charging and discharging data respectively, and performing numerical comparison on the extrusion data, external temperature data, and charging and discharging data: if the value of the extrusion data is the largest, a space optimization signal is generated and sent to the mobile phone terminal of the administrator; if the value of the external temperature data is the largest, an environment optimization signal is generated and sent to the mobile phone terminal of the administrator; if the value of the charging and discharging data is the largest, a usage optimization signal is generated and sent to the mobile phone terminal of the administrator.

8. A lithium battery short circuit diagnosis and detection method, characterized in that: The following steps are involved: Step 1: Perform short circuit detection and analysis on the lithium battery: generate a detection cycle and divide the detection cycle into several detection time periods, and mark the detection time periods as short circuit normal time periods or short circuit abnormal time periods; Step 2: Conduct risk assessment and analysis on lithium batteries: mark the detection period as a safe normal period or a safe abnormal period; Step 3: Investigate and analyze the factors affecting the short circuit of the lithium battery and mark the factors affecting the abnormal short circuit period as physical squeezing, external temperature or overcharge and discharge; Step 4: Perform short-circuit optimization analysis on the lithium battery: generate a detection optimization signal, a space optimization signal, an environment optimization signal or a usage optimization signal and send it to the manager's mobile terminal.

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