A method and system for diagnosing and detecting short circuits in lithium batteries

Abstract

The present invention belongs to the field of battery short - circuit detection, involves data analysis technology, and is used to solve the problem that the prior art cannot verify the short - circuit detection results in combination with risk analysis. Specifically, it is a lithium - battery short - circuit diagnosis and detection method and system, including 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; the short - circuit detection module is used to perform short - circuit detection and analysis on the lithium battery: generate a detection period and divide the detection period into several detection time periods, mark the lithium battery as the detection object, and mark the detection time periods as short - circuit normal time periods or short - circuit abnormal time periods through the short - circuit coefficient DL; the present invention can perform short - circuit detection and analysis on the lithium battery, collect and calculate the operating parameters of the lithium battery in each detection time period to obtain the short - circuit coefficient, feedback the short - circuit probability of the lithium battery through the short - circuit coefficient, and then differentially mark the detection time periods.

Description

Technical Field

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

[0002] ‌A short - circuit of a lithium battery‌ refers to the direct contact between the positive and negative electrodes in the battery, resulting in a short - circuit of the electrolyte inside the battery. An instantaneous burst of current occurs, the battery gets out of control, and problems such as overheating and fire are generated; a short - circuit of a lithium battery is a serious safety problem, which is prone to dangers such as fire and explosion.

[0003] The invention patent with the publication number CN110376530B discloses a battery internal short - circuit detection device and method. During the detection process, each process data is not affected by the working conditions, and has a good detection effect. Moreover, this method can detect the internal short - circuit before thermal runaway occurs, thus greatly reducing the harm caused by thermal runaway; however, this method cannot combine risk analysis to verify the short - circuit detection result, resulting in the inability to guarantee the accuracy of the short - circuit detection result, and at the same time, it is impossible to optimize the maintenance of lithium batteries according to the analysis result of short - circuit influencing factors.

[0004] In view of the above - mentioned 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 combine risk analysis to verify the short - circuit detection result;

[0006] The technical problem that the present invention needs to solve is: how to provide a lithium - battery short - circuit diagnosis and detection method and system that can combine risk analysis to verify the short - circuit detection result.

[0007] The purpose of the present invention can be realized through the following technical solutions:

[0008] A lithium - battery short - circuit diagnosis and detection system includes a diagnosis and detection platform, and 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;

[0009] The short - circuit detection module is used to perform short - circuit detection and analysis on the lithium battery: generate a detection period and divide the detection period into several detection time segments, mark the lithium battery as the detection object, obtain the temperature difference data WC and pressure difference data YC of the detection object at the end of the detection time segment, and perform numerical calculation to obtain a short - circuit coefficient DL; mark the detection time segment as a short - circuit normal time segment or a short - circuit abnormal time segment through the short - circuit coefficient DL;

[0010] The risk assessment module is used to conduct risk assessment and analysis on the lithium battery: by using a smoke sensor and a spark sensor to collect the smoke concentration in the operating environment and the spark discharge voltage of the detection object respectively, marking the maximum values of the smoke concentration in the operating environment and the spark discharge voltage during the detection period as the smoke concentration data and the spark data respectively, and marking the detection period as a safe and normal period or a safe and abnormal period through the smoke concentration data and the spark data;

[0011] The factor troubleshooting module is used to conduct troubleshooting and analysis on the short - circuit influencing factors of the lithium battery;

[0012] The optimization analysis module is used to conduct short - circuit optimization analysis on the lithium battery: at the end of the detection cycle, marking the detection periods that are simultaneously marked as short - circuit normal periods and safe and abnormal periods as ignored periods, marking the ratio of the number of ignored periods to the total number of detection periods as the ignoring coefficient, and determining whether the detection link needs to be optimized through the ignoring coefficient.

[0013] Furthermore, the process of obtaining the temperature difference data WC and the pressure difference data YC includes: obtaining 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.

[0014] Furthermore, the specific process of marking the detection period as a short - circuit normal period or a short - circuit abnormal period includes: obtaining the short - circuit threshold DLmax through the storage module, and comparing the short - circuit coefficient DL of the detection object during 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 characteristic during 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 characteristic during the detection period, and the corresponding detection period is marked as a short - circuit abnormal period, generating a troubleshooting analysis signal and sending the troubleshooting analysis signal to the factor troubleshooting module through the diagnostic detection platform.

[0015] Further, the specific process of marking the detection period as a safe normal period or a safe abnormal period includes: obtaining the smoke concentration threshold and the spark threshold through the storage module, and comparing the smoke concentration data and the spark data with the smoke concentration threshold and the spark threshold respectively. If the smoke concentration data is less than the smoke concentration 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, the corresponding detection period is marked as a safe abnormal period, a risk warning signal is generated and the risk warning signal is sent to the mobile terminal of the management personnel through the diagnostic detection platform.

[0016] Further, the specific process of the factor investigation module for investigating and analyzing the short-circuit influencing factors of the lithium battery includes: analyzing whether the detection object is mechanically squeezed during the short-circuit abnormal period. If so, the influencing factor of the short-circuit abnormal period is marked as physical extrusion; 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 over-discharge.

[0017] Further, 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, a detection optimization signal is generated and the detection optimization signal is sent to the mobile terminal of the management personnel; 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 in-depth analysis is performed.

[0018] Further, the specific process of in-depth analysis includes: marking the detection period that is simultaneously marked as the short-circuit abnormal period and the safe abnormal period as the in-depth analysis period, obtaining the marking result of the influencing factor of the in-depth analysis period, marking the number of times that the influencing factor of the in-depth analysis period is marked as physical extrusion, external temperature, and overcharge and over-discharge as the extrusion data, the external temperature data, and the charge and discharge data respectively, and comparing the extrusion data, the external temperature data, and the charge and discharge data numerically. 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 management personnel; 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 management personnel; if the value of the charge and discharge data is the largest, a usage optimization signal is generated and the usage optimization signal is sent to the mobile terminal of the management personnel.

[0019] A lithium battery short-circuit diagnosis and detection method includes the following steps:

[0020] Step 1: Conduct short - circuit detection and analysis on the lithium - ion battery: Generate a detection period and divide the detection period into several detection time segments, and mark the detection time segments as short - circuit normal time segments or short - circuit abnormal time segments;

[0021] Step 2: Conduct risk assessment and analysis on the lithium - ion battery: Mark the detection time segments as safe normal time segments or safe abnormal time segments;

[0022] Step 3: Conduct investigation and analysis on the short - circuit influencing factors of the lithium - ion battery and mark the influencing factors in the short - circuit abnormal time segments as physical extrusion, external temperature, or over - charge / discharge;

[0023] Step 4: Conduct short - circuit optimization analysis on the lithium - ion battery: Generate detection optimization signals, space optimization signals, environmental optimization signals, or usage optimization signals and send them to the mobile terminals of the management personnel.

[0024] The present invention has the following beneficial effects:

[0025] Through the short - circuit detection module, short - circuit detection and analysis can be carried out on the lithium - ion battery. The operating parameters of the lithium - ion battery in each detection time segment are collected and calculated in a periodic detection manner to obtain a short - circuit coefficient. The short - circuit probability of the lithium - ion battery is fed back through the short - circuit coefficient, and then the detection time segments are differentially marked, providing data support for the optimization analysis process;

[0026] Through the risk assessment module, risk assessment and analysis can be carried out on the lithium - ion battery. The operating safety of the lithium - ion battery is evaluated by combining the smoke concentration and spark discharge voltage in the operating environment, so as to give an alarm in time when the lithium - ion battery has risk characteristics, and avoid safety accidents caused by the short - circuit of the lithium - ion battery;

[0027] Through the factor investigation module, investigation and analysis can be carried out on the short - circuit influencing factors of the lithium - ion battery. When the lithium - ion battery has short - circuit characteristics, the external factors causing the short - circuit are gradually investigated and analyzed, improving the efficiency of short - circuit abnormal handling and providing data support for the optimization analysis process at the same time;

[0028] 4. Through the optimization analysis module, short - circuit optimization analysis can be carried out on the lithium - ion battery. The short - circuit detection results and safety assessment results within the detection period are compared and analyzed. According to the analysis results, a neglect coefficient is generated, and the necessity of optimizing the detection link is evaluated through the neglect coefficient, thereby improving the accuracy of subsequent short - circuit detection results. Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 It is the system block diagram of Embodiment 1 of the present invention;

[0031] Figure 2 It is the method flowchart of Embodiment 2 of the present invention. Detailed implementation manners

[0032] Next, the technical solution of the present invention will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0033] Embodiment 1: As Figure 1 shown, a lithium battery short - circuit diagnosis and detection system includes a diagnosis and detection platform, and the diagnosis and detection platform 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.

[0034] The short - circuit detection module is used to perform short - circuit detection and analysis on a lithium battery: generate a detection period and divide the detection period into several detection time segments, 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 time segment, mark the maximum and minimum values of the surface temperature value of the detection object during the detection time segment as the 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 time segment, mark the maximum and minimum values of the circuit voltage value of the detection object during the detection time segment as the high - voltage value and low - voltage value respectively, and mark the difference between the high - voltage value and the low - voltage value as the pressure difference data YC of the detection object during the detection time segment; obtain the short - circuit coefficient DL of the detection object during the detection time segment through the formula DL = c1×WC + c2×YC, where c1 and c2 are both proportionality coefficients, and the value - taking process of c1 and c2 includes: retrieve the normal temperature range and normal voltage range of the detection object, mark the difference between the maximum and minimum values of the normal temperature range as the temperature range value WF, mark the difference between the maximum and minimum values of the normal voltage range as the voltage range value DF, mark the sum value of the temperature range value and the voltage range value as the range mark value FB, and obtain the values of c1 and c2 through the formulas c1 = 1 - WF / FB and c2 = 1 - DF / FB; obtain the short - circuit threshold DLmax through the storage module, and compare the short - circuit coefficient DL of the detection object during the detection time segment 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 characteristic during the detection time segment, and mark the corresponding detection time segment as a short - circuit normal time segment; 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 characteristic during the detection time segment, mark the corresponding detection time segment as a short - circuit abnormal time segment, generate a troubleshooting analysis signal and send the troubleshooting analysis signal to the factor troubleshooting module through the diagnostic detection platform; collect and calculate the operating parameters of the lithium battery in each detection time segment in a periodic detection manner to obtain the short - circuit coefficient, feedback the short - circuit probability of the lithium battery through the short - circuit coefficient, and then perform differential marking on the detection time segments to provide data support for optimizing the analysis process.

[0035] The risk assessment module is used to conduct risk assessment and analysis on lithium batteries: the operating environment smoke concentration and spark discharge voltage of the detection object are collected through a smoke sensor and a spark sensor respectively, the maximum values of the operating environment smoke concentration and spark discharge voltage during the detection period are marked as smoke concentration data and spark data respectively, the smoke concentration threshold and spark threshold are obtained through the storage module, and the smoke concentration data and spark data are compared with the smoke concentration threshold and spark threshold respectively: if the smoke concentration data is less than the smoke concentration 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, the corresponding detection period is marked as a safe abnormal period, a risk warning signal is generated and the risk warning signal is sent to the mobile terminal of the management personnel through the diagnostic detection platform; the operating safety of the lithium battery is evaluated by combining the operating environment smoke concentration and spark discharge voltage, so as to give an alarm in time when the lithium battery has risk characteristics and avoid safety accidents caused by the short circuit of the lithium battery.

[0036] The factor investigation module is used to investigate and analyze the influencing factors of the short circuit of lithium batteries: analyze whether the detection object is mechanically squeezed during the short circuit abnormal period: if so, mark the influencing factor of the short circuit abnormal period as physical extrusion; if not, obtain the maximum value of the operating environment temperature 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 factor of the short circuit abnormal period as external temperature; if not, mark the influencing factor of the short circuit abnormal period as overcharge and discharge; gradually investigate and analyze the external factors causing the short circuit when the lithium battery has short circuit characteristics, improve the efficiency of short circuit abnormal handling, and provide data support for optimizing the analysis process.

[0037] The optimization analysis module is used to perform short - circuit optimization analysis on lithium batteries: at the end of the detection period, the detection periods that are simultaneously marked as short - circuit normal periods and safety abnormal periods are marked as ignored periods, the ratio of the number of ignored periods to the total number of detection periods is marked as the ignoring coefficient, and the ignoring coefficient is compared with a preset ignoring threshold: if the ignoring coefficient is greater than or equal to the ignoring threshold, it is determined that the short - circuit detection link needs to be optimized, a detection optimization signal is generated and sent to the mobile terminal of the management personnel; if the ignoring coefficient is less than the ignoring threshold, it is determined that the short - circuit detection link does not need to be optimized, and in - depth analysis is carried out: the detection periods that are simultaneously marked as short - circuit abnormal periods and safety abnormal periods are marked as in - depth analysis periods, the influence factor marking results of the in - depth analysis periods are obtained, the influence factors of the in - depth analysis periods marked as physical extrusion, external temperature, and the number of over - charge and discharge are respectively marked as extrusion data, external temperature data, and charge - discharge data, and the extrusion data, external temperature data, and charge - discharge data are numerically compared: if the value of the extrusion data is the largest, a space optimization signal is generated and sent to the mobile terminal of the management personnel; if the value of the external temperature data is the largest, an environment optimization signal is generated and sent to the mobile terminal of the management personnel; if the value of the charge - discharge data is the largest, a usage optimization signal is generated and sent to the mobile terminal of the management personnel; the short - circuit detection results within the detection period are compared and analyzed with the safety assessment results, an ignoring coefficient is generated according to the analysis results, and the necessity of optimizing the detection link is evaluated through the ignoring coefficient, so as to improve the accuracy of subsequent short - circuit detection results.

[0038] Embodiment 2: As Figure 2 shown, a method for diagnosing and detecting short - circuits in lithium batteries includes the following steps:

[0039] Step 1: Perform short - circuit detection and analysis on the lithium battery: generate a detection period and divide the detection period into several detection periods, mark the lithium battery as the detection object, obtain the pressure difference data YC and temperature difference data WC of the detection object during the detection period and perform numerical calculation to obtain the short - circuit coefficient DL, and mark the detection period as a short - circuit normal period or a short - circuit abnormal period through the short - circuit coefficient DL;

[0040] Step 2: Perform risk assessment and analysis on the lithium battery: respectively collect the smoke concentration in the operating environment and the spark discharge voltage of the detection object through a smoke sensor and an electric spark sensor, and mark the detection period as a safety normal period or a safety abnormal period through the smoke concentration in the operating environment and the spark discharge voltage;

[0041] Step 3: Investigate and analyze the short - circuit influencing factors of the lithium battery and mark the influencing factors of the short - circuit abnormal period as physical extrusion, external temperature, or over - charge and discharge;

[0042] Step 4: Conduct short - circuit optimization analysis on the lithium - ion battery: Generate detection optimization signals, space optimization signals, environment optimization signals, or usage optimization signals and send them to the mobile terminal of the management personnel.

[0043] A short - circuit diagnosis and detection method and system for a lithium - ion battery. During operation, generate a detection period and divide the detection period into several detection time intervals. Mark the lithium - ion battery as the detection object, obtain the pressure difference data YC and temperature difference data WC of the detection object within the detection time interval and perform numerical calculations to obtain the short - circuit coefficient DL. Mark the detection time interval as a normal short - circuit time interval or an abnormal short - circuit time interval through the short - circuit coefficient DL. Collect the smoke concentration in the operating environment and the spark discharge voltage of the detection object through a smoke sensor and a spark sensor respectively, and mark the detection time interval as a normal safety time interval or an abnormal safety time interval through the smoke concentration in the operating environment and the spark discharge voltage. Conduct a troubleshooting analysis on the short - circuit influencing factors of the lithium - ion battery and mark the influencing factors in the abnormal short - circuit time interval as physical extrusion, external temperature, or over - charge / discharge. Conduct short - circuit optimization analysis on the lithium - ion battery. Generate detection optimization signals, space optimization signals, environment optimization signals, or usage optimization signals and send them to the mobile terminal of the management personnel.

[0044] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications, supplements, or use similar methods to replace the specific embodiments described, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

[0045] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above - mentioned terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0046] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate on all details and do not limit the invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principle and practical application of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claim book and its 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: Use smoke sensors and spark sensors to collect smoke concentration and spark discharge voltage in the operating environment of the test object, respectively, and mark the maximum values ​​of smoke concentration and spark discharge voltage in the operating environment during the test period as smoke density data and spark data, respectively. Use the smoke density data and spark data to mark the test 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 lithium batteries: At the end of the detection cycle, the detection periods that are marked as 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 the ignored coefficient. The ignored coefficient is used to determine whether the detection link needs to be optimized, and a detection optimization signal, a space optimization signal, an environmental optimization signal or a usage optimization signal is generated and sent to the manager's mobile terminal.

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