A new energy battery module intelligent detection method and related system

Abstract

The present application provides a new energy battery module intelligent detection method and related system, the method comprising: obtaining a voltage data set of a target detection point of the new energy battery module; determining a temperature data set of the target detection point within a preset time period; performing fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within a preset time period; obtaining a first slope of the first fitting straight line; determining a maximum value and a minimum value corresponding to the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values; determining a target stability evaluation parameter of the new energy battery module according to the first slope, multiple maximum values ​​and multiple minimum values; performing fitting according to the temperature data set to obtain a second fitting straight line; obtaining a second slope of the second fitting straight line; determining a target abnormality probability of the new energy battery module according to the target stability evaluation parameter and the second slope; when the target abnormality probability is greater than a preset probability value, determining a warning strategy corresponding to the target abnormality probability.

Description

Technical Field

[0001] The present application relates to the field of motor vehicle technology or new energy technology, and specifically to a new energy battery module intelligent detection method and related system. Background Art

[0002] With the rapid advancement of science and technology, the new energy vehicle industry has also developed rapidly. For new energy vehicles, the importance of new energy battery modules is self-evident. Therefore, the testing of new energy battery modules is also very critical. Only when the new energy battery modules have undergone rigorous testing can the safety of new energy vehicles be further guaranteed. Therefore, how to improve the efficiency of intelligent detection of new energy battery modules needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a new energy battery module intelligent detection method and related system, which can improve the efficiency of new energy battery module intelligent detection.

[0004] In a first aspect, an embodiment of the present application provides a new energy battery module intelligent detection method, which is applied to a robot, wherein the robot is communicatively connected to a new energy battery module, and the new energy battery module includes a temperature sensor, and the method includes:

[0005] Acquire a voltage data set of a target detection point of the new energy battery module within a preset time period;

[0006] Determine a temperature data set of the target detection point within the preset time period by the temperature sensor;

[0007] Performing fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within the preset time period, wherein the horizontal axes of the first fitting straight line and the fitting curve segment are both time and the vertical axes are both voltage values;

[0008] Obtaining a first slope of the first fitting straight line;

[0009] Determine the maximum and minimum values ​​corresponding to the fitting curve segment to obtain multiple maximum and multiple minimum values;

[0010] Determine a target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values;

[0011] Perform fitting according to the temperature data set to obtain a second fitting straight line, wherein the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value;

[0012] Obtaining a second slope of the second fitting straight line;

[0013] Determining a target abnormality probability of the new energy battery module according to the target stability evaluation parameter and the second slope;

[0014] When the target abnormality probability is greater than a preset probability value, a warning strategy corresponding to the target abnormality probability is determined and the warning strategy is executed.

[0015] In a second aspect, an embodiment of the present application provides a new energy battery module intelligent detection system, which is applied to a robot, wherein the robot is connected to a new energy battery module in communication, and the new energy battery module includes a temperature sensor. The system includes: an acquisition unit, a determination unit, and a fitting unit, wherein:

[0016] The acquisition unit is used to acquire a voltage data set of a target detection point of the new energy battery module within a preset time period;

[0017] The determining unit is used to determine a temperature data set of the target detection point within the preset time period through the temperature sensor;

[0018] The fitting unit is used to perform fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within the preset time period, wherein the horizontal axes of the first fitting straight line and the fitting curve segment are both time and the vertical axes are both voltage values;

[0019] The acquisition unit is further used to acquire a first slope of the first fitting straight line;

[0020] The determination unit is further used to determine the maximum value and the minimum value corresponding to the fitting curve segment, and obtain multiple maximum values ​​and multiple minimum values; determine the target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values;

[0021] The fitting unit is further used to perform fitting according to the temperature data set to obtain a second fitting straight line, wherein the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value;

[0022] The acquisition unit is further used to acquire a second slope of the second fitting straight line;

[0023] The determination unit is also used to determine the target abnormality probability of the new energy battery module according to the target stability evaluation parameter and the second slope; when the target abnormality probability is greater than a preset probability value, determine a warning strategy corresponding to the target abnormality probability and execute the warning strategy.

[0024] In a third aspect, an embodiment of the present application provides a robot comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program comprises instructions for executing the steps in the first aspect of the embodiment of the present application.

[0025] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0026] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0027] The implementation of the embodiments of the present application has the following beneficial effects:

[0028] It can be seen that the new energy battery module intelligent detection method and related system described in the embodiment of the present application are applied to a robot, and the robot is communicatively connected with the new energy battery module. The new energy battery module includes a temperature sensor, and the method includes: obtaining a voltage data set of a target detection point of the new energy battery module within a preset time period; determining a temperature data set of the target detection point within a preset time period through a temperature sensor; fitting according to the voltage data set to obtain a fitting straight line and a fitting curve segment within a preset time period, the horizontal axis of the first fitting straight line and the fitting curve segment are both time and the vertical axis are both voltage values; obtaining a first slope of the first fitting straight line; determining the maximum and minimum values ​​corresponding to the fitting curve segment, and obtaining multiple maximum and multiple minimum values; determining the new energy battery module according to the first slope, multiple maximum and multiple minimum values. The target stability evaluation parameters of the battery module are obtained; fitting is performed according to the temperature data set to obtain a second fitting straight line, the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value; the second slope of the second fitting straight line is obtained; the target abnormal probability of the new energy battery module is determined according to the target stability evaluation parameters and the second slope; when the target abnormal probability is greater than the preset probability value, the early warning strategy corresponding to the target abnormal probability is determined, and the early warning strategy is executed. Firstly, the robot can be used to perform single-point (detection point) or multi-point battery monitoring on the new energy battery module, and the voltage and temperature dimensions can be used to determine the abnormal probability, and then, the abnormal probability can be accurately determined, which is helpful to perform accurate early warning, and then, the intelligent detection efficiency of the new energy battery module can be improved, thereby ensuring the safety of the new energy battery module. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 It is a flow chart of a new energy battery module intelligent detection method provided in an embodiment of the present application;

[0031] Figure 2 is a schematic diagram of the structure of a robot provided in an embodiment of the present application;

[0032] Figure 3 This is a block diagram of the functional units of a new energy battery module intelligent detection system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but in a possible example also includes steps or units that are not listed, or in a possible example also includes other steps or units inherent to these processes, methods, products or devices.

[0034] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0035] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] The robots involved in the embodiments of the present application may include but are not limited to: automobile processing robots, industrial robots, monitoring robots, automobile processing machine tools, humanoid robots or other robots with mobile or motion functions.

[0037] See also Figure 1 , Figure 1 This is a flow chart of a new energy battery module intelligent detection method provided by an embodiment of the present application. As shown in the figure, it is applied to a robot. The robot is connected to a new energy battery module in communication. The new energy battery module includes a temperature sensor. The new energy battery module intelligent detection method includes:

[0038] 101. Obtain a voltage data set of a target detection point of the new energy battery module within a preset time period.

[0039] The preset time period may be preset or set by system default. The preset time period may be a time period before the current moment.

[0040] In a specific implementation, the robot is communicated with a new energy battery module, and the new energy battery module includes a temperature sensor.

[0041] The target detection point may be any detection point in the new energy battery module. The new energy battery module may include one or more batteries. The battery may include at least one of the following: a lithium battery, a blade battery, a lithium iron phosphate battery, etc., which are not limited here.

[0042] In the embodiment of the present application, the voltage data set may include multiple voltage data. Specifically, the voltage data set of the target detection point of the new energy battery module within a preset time period may be obtained, that is, the voltage data is collected every first preset time interval to obtain multiple voltage data, and each voltage data corresponds to a sampling moment. The voltage data set can reflect the working stability of the new energy battery module to a certain extent. The first preset time interval can be preset or system default.

[0043] 102. Determine a temperature data set of the target detection point within the preset time period by using the temperature sensor.

[0044] In a specific implementation, a temperature data set of a target detection point within a preset time period can be determined by a temperature sensor. The temperature data set can include multiple temperature data, that is, the temperature sensor can collect temperature data at every second preset time interval, and each temperature data corresponds to a sampling moment. The temperature data set can also reflect the working stability of the new energy battery to a certain extent. The second preset time interval can be preset or system default.

[0045] 103. Perform fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within the preset time period, wherein the horizontal axes of the first fitting straight line and the fitting curve segment are both time and the vertical axes are both voltage values.

[0046] In the specific implementation, since each voltage data in the voltage data set corresponds to a sampling moment, each voltage data and its corresponding sampling moment can be regarded as a coordinate point. Based on this method, multiple coordinate points can be obtained, that is, each voltage data corresponds to a coordinate point. Then, fitting can be performed based on multiple coordinate points to obtain a fitting straight line and a fitting curve segment within a preset time period. The horizontal axis of the first fitting straight line and the fitting curve segment are both time and the vertical axis are both voltage values. That is, the first fitting straight line and the fitting curve segment can be used to analyze the working stability of the new energy battery module.

[0047] 104. Obtain a first slope of the first fitting straight line.

[0048] In a specific implementation, the first slope of the first fitting straight line can be obtained, and the first slope reflects the working stability of the new energy battery module or the change trend of the new energy battery module to a certain extent.

[0049] 105. Determine the maximum value and the minimum value corresponding to the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values.

[0050] In a specific implementation, the fitting curve segment is the actual working condition of the new energy battery module in a preset time period, and then, multiple maximum values ​​and multiple minimum values ​​reflect the working stability under actual conditions.

[0051] 106. Determine a target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values, and the multiple minimum values.

[0052] Among them, the first slope can reflect the working stability of the new energy battery module from the horizontal direction (horizontal axis), and multiple maximum values ​​and multiple minimum values ​​can reflect the working stability of the new energy battery module from the vertical direction (vertical axis). In this way, the target stability evaluation parameter of the new energy battery module can be determined from the horizontal and vertical directions according to the first slope, multiple maximum values ​​and multiple minimum values. The larger the target stability evaluation parameter, the lower the abnormal probability.

[0053] Optionally, the above step 106, determining the first stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values, may include the following steps:

[0054] determining a first stability evaluation parameter corresponding to the first slope;

[0055] Performing a mean square error operation on the multiple maximum values ​​and the multiple minimum values ​​to obtain a first mean square error;

[0056] determining a second stability evaluation parameter corresponding to the first mean square error;

[0057] Determine a second mean square error corresponding to the multiple maximum values;

[0058] Determine a third mean square error corresponding to the multiple minimum values;

[0059] Determine an absolute value of a difference between the second mean square error and the third mean square error, and determine a second weight corresponding to the absolute value, wherein the smaller the absolute value, the larger the second weight;

[0060] Determine a first weight according to the second weight, wherein the sum of the first weight and the second weight is 1;

[0061] A weighted operation is performed based on the first weight, the second weight, the first stability evaluation parameter, and the second stability evaluation parameter to obtain the target stability evaluation parameter.

[0062] In a specific implementation, the mapping relationship between the preset slope (the fitting straight line obtained by fitting the voltage data) and the stability evaluation parameter can be pre-stored, and then, the first stability evaluation parameter corresponding to the first slope can be determined based on the mapping relationship. The first slope reflects the working stability of the new energy battery module to a certain extent.

[0063] Furthermore, a mean square error calculation can be performed based on multiple maximum values ​​and multiple minimum values ​​to obtain a first mean square error. The maximum value and the minimum value reflect the amplitude fluctuation of the voltage and vertically reflect the working stability of the new energy battery module. Furthermore, a mapping relationship between a preset mean square error and a stability evaluation parameter can be pre-stored, and then, a second stability evaluation parameter corresponding to the first mean square error can be determined based on the mapping relationship.

[0064] Next, the second mean square error corresponding to multiple maximum values ​​and the third mean square error corresponding to multiple minimum values ​​can also be determined. The second mean square error reflects the fluctuation of the maximum value, and the third mean square error reflects the fluctuation of the minimum value. Furthermore, the absolute value of the difference between the second mean square error and the third mean square error is determined. The absolute value = |second mean square error - third mean square error|. The smaller the absolute value, the greater the weight. On the contrary, the larger the absolute value, the smaller the weight. The smaller the absolute value, the better the stability reflected by the voltage level, and the greater the weight. On the contrary, the larger the absolute value, the worse the stability reflected by the voltage level, and the smaller the weight. That is, the mapping relationship between the preset absolute value and the weight can be stored in advance, and the second weight corresponding to the corresponding absolute value can be determined based on the mapping relationship. Then, the first weight is determined according to the second weight. The sum of the first weight and the second weight is 1. Then, a weighted operation can be performed according to the first weight, the second weight, the first stability evaluation parameter and the second stability evaluation parameter to obtain the target stability evaluation parameter, as follows: target stability evaluation parameter = first weight * first stability evaluation parameter + second weight * second stability evaluation parameter. In this way, not only can the target stability evaluation parameter of the new energy battery module be determined in the horizontal and vertical directions in combination with the first slope, multiple maximum values ​​and multiple minimum values, but the weights in the horizontal and vertical directions can also be dynamically determined by considering the fluctuations of the maximum and minimum values, so that the target stability evaluation parameter of the new energy battery module can be determined more accurately.

[0065] 107. Perform fitting according to the temperature data set to obtain a second fitting straight line, wherein the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value.

[0066] In a specific implementation, since each temperature data in the temperature data set corresponds to a sampling moment, each temperature data and its corresponding sampling moment can be regarded as a coordinate point. Based on this method, multiple coordinate points can be obtained, and then fitting is performed based on the multiple coordinate points to obtain a second fitting straight line. The horizontal axis of the second fitting straight line is time and the vertical axis is temperature value.

[0067] 108. Obtain a second slope of the second fitting straight line.

[0068] In a specific implementation, the second slope of the second fitting straight line can be obtained, and the second slope reflects the working stability of the new energy battery module or the changing trend of the new energy battery module to a certain extent.

[0069] 109. Determine a target abnormal probability of the new energy battery module according to the target stability evaluation parameter and the second slope.

[0070] In the embodiment of the present application, the target stability evaluation parameter is the stability determined based on the voltage data, and the second slope is the stability reflected by the temperature change. The second slope also reflects the corresponding changes in the surrounding caused by the components that generate the voltage data.

[0071] Optionally, the above step 109, determining the target abnormal probability of the new energy battery module according to the target stability evaluation parameter and the second slope, may include the following steps:

[0072] When the target stability evaluation parameter is less than a first threshold and the second slope is within a preset range, determining a first deviation between the first threshold and the target stability evaluation parameter; the preset range includes an upper threshold and a lower threshold;

[0073] Determining the target abnormality probability according to the first deviation;

[0074] or,

[0075] When the target stability evaluation parameter is less than the first threshold and the second slope is not within the preset range, determining a monotonic relationship between the first slope and the second slope;

[0076] Determine the target abnormality probability according to the monotonic relationship, the target stability evaluation parameter and the second slope;

[0077] or,

[0078] When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is within the preset range, determining that the target abnormality probability is 0;

[0079] or,

[0080] When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is not within the preset range, if the second slope is greater than the upper threshold, determine the second deviation between the second slope and the upper threshold, and determine the target abnormality probability based on the second deviation; if the second slope is less than the lower threshold, determine the third deviation between the lower threshold and the second slope, and determine the target abnormality probability based on the third deviation.

[0081] The first threshold value may be preset or set by the system by default, and the preset range may also be preset or set by the system by default. The preset range includes an upper threshold value and a lower threshold value.

[0082] In the embodiment of the present application, when the target stability evaluation parameter is less than the first threshold and the second slope is within the preset range, it means that from the voltage point of view, the new energy battery module is unstable and may be abnormal, and from the temperature point of view, the new energy battery module is stable, that is, the first deviation between the first threshold and the target stability evaluation parameter can be determined, that is, the first deviation = (first threshold-target stability evaluation parameter) / first threshold, and the mapping relationship between the preset deviation and the abnormal probability can be pre-stored, and then, the target abnormal probability corresponding to the first deviation can be determined based on the mapping relationship, and then, the abnormal probability can be accurately determined, which is helpful to perform accurate early warning.

[0083] In an embodiment of the present application, when the target stability evaluation parameter is less than the first threshold value and the second slope is not within the preset range, it means that from the voltage point of view, the new energy battery module is unstable and may have abnormalities, and from the temperature point of view, the new energy battery module is unstable and may have abnormalities. The monotonic relationship between the first slope and the second slope can be determined. According to the monotonic relationship, the target stability evaluation parameter and the second slope determine the target abnormality probability, that is, the monotonicity of the two dimensions can be used to determine the abnormality probability, and then, the abnormality probability can be accurately determined, which is helpful to perform accurate early warning.

[0084] In an embodiment of the present application, when the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is within a preset range, it means that the new energy battery module is working stably from the voltage perspective, and the new energy battery module is working stably from the temperature perspective, that is, the new energy battery module is working normally, and the target abnormality probability is determined to be 0.

[0085] In the embodiment of the present application, when the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is not within the preset range, it means that from the voltage point of view, the new energy battery module is stable, and from the temperature point of view, the new energy battery module is unstable and may be abnormal. If the second slope is greater than the upper threshold, the second deviation between the second slope and the upper threshold is determined, that is, the second deviation = (second slope-upper threshold) / (upper threshold + second slope), that is, the mapping relationship between the preset deviation (deviation related to the upper threshold) and the abnormal probability can be pre-stored, and then the target abnormal probability corresponding to the second deviation can be determined based on the mapping relationship. If the second slope is less than the lower threshold, the third deviation between the lower threshold and the second slope can be determined, the third deviation = (lower threshold-second slope) / (lower threshold + second slope), and the mapping relationship between the preset deviation (deviation related to the lower threshold) and the abnormal probability can also be pre-stored, and then the target abnormal probability corresponding to the third deviation can be determined based on the mapping relationship, and then the abnormal probability can be accurately determined, which is helpful for performing accurate early warning.

[0086] Optionally, the above step of determining the target abnormality probability according to the monotonicity relationship, the target stability evaluation parameter and the second slope may include the following steps:

[0087] determining a fourth deviation between the first threshold and the target stability evaluation parameter;

[0088] Determining a first reference abnormality probability according to the fourth deviation;

[0089] Determining a second reference abnormal probability according to the second slope and the preset range, specifically: if the second slope is greater than the upper threshold, determining a fifth deviation between the second slope and the upper threshold, and determining the second reference abnormal probability according to the fifth deviation; if the second slope is less than the lower threshold, determining a sixth deviation between the lower threshold and the second slope, and determining the second reference abnormal probability according to the sixth deviation;

[0090] The target abnormality probability is determined according to the monotonic relationship, the first reference abnormality probability, and the second reference abnormality probability.

[0091] In a specific implementation, the fourth deviation between the first threshold and the target stability evaluation parameter can be determined, the fourth deviation = (first threshold - target stability evaluation parameter) / target stability evaluation parameter, that is, the mapping relationship between the preset deviation and the abnormal probability can be pre-stored, and then, the first reference abnormal probability corresponding to the fourth deviation can be determined based on the mapping relationship, and then, the second reference abnormal probability can be determined according to the second slope and the preset range, specifically: if the second slope is greater than the upper limit threshold, the fifth deviation between the second slope and the upper limit threshold can be determined, that is, the fifth deviation = (second slope - upper limit threshold) / (upper limit threshold + second slope), that is, the mapping relationship between the preset deviation (deviation related to the upper limit threshold) and the abnormal probability can be pre-stored, and then, the second reference abnormal probability corresponding to the fifth deviation can be determined based on the mapping relationship. If the second slope is less than the lower threshold, the sixth deviation between the lower threshold and the second slope can be determined, and the sixth deviation = (lower threshold-second slope) / (lower threshold+second slope). The mapping relationship between the preset deviation (deviation related to the lower threshold) and the abnormal probability can also be pre-stored. Then, the second reference abnormal probability corresponding to the sixth deviation can be determined based on the mapping relationship. Then, the target abnormal probability can be determined according to the monotonicity relationship, the first reference abnormal probability and the second reference abnormal probability. In this way, when the new energy battery module is unstable and may have abnormalities from the voltage point of view, and when the new energy battery module is unstable and may have abnormalities from the temperature point of view, the monotonicity of the two dimensions can be used to determine the abnormal probability. Then, the abnormal probability can be accurately determined, which is helpful to perform accurate early warning.

[0092] Optionally, the above step of determining the target abnormality probability according to the monotonic relationship, the first reference abnormality probability and the second reference abnormality probability may include the following steps:

[0093] When the monotonic relationship includes that the first slope and the second slope are monotonically consistent, performing a sum operation according to the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability;

[0094] When the monotonic relationship includes a monotonic inconsistency between the first slope and the second slope, determining a target absolute value of a ratio between the first slope and the second slope;

[0095] Determining a target weight pair corresponding to the target absolute value;

[0096] A weighted operation is performed on the target weight pair and the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability.

[0097] In an embodiment of the present application, when the monotonic relationship includes the monotonicity consistency between the first slope and the second slope, that is, both are monotonically upward or monotonically downward, then the first reference abnormal probability and the second reference abnormal probability can be summed to obtain the target abnormal probability, that is, the target abnormal probability = the first reference abnormal probability + the second reference abnormal probability, that is, from the voltage point of view, the new energy battery module is unstable and may have abnormalities, and from the temperature point of view, the new energy battery module is unstable and may have abnormalities, then the two dimensions are consistent in their strength directions, and then, the abnormal probabilities of the two dimensions can be summed to determine the final abnormal probability, and then, the abnormal probability can be accurately determined, which is helpful to perform accurate early warning.

[0098] Furthermore, when the monotonic relationship includes the inconsistency of monotonicity between the first slope and the second slope, that is, although the new energy battery module is unstable and may be abnormal from the voltage perspective, and the new energy battery module is unstable and may be abnormal from the temperature perspective, the directions of the intensity determination of the two dimensions are inconsistent. Therefore, the target absolute value of the ratio between the first slope and the second slope can be determined, the target absolute value = |first slope / second slope|, and then the target weight pair corresponding to the target absolute value is determined, for example, c = |a| / |b|, c represents the target absolute value, then the target first weight = |a| / (|a|+| b|), the target second weight = |b| / (|a|+|b|), the target weight pair includes the target first weight and the target second weight, and then, a weighted operation can be performed based on the target weight pair and the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability, that is, the target abnormal probability = target first weight * first reference abnormal probability + target second weight * second reference abnormal probability, that is, when the intensity directions of the two dimensions are inconsistent, the abnormal probabilities of the two dimensions can be summed to determine the final abnormal probability, so that the abnormal probability determination is more in line with the actual situation, and then, the abnormal probability can be accurately determined, which is helpful to perform accurate early warning.

[0099] 110. When the target abnormality probability is greater than a preset probability value, determine a warning strategy corresponding to the target abnormality probability, and execute the warning strategy.

[0100] In the embodiment of the present application, the preset probability value can be preset or the system defaults. The early warning strategy can include at least one of the following: alarm, voice prompt, SMS prompt, power-off operation, voltage reduction, voltage increase, battery heating, battery cooling, current reduction, current increase, charging stop, discharging stop, etc., which are not limited here.

[0101] Among them, different abnormal probabilities can also correspond to different abnormal causes. For example, the mapping relationship between the preset abnormal probability and the abnormal cause can be pre-stored, and then the target abnormal cause corresponding to the target abnormal probability can be determined based on the mapping relationship. The abnormal cause may include at least one of the following: charging abnormality, layout heating, leakage, battery expansion, etc., which are not limited here.

[0102] In a specific implementation, when the target abnormal probability is greater than the preset probability value, it means that an abnormality is likely to occur, and a certain response strategy needs to be adopted. Specifically, the mapping relationship between the preset abnormal probability and the early warning strategy can be pre-stored, and then the early warning strategy corresponding to the target abnormal probability can be determined based on the mapping relationship, and the early warning strategy is executed. Of course, the target probability difference between the target abnormal probability and the preset probability value can also be determined first, and the mapping relationship between the preset probability difference and the early warning strategy can be pre-stored, and then the early warning strategy corresponding to the target probability difference can be determined based on the mapping relationship, and the early warning strategy is executed.

[0103] Among them, when the target abnormal probability is less than or equal to the preset probability value, it means that there is no abnormality, and the early warning strategy may not be executed.

[0104] Optionally, the robot includes an environmental sensor and may further include the following steps:

[0105] Acquiring target environmental parameters through the environmental sensor;

[0106] Acquire a first attribute parameter of the target detection point;

[0107] Obtaining target device attribute parameters of the new energy battery module;

[0108] determining a reference threshold corresponding to the first attribute parameter;

[0109] Determining a first adjustment parameter corresponding to the target device attribute parameter;

[0110] Determining a first fine-tuning parameter corresponding to the target environmental parameter;

[0111] The first threshold is determined according to the first adjustment parameter, the first fine-tuning parameter, and the reference threshold.

[0112] The target environmental parameters may include at least one of the following: ambient temperature, ambient humidity, air composition, atmospheric pressure, ambient light intensity, etc., which are not limited here.

[0113] The first attribute parameter may include at least one of the following: material, position, composition, thickness, area, volume, density, function, etc., which are not limited here.

[0114] The target device attribute parameters may include at least one of the following: battery model, battery capacity, battery structure, battery material, battery composition, battery weight, battery volume, etc., which are not limited here.

[0115] In the embodiment of the present application, the target environmental parameter can be obtained through the environmental sensor, the first attribute parameter of the target detection point can be obtained, and the target device attribute parameter of the new energy battery module can be obtained, and the mapping relationship between the preset attribute parameter and the threshold can be pre-stored, and then, the reference threshold corresponding to the first attribute parameter can be determined based on the mapping relationship, and the mapping relationship between the preset device attribute parameter and the adjustment parameter can be pre-stored, and then, the first adjustment parameter corresponding to the target device attribute parameter can be determined based on the mapping relationship, and the mapping relationship between the preset environmental parameter and the fine-tuning parameter can be pre-stored, and then, the first fine-tuning parameter corresponding to the target environmental parameter can be determined based on the mapping relationship, and then the first threshold is determined according to the first adjustment parameter, the first fine-tuning parameter and the reference threshold, that is, the first threshold = reference threshold * (1 + The first adjustment parameter) * (1+the first fine-tuning parameter), since the first attribute parameter of the detection point reflects the characteristics of the detection point to a certain extent, different positions have different parameters (material, position, composition, thickness, area, volume, density, function), that is, the corresponding reference threshold can be determined based on its own characteristics. Secondly, the device attribute parameters of the new energy battery module reflect the performance of the battery itself to a certain extent, and then, the threshold can be dynamically adjusted based on its own performance, so that the threshold is more in line with the personality of the new energy battery module itself. Then, the environmental parameters reflect the influence of the physical space to a certain extent, and then, the threshold can be deeply fine-tuned in combination with the influence of the physical space, so that the final first threshold is consistent with the detection point, the new energy battery module itself and the actual physical environment, thereby helping to accurately determine the probability of abnormalities in the subsequent period and helping to perform accurate early warnings.

[0116] It can be seen that the new energy battery module intelligent detection method described in the embodiment of the present application is applied to a robot, and the robot is communicatively connected with the new energy battery module. The new energy battery module includes a temperature sensor, and the method includes: obtaining a voltage data set of a target detection point of the new energy battery module within a preset time period; determining a temperature data set of the target detection point within a preset time period through a temperature sensor; fitting according to the voltage data set to obtain a fitting straight line and a fitting curve segment within a preset time period, the horizontal axis of the first fitting straight line and the fitting curve segment are both time and the vertical axis is both voltage value; obtaining a first slope of the first fitting straight line; determining the maximum and minimum values ​​corresponding to the fitting curve segment, and obtaining multiple maximum values ​​and multiple minimum values; determining the new energy battery module according to the first slope, multiple maximum values ​​and multiple minimum values. The target stability evaluation parameters of the group are obtained; fitting is performed according to the temperature data set to obtain a second fitting straight line, the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value; the second slope of the second fitting straight line is obtained; the target abnormality probability of the new energy battery module is determined according to the target stability evaluation parameters and the second slope; when the target abnormality probability is greater than the preset probability value, the early warning strategy corresponding to the target abnormality probability is determined, and the early warning strategy is executed. First, the robot can be used to perform single-point (detection point) or multi-point battery monitoring on the new energy battery module, and the voltage and temperature dimensions can be used to determine the abnormality probability. Then, the abnormality probability can be accurately determined, which is helpful to perform accurate early warning, and then, the intelligent detection efficiency of the new energy battery module can be improved, thereby ensuring the safety of the new energy battery module.

[0117] In accordance with the above embodiment, please refer to Figure 2 , Figure 2 : is a schematic diagram of the structure of a robot provided in an embodiment of the present application. As shown in the figure, the robot includes a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor. The robot is connected to a new energy battery module in communication, and the new energy battery module includes a temperature sensor. In the embodiment of the present application, the program includes instructions for executing the following steps:

[0118] Acquire a voltage data set of a target detection point of the new energy battery module within a preset time period;

[0119] Determine a temperature data set of the target detection point within the preset time period by the temperature sensor;

[0120] Performing fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within the preset time period, wherein the horizontal axes of the first fitting straight line and the fitting curve segment are both time and the vertical axes are both voltage values;

[0121] Obtaining a first slope of the first fitting straight line;

[0122] Determine the maximum and minimum values ​​corresponding to the fitting curve segment to obtain multiple maximum and multiple minimum values;

[0123] Determine a target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values;

[0124] Perform fitting according to the temperature data set to obtain a second fitting straight line, wherein the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value;

[0125] Obtaining a second slope of the second fitting straight line;

[0126] Determining a target abnormality probability of the new energy battery module according to the target stability evaluation parameter and the second slope;

[0127] When the target abnormality probability is greater than a preset probability value, a warning strategy corresponding to the target abnormality probability is determined and the warning strategy is executed.

[0128] Optionally, in determining the first stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values, the program includes instructions for executing the following steps:

[0129] determining a first stability evaluation parameter corresponding to the first slope;

[0130] Performing a mean square error operation on the multiple maximum values ​​and the multiple minimum values ​​to obtain a first mean square error;

[0131] determining a second stability evaluation parameter corresponding to the first mean square error;

[0132] Determine a second mean square error corresponding to the multiple maximum values;

[0133] Determine a third mean square error corresponding to the multiple minimum values;

[0134] Determine an absolute value of a difference between the second mean square error and the third mean square error, and determine a second weight corresponding to the absolute value, wherein the smaller the absolute value, the larger the second weight;

[0135] Determine a first weight according to the second weight, wherein the sum of the first weight and the second weight is 1;

[0136] A weighted operation is performed based on the first weight, the second weight, the first stability evaluation parameter, and the second stability evaluation parameter to obtain the target stability evaluation parameter.

[0137] Optionally, in determining the target abnormal probability of the new energy battery module according to the target stability evaluation parameter and the second slope, the program includes instructions for executing the following steps:

[0138] When the target stability evaluation parameter is less than a first threshold and the second slope is within a preset range, determining a first deviation between the first threshold and the target stability evaluation parameter; the preset range includes an upper threshold and a lower threshold;

[0139] Determining the target abnormality probability according to the first deviation;

[0140] or,

[0141] When the target stability evaluation parameter is less than the first threshold and the second slope is not within the preset range, determining a monotonic relationship between the first slope and the second slope;

[0142] Determine the target abnormality probability according to the monotonic relationship, the target stability evaluation parameter and the second slope;

[0143] or,

[0144] When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is within the preset range, determining that the target abnormality probability is 0;

[0145] or,

[0146] When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is not within the preset range, if the second slope is greater than the upper threshold, determine the second deviation between the second slope and the upper threshold, and determine the target abnormality probability based on the second deviation; if the second slope is less than the lower threshold, determine the third deviation between the lower threshold and the second slope, and determine the target abnormality probability based on the third deviation.

[0147] Optionally, in determining the target abnormality probability according to the monotonicity relationship, the target stability evaluation parameter and the second slope, the program includes instructions for executing the following steps:

[0148] determining a fourth deviation between the first threshold and the target stability evaluation parameter;

[0149] Determining a first reference abnormality probability according to the fourth deviation;

[0150] Determining a second reference abnormal probability according to the second slope and the preset range, specifically: if the second slope is greater than the upper threshold, determining a fifth deviation between the second slope and the upper threshold, and determining the second reference abnormal probability according to the fifth deviation; if the second slope is less than the lower threshold, determining a sixth deviation between the lower threshold and the second slope, and determining the second reference abnormal probability according to the sixth deviation;

[0151] The target abnormality probability is determined according to the monotonic relationship, the first reference abnormality probability, and the second reference abnormality probability.

[0152] Optionally, in determining the target abnormality probability according to the monotonic relationship, the first reference abnormality probability and the second reference abnormality probability, the program includes instructions for performing the following steps:

[0153] When the monotonic relationship includes that the first slope and the second slope are monotonically consistent, performing a sum operation according to the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability;

[0154] When the monotonic relationship includes a monotonic inconsistency between the first slope and the second slope, determining a target absolute value of a ratio between the first slope and the second slope;

[0155] Determining a target weight pair corresponding to the target absolute value;

[0156] A weighted operation is performed on the target weight pair and the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability.

[0157] It can be seen that the robot described in the embodiment of the present application is communicatively connected with the new energy battery module, and the new energy battery module includes a temperature sensor. The method includes: obtaining a voltage data set of a target detection point of the new energy battery module within a preset time period; determining a temperature data set of the target detection point within a preset time period through a temperature sensor; fitting according to the voltage data set to obtain a fitting straight line and a fitting curve segment within a preset time period, wherein the horizontal axis of the first fitting straight line and the fitting curve segment are both time and the vertical axis are both voltage values; obtaining a first slope of the first fitting straight line; determining the maximum and minimum values ​​corresponding to the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values; determining a target stability evaluation value of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values. price parameter; fitting is performed according to the temperature data set to obtain a second fitting straight line, the horizontal axis of the second fitting straight line is both time and the vertical axis is both temperature value; the second slope of the second fitting straight line is obtained; the target abnormal probability of the new energy battery module is determined according to the target stability evaluation parameter and the second slope; when the target abnormal probability is greater than the preset probability value, the early warning strategy corresponding to the target abnormal probability is determined, and the early warning strategy is executed. First, the robot can be used to perform single-point (detection point) or multi-point battery monitoring on the new energy battery module, and the voltage and temperature dimensions can be used to determine the abnormal probability, and then, the abnormal probability can be accurately determined, which is helpful to perform accurate early warning, and then, the intelligent detection efficiency of the new energy battery module can be improved, thereby ensuring the safety of the new energy battery module.

[0158] Figure 3 It is a functional unit composition block diagram of a new energy battery module intelligent detection system 300 involved in an embodiment of the present application. The new energy battery module intelligent detection system 300 is applied to a robot. The robot is connected to a new energy battery module in communication. The new energy battery module includes a temperature sensor. The new energy battery module intelligent detection system 300 includes: an acquisition unit 301, a determination unit 302, and a fitting unit 303, wherein:

[0159] The acquisition unit 301 is used to acquire a voltage data set of a target detection point of the new energy battery module within a preset time period;

[0160] The determining unit 302 is used to determine a temperature data set of the target detection point within the preset time period through the temperature sensor;

[0161] The fitting unit 303 is used to perform fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within the preset time period, wherein the horizontal axes of the first fitting straight line and the fitting curve segment are both time and the vertical axes are both voltage values;

[0162] The acquisition unit 301 is further configured to acquire a first slope of the first fitting straight line;

[0163] The determining unit 302 is further used to determine the maximum value and the minimum value corresponding to the fitting curve segment, and obtain multiple maximum values ​​and multiple minimum values; determine the target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values;

[0164] The fitting unit 303 is further used to perform fitting according to the temperature data set to obtain a second fitting straight line, wherein the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value;

[0165] The acquisition unit 301 is further used to acquire a second slope of the second fitting straight line;

[0166] The determination unit 302 is also used to determine the target abnormality probability of the new energy battery module according to the target stability evaluation parameter and the second slope; when the target abnormality probability is greater than a preset probability value, determine a warning strategy corresponding to the target abnormality probability and execute the warning strategy.

[0167] Optionally, in determining the first stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values, the determining unit 302 is specifically used to:

[0168] determining a first stability evaluation parameter corresponding to the first slope;

[0169] Performing a mean square error operation on the multiple maximum values ​​and the multiple minimum values ​​to obtain a first mean square error;

[0170] determining a second stability evaluation parameter corresponding to the first mean square error;

[0171] Determine a second mean square error corresponding to the multiple maximum values;

[0172] Determine a third mean square error corresponding to the multiple minimum values;

[0173] Determine an absolute value of a difference between the second mean square error and the third mean square error, and determine a second weight corresponding to the absolute value, wherein the smaller the absolute value, the larger the second weight;

[0174] Determine a first weight according to the second weight, wherein the sum of the first weight and the second weight is 1;

[0175] A weighted operation is performed based on the first weight, the second weight, the first stability evaluation parameter, and the second stability evaluation parameter to obtain the target stability evaluation parameter.

[0176] Optionally, in determining the target abnormal probability of the new energy battery module according to the target stability evaluation parameter and the second slope, the determining unit 302 is specifically used to:

[0177] When the target stability evaluation parameter is less than a first threshold and the second slope is within a preset range, determining a first deviation between the first threshold and the target stability evaluation parameter; the preset range includes an upper threshold and a lower threshold;

[0178] Determining the target abnormality probability according to the first deviation;

[0179] or,

[0180] When the target stability evaluation parameter is less than the first threshold and the second slope is not within the preset range, determining a monotonic relationship between the first slope and the second slope;

[0181] Determine the target abnormality probability according to the monotonic relationship, the target stability evaluation parameter and the second slope;

[0182] or,

[0183] When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is within the preset range, determining that the target abnormality probability is 0;

[0184] or,

[0185] When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is not within the preset range, if the second slope is greater than the upper threshold, determine the second deviation between the second slope and the upper threshold, and determine the target abnormality probability based on the second deviation; if the second slope is less than the lower threshold, determine the third deviation between the lower threshold and the second slope, and determine the target abnormality probability based on the third deviation.

[0186] Optionally, in determining the target abnormality probability according to the monotonicity relationship, the target stability evaluation parameter and the second slope, the determining unit 302 is specifically configured to:

[0187] determining a fourth deviation between the first threshold and the target stability evaluation parameter;

[0188] Determining a first reference abnormality probability according to the fourth deviation;

[0189] Determining a second reference abnormal probability according to the second slope and the preset range, specifically: if the second slope is greater than the upper threshold, determining a fifth deviation between the second slope and the upper threshold, and determining the second reference abnormal probability according to the fifth deviation; if the second slope is less than the lower threshold, determining a sixth deviation between the lower threshold and the second slope, and determining the second reference abnormal probability according to the sixth deviation;

[0190] The target abnormality probability is determined according to the monotonic relationship, the first reference abnormality probability, and the second reference abnormality probability.

[0191] Optionally, in determining the target abnormality probability according to the monotonic relationship, the first reference abnormality probability and the second reference abnormality probability, the determining unit 302 is specifically configured to:

[0192] When the monotonic relationship includes that the first slope and the second slope are monotonically consistent, performing a sum operation according to the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability;

[0193] When the monotonic relationship includes a monotonic inconsistency between the first slope and the second slope, determining a target absolute value of a ratio between the first slope and the second slope;

[0194] Determining a target weight pair corresponding to the target absolute value;

[0195] A weighted operation is performed on the target weight pair and the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability.

[0196] It can be seen that the new energy battery module intelligent detection system described in the embodiment of the present application is applied to a robot, and the robot is communicatively connected with the new energy battery module. The new energy battery module includes a temperature sensor, and the method includes: obtaining a voltage data set of a target detection point of the new energy battery module within a preset time period; determining a temperature data set of the target detection point within a preset time period through a temperature sensor; fitting according to the voltage data set to obtain a fitting straight line and a fitting curve segment within a preset time period, the horizontal axis of the first fitting straight line and the fitting curve segment are both time and the vertical axis is both voltage value; obtaining a first slope of the first fitting straight line; determining the maximum and minimum values ​​corresponding to the fitting curve segment, and obtaining multiple maximum values ​​and multiple minimum values; determining the new energy battery module according to the first slope, multiple maximum values ​​and multiple minimum values. The target stability evaluation parameters of the group are obtained; fitting is performed according to the temperature data set to obtain a second fitting straight line, the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value; the second slope of the second fitting straight line is obtained; the target abnormality probability of the new energy battery module is determined according to the target stability evaluation parameters and the second slope; when the target abnormality probability is greater than the preset probability value, the early warning strategy corresponding to the target abnormality probability is determined, and the early warning strategy is executed. First, the robot can be used to perform single-point (detection point) or multi-point battery monitoring on the new energy battery module, and the voltage and temperature dimensions can be used to determine the abnormality probability. Then, the abnormality probability can be accurately determined, which is helpful to perform accurate early warning, and then, the intelligent detection efficiency of the new energy battery module can be improved, thereby ensuring the safety of the new energy battery module.

[0197] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes a robot.

[0198] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes a robot.

[0199] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0200] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0201] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0202] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0203] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0204] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0205] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0206] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A new energy battery module intelligent detection method, characterized in that: Applied to a robot, the robot is communicatively connected to a new energy battery module, the new energy battery module includes a temperature sensor, and the method includes: Acquire a voltage data set of a target detection point of the new energy battery module within a preset time period; Determine a temperature data set of the target detection point within the preset time period by the temperature sensor; Performing fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within the preset time period, wherein the horizontal axes of the first fitting straight line and the fitting curve segment are both time and the vertical axes are both voltage values; Obtaining a first slope of the first fitting straight line; Determine the maximum and minimum values ​​corresponding to the fitting curve segment to obtain multiple maximum and multiple minimum values; Determine a target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values; Perform fitting according to the temperature data set to obtain a second fitting straight line, wherein the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value; Obtaining a second slope of the second fitting straight line; Determining a target abnormality probability of the new energy battery module according to the target stability evaluation parameter and the second slope; When the target abnormality probability is greater than a preset probability value, a warning strategy corresponding to the target abnormality probability is determined and the warning strategy is executed.

2. The method according to claim 1, characterized in that The step of determining the target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values, and the multiple minimum values ​​includes: determining a first stability evaluation parameter corresponding to the first slope; Performing a mean square error operation on the multiple maximum values ​​and the multiple minimum values ​​to obtain a first mean square error; determining a second stability evaluation parameter corresponding to the first mean square error; Determine a second mean square error corresponding to the multiple maximum values; Determine a third mean square error corresponding to the multiple minimum values; Determine an absolute value of a difference between the second mean square error and the third mean square error, and determine a second weight corresponding to the absolute value, wherein the smaller the absolute value, the larger the second weight; Determine a first weight according to the second weight, wherein the sum of the first weight and the second weight is 1; A weighted operation is performed based on the first weight, the second weight, the first stability evaluation parameter, and the second stability evaluation parameter to obtain the target stability evaluation parameter.

3. The method according to claim 2, characterized in that The step of determining the target abnormal probability of the new energy battery module according to the target stability evaluation parameter and the second slope includes: When the target stability evaluation parameter is less than a first threshold and the second slope is within a preset range, determining a first deviation between the first threshold and the target stability evaluation parameter; the preset range includes an upper threshold and a lower threshold; Determining the target abnormality probability according to the first deviation; or, When the target stability evaluation parameter is less than the first threshold and the second slope is not within the preset range, determining a monotonic relationship between the first slope and the second slope; Determine the target abnormality probability according to the monotonic relationship, the target stability evaluation parameter and the second slope; or, When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is within the preset range, determining that the target abnormality probability is 0; or, When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is not within the preset range, if the second slope is greater than the upper threshold, determine the second deviation between the second slope and the upper threshold, and determine the target abnormality probability based on the second deviation; if the second slope is less than the lower threshold, determine the third deviation between the lower threshold and the second slope, and determine the target abnormality probability based on the third deviation.

4. The method according to claim 3, characterized in that The determining the target abnormality probability according to the monotonicity relationship, the target stability evaluation parameter and the second slope includes: determining a fourth deviation between the first threshold and the target stability evaluation parameter; Determining a first reference abnormality probability according to the fourth deviation; Determining a second reference abnormal probability according to the second slope and the preset range, specifically: if the second slope is greater than the upper threshold, determining a fifth deviation between the second slope and the upper threshold, and determining the second reference abnormal probability according to the fifth deviation; if the second slope is less than the lower threshold, determining a sixth deviation between the lower threshold and the second slope, and determining the second reference abnormal probability according to the sixth deviation; The target abnormality probability is determined according to the monotonic relationship, the first reference abnormality probability, and the second reference abnormality probability.

5. The method according to claim 4, characterized in that The determining the target abnormality probability according to the monotonic relationship, the first reference abnormality probability and the second reference abnormality probability comprises: When the monotonic relationship includes that the first slope and the second slope are monotonically consistent, performing a sum operation according to the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability; When the monotonic relationship includes a monotonic inconsistency between the first slope and the second slope, determining a target absolute value of a ratio between the first slope and the second slope; Determining a target weight pair corresponding to the target absolute value; A weighted operation is performed on the target weight pair and the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability.

6. A new energy battery module intelligent detection system, characterized in that: Applied to a robot, the robot is connected to a new energy battery module for communication, the new energy battery module includes a temperature sensor, the system includes: an acquisition unit, a determination unit, and a fitting unit, wherein: The acquisition unit is used to acquire a voltage data set of a target detection point of the new energy battery module within a preset time period; The determining unit is used to determine a temperature data set of the target detection point within the preset time period through the temperature sensor; The fitting unit is used to perform fitting according to the voltage data set to obtain a first fitting straight line and a fitting curve segment within the preset time period, wherein the horizontal axes of the first fitting straight line and the fitting curve segment are both time and the vertical axes are both voltage values; The acquisition unit is further used to acquire a first slope of the first fitting straight line; The determination unit is further used to determine the maximum value and the minimum value corresponding to the fitting curve segment, and obtain multiple maximum values ​​and multiple minimum values; determine the target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values; The fitting unit is further used to perform fitting according to the temperature data set to obtain a second fitting straight line, wherein the horizontal axis of the second fitting straight line is time and the vertical axis is temperature value; The acquisition unit is further used to acquire a second slope of the second fitting straight line; The determination unit is also used to determine the target abnormality probability of the new energy battery module according to the target stability evaluation parameter and the second slope; when the target abnormality probability is greater than a preset probability value, determine a warning strategy corresponding to the target abnormality probability and execute the warning strategy.

7. The system according to claim 6, characterized in that In determining the target stability evaluation parameter of the new energy battery module according to the first slope, the multiple maximum values ​​and the multiple minimum values, the determining unit is specifically used for: determining a first stability evaluation parameter corresponding to the first slope; Performing a mean square error operation on the multiple maximum values ​​and the multiple minimum values ​​to obtain a first mean square error; determining a second stability evaluation parameter corresponding to the first mean square error; Determine a second mean square error corresponding to the multiple maximum values; Determine a third mean square error corresponding to the multiple minimum values; Determine an absolute value of a difference between the second mean square error and the third mean square error, and determine a second weight corresponding to the absolute value, wherein the smaller the absolute value, the larger the second weight; Determine a first weight according to the second weight, wherein the sum of the first weight and the second weight is 1; A weighted operation is performed based on the first weight, the second weight, the first stability evaluation parameter, and the second stability evaluation parameter to obtain the target stability evaluation parameter.

8. The system according to claim 7, characterized in that In determining the target abnormal probability of the new energy battery module according to the target stability evaluation parameter and the second slope, the determining unit is specifically used for: When the target stability evaluation parameter is less than a first threshold and the second slope is within a preset range, determining a first deviation between the first threshold and the target stability evaluation parameter; the preset range includes an upper threshold and a lower threshold; Determining the target abnormality probability according to the first deviation; or, When the target stability evaluation parameter is less than the first threshold and the second slope is not within the preset range, determining a monotonic relationship between the first slope and the second slope; Determine the target abnormality probability according to the monotonic relationship, the target stability evaluation parameter and the second slope; or, When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is within the preset range, determining that the target abnormality probability is 0; or, When the target stability evaluation parameter is greater than or equal to the first threshold and the second slope is not within the preset range, if the second slope is greater than the upper threshold, determine the second deviation between the second slope and the upper threshold, and determine the target abnormality probability based on the second deviation; if the second slope is less than the lower threshold, determine the third deviation between the lower threshold and the second slope, and determine the target abnormality probability based on the third deviation.

9. The system according to claim 8, characterized in that In the aspect of determining the target abnormality probability according to the monotonicity relationship, the target stability evaluation parameter and the second slope, the determining unit is specifically used for: determining a fourth deviation between the first threshold and the target stability evaluation parameter; Determining a first reference abnormality probability according to the fourth deviation; Determining a second reference abnormal probability according to the second slope and the preset range, specifically: if the second slope is greater than the upper threshold, determining a fifth deviation between the second slope and the upper threshold, and determining the second reference abnormal probability according to the fifth deviation; if the second slope is less than the lower threshold, determining a sixth deviation between the lower threshold and the second slope, and determining the second reference abnormal probability according to the sixth deviation; The target abnormality probability is determined according to the monotonic relationship, the first reference abnormality probability, and the second reference abnormality probability.

10. The system according to claim 9, characterized in that In determining the target abnormality probability according to the monotonic relationship, the first reference abnormality probability and the second reference abnormality probability, the determining unit is specifically configured to: When the monotonic relationship includes that the first slope and the second slope are monotonically consistent, performing a sum operation according to the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability; When the monotonic relationship includes a monotonic inconsistency between the first slope and the second slope, determining a target absolute value of a ratio between the first slope and the second slope; Determining a target weight pair corresponding to the target absolute value; A weighted operation is performed on the target weight pair and the first reference abnormal probability and the second reference abnormal probability to obtain the target abnormal probability.

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