Automobile emergency power supply real-time detection method, electronic equipment and storage medium

Abstract

The invention discloses an automobile emergency power supply real-time detection method, electronic equipment and a storage medium, and relates to the technical field of automobile emergency power supply detection, and the method comprises the following steps: carrying out the statistics of historical state parameters based on big data and a database of a manufacturer; analyzing an abnormal data set of the automobile emergency power supply through abnormal data in the historical state parameters; analyzing a security relationship between historical state parameters in the abnormal data set; operating parameters of the automobile emergency power supply are collected in real time, meanwhile, independent safety analysis is conducted on the operating parameters, and whether the operating parameters are abnormal or not is judged; analyzing whether different operation parameters accord with a safety relation so as to judge whether the automobile emergency power supply is abnormal; the method and the device are used for solving the problem that the judgment accuracy of the health state is relatively low due to the fact that factors considered when the health state of the automobile emergency battery is judged in the existing automobile emergency power supply detection technology are not comprehensive enough.

Description

Technical Field

[0001] This invention relates to the field of automotive emergency power supply testing technology, specifically to a real-time testing method, electronic device, and storage medium for automotive emergency power supplies. Background Technology

[0002] Automotive emergency power supply testing technology refers to the general term for technologies that use specific methods and equipment to measure, analyze, and evaluate the key performance parameters of automotive emergency jump starters in order to determine their current status, remaining availability, and safety in real time or periodically.

[0003] Existing automotive emergency power supply testing technologies typically employ safety threshold judgments for each parameter to monitor for anomalies in real time. This method analyzes each parameter independently, but there is a certain balance relationship between different parameters. If this balance is disrupted, it can also lead to anomalies in the automotive emergency power supply. Furthermore, existing automotive emergency power supply testing technologies do not consider all factors when assessing the health status of automotive emergency batteries, resulting in low accuracy in health status assessments. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It utilizes big data and manufacturer databases to statistically analyze historical state parameters, then analyzes normal and abnormal segments between any two historical state parameters. Based on these normal and abnormal segments, it analyzes abnormal data groups of the vehicle emergency power supply, then analyzes the safety relationships between historical state parameters within these abnormal data groups. It also collects the vehicle emergency power supply's operating parameters in real time and performs independent safety analysis on these parameters to determine if they are abnormal. Finally, it analyzes whether different operating parameters conform to safety relationships to determine if the vehicle emergency power supply is abnormal. This addresses the problem that existing vehicle emergency power supply detection technologies do not consider all factors comprehensively when assessing the health status of vehicle emergency batteries, resulting in low accuracy in health status assessments.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for real-time detection of automotive emergency power supplies, comprising the following steps:

[0006] Historical status parameters are statistically analyzed based on big data and the manufacturer's database;

[0007] Analyze abnormal data groups of the vehicle emergency power supply by identifying anomalous data in historical status parameters;

[0008] Analyze the security relationships among historical state parameters in abnormal data groups;

[0009] Real-time acquisition of the vehicle's emergency power supply's operating parameters, and independent safety analysis of the operating parameters to determine whether the operating parameters are abnormal;

[0010] Analyze whether different operating parameters conform to safe relationships to determine if the vehicle's emergency power supply is malfunctioning.

[0011] Furthermore, based on big data and the manufacturer's database, the historical status parameters are statistically analyzed, including the following sub-steps:

[0012] Historical status parameters of commercially available car emergency power supplies are obtained through big data analysis.

[0013] The historical status parameters of the vehicle emergency power supply can be obtained from the database saved by the manufacturer when testing the vehicle emergency power supply.

[0014] The historical status parameters are historical data of the operating parameters of the vehicle emergency power supply. The operating parameters include battery voltage, battery current, battery internal resistance, battery temperature, and self-discharge rate. The operating parameters in the historical status parameters are named historical voltage, historical current, historical internal resistance, historical temperature, and historical rate, respectively. The historical status parameters also record the power supply status, which includes normal status and abnormal status.

[0015] Furthermore, analyzing the abnormal data sets of the vehicle emergency power supply through anomalies in historical status parameters includes the following sub-steps:

[0016] Analyze the normal and abnormal line segments between any two historical state parameters;

[0017] Analysis of abnormal data groups in automotive emergency power supplies based on normal and abnormal line segments.

[0018] Furthermore, analyzing the normal and abnormal segments between any two historical state parameters includes the following sub-steps:

[0019] Choose any two historical state parameters as the first and second parameters;

[0020] Establish a two-dimensional coordinate system with the first parameter as the X-axis and the second parameter as the Y-axis, and name it the parameter relationship diagram. Enter the first parameter and the second parameter into the parameter relationship diagram to form parameter coordinate points.

[0021] Enter the first and second parameters corresponding to the historical state parameters when the power supply is in normal state into the parameter relationship diagram, and name the obtained parameter coordinate points as parameter normal points. Enter the first and second parameters corresponding to the historical state parameters when the power supply is in abnormal state into the parameter relationship diagram, and name the obtained parameter coordinate points as parameter abnormal points.

[0022] Get the point with the smallest X-axis among the parameter coordinate points and name it point 1; get the point with the largest X-axis among the parameter coordinate points and name it point 2; get the point with the smallest Y-axis among the parameter coordinate points and name it point 3; get the point with the largest Y-axis among the parameter coordinate points and name it point 4.

[0023] Connect point 1 to point 3 to obtain edge line 1. Connect point 1 to point 4 to obtain edge line 2. Connect point 2 to point 3 to obtain edge line 3. Connect point 2 to point 4 to obtain edge line 4. Edge lines 1, 2, 3 and 4 are collectively referred to as set boundary lines.

[0024] The first, second, third, and fourth edge lines obtained from the normal point analysis are named the first normal edge line, the second normal edge line, the third normal edge line, and the fourth normal edge line, respectively, and are collectively referred to as normal line segments.

[0025] The first, second, third, and fourth edge lines obtained from the parameter anomaly point analysis are named the first anomaly edge line, the second anomaly edge line, the third anomaly edge line, and the fourth anomaly edge line, respectively, and are collectively referred to as anomaly line segments.

[0026] Furthermore, analyzing abnormal data sets of automotive emergency power supplies based on normal and abnormal line segments includes the following sub-steps:

[0027] The area enclosed by edge line 1, edge line 2, edge line 3, and edge line 4 is named the feature area, and the parameter coordinate points outside the feature area are named the outside coordinates.

[0028] For any set of coordinates outside the region, obtain the boundary line of the set that is closest to the coordinates outside the region and name it the proximity line segment. When analyzing any set of boundary lines, name it the line segment to be analyzed. Name the coordinates outside the region that are the proximity line segments to be analyzed as the coordinates to be analyzed.

[0029] Name the coordinate furthest from the line segment to be analyzed as the outer endpoint. Name the two endpoints of the line segment to be analyzed as the first endpoint and the second endpoint, respectively. Connect the first endpoint to the outer endpoint and name it the first outer line. At the same time, connect the second endpoint to the outer endpoint and name it the second outer line.

[0030] Remove the line segment to be analyzed from the set boundary line, and include the first outer line and the second outer line into the set boundary line. Perform the same analysis on each set boundary line until there are no more coordinates outside the area, and obtain the final set boundary line.

[0031] The region enclosed by the final set boundary line is named the effective feature region, the effective feature region obtained from the analysis of normal line segments is named the normal effective feature region, and the effective feature region obtained from the analysis of abnormal line segments is named the abnormal effective feature region.

[0032] Construct the circumscribed ellipses of the normal and abnormal feature regions and the normal feature circle and the abnormal feature circle, respectively. Obtain the centers of the normal and abnormal feature circles and name them the normal center and the abnormal center, respectively.

[0033] Calculate the difference between the normal and abnormal circle centers, naming it the characteristic difference. Combine historical state parameters pairwise to obtain parameter sets. Analyze the characteristic difference of each parameter set, numbering them in ascending order and using the symbol P. n express;

[0034] With n as the horizontal axis, P n Establish a two-dimensional coordinate system for the vertical axis and name it the feature distribution map. Name the coordinate points in the feature distribution map the feature distribution points. Connect two adjacent feature distribution points to obtain the feature distribution lines. Name the angle between two adjacent feature distribution lines the feature distribution angle.

[0035] Find the minimum value among the feature distribution angles, name it the turning angle, and assign the P value corresponding to the feature distribution point at the turning angle. n Marked as P m P for n>m n The corresponding parameter group is named the abnormal data group.

[0036] Furthermore, analyzing the security relationships between historical state parameters in the abnormal data set includes the following sub-steps:

[0037] When analyzing any abnormal data set, name it the data set to be analyzed, and name the two historical state parameters in the data set to be analyzed as parameter A and parameter B respectively.

[0038] Establish a two-dimensional coordinate system with parameter A as the X-axis and parameter B as the Y-axis, and name it the parameter relationship analysis diagram. Enter parameter A and parameter B into the parameter relationship analysis diagram.

[0039] The normal and abnormal feature circles between parameter A and parameter B in the parameter relationship analysis diagram are named the normal parameter circle and the abnormal parameter circle, respectively.

[0040] Analyze the normal parameter circles and abnormal parameter circles between the historical state parameters in each abnormal data group; these normal parameter circles and abnormal parameter circles represent the safety relationships.

[0041] Furthermore, the real-time acquisition of the vehicle's emergency power supply's operating parameters, along with independent safety analysis of these parameters to determine whether any abnormalities exist, includes the following sub-steps:

[0042] Real-time acquisition of the vehicle's emergency power supply's operating parameters, with each parameter having a corresponding preset safety threshold;

[0043] Determine whether the operating parameters are within the preset safety threshold. If so, output a normal parameter signal; otherwise, output an abnormal parameter signal.

[0044] If an abnormal output parameter signal is detected, it indicates that the vehicle's emergency power supply is malfunctioning.

[0045] Furthermore, analyzing whether different operating parameters conform to safety relationships to determine whether the vehicle's emergency power supply is malfunctioning includes the following sub-steps:

[0046] When analyzing any abnormal data set, name it the target analysis set, and label the running parameters corresponding to the two historical state parameters in the target analysis set as α and β respectively.

[0047] Enter α and β into the parameter relationship analysis diagram corresponding to the target analysis group, and name the obtained coordinate points as target points;

[0048] If the target point is within the normal parameter circle, the output parameters are combined with the normal signal; if the target point is within the abnormal parameter circle, the output parameters are combined with the abnormal signal; otherwise, the output parameters are combined with the undetermined signal.

[0049] If the output parameters are combined with the undetermined signal, then obtain the centers of the normal parameter circle and the abnormal parameter circle, and name them the first center and the second center respectively. Calculate the distance between the target point and the first center, and name it the normal discrete value. Calculate the distance between the target point and the second center, and name it the abnormal discrete value.

[0050] If the normal discrete value is greater than the abnormal discrete value, the output parameters are combined with the normal signal; otherwise, the output parameters are combined with the abnormal signal.

[0051] If the output parameters show an abnormal signal, it indicates that the vehicle's emergency power supply is malfunctioning.

[0052] If the vehicle emergency power supply is flagged as malfunctioning, the information will be sent to the user via wireless communication.

[0053] The operating parameters also include battery level;

[0054] The system independently detects battery power and notifies the user via wireless communication if the battery level falls below the optimal threshold, reminding the user to charge the battery in time.

[0055] Secondly, this application provides an electronic device including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method described above are performed.

[0056] Thirdly, this application provides a storage medium on which a computer program is stored, which, when executed by a processor, performs the steps of the method described above.

[0057] The beneficial effects of this invention are as follows: By statistically analyzing historical status parameters based on big data and manufacturer databases, and then analyzing the normal and abnormal segments between any two historical status parameters, and further analyzing the abnormal data groups of the vehicle emergency power supply based on the normal and abnormal segments, the advantage is that by analyzing two operating parameters that have a certain balance relationship with each other, the combination analysis of different operating parameters can be performed, thereby improving the accuracy and comprehensiveness of vehicle emergency power supply detection.

[0058] This invention analyzes the safety relationships between historical state parameters in abnormal data sets, collects the operating parameters of the vehicle emergency power supply in real time, and performs independent safety analysis on the operating parameters to determine whether the operating parameters are abnormal. Finally, it analyzes whether the different operating parameters conform to the safety relationships to determine whether the vehicle emergency power supply is abnormal. The advantage is that it not only analyzes whether each operating parameter exceeds the limit, but also analyzes whether the balance relationship between different operating parameters is broken, which may lead to abnormalities when the vehicle emergency power supply is activated, thus improving the accuracy and effectiveness of vehicle emergency power supply detection. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0060] Figure 2 This is a schematic diagram of the parameter relationship of the present invention;

[0061] Figure 3 This is a schematic diagram of the boundary lines of the set in this invention;

[0062] Figure 4 This is a schematic diagram of the abnormal line segment of the present invention;

[0063] Figure 5 This is a schematic diagram of the line segment to be analyzed and the coordinates to be analyzed in this invention;

[0064] Figure 6 This is a schematic diagram of the first and second outer rays of the present invention;

[0065] Figure 7 This is a schematic diagram of the new set boundary line of the present invention;

[0066] Figure 8 This is a schematic diagram of the effective feature area of ​​the present invention;

[0067] Figure 9 This is a schematic diagram of the normal feature circle and the abnormal feature circle of the present invention;

[0068] Figure 10 This is a schematic diagram of the feature distribution of the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Example 1, please refer to Figure 1 As shown, this application provides a method for real-time detection of automotive emergency power supplies, including the following steps:

[0071] Step S1 involves statistically analyzing historical status parameters based on big data and the manufacturer's database. Step S1 includes the following sub-steps:

[0072] Step S101: Obtain historical status parameters of commercially available car emergency power supplies through big data.

[0073] Step S102: Obtain the historical status parameters of the vehicle emergency power supply from the database saved by the manufacturer when testing the vehicle emergency power supply.

[0074] Step S103: The historical status parameters are historical data of the operating parameters of the vehicle emergency power supply. The operating parameters include battery voltage, battery current, battery internal resistance, battery temperature and self-discharge rate. The operating parameters in the historical status parameters are named historical voltage, historical current, historical internal resistance, historical temperature and historical rate respectively. At the same time, the power supply status is also recorded in the historical status parameters. The power supply status includes normal status and abnormal status.

[0075] In specific implementation, the operating parameters also include battery power. Collecting historical status parameters and power status is actually to establish a dataset for analysis and reference in this embodiment, which is used to reveal the numerical relationship between the various operating parameters of the vehicle emergency power supply under different working environments. The historical status parameters are all data where all operating parameters do not exceed their own safety thresholds. This embodiment does not provide a detailed description of the data collection process.

[0076] Step S2 involves analyzing the abnormal data groups of the vehicle emergency power supply through data analysis of anomalies in the historical status parameters; Step S2 includes the following sub-steps:

[0077] Step S201: Analyze the normal and abnormal line segments between any two historical state parameters;

[0078] Step S201 includes the following sub-steps:

[0079] Step S201.1: Select any two historical state parameters as the first parameter and the second parameter;

[0080] Please see Figure 2 As shown, in step S201.2, a two-dimensional coordinate system is established with the first parameter as the X-axis and the second parameter as the Y-axis, named the parameter relationship diagram, and the first parameter and the second parameter are entered into the parameter relationship diagram to form parameter coordinate points;

[0081] Step S201.3: Enter the first and second parameters corresponding to the historical state parameters when the power supply is in normal state into the parameter relationship diagram, and name the obtained parameter coordinate points as parameter normal points. Enter the first and second parameters corresponding to the historical state parameters when the power supply is in abnormal state into the parameter relationship diagram, and name the obtained parameter coordinate points as parameter abnormal points.

[0082] Step S201.4: Obtain the coordinate point with the smallest X among the parameter coordinate points and name it point 1; obtain the coordinate point with the largest X among the parameter coordinate points and name it point 2; obtain the coordinate point with the smallest Y among the parameter coordinate points and name it point 3; obtain the coordinate point with the largest Y among the parameter coordinate points and name it point 4.

[0083] Please see Figure 3 As shown, in step S201.5, connect point 1 to point 3 to obtain edge line 1, connect point 1 to point 4 to obtain edge line 2, connect point 2 to point 3 to obtain edge line 3, and connect point 2 to point 4 to obtain edge line 4. Edge lines 1, 2, 3 and 4 are collectively referred to as set boundary lines.

[0084] Step S201.6: The first edge line, the second edge line, the third edge line and the fourth edge line obtained from the normal point analysis are named as the first normal edge line, the second normal edge line, the third normal edge line and the fourth normal edge line respectively, and are collectively referred to as normal line segments.

[0085] Please see Figure 4 As shown in step S201.7, the first, second, third and fourth edge lines obtained from the parameter anomaly point analysis are named as first abnormal edge line, second abnormal edge line, third abnormal edge line and fourth abnormal edge line respectively, and are collectively referred to as abnormal line segments.

[0086] In practical implementation, taking historical temperature and historical rate as examples, assuming historical temperature is the first parameter and historical rate is the second parameter, the parameter relationship diagram is constructed as follows: Figure 2 As shown, Figure 2 The text displays the parameter coordinates points constructed from historical state parameters when the power supply was in a normal state, and the resulting set boundary lines are shown below. Figure 3 As shown, Figure 3The boundary lines of the set in the example are normal line segments; similarly, abnormal line segments can be obtained as follows: Figure 4 As shown;

[0087] Step S202: Analyze the abnormal data groups of the vehicle emergency power supply based on normal and abnormal line segments;

[0088] Step S202 includes the following sub-steps:

[0089] Step S202.1: Name the area enclosed by edge line 1, edge line 2, edge line 3 and edge line 4 as the feature area, and name the parameter coordinate points outside the feature area as the outside coordinates.

[0090] Please see Figure 5 As shown, in step S202.2, for any outside coordinate, obtain the set boundary line that is closest to the outside coordinate and name it the near distance line segment. When analyzing any set boundary line, name it the line segment to be analyzed and name the outside coordinate that is the line segment to be analyzed.

[0091] In specific implementation, when calculating the distance between the external coordinates and the boundary line of the set, if there is a perpendicular line between the external coordinates and the boundary line, the length of the perpendicular line is calculated. If there is no perpendicular line between the external coordinates and the boundary line, the distance between the two endpoints on the boundary line closest to the external coordinates and the external coordinates is calculated. This distance is used as the distance between the external coordinates and the boundary line, thereby finding the shortest line segment. In this embodiment, we use... Figure 3 Taking the third boundary line in the diagram as an example, the analysis process of the effective feature region is illustrated. Figure 5 The diagram shows the line segment to be analyzed and the coordinates to be analyzed. The dashed line represents the line segment to be analyzed, and the unfilled hollow circle represents the coordinates to be analyzed.

[0092] Please see Figure 6 As shown, in step S202.3, the coordinate furthest from the line segment to be analyzed is named the outer endpoint, the two endpoints of the line segment to be analyzed are named the first endpoint and the second endpoint respectively, the first endpoint is connected to the outer endpoint and named the first outer line, and the second endpoint is connected to the outer endpoint and named the second outer line.

[0093] Please see Figure 7 As shown, in step S202.4, the line segment to be analyzed is removed from the set boundary line, and the first outer line and the second outer line are included in the set boundary line. The same analysis is performed on each set boundary line until there are no more coordinates outside the area, and the final set boundary line is obtained.

[0094] Please see Figure 8As shown, in step S202.5, the region enclosed by the final set boundary line is named the feature effective region, the feature effective region obtained from the normal line segment analysis is named the feature normal effective region, and the feature effective region obtained from the abnormal line segment analysis is named the feature abnormal effective region.

[0095] Please see Figure 9 As shown, in step S202.6, construct the circumscribed ellipse of the normal effective region and the abnormal effective region of the feature, and name them the normal feature circle and the abnormal feature circle, respectively. Obtain the center of the normal feature circle and the abnormal feature circle, and name them the normal center and the abnormal center, respectively.

[0096] In specific implementation, the first and second external lines are obtained by connection, as follows: Figure 6 As shown, after removing the line segment to be analyzed from the set boundary line and incorporating the first and second outer lines into the set boundary line, a new set boundary line is obtained as follows. Figure 7 As shown, similarly, each set boundary line is analyzed to ultimately obtain the effective feature region, as shown below. Figure 8 As shown, the effective feature region reveals the distribution characteristics of the magnitudes between the first and second parameters. Then, normal feature circles and abnormal feature circles are constructed as follows: Figure 9 As shown;

[0097] Step S202.7: Calculate the difference between the normal circle center and the abnormal circle center, named the characteristic difference. Combine the historical state parameters pairwise to obtain parameter groups. Analyze the characteristic difference of each parameter group, and number the characteristic differences in ascending order, using the symbol P. n express;

[0098] In practice, the characteristic difference was 7.35. Historical voltage, historical current, historical internal resistance, historical temperature, and historical rate were combined in pairs to obtain a total of 10 parameter groups. Each parameter group was analyzed to obtain 10 characteristic differences, which were numbered P1 to P... 10Because different parameters have different numerical units and their magnitudes vary significantly, the calculated characteristic differences also differ considerably. Therefore, in actual calculations, a parameter set must be selected as a benchmark. For example, when using the historical temperature and historical rate listed in this embodiment as benchmarks, the first parameter is the historical temperature, with a value range of 21 to 45, and the second parameter is the historical rate, with a value range of 2 to 5. Therefore, when analyzing other historical state parameters, they need to be adjusted to values ​​corresponding to 21 to 45 and 2 to 5. For example, when the first parameter is the historical current and the second parameter is the historical voltage, assuming... Assuming the historical current range is 100A to 500A and the historical voltage range is 50V to 100V, the normalized data for each historical current and historical voltage is calculated. For example, if a historical current is 200A, its normalized data is (200-100) / (500-100) = 0.25. Then, the normalized data is converted to a value corresponding to the historical temperature range using 0.25×(45-21)+21. This gives the X-axis value of 27 for the 200A historical current in the parameter relationship graph, thus balancing the value of each feature difference.

[0099] Please see Figure 10 As shown, in step S202.8, with n as the horizontal axis, P n Establish a two-dimensional coordinate system for the vertical axis and name it the feature distribution map. Name the coordinate points in the feature distribution map the feature distribution points. Connect two adjacent feature distribution points to obtain the feature distribution lines. Name the angle between two adjacent feature distribution lines the feature distribution angle.

[0100] Step S202.9: Obtain the minimum value among the feature distribution angles, name it the turning angle, and assign the P value corresponding to the feature distribution point at the turning angle to the minimum value. n Marked as P m P for n>m n The corresponding parameter group is named the abnormal data group;

[0101] In practice, since the historical state parameters are all data where all operating parameters do not exceed their own safety thresholds, there are two operating parameters that have a certain balance relationship with each other. Therefore, the feature difference calculated by the parameter set contains two types of data: one is the feature difference calculated by the parameter set without a balance relationship, and the other is the feature difference calculated by the parameter set with a balance relationship. Typically, the feature difference calculated by the parameter set without a balance relationship is very small, while the feature difference calculated by the parameter set with a balance relationship is much larger than that calculated by the parameter set without a balance relationship. There is a boundary between them, namely the turning angle. The resulting feature distribution map is shown below. Figure 10 As shown, P7 to P are finally obtained. 10All of these are abnormal data sets, corresponding to parameter sets for historical temperature and historical rate, historical voltage and historical current, historical temperature and historical internal resistance, and historical voltage and historical internal resistance, respectively.

[0102] Step S3: Analyze the security relationships between historical state parameters in the abnormal data group; Step S3 includes the following sub-steps:

[0103] Step S301: When analyzing any abnormal data group, name it the data group to be analyzed, and name the two historical state parameters in the data group to be analyzed as parameter A and parameter B respectively.

[0104] Step S302: Establish a two-dimensional coordinate system with parameter A as the X-axis and parameter B as the Y-axis, and name it the parameter relationship analysis diagram. Enter parameter A and parameter B into the parameter relationship analysis diagram.

[0105] Step S303: Analyze the normal and abnormal feature circles between parameter A and parameter B in the parameter relationship analysis diagram, and name them as normal parameter circle and abnormal parameter circle, respectively.

[0106] Step S304: Analyze the normal parameter circle and abnormal parameter circle between the historical state parameters in each abnormal data group. The normal parameter circle and abnormal parameter circle are the safety relationships.

[0107] In practice, since the normal parameter circle and the abnormal parameter circle represent a safety relationship, and the analysis process for analyzing the normal parameter circle and the abnormal parameter circle has been detailed in step S2, it will not be described in detail in this embodiment. Refer to [link / reference]. Figure 9 The normal parameter circle and the abnormal parameter circle are sufficient.

[0108] Step S4 involves real-time acquisition of the vehicle's emergency power supply's operating parameters, along with independent safety analysis of these parameters to determine if any abnormalities exist. Step S4 includes the following sub-steps:

[0109] Step S401: Real-time acquisition of the operating parameters of the vehicle emergency power supply; each operating parameter has a corresponding preset safety threshold.

[0110] Step S402: Determine whether the operating parameters are within the preset safety threshold. If so, output a normal parameter signal; otherwise, output an abnormal parameter signal.

[0111] Step S403: If an abnormal output parameter signal is detected, then mark the vehicle emergency power supply as abnormal.

[0112] In specific implementation, the method of judging by preset safety threshold is existing technology. This embodiment will not describe it in detail. It is only used to show that this embodiment not only considers whether there is an abnormality in a single operating parameter, but also whether there is an abnormality in the balance relationship between multiple operating parameters.

[0113] Step S5 involves analyzing whether different operating parameters conform to safety relationships to determine if the vehicle's emergency power supply is malfunctioning. Step S5 includes the following sub-steps:

[0114] Step S501: When analyzing any abnormal data group, name it the target analysis group and label the running parameters corresponding to the two historical state parameters in the target analysis group as α and β respectively.

[0115] Step S502: Input α and β into the parameter relationship analysis diagram corresponding to the target analysis group, and name the obtained coordinate points as target points;

[0116] Step S503: If the target point is within the normal parameter circle, output the parameter combined normal signal; if the target point is within the abnormal parameter circle, output the parameter combined abnormal signal; otherwise, output the parameter combined undetermined signal.

[0117] Step S504: If the output parameters are combined with the undetermined signal, then obtain the center of the normal parameter circle and the abnormal parameter circle, and name them as the first center and the second center respectively. Calculate the distance between the target point and the first center and name it as the normal discrete value. Calculate the distance between the target point and the second center and name it as the abnormal discrete value.

[0118] Step S505: If the normal discrete value is greater than the abnormal discrete value, then the output parameters are combined with the normal signal; otherwise, the output parameters are combined with the abnormal signal.

[0119] Step S506: If the output parameters are combined with an abnormal signal, then the vehicle emergency power supply is marked as abnormal; if the vehicle emergency power supply is marked as abnormal, then the information that the vehicle emergency power supply is abnormal is sent to the user via wireless communication.

[0120] Step S507: Independently detect the battery level. If the battery level is lower than the optimal power threshold, notify the user wirelessly to remind them to charge the battery in time.

[0121] In practice, assuming we analyze battery temperature and self-discharge rate, let battery temperature be α and self-discharge rate be β. We will then input α and β. Figure 9 The fact that the target point lies within the normal parameter circle indicates that the battery temperature and self-discharge rate corresponding to the target point more closely match the distribution characteristics of historical state parameters under normal conditions. Therefore, the output parameters, combined with the normal signal, indicate that the balance between battery temperature and self-discharge rate is within the normal range. If P7 to P... 10If the corresponding abnormal data group outputs a combined normal signal, it means that all operating parameters are in a normal balance state, that is, the car emergency power supply is in a normal state. At the same time, during the detection of the battery power parameter in step S507, this process is an independent detection and has no sequential execution relationship with other steps. The battery power can be detected independently, and an optimal power threshold is set. If the battery power is less than the optimal power threshold, the user will be notified wirelessly via Bluetooth or Wi-Fi to remind the user to charge in time.

[0122] Example 2: This application provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions. The processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a real-time detection method for automotive emergency power supplies are performed to achieve the following functions: statistically analyzing historical status parameters based on big data and the manufacturer's database; analyzing abnormal data groups of the automotive emergency power supply through abnormal data in the historical status parameters; analyzing the safety relationships between historical status parameters in the abnormal data groups; real-time acquisition of the operating parameters of the automotive emergency power supply, and simultaneously performing independent safety analysis on the operating parameters to determine whether the operating parameters are abnormal; analyzing whether different operating parameters conform to safety relationships to determine whether the automotive emergency power supply is abnormal.

[0123] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a real-time detection method for automotive emergency power supplies provided by the above methods. This method includes: statistically analyzing historical status parameters based on big data and the manufacturer's database; analyzing abnormal data groups of the automotive emergency power supply through abnormal data in the historical status parameters; analyzing the safety relationships between historical status parameters in the abnormal data groups; collecting the operating parameters of the automotive emergency power supply in real time, and simultaneously performing independent safety analysis on the operating parameters to determine whether the operating parameters are abnormal; analyzing whether different operating parameters conform to safety relationships to determine whether the automotive emergency power supply is abnormal.

[0125] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-described real-time detection method for automotive emergency power supplies to achieve the following functions: statistically analyzing historical status parameters based on big data and the manufacturer's database; analyzing abnormal data groups of the automotive emergency power supply through abnormal data in the historical status parameters; analyzing the safety relationships between historical status parameters in the abnormal data groups; collecting the operating parameters of the automotive emergency power supply in real time, and simultaneously performing independent safety analysis on the operating parameters to determine whether the operating parameters are abnormal; analyzing whether different operating parameters conform to safety relationships to determine whether the automotive emergency power supply is abnormal.

[0126] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the technical solutions described above, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products can be stored in computer-readable storage media, such as ROM / RAM, magnetic disks, optical disks, etc., and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0127] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for real-time detection of automotive emergency power supplies, characterized in that, Includes the following steps: Historical status parameters are statistically analyzed based on big data and the manufacturer's database; Analyze abnormal data groups of the vehicle emergency power supply by identifying anomalous data in historical status parameters; Analyze the security relationships among historical state parameters in abnormal data groups; Real-time acquisition of the vehicle's emergency power supply's operating parameters, and independent safety analysis of the operating parameters to determine whether the operating parameters are abnormal; Analyze whether different operating parameters conform to safe relationships to determine if the vehicle's emergency power supply is malfunctioning.

2. The method for real-time detection of automotive emergency power supply according to claim 1, characterized in that, Based on big data and the manufacturer's database, the historical status parameters are statistically analyzed, including the following sub-steps: Historical status parameters of commercially available car emergency power supplies are obtained through big data analysis. The historical status parameters of the vehicle emergency power supply can be obtained from the database saved by the manufacturer when testing the vehicle emergency power supply. The historical status parameters are historical data of the operating parameters of the vehicle emergency power supply. The operating parameters include battery voltage, battery current, battery internal resistance, battery temperature, and self-discharge rate. The operating parameters in the historical status parameters are named historical voltage, historical current, historical internal resistance, historical temperature, and historical rate, respectively. The historical status parameters also record the power supply status, which includes normal status and abnormal status.

3. The method for real-time detection of automotive emergency power supply according to claim 2, characterized in that, Analyzing abnormal data sets of automotive emergency power supplies by identifying anomalies in historical status parameters includes the following sub-steps: Analyze the normal and abnormal line segments between any two historical state parameters; Analysis of abnormal data groups in automotive emergency power supplies based on normal and abnormal line segments.

4. The method for real-time detection of automotive emergency power supply according to claim 3, characterized in that, Analyzing the normal and abnormal segments between any two historical state parameters includes the following sub-steps: Choose any two historical state parameters as the first and second parameters; Establish a two-dimensional coordinate system with the first parameter as the X-axis and the second parameter as the Y-axis, and name it the parameter relationship diagram. Enter the first parameter and the second parameter into the parameter relationship diagram to form parameter coordinate points. Enter the first and second parameters corresponding to the historical state parameters when the power supply is in normal state into the parameter relationship diagram, and name the obtained parameter coordinate points as parameter normal points. Enter the first and second parameters corresponding to the historical state parameters when the power supply is in abnormal state into the parameter relationship diagram, and name the obtained parameter coordinate points as parameter abnormal points. Get the point with the smallest X-axis among the parameter coordinate points and name it point 1; get the point with the largest X-axis among the parameter coordinate points and name it point 2; get the point with the smallest Y-axis among the parameter coordinate points and name it point 3; get the point with the largest Y-axis among the parameter coordinate points and name it point 4. Connect point 1 to point 3 to obtain edge line 1. Connect point 1 to point 4 to obtain edge line 2. Connect point 2 to point 3 to obtain edge line 3. Connect point 2 to point 4 to obtain edge line 4. Edge lines 1, 2, 3 and 4 are collectively referred to as set boundary lines. The first, second, third, and fourth edge lines obtained from the normal point analysis are named the first normal edge line, the second normal edge line, the third normal edge line, and the fourth normal edge line, respectively, and are collectively referred to as normal line segments. The first, second, third, and fourth edge lines obtained from the parameter anomaly point analysis are named the first anomaly edge line, the second anomaly edge line, the third anomaly edge line, and the fourth anomaly edge line, respectively, and are collectively referred to as anomaly line segments.

5. The method for real-time detection of automotive emergency power supply according to claim 4, characterized in that, Analyzing abnormal data sets of automotive emergency power supplies based on normal and abnormal line segments includes the following sub-steps: The area enclosed by edge line 1, edge line 2, edge line 3, and edge line 4 is named the feature area, and the parameter coordinate points outside the feature area are named the outside coordinates. For any set of coordinates outside the region, obtain the boundary line of the set that is closest to the coordinates outside the region and name it the proximity line segment. When analyzing any set of boundary lines, name it the line segment to be analyzed. Name the coordinates outside the region that are the proximity line segments to be analyzed as the coordinates to be analyzed. Name the coordinate furthest from the line segment to be analyzed as the outer endpoint. Name the two endpoints of the line segment to be analyzed as the first endpoint and the second endpoint, respectively. Connect the first endpoint to the outer endpoint and name it the first outer line. At the same time, connect the second endpoint to the outer endpoint and name it the second outer line. Remove the line segment to be analyzed from the set boundary line, and include the first outer line and the second outer line into the set boundary line. Perform the same analysis on each set boundary line until there are no more coordinates outside the area, and obtain the final set boundary line. The region enclosed by the final set boundary line is named the effective feature region, the effective feature region obtained from the analysis of normal line segments is named the normal effective feature region, and the effective feature region obtained from the analysis of abnormal line segments is named the abnormal effective feature region. Construct the circumscribed ellipses of the normal and abnormal feature regions and the normal feature circle and the abnormal feature circle, respectively. Obtain the centers of the normal and abnormal feature circles and name them the normal center and the abnormal center, respectively. Calculate the difference between the normal and abnormal circle centers, naming it the characteristic difference. Combine historical state parameters pairwise to obtain parameter sets. Analyze the characteristic difference of each parameter set, numbering them in ascending order and using the symbol P. n express; With n as the horizontal axis, P n Establish a two-dimensional coordinate system for the vertical axis and name it the feature distribution map. Name the coordinate points in the feature distribution map the feature distribution points. Connect two adjacent feature distribution points to obtain the feature distribution lines. Name the angle between two adjacent feature distribution lines the feature distribution angle. Find the minimum value among the feature distribution angles, name it the turning angle, and assign the P value corresponding to the feature distribution point at the turning angle. n Marked as P m P for n>m n The corresponding parameter group is named the abnormal data group.

6. The method for real-time detection of automotive emergency power supply according to claim 5, characterized in that, Analyzing the security relationships between historical state parameters in anomaly data sets includes the following sub-steps: When analyzing any abnormal data set, name it the data set to be analyzed, and name the two historical state parameters in the data set to be analyzed as parameter A and parameter B respectively. Establish a two-dimensional coordinate system with parameter A as the X-axis and parameter B as the Y-axis, and name it the parameter relationship analysis diagram. Enter parameter A and parameter B into the parameter relationship analysis diagram. The normal and abnormal feature circles between parameter A and parameter B in the parameter relationship analysis diagram are named the normal parameter circle and the abnormal parameter circle, respectively. Analyze the normal parameter circles and abnormal parameter circles between the historical state parameters in each abnormal data group; these normal parameter circles and abnormal parameter circles represent the safety relationships.

7. The method for real-time detection of automotive emergency power supply according to claim 6, characterized in that, The real-time acquisition of the vehicle's emergency power supply's operating parameters, along with independent safety analysis of these parameters to determine whether any abnormalities exist, includes the following sub-steps: Real-time acquisition of the vehicle's emergency power supply's operating parameters, with each parameter having a corresponding preset safety threshold; Determine whether the operating parameters are within the preset safety threshold. If so, output a normal parameter signal; otherwise, output an abnormal parameter signal. If an abnormal signal is output, it indicates that there is an abnormality in the vehicle's emergency power supply.

8. The method for real-time detection of automotive emergency power supply according to claim 7, characterized in that, Analyzing whether different operating parameters conform to safe relationships to determine if the vehicle's emergency power supply is malfunctioning includes the following sub-steps: When analyzing any abnormal data set, name it the target analysis set, and label the running parameters corresponding to the two historical state parameters in the target analysis set as α and β respectively. Enter α and β into the parameter relationship analysis diagram corresponding to the target analysis group, and name the obtained coordinate points as target points; If the target point is within the normal parameter circle, the output parameters are combined with the normal signal; if the target point is within the abnormal parameter circle, the output parameters are combined with the abnormal signal; otherwise, the output parameters are combined with the undetermined signal. If the output parameters are combined with the undetermined signal, then obtain the centers of the normal parameter circle and the abnormal parameter circle, and name them the first center and the second center respectively. Calculate the distance between the target point and the first center, and name it the normal discrete value. Calculate the distance between the target point and the second center, and name it the abnormal discrete value. If the normal discrete value is greater than the abnormal discrete value, the output parameters are combined with the normal signal; otherwise, the output parameters are combined with the abnormal signal. If the output parameters show an abnormal signal, it indicates that the vehicle's emergency power supply is malfunctioning. If the vehicle emergency power supply is flagged as malfunctioning, the information will be sent to the user via wireless communication. The operating parameters also include battery level; The system independently detects battery power and notifies the user via wireless communication if the battery level falls below the optimal threshold, reminding the user to charge the battery in time.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method as described in any one of claims 1-8.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the steps of the method as described in any one of claims 1-8.

Images