Insulation fault detection method, device and equipment and computer readable storage medium

Abstract

The invention discloses an insulation fault detection method, device and equipment and a computer readable storage medium. The method comprises the following steps: carrying out insulation detection according to a preset period, and recording an insulation resistance value measured value obtained by each insulation detection; obtaining an insulation resistance value prediction value based on N newly recorded insulation resistance value measurement values, wherein N is a positive integer greater than 1; detecting whether the insulation resistance value prediction value and the N insulation resistance value measurement values meet fault detection conditions or not; if the fault detection condition is satisfied, whether an insulation fault exists is detected based on the insulation resistance value prediction value and the N insulation resistance value measurement values; and if the insulation fault exists, fault reminding is carried out. According to the insulation fault detection method and device, insulation fault detection is carried out based on the insulation resistance value predicted value and the N insulation resistance value measured values, the influence of measurement errors on an insulation fault detection result is weakened to a certain degree, and the situation that the insulation fault is misreported or missed to be reported is reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle detection technology, and in particular to an insulation fault detection method, device, equipment, and computer-readable storage medium. Background Art

[0002] To ensure the safety and reliability of automotive electrical systems, detecting insulation faults is crucial. Currently, insulation resistance meters are commonly used to measure the insulation resistance of a vehicle's electrical system and wiring harness. These measurements are then compared to a threshold to determine if an insulation fault exists. However, due to detection errors, the measured insulation resistance value can differ from the actual insulation resistance value, leading to false or missed insulation fault reports. Summary of the Invention

[0003] The present application provides an insulation fault detection method, device, equipment and computer-readable storage medium, which can solve the technical problem in the prior art that insulation faults are easily falsely reported or missed.

[0004] In a first aspect, an embodiment of the present application provides an insulation fault detection method, the insulation fault detection method comprising:

[0005] Perform insulation testing according to the preset period and record the insulation resistance measurement value obtained during each insulation test;

[0006] Obtaining an insulation resistance prediction value based on the latest recorded N insulation resistance measurement values, where N is a positive integer greater than 1;

[0007] Detect whether the insulation resistance prediction value and N insulation resistance measurement values meet the fault detection conditions;

[0008] If the fault detection condition is met, whether there is an insulation fault is detected based on the insulation resistance prediction value and the N insulation resistance measurement values;

[0009] If there is an insulation fault, a fault reminder will be issued.

[0010] In conjunction with the first aspect, in one embodiment, the step of obtaining an insulation resistance prediction value based on the most recently recorded N insulation resistance measurement values includes:

[0011] Input the latest recorded N insulation resistance measurement values into the prediction model, which is based on the training of wavelet neural network;

[0012] Get the insulation resistance prediction value output by the prediction model.

[0013] In conjunction with the first aspect, in one embodiment, the step of detecting whether the insulation resistance prediction value and the N insulation resistance measurement values meet the fault detection condition includes:

[0014] Detect whether the M most recently recorded insulation resistance measurement values and the insulation resistance prediction value among the N insulation resistance measurement values are all outside a preset range, where M is a positive integer less than N;

[0015] If the M insulation resistance measurement values and the insulation resistance prediction values do not all exceed the preset range, it is determined that the fault detection condition is met.

[0016] In conjunction with the first aspect, in one embodiment, the step of detecting whether an insulation fault exists based on the insulation resistance prediction value and the N insulation resistance measurement values includes:

[0017] Calculating a predicted insulation resistance value and a first average value of N insulation resistance measurement values;

[0018] detecting whether the first average value is less than or equal to a threshold;

[0019] If the first average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

[0020] In conjunction with the first aspect, in one embodiment, the step of detecting whether an insulation fault exists based on the insulation resistance prediction value and the N insulation resistance measurement values includes:

[0021] Filtering the target insulation resistance measurement value that exceeds a preset range among the N insulation resistance measurement values to obtain a new target insulation resistance measurement value;

[0022] Calculating the insulation resistance prediction value, the new target insulation resistance measurement value, and a second average value of the insulation resistance measurement values that do not exceed a preset range among the N insulation resistance measurement values;

[0023] detecting whether the second average value is less than or equal to a threshold;

[0024] If the second average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

[0025] In conjunction with the first aspect, in one embodiment, the step of filtering the target insulation resistance measurement value that exceeds a preset range among the N insulation resistance measurement values to obtain a new target insulation resistance measurement value includes:

[0026] Determining a target insulation resistance measurement value that exceeds a preset range among the N insulation resistance measurement values;

[0027] For each target insulation resistance measurement value, the most recently recorded insulation resistance measurement value that does not exceed the preset range is used as the corresponding new target insulation resistance measurement value.

[0028] In combination with the first aspect, in one implementation, the threshold is determined according to a state of the power battery system.

[0029] In a second aspect, an embodiment of the present application provides an insulation fault detection device, the insulation fault detection device comprising:

[0030] A recording module is used to perform insulation testing according to a preset period and record the insulation resistance measurement value obtained during each insulation test;

[0031] A prediction module, configured to obtain a predicted insulation resistance value based on the latest recorded N insulation resistance measurement values, where N is a positive integer greater than 1;

[0032] A first detection module is used to detect whether the insulation resistance prediction value and N insulation resistance measurement values meet the fault detection condition;

[0033] A second detection module is configured to detect whether an insulation fault exists based on the insulation resistance prediction value and the N insulation resistance measurement values if a fault detection condition is met;

[0034] The reminder module is used to issue a fault reminder if an insulation fault occurs.

[0035] In a third aspect, an embodiment of the present application provides an insulation fault detection device, which includes a processor, a memory, and an insulation fault detection program stored in the memory and executable by the processor, wherein when the insulation fault detection program is executed by the processor, the steps of the insulation fault detection method described in the first aspect are implemented.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which an insulation fault detection program is stored, wherein when the insulation fault detection program is executed by a processor, the steps of the insulation fault detection method described in the first aspect are implemented.

[0037] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0038] In an embodiment of the present application, insulation testing is performed according to a preset cycle, and the insulation resistance measurement value obtained from each insulation test is recorded; an insulation resistance prediction value is obtained based on the most recently recorded N insulation resistance measurement values, where N is a positive integer greater than 1; the insulation resistance prediction value and the N insulation resistance measurement values are detected to see whether a fault detection condition is met; if the fault detection condition is met, an insulation fault is detected based on the insulation resistance prediction value and the N insulation resistance measurement values; if an insulation fault is present, a fault reminder is issued. Through the embodiment of the present application, insulation fault detection is performed based on the insulation resistance prediction value and the N insulation resistance measurement values, which, to a certain extent, reduces the impact of measurement errors on the insulation fault detection results and reduces the occurrence of false insulation fault reports or missed insulation fault reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of an embodiment of the insulation fault detection method of the present application;

[0040] Figure 2 This is a functional module diagram of an insulation fault detection device according to an embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of the hardware structure of the insulation fault detection device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0044] In a first aspect, an embodiment of the present application provides an insulation fault detection method.

[0045] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of the insulation fault detection method of the present application. Figure 1 As shown, the insulation fault detection method includes:

[0046] Step S10, performing insulation testing according to a preset period, and recording the insulation resistance measurement value obtained from each insulation testing;

[0047] In this embodiment, insulation testing is performed in a conventional manner, for example, by measuring the insulation resistance of the vehicle's electrical system and wiring harness using an insulation resistance meter.

[0048] The preset period is set according to actual needs, for example, an insulation test is performed every 3 minutes, and the insulation resistance measurement value obtained by each insulation test is recorded.

[0049] Step S20, obtaining an insulation resistance prediction value based on the latest recorded N insulation resistance measurement values, where N is a positive integer greater than 1;

[0050] In this embodiment, N is determined according to actual needs. For example, if N is 4, the insulation resistance prediction value Y1 is obtained based on the insulation resistance measurement value C1 recorded for the first time, the insulation resistance measurement value C2 recorded for the second time, the insulation resistance measurement value C3 recorded for the third time, and the insulation resistance measurement value C4 recorded for the fourth time. Similarly, the insulation resistance prediction value Y2 is obtained based on the insulation resistance measurement value C2 recorded for the second time, the insulation resistance measurement value C3 recorded for the third time, the insulation resistance measurement value C4 recorded for the fourth time, and the insulation resistance measurement value C5 recorded for the fifth time; and so on.

[0051] Furthermore, in one embodiment, step S20 includes:

[0052] Step S201, inputting the latest recorded N insulation resistance measurement values into a prediction model, where the prediction model is obtained by training a wavelet neural network;

[0053] Step S202: Obtain the insulation resistance prediction value output by the prediction model.

[0054] In this embodiment, the process of training the wavelet neural network to obtain the prediction model is as follows:

[0055] S1: Network initialization, including random initialization of wavelet function scaling factor, translation factor and network connection weight, and setting network learning rate;

[0056] S2: Sample classification, including dividing the insulation resistance samples into training samples and test samples. The training samples are used to train the network, and the test samples are used to test the network prediction accuracy.

[0057] S2: Model training. Input the divided training set into the wavelet neural network for training. When the set error value or the maximum number of training times is reached, the training stops and the prediction model is output.

[0058] S4: Input the test set data into the prediction model for testing. If the test passes, it is used in step S201.

[0059] By inputting the latest recorded N insulation resistance measurement values into the prediction model, the insulation resistance prediction value output by the prediction model can be obtained.

[0060] Step S30, detecting whether the insulation resistance prediction value and the N insulation resistance measurement values meet the fault detection condition;

[0061] In this embodiment, in order to ensure the accuracy of the final insulation fault detection result, it is necessary to determine whether the insulation resistance prediction value and the N insulation resistance measurement values can be used for insulation fault detection, that is, to determine whether these values meet the fault detection conditions.

[0062] Furthermore, in one embodiment, step S30 includes:

[0063] Step S301, detecting whether the M insulation resistance measurement values and the insulation resistance prediction value recorded most recently among the N insulation resistance measurement values are both outside a preset range, where M is a positive integer less than N;

[0064] Step S302: If the M insulation resistance measurement values and the insulation resistance prediction values do not all exceed the preset range, it is determined that the fault detection condition is met.

[0065] In this embodiment, N is 4, M is 2, the four insulation resistance measurement values include insulation resistance measurement value C1, insulation resistance measurement value C2, insulation resistance measurement value C3 and insulation resistance measurement value C4, and the insulation resistance prediction value is Y1.

[0066] The two most recently recorded insulation resistance measurement values among the four insulation resistance measurement values, namely the insulation resistance measurement value C3 and the insulation resistance measurement value C4, are judged whether the insulation resistance prediction value Y1, the insulation resistance measurement value C3, and the insulation resistance measurement value C4 are all outside the preset range.

[0067] If all exceed the preset range, it is determined that the fault detection condition is not met; if all do not exceed the preset range, that is, at least one of the insulation resistance predicted value Y1, the insulation resistance measured value C3 and the insulation resistance measured value C4 is within the preset range, it is determined that the fault detection condition is met.

[0068] The above preset range is set according to actual conditions. For example, the threshold is R, the lower limit of the preset range is 95%R, and the upper limit of the preset range is 105%R.

[0069] Step S40: If the fault detection condition is met, detecting whether there is an insulation fault based on the insulation resistance prediction value and the N insulation resistance measurement values;

[0070] In this embodiment, when the insulation resistance prediction value and the N insulation resistance measurement values meet the fault detection condition, a preset operation is performed on these values to obtain an insulation fault detection result.

[0071] For example, the average value of these values is calculated, and then the average value is compared with a threshold value. If the average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

[0072] Furthermore, in one embodiment, step S40 includes:

[0073] Step S401, calculating an insulation resistance prediction value and a first average value of N insulation resistance measurement values;

[0074] Step S402, detecting whether the first average value is less than or equal to a threshold;

[0075] Step S403: If the first average value is less than or equal to the threshold, it is determined that an insulation fault exists.

[0076] Furthermore, in one embodiment, step S40 includes:

[0077] Step S404: filtering the target insulation resistance measurement value that exceeds the preset range among the N insulation resistance measurement values to obtain a new target insulation resistance measurement value;

[0078] Step S405, calculating the insulation resistance prediction value, the new target insulation resistance measurement value, and a second average value of the insulation resistance measurement values that do not exceed a preset range among the N insulation resistance measurement values;

[0079] Step S406, detecting whether the second average value is less than or equal to a threshold;

[0080] Step S407: If the second average value is less than or equal to the threshold, it is determined that an insulation fault exists.

[0081] In this embodiment, to further mitigate the impact of measurement errors on insulation fault detection results, filtering is performed on target insulation resistance values that exceed a preset range among the N insulation resistance measurements. For example, the four insulation resistance measurements include insulation resistance measurement value C1, insulation resistance measurement value C2, insulation resistance measurement value C3, and insulation resistance measurement value C4, and the insulation resistance prediction value is Y1.

[0082] If the insulation resistance measurement value C2 exceeds the target insulation resistance measurement value within the preset range, filtering is performed on C2 to obtain a new target insulation resistance measurement value, which is recorded as C2′.

[0083] Furthermore, in one embodiment, step S404 includes:

[0084] Step S4041, determining the target insulation resistance measurement value that exceeds the preset range among the N insulation resistance measurement values; Step S4042, for each target insulation resistance measurement value, using the most recently recorded insulation resistance measurement value that does not exceed the preset range as its corresponding new target insulation resistance measurement value.

[0085] In this embodiment, in combination with the above example, the target insulation resistance measurement value is C2. Before recording C2, the latest recorded insulation resistance measurement value that does not exceed the preset range is C1. Then C1 = the new target insulation resistance measurement value C2'.

[0086] Then, a second average value of C1, C2', C3, C4 and Y1 is calculated and compared with a threshold value. If the second average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

[0087] The threshold value is set according to actual needs and is not limited here. The threshold value can be a fixed value or can change dynamically.

[0088] Furthermore, in one embodiment, the threshold is determined according to the state of the power battery system.

[0089] In this embodiment, the threshold for insulation fault detection varies depending on the power battery system's state. For example, when the power battery system is in high-voltage power-up or slow charging, the threshold is 500Ω; when the power battery system is in fast charging, the threshold is 200Ω. This is merely an illustrative example of the threshold and does not constitute a limitation on the threshold values.

[0090] Step S50: If an insulation fault exists, a fault reminder is issued.

[0091] In this embodiment, the fault reminder method is selected according to actual needs, for example, displaying a warning icon on the vehicle instrument and / or issuing a warning sound through the vehicle speaker and / or sending a warning prompt to the client.

[0092] It is easy to understand that if there is no insulation fault, the process returns to step S20 to continue detecting the insulation fault.

[0093] In an embodiment of the present application, insulation testing is performed according to a preset cycle, and the insulation resistance measurement value obtained from each insulation test is recorded; an insulation resistance prediction value is obtained based on the most recently recorded N insulation resistance measurement values, where N is a positive integer greater than 1; the insulation resistance prediction value and the N insulation resistance measurement values are detected to see whether a fault detection condition is met; if the fault detection condition is met, an insulation fault is detected based on the insulation resistance prediction value and the N insulation resistance measurement values; if an insulation fault is present, a fault reminder is issued. Through the embodiment of the present application, insulation fault detection is performed based on the insulation resistance prediction value and the N insulation resistance measurement values, which, to a certain extent, reduces the impact of measurement errors on the insulation fault detection results and reduces the occurrence of false insulation fault reports or missed insulation fault reports.

[0094] In a second aspect, an embodiment of the present application further provides an insulation fault detection device.

[0095] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of an embodiment of the insulation fault detection device of the present application. Figure 2 As shown, the insulation fault detection device includes:

[0096] The recording module 10 is used to perform insulation testing according to a preset period and record the insulation resistance measurement value obtained during each insulation testing;

[0097] A prediction module 20 is configured to obtain a predicted insulation resistance value based on the latest recorded N insulation resistance measurement values, where N is a positive integer greater than 1;

[0098] A first detection module 30 is used to detect whether the insulation resistance prediction value and the N insulation resistance measurement values meet the fault detection condition;

[0099] A second detection module 40 is configured to detect whether an insulation fault exists based on the insulation resistance prediction value and the N insulation resistance measurement values if the fault detection condition is met;

[0100] The reminder module 50 is used to issue a fault reminder if an insulation fault occurs.

[0101] Furthermore, in one embodiment, the prediction module 20 is configured to:

[0102] Input the latest recorded N insulation resistance measurement values into the prediction model, which is based on the training of wavelet neural network;

[0103] Get the insulation resistance prediction value output by the prediction model.

[0104] Furthermore, in one embodiment, the first detection module 30 is configured to:

[0105] Detect whether the M most recently recorded insulation resistance measurement values and the insulation resistance prediction value among the N insulation resistance measurement values are all outside a preset range, where M is a positive integer less than N;

[0106] If the M insulation resistance measurement values and the insulation resistance prediction values do not all exceed the preset range, it is determined that the fault detection condition is met.

[0107] Furthermore, in one embodiment, the second detection module 40 is configured to:

[0108] Calculating a predicted insulation resistance value and a first average value of N insulation resistance measurement values;

[0109] detecting whether the first average value is less than or equal to a threshold;

[0110] If the first average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

[0111] Furthermore, in one embodiment, the second detection module 40 is configured to:

[0112] Filtering the target insulation resistance measurement value that exceeds a preset range among the N insulation resistance measurement values to obtain a new target insulation resistance measurement value;

[0113] Calculating the insulation resistance prediction value, the new target insulation resistance measurement value, and a second average value of the insulation resistance measurement values that do not exceed a preset range among the N insulation resistance measurement values;

[0114] detecting whether the second average value is less than or equal to a threshold;

[0115] If the second average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

[0116] Furthermore, in one embodiment, the second detection module 40 is configured to:

[0117] Determining a target insulation resistance measurement value that exceeds a preset range among the N insulation resistance measurement values;

[0118] For each target insulation resistance measurement value, the most recently recorded insulation resistance measurement value that does not exceed the preset range is used as the corresponding new target insulation resistance measurement value.

[0119] Furthermore, in one embodiment, the threshold is determined according to the state of the power battery system.

[0120] The functional implementation of each module in the above insulation fault detection device corresponds to each step in the above insulation fault detection method embodiment, and their functions and implementation processes are not described here one by one.

[0121] In a third aspect, an embodiment of the present application provides an insulation fault detection device, which may be a device with data processing capabilities, such as a personal computer (PC), a laptop computer, or a server.

[0122] Reference Figure 3 , Figure 3 FIG1 is a schematic diagram of the hardware structure of the insulation fault detection device involved in the embodiment of the present application. In the embodiment of the present application, the insulation fault detection device may include a processor, a memory, a communication interface, and a communication bus.

[0123] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0124] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces, which are used to interconnect components within the insulation fault detection device and to connect the device to other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet, fiber, or ATM interfaces; user equipment can be displays or keyboards.

[0125] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0126] The processor may be a general-purpose processor, and the general-purpose processor may call the insulation fault detection program stored in the memory and execute the insulation fault detection method provided by the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). Wherein, the method executed when the insulation fault detection program is called may refer to the various embodiments of the insulation fault detection method of the present application, and will not be repeated here.

[0127] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0128] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0129] The computer-readable storage medium of the present application stores an insulation fault detection program, wherein when the insulation fault detection program is executed by a processor, the steps of the insulation fault detection method described above are implemented.

[0130] Among them, the method implemented when the insulation fault detection program is executed can refer to the various embodiments of the insulation fault detection method of the present application, and will not be repeated here.

[0131] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0132] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings 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 optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0133] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0134] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0135] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0136] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0137] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for detecting insulation faults, characterized in that: The insulation fault detection method comprises: Perform insulation testing according to the preset period and record the insulation resistance measurement value obtained during each insulation test; Obtaining an insulation resistance prediction value based on the latest recorded N insulation resistance measurement values, where N is a positive integer greater than 1; Detect whether the insulation resistance prediction value and N insulation resistance measurement values meet the fault detection conditions; If the fault detection condition is met, whether there is an insulation fault is detected based on the insulation resistance prediction value and the N insulation resistance measurement values; If there is an insulation fault, a fault reminder will be issued.

2. The insulation fault detection method according to claim 1, wherein: The step of obtaining the insulation resistance prediction value based on the latest recorded N insulation resistance measurement values includes: Input the latest recorded N insulation resistance measurement values into the prediction model, which is based on the training of wavelet neural network; Get the insulation resistance prediction value output by the prediction model.

3. The insulation fault detection method according to claim 1, wherein: The step of detecting whether the insulation resistance prediction value and the N insulation resistance measurement values meet the fault detection condition includes: Detect whether the M most recently recorded insulation resistance measurement values and the insulation resistance prediction value among the N insulation resistance measurement values are all outside a preset range, where M is a positive integer less than N; If the M insulation resistance measurement values and the insulation resistance prediction values do not all exceed the preset range, it is determined that the fault detection condition is met.

4. The insulation fault detection method according to claim 1, wherein: The step of detecting whether there is an insulation fault based on the insulation resistance prediction value and N insulation resistance measurement values includes: Calculating a predicted insulation resistance value and a first average value of N insulation resistance measurement values; detecting whether the first average value is less than or equal to a threshold; If the first average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

5. The insulation fault detection method according to claim 1, wherein: The step of detecting whether there is an insulation fault based on the insulation resistance prediction value and N insulation resistance measurement values includes: Filtering the target insulation resistance measurement value that exceeds a preset range among the N insulation resistance measurement values to obtain a new target insulation resistance measurement value; Calculating the insulation resistance prediction value, the new target insulation resistance measurement value, and a second average value of the insulation resistance measurement values that do not exceed a preset range among the N insulation resistance measurement values; detecting whether the second average value is less than or equal to a threshold; If the second average value is less than or equal to the threshold value, it is determined that an insulation fault exists.

6. The insulation fault detection method according to claim 5, wherein: The step of filtering the target insulation resistance measurement value that exceeds the preset range among the N insulation resistance measurement values to obtain a new target insulation resistance measurement value includes: Determining a target insulation resistance measurement value that exceeds a preset range among the N insulation resistance measurement values; For each target insulation resistance measurement value, the most recently recorded insulation resistance measurement value that does not exceed the preset range is used as the corresponding new target insulation resistance measurement value.

7. The insulation fault detection method according to any one of claims 4 to 6, characterized in that: The threshold is determined according to the state of the power battery system.

8. An insulation fault detection device, characterized in that: The insulation fault detection device comprises: A recording module is used to perform insulation testing according to a preset period and record the insulation resistance measurement value obtained during each insulation test; A prediction module, configured to obtain a predicted insulation resistance value based on the latest recorded N insulation resistance measurement values, where N is a positive integer greater than 1; A first detection module is used to detect whether the insulation resistance prediction value and N insulation resistance measurement values meet the fault detection condition; A second detection module is configured to detect whether an insulation fault exists based on the insulation resistance prediction value and the N insulation resistance measurement values if a fault detection condition is met; The reminder module is used to issue a fault reminder if an insulation fault occurs.

9. An insulation fault detection device, characterized in that: The insulation fault detection device includes a processor, a memory, and an insulation fault detection program stored in the memory and executable by the processor, wherein when the insulation fault detection program is executed by the processor, the steps of the insulation fault detection method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores an insulation fault detection program, wherein when the insulation fault detection program is executed by a processor, the steps of the insulation fault detection method according to any one of claims 1 to 7 are implemented.

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