Insulation unit monitoring method, device, equipment and storage medium

Abstract

This application discloses an insulation unit monitoring method, apparatus, device, and storage medium, relating to the field of power battery technology. The method comprises obtaining an insulation unit parameter set, obtaining insulation unit information through an insulation unit monitoring circuit, determining an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set, constructing an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set, and monitoring the insulation unit state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and triggering an intelligent management mechanism to implement electric vehicle insulation control. The present application obtains insulation state information in real time through an insulation unit monitoring circuit, thereby constructing an insulation unit state identification and diagnosis model, accurately identifying and predicting the insulation state, ensuring safe vehicle operation, and improving electric vehicle safety.

Description

Technical Field

[0001] The present application relates to the field of power battery technology, and in particular to an insulation unit monitoring method, device, equipment, and storage medium. Background Art

[0002] With the development of electric and hybrid vehicles (EVs), the safety of power battery systems has become a focus of public attention. In these vehicles, the power battery serves as the core energy source, and the insulation performance of its high-voltage system is directly related to the safe operation of the entire vehicle. Monitoring insulation cells is a key technology for ensuring EV safety, preventing electrical failures and safety hazards caused by degraded insulation performance.

[0003] At present, the existing technology is to collect relevant electrical signals through the insulation detection circuit and calculate the equivalent resistance between the battery module and the vehicle body through the insulation detection algorithm, that is, the insulation unit. The resistance value of the insulation unit can reflect the insulation performance of the electrical equipment of hybrid vehicles and electric vehicles.

[0004] However, the existing technology is easily affected by other components in the circuit during the measurement process, resulting in inaccurate measurement results. It also lacks an effective fault warning and alarm mechanism, making it difficult to respond to the degradation of insulation performance in a timely manner. It relies too much on a single detection technology and lacks comprehensive diagnostic capabilities, resulting in insufficient detection accuracy. Therefore, how to accurately and reliably monitor insulation units is a technical problem that needs to be solved urgently.

[0005] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide an insulation unit monitoring method, device, equipment and storage medium, aiming to solve the technical problem of how to accurately and reliably monitor insulation units.

[0007] To achieve the above objectives, the present application proposes an insulation unit monitoring method, which is applied to an insulation unit monitoring circuit. The insulation unit monitoring circuit includes: a power battery, a positive control switch, a start switch, a negative control switch, a positive insulation unit, a negative insulation unit, a first insulation fault diagnosis unit, a second insulation fault diagnosis unit, a third insulation fault diagnosis unit, and a fourth insulation fault diagnosis unit;

[0008] The positive electrode of the power battery is connected to the first end of the positive control switch and the first end of the positive insulation unit, the second end of the positive control switch is connected to the second end of the first insulation fault diagnosis unit, the first end of the first insulation fault diagnosis unit is connected to the first end of the second insulation fault diagnosis unit, the second end of the second insulation fault diagnosis unit is connected to the second end of the starting switch and the second end of the third insulation fault diagnosis unit, the first end of the starting switch is connected to the second end of the positive insulation unit and the second end of the negative insulation unit, the first end of the third insulation fault diagnosis unit is respectively connected to the first end of the fourth insulation fault diagnosis unit, the second end of the fourth insulation fault diagnosis unit is connected to the second end of the negative control switch, and the first end of the negative control switch is connected to the first end of the negative insulation unit and the negative electrode of the power battery. The method includes:

[0009] Obtaining an insulation unit parameter set, and obtaining insulation unit information through an insulation unit monitoring circuit, and determining an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set;

[0010] An insulation unit state identification and diagnosis model is constructed based on insulation unit information, insulation unit parameter set, and insulation unit fault state parameter set;

[0011] Based on the insulation unit information, insulation unit parameter set, insulation unit fault state parameter set and insulation unit state identification and diagnosis model, the insulation unit state is monitored, and an intelligent management mechanism is triggered to realize the insulation control of electric vehicles.

[0012] In one embodiment, the steps of obtaining an insulation unit parameter set, obtaining insulation unit information through an insulation unit monitoring circuit, and determining an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set include:

[0013] Obtaining the insulation unit critical safety value, insulation unit safety insulation value, power battery rated voltage, positive and negative pole switch information, and insulation fault diagnosis unit information set, where the positive and negative pole switch information includes positive pole control switch information and negative pole control switch information;

[0014] Based on the positive control switch information and the negative control switch information, the insulation unit monitoring circuit is measured to obtain the positive and negative voltage information, where the positive and negative voltage information includes the positive voltage information and the negative voltage information;

[0015] determining insulation unit information based on the insulation fault diagnosis unit information set, the positive pole voltage information, and the negative pole voltage information;

[0016] Obtain an insulation unit parameter set based on the insulation unit critical safety value and the insulation unit safety insulation value;

[0017] An insulation unit fault state parameter set is determined based on the insulation unit information, the insulation unit parameter set, and the power battery rated voltage.

[0018] In one embodiment, the step of constructing an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set includes:

[0019] Get the model training cycle;

[0020] Converting insulation unit information, insulation unit parameter sets, and insulation unit fault state parameter sets into standardized forms, and obtaining insulation unit sample sets;

[0021] Training the insulation unit sample set to determine the category confidence and obtain the insulation unit condition monitoring model;

[0022] Based on the model training cycle, the pre-classification results of the insulation unit state monitoring model and the insulation unit sample set are trained to determine the insulation unit state recognition and diagnosis model.

[0023] In one embodiment, the steps of training the insulation unit sample set to determine the category confidence and obtaining the insulation unit state monitoring model include:

[0024] Get the scoring function, tree regression algorithm, loop iterations, separation vector, truncation error, gradient factor, training time, and dilation step size;

[0025] The features in the isolation unit sample set are combined into scoring features through a scoring function;

[0026] The insulation unit sample set is trained using a tree regression algorithm to obtain an insulation unit sample subset;

[0027] Calculate the starting value of the weight based on the isolation unit sample subset, the scoring feature, the loop iteration, the separation vector, and the truncation error;

[0028] Calculate the weighted fusion value based on the weight starting value, gradient factor, training time and expansion step size;

[0029] An insulation unit basic classifier is obtained based on the weighted fusion value, and an insulation unit condition monitoring model is determined based on the insulation unit basic classifier.

[0030] In one embodiment, the steps of obtaining an insulation unit basic classifier based on the weighted fusion value and determining an insulation unit state monitoring model based on the insulation unit basic classifier include:

[0031] Obtain sample volatility, empirical constant, Gini coefficient, necessary sample size, ensemble learning rate, linear interpolation function, Lagrange multiplier, constraint conditions, average granularity, insulation unit fault state basic classification set, and category set;

[0032] Calculate the initial tree cycle value based on the weighted fusion value, sample volatility, empirical constant and Gini coefficient;

[0033] Calculate the initial tree level based on the initial tree cycle value, the required sample size, the ensemble learning rate, and the linear interpolation function;

[0034] Calculate the maximum distance based on the initial tree level, Lagrange multipliers and constraints;

[0035] An insulation unit basic classifier is calculated based on the maximized distance, the average granularity, the insulation unit fault state basic classification set and the category set, and an insulation unit state monitoring model is determined based on the insulation unit basic classifier.

[0036] In one embodiment, the steps of training the insulation unit state monitoring model based on the pre-classification results and insulation unit sample set based on the model training cycle and determining the insulation unit state recognition and diagnosis model include:

[0037] Obtain the number of hidden layers, the number of neurons in the hidden layer, and the neural network function. The neural network function includes the ReLU activation function, the softmax activation function, and the cross entropy loss function.

[0038] Based on the model training cycle, the number of hidden layers, the number of neurons in the hidden layer, and the neural network function, the pre-classification results of the insulation unit condition monitoring model and the insulation unit sample set are trained to determine the insulation unit optimization classifier;

[0039] An insulation unit state recognition and diagnosis model is obtained based on the insulation unit optimization classifier.

[0040] In one embodiment, the steps of monitoring the insulation unit status based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit status identification and diagnosis model, and triggering the intelligent management mechanism to implement insulation control of the electric vehicle include:

[0041] Determine the insulation unit fault state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and obtain a determination result;

[0042] Based on the judgment result, the insulation state category of the insulation unit is obtained, and the intelligent management mechanism is triggered according to the insulation state category to realize the insulation control of the electric vehicle.

[0043] In addition, to achieve the above-mentioned purpose, the present application also proposes an insulation unit monitoring device, which includes:

[0044] an acquisition module, configured to acquire an insulation unit parameter set, obtain insulation unit information through an insulation unit monitoring circuit, and determine an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set;

[0045] A processing module, configured to construct an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set;

[0046] The execution module is used to monitor the insulation unit status based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set and the insulation unit status identification and diagnosis model, and trigger the intelligent management mechanism to realize the insulation control of the electric vehicle.

[0047] In addition, to achieve the above objectives, the present application also proposes an insulation unit monitoring device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the insulation unit monitoring method described above.

[0048] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the insulation unit monitoring method as described above are implemented.

[0049] One or more technical solutions proposed in this application have at least the following technical effects:

[0050] The present application proposes an insulation unit monitoring method, which obtains an insulation unit parameter set, obtains insulation unit information through an insulation unit monitoring circuit, determines an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set; constructs an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set; monitors the insulation unit state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and triggers an intelligent management mechanism to achieve electric vehicle insulation control. The present application obtains insulation state information of the electric vehicle power battery system in real time through an insulation unit monitoring circuit, thereby constructing an insulation unit state identification and diagnosis model, accurately identifying and predicting the insulation state. When insulation performance degradation or failure is detected, the intelligent management mechanism responds immediately to ensure the safe operation of the vehicle, thereby improving the safety and reliability of electric vehicles, protecting the personal safety of drivers and passengers, and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 This is a schematic diagram of the structure of the intelligent monitoring circuit system for safe isolation of the insulation unit involved in the insulation unit monitoring method of this application;

[0054] Figure 2 A schematic diagram of a flow chart provided for Example 1 of the insulation unit monitoring method of the present application;

[0055] Figure 3 This is a detailed structural diagram of an insulation unit monitoring circuit involved in the insulation unit monitoring method of this application;

[0056] Figure 4 The insulation unit fault state parameter set, insulation unit fault state judgment result and insulation state category diagram of the insulation unit monitoring method of this application;

[0057] Figure 5 A schematic diagram of a flow chart provided for Example 2 of the insulation unit monitoring method of the present application;

[0058] Figure 6 This is a schematic diagram of the module structure of the insulation unit monitoring device according to an embodiment of the present application;

[0059] Figure 7 Schematic diagram of the equipment structure of the hardware operating environment involved in the insulation unit monitoring method in the embodiment of the present application.

[0060] Description of Figure Numbers:

[0061] 10. Power battery; 20. Power battery main control module; 30. Communication module; 40. Insulation fault intelligent management module; 50. Insulation unit monitoring circuit; 60. Voltage measurement module; 70. Insulation state identification and diagnosis module; 80. Insulation fault early warning module; 90. Insulation fault alarm module; 100. Insulation unit monitoring module; 110. Insulation fault diagnosis unit; 120. Switch module; S0, starting switch; S1, positive control switch; S2, negative control switch; R+, positive insulation unit; R-, negative insulation unit; R1, first insulation fault diagnosis unit; R2, second insulation fault diagnosis unit; R3, third insulation fault diagnosis unit; R4, fourth insulation fault diagnosis unit.

[0062] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0064] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0065] The main solutions of the embodiments of the present application are: obtaining an insulation unit parameter set, and obtaining insulation unit information through an insulation unit monitoring circuit, and determining an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set; constructing an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set; monitoring the insulation unit state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and triggering an intelligent management mechanism to realize electric vehicle insulation control.

[0066] In this embodiment, for ease of description, the following description is made with identification of the power battery control module as the execution subject.

[0067] Because existing technologies are easily affected by other components in the circuit during the measurement process, resulting in inaccurate measurement results, and lack of effective fault warning and alarm mechanisms, it is difficult to respond to the degradation of insulation performance in a timely manner. They rely too much on a single detection technology and lack comprehensive diagnostic capabilities, resulting in insufficient detection accuracy. Therefore, how to accurately and reliably monitor insulation units is a technical problem that needs to be solved urgently.

[0068] The present application provides a solution, which obtains an insulation unit parameter set, obtains insulation unit information through an insulation unit monitoring circuit, determines an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set; constructs an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set; monitors the insulation unit state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and triggers an intelligent management mechanism to realize electric vehicle insulation control.

[0069] It can be seen from the above embodiments that the present application obtains the insulation status information of the electric vehicle power battery system in real time through the insulation unit monitoring circuit, thereby constructing an insulation unit status recognition and diagnosis model to accurately identify and predict the insulation status. When insulation performance degradation or failure is detected, the intelligent management mechanism responds immediately to ensure the safe operation of the vehicle, improve the safety and reliability of electric vehicles, protect the personal safety of drivers and passengers, and reduce maintenance costs.

[0070] It should be noted that the safety isolation intelligent monitoring circuit system of the insulation unit includes but is not limited to the power battery 10, the power battery main control module 20, the communication module 30, the insulation fault intelligent management module 40, the insulation unit monitoring circuit 50 and the voltage measurement module 60. Figure 1 As shown, Figure 1 This is a schematic diagram of the architecture of the intelligent monitoring circuit system for bridge safety isolation of the insulation unit of the insulation unit monitoring method of this application.

[0071] It can be understood that the power battery 10 is a power supply device, the power battery main control module 20 is used to process the information of the insulation fault intelligent management module 40, and send corresponding control information based on the judgment results, the communication module 30 is used to output the insulation unit fault status information monitored by the insulation unit's safety isolation intelligent monitoring circuit system to the vehicle controller, so as to optimize the vehicle control strategy, the insulation fault intelligent management module 40 is used to monitor the insulation unit fault status information to take corresponding maintenance suggestions and protection measures, the insulation unit monitoring circuit 50 is used to monitor the insulation unit status in real time, and the voltage measurement module 60 is used to detect the voltage of the positive and negative bus high voltage of the power battery 10 to the electric chassis of the electric vehicle.

[0072] Based on this, the embodiment of the present application provides an insulation unit monitoring method, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the insulation unit monitoring method of the present application.

[0073] In this embodiment, the insulation unit monitoring method includes steps S10 to S30:

[0074] Step S10, obtaining an insulation unit parameter set, and obtaining insulation unit information through an insulation unit monitoring circuit, and determining an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set;

[0075] It should be noted that if Figure 3 As shown, Figure 3 This is a detailed structural diagram of the insulation unit monitoring circuit involved in the insulation unit monitoring method of the present application. The insulation fault intelligent management module 40 includes an insulation state identification and diagnosis module 70, an insulation fault warning module 80 and an insulation fault alarm module 90. The insulation unit monitoring circuit 50 includes a power battery 10, an insulation unit monitoring module 100, an insulation fault diagnosis unit 110 and a switch module 120. The insulation unit monitoring module 100 includes a positive insulation unit R+ and a negative insulation unit R-. The insulation fault diagnosis unit 110 includes a first insulation fault diagnosis unit R1, a second insulation fault diagnosis unit R2, a third insulation fault diagnosis unit R3 and a fourth insulation fault diagnosis unit R4. The switch module 120 includes a starting switch S0, a positive control switch S1 and a negative control switch S2.

[0076] It can be understood that the power battery 10 is a power supply device, and the positive control switch S1, the negative switch S2 and the starting switch S0 are automatic switches of the insulation unit monitoring circuit, wherein the starting switch S0 is connected in series between the vehicle body ground and the remaining resistance switch circuits to avoid the degradation of insulation performance caused by failure of components in the insulation detection circuit. When S0 is closed, the resistance switch network is activated, and the measurement process of the insulation unit can be started. The positive control switch S1 and the negative control switch S2 are controlled as needed to change the resistance path of the positive and negative poles to the ground, and S1 and S2 are alternately closed and opened to measure the voltage values ​​under different configurations. According to the measured voltage value, using Ohm's law and circuit analysis, the equivalent resistance value of the insulation unit of the positive and negative poles to the ground can be obtained. The positive insulation unit R+ is the insulation unit that connects the high voltage of the positive busbar of the power battery 10 to the electric chassis of the electric vehicle. The equivalent resistance value of the positive insulation unit R+ is referred to as the positive insulation resistance. The negative insulation unit R- is the insulation unit that connects the high voltage of the negative busbar of the power battery 10 to the electric chassis of the electric vehicle. The equivalent resistance value of the negative insulation unit R+ is referred to as the negative insulation resistance. The first insulation fault diagnosis unit R1, the second insulation fault diagnosis unit R2, the third insulation fault diagnosis unit R3 and the fourth insulation fault diagnosis unit R4 are insulation fault diagnosis resistors. Their resistance values ​​are known, and the resistance value of the first insulation fault diagnosis unit R1 is equal to the resistance value of the second insulation fault diagnosis unit R2, and the resistance value of the third insulation fault diagnosis unit R3 is equal to the resistance value of the fourth insulation fault diagnosis unit R4.

[0077] In addition, it should be noted that by controlling the switching of the switch, the influence of other components in the circuit on the measurement results can be reduced, thereby improving the accuracy and reliability of the measurement. When the positive control switch S1 or the negative control switch S2 is closed, the first insulation fault diagnosis unit R1 and the third insulation fault diagnosis unit R3 in the insulation unit monitoring circuit are connected in series, and the second insulation fault diagnosis unit R2 and the fourth insulation fault diagnosis unit R4 are connected in series. By dispersing the current, the thermal effect of the insulation unit is reduced to ensure the safety of the circuit and improve the adaptability of the circuit to the insulation unit, thereby stabilizing the resistance value of the insulation unit. In addition, the resistance value of the insulation fault diagnosis resistor is usually set to a larger value, but when the insulation fault diagnosis resistor is short-circuited or the resistance decreases and fails, it is also easy to cause insulation fault. By configuring the insulation units in series, at this time, if there is a problem with one of the insulation fault diagnosis resistors, other insulation fault diagnosis resistors can still work normally, thereby reducing the risk of insulation failure.

[0078] For ease of understanding, the example of obtaining insulation unit information is used for explanation, wherein the information acquisition device is an information acquisition module, and the storage device is a memory.

[0079] The information acquisition module obtains an insulation unit parameter set, such as an insulation unit critical safety value and an insulation unit safety insulation value, and obtains insulation unit information, such as a positive insulation resistance value and a negative insulation resistance value, through an insulation unit monitoring circuit. Based on the insulation unit information, an insulation unit fault state parameter set, such as an emergency alarm state, a warning state, and a safety state, is determined, and subsequent processing is performed based on the insulation unit parameter set and the insulation unit fault state parameter set.

[0080] In a feasible implementation, step S10 may include steps A11 to A15:

[0081] Step A11: Acquire the insulation unit critical safety value, insulation unit safety insulation value, power battery rated voltage, positive and negative pole switch information, and insulation fault diagnosis unit information set, where the positive and negative pole switch information includes positive pole control switch information and negative pole control switch information;

[0082] It should be noted that the insulation unit parameter set includes the insulation unit critical safety value and the insulation unit safety insulation value. The magnitude of these resistance values ​​reflects the insulation performance between the high and low voltage systems of electric vehicles, and determines the insulation performance level, which is generally determined by safety requirements and equipment category. For example, Class I equipment requires an insulation resistance of ≥100Ω / V, and Class II equipment requires an insulation resistance of ≥500Ω / V. The insulation unit critical safety value can be set to 100Ω / V, and the insulation unit safety insulation value can be set to 500Ω / V. The insulation unit critical safety value reflects the characteristics of the insulation performance in a critical safety state, and the insulation unit safety insulation value reflects the characteristics of the insulation performance in a safe state. The power battery rated voltage reflects the characteristics of the battery operating voltage under specified conditions. The positive and negative pole switches reflect the characteristics of the dynamic control of the circuit. The insulation fault diagnosis unit information set reflects the characteristics of measuring the equivalent resistance between the battery module and the vehicle body ground.

[0083] It can be understood that the critical safety value of the insulation unit is the minimum insulation unit equivalent resistance value when the insulation performance is in a critical safety state, and the safety insulation value of the insulation unit is the minimum insulation unit equivalent resistance value when the insulation performance is in a safe state. Usually, the safety insulation value of the insulation unit is higher than the critical safety value of the insulation unit. The rated voltage of the power battery is an ideal voltage value without considering the external load and charging status. The positive and negative pole switch information controls the direction of current flow so as to obtain accurate voltage and resistance data under different measurement conditions. The insulation fault diagnosis unit information set can provide the status of each diagnostic unit and the corresponding fault status.

[0084] Step A12: measuring the insulation unit monitoring circuit based on the positive electrode control switch information and the negative electrode control switch information to obtain positive and negative electrode voltage information, where the positive and negative electrode voltage information includes positive electrode voltage information and negative electrode voltage information;

[0085] It should be noted that the positive and negative pole voltage information reflects the characteristics of the insulation state at a specific moment, which is the voltage of the power battery positive bus high voltage to the electric vehicle chassis and the voltage of the power battery negative bus high voltage to the electric vehicle chassis measured by the insulation unit monitoring circuit.

[0086] It can be understood that based on the positive and negative pole voltage information, the equivalent resistance value of the insulation unit of the positive and negative poles to the ground can be calculated, and then whether the insulation status is normal can be evaluated. In addition, the positive and negative pole voltage information can reveal whether there is a circuit break or short circuit problem in the circuit. If the voltage is abnormal or there are abnormal fluctuations, it indicates that there is a fault in the circuit and the battery system is aging or has other performance degradation problems.

[0087] For ease of understanding, obtaining positive and negative voltage information is taken as an example for explanation, wherein the voltage measuring device is a voltage measuring module, the storage device is a memory, and the processing device is a processing module.

[0088] Close switch S0, activate the resistance switch network, close the positive control switch S1 and the negative control switch S2, and obtain the voltage of the high-voltage power of the positive busbar of the power battery to the electric chassis of the electric vehicle through the voltage measurement module, which is recorded as U+, and obtain the voltage of the high-voltage power of the negative busbar of the power battery to the electric chassis of the electric vehicle, which is recorded as U-, so as to obtain the positive and negative voltage information, and perform subsequent processing based on the positive and negative voltage information.

[0089] Step A13, determining insulation unit information based on the insulation fault diagnosis unit information set, the positive electrode voltage information, and the negative electrode voltage information;

[0090] It should be noted that the insulation unit information reflects the characteristics of the insulation unit of the positive and negative bus high-voltage electricity of the power battery to the electric chassis of the electric vehicle, including the positive insulation unit information and the negative insulation unit information. The positive insulation unit information refers to the equivalent resistance value of the positive insulation unit R+, referred to as the positive insulation resistance value. The negative insulation unit information refers to the equivalent resistance value of the negative insulation unit R-, referred to as the negative insulation resistance value.

[0091] It is understandable that by monitoring the insulation unit information, the changing trend of the insulation unit equivalent resistance value over time can be observed, thereby evaluating whether the insulation state is stable or whether there is performance degradation, and identifying whether there is an insulation fault.

[0092] In addition, it should be noted that the insulation unit information can be used to evaluate the safety of the entire battery system, and the degradation of insulation performance may be caused by material aging, changes in environmental conditions or mechanical damage. Regular monitoring of insulation unit information can enable timely maintenance, such as replacing components with degraded insulation performance, to maintain the optimal performance of the battery system.

[0093] For ease of understanding, obtaining insulation unit information is taken as an example for explanation, wherein the voltage measuring device is a voltage measuring module, the storage device is a memory, and the processing device is a processing module.

[0094] Close switch S0, activate the resistance switch network, close the positive control switch S1 and the negative control switch S2, and obtain the voltage of the high-voltage power of the positive busbar of the power battery to the electric chassis of the electric vehicle through the voltage measurement module, recorded as U+, and obtain the voltage of the high-voltage power of the negative busbar of the power battery to the electric chassis of the electric vehicle, recorded as U-.

[0095] Thus, the ratio N0 is calculated and the first corresponding relationship is obtained:

[0096]

[0097] If N0>1, close the positive control switch S1 and open the negative control switch S2. The voltage measurement module obtains the voltage between the high-voltage power busbar of the power battery and the electric vehicle chassis, denoted as U1+, and the voltage between the high-voltage power busbar of the power battery and the electric vehicle chassis, denoted as U1-.

[0098] Using Ohm's law and circuit analysis, the ratio N1 can be calculated to obtain the second corresponding relationship:

[0099]

[0100] If N0 < 1, open the positive control switch S1 and close the negative control switch S2. The voltage measurement module obtains the voltage between the high-voltage power busbar of the power battery and the electric vehicle chassis, denoted as U2+, and the voltage between the high-voltage power busbar of the power battery and the electric vehicle chassis, denoted as U2-.

[0101] Using Ohm's law and circuit analysis, the ratio N2 can be calculated to obtain the third corresponding relationship:

[0102]

[0103] Solving the first and second corresponding relations together, we can get:

[0104] When N0>1

[0105] Solving the first and third corresponding relations together, we can obtain:

[0106] When N0<1

[0107] By controlling the switching of the switch, the influence of other components in the circuit on the measurement results can be reduced, thereby improving the accuracy and reliability of the measurement.

[0108] The path dynamic adjustment mechanism of the insulation unit is:

[0109] If N0>1, open the positive control switch S1 and close the negative control switch S2. Through the voltage measurement module, obtain the voltage of the power battery positive bus high voltage to the electric vehicle chassis, and the voltage of the power battery negative bus high voltage to the electric vehicle chassis.

[0110] If N0 < 1, close the positive control switch S1 and open the negative control switch S2. The voltage measurement module obtains the voltage between the high voltage of the positive busbar of the power battery and the electric chassis of the electric vehicle, and the voltage between the high voltage of the negative busbar of the power battery and the electric chassis of the electric vehicle.

[0111] At this time, the second and third corresponding relationship equations are solved together to obtain:

[0112]

[0113] Set the monitoring interval and monitor the insulation unit information regularly.

[0114] Step A14, obtaining an insulation unit parameter set based on the insulation unit critical safety value and the insulation unit safety insulation value;

[0115] It should be noted that the insulation unit parameter set reflects the characteristics of the preset threshold values ​​in the insulation unit, including but not limited to the insulation unit critical safety value and the insulation unit safety insulation value.

[0116] It can be understood that by obtaining the equivalent resistance value of the insulation unit, including the positive insulation resistance value, the negative insulation resistance value, the critical safety value of the insulation unit and the safe insulation value of the insulation unit, it is possible to evaluate whether the insulation unit is still within the safe working range. If the equivalent resistance value of the insulation unit is close to or lower than the critical safety value, it is necessary to check or replace it in time to avoid failure. The critical safety value can be used as a threshold for setting an alarm or early warning state. When the monitored insulation unit value is lower than this critical safety value, the system will issue an alarm prompt, and take corresponding maintenance or repair measures. The safe insulation value of the insulation unit can be used as a threshold for setting an early warning or safe state. When the obtained equivalent resistance value of the insulation unit is higher than the safe insulation value of the insulation unit, the system will issue a safe state prompt, and take corresponding regular monitoring measures.

[0117] Step A15: determining an insulation unit fault state parameter set based on the insulation unit information, the insulation unit parameter set, and the power battery rated voltage.

[0118] It can be understood that the insulation unit fault state parameter set reflects the characteristics of identifying the insulation fault state. By comparing the actually measured insulation unit information with the insulation unit parameter set, it can be determined whether there is an insulation fault in the battery system, and information on the type of fault state can be provided, thereby determining whether maintenance is required to improve the safety and reliability of electric vehicles.

[0119] Additionally, it should be noted that the insulation unit fault state parameter set includes an emergency alarm state, a warning state, and a safety state.

[0120] For ease of understanding, the example of obtaining insulation unit information is used for explanation, wherein the information acquisition device is an information acquisition module, and the storage device is a memory.

[0121] The information acquisition module obtains the insulation unit information R+ and R-, and obtains the insulation unit parameter set R saf,C and R saf,S , Power battery rated voltage V cell , determine the insulation unit fault state parameter set based on the insulation unit information, the insulation unit parameter set and the power battery rated voltage. For example, R Saf,c ≥100Ω / V, R Saf,S ≥500Ω / V, not specifically limited thereto.

[0122] Step S20, constructing an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set;

[0123] It is understandable that a tree-based regression algorithm can be used to construct a basic insulation unit classifier, and a neural network can be used to construct an optimized insulation unit classifier. A combination of these two classifiers can be used to construct an insulation unit state identification and diagnosis model. The tree-based regression algorithm excels in capturing data features and performing preliminary classification, while the neural network can handle complex nonlinear relationships. The ensemble method improves the accuracy and precision of insulation unit state detection by combining the predictions of multiple classifiers. It also enhances the model's ability to generalize to new data, reduces the risk of overfitting, and makes the insulation unit state identification and diagnosis model more stable, reliable, and capable of greater generalization in practical applications.

[0124] For ease of understanding, the construction of an insulation unit state identification and diagnosis model is taken as an example for explanation, wherein the information acquisition device is an information acquisition module, the storage device is a memory, and the processing device is a processing module.

[0125] The information acquisition module obtains the insulation unit information R+, R-, and the rated voltage of the power battery V cell , insulation unit critical safety threshold R Saf,C , insulation unit safety insulation threshold R Saf,S, and extract characteristic data based on the insulation unit information and convert it into a standardized form to eliminate the influence of different dimensions and value ranges, such as using the minimum-maximum standard method for standardization to obtain R+′, R-′, V′ cell , R′ Saf,C , R′ Saf,S .

[0126] Step S30 , monitoring the insulation unit status based on the insulation unit information, the insulation unit parameter set, the insulation unit fault status parameter set, and the insulation unit status identification and diagnosis model, and triggering an intelligent management mechanism to implement electric vehicle insulation control.

[0127] In a feasible implementation, step S30 may include steps B11 to B12:

[0128] Step B11, judging the state of the insulation unit based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and obtaining a judgment result;

[0129] It should be noted that the judgment result reflects the characteristics of the identified pattern value.

[0130] It is understandable that the judgment result output by the insulation unit state identification and diagnosis model is one or more mode values, including mode 1 to mode 9, such as Figure 4 As shown, Figure 4 The insulation unit fault state parameter set, insulation unit fault state judgment result and insulation state category diagram of the insulation unit monitoring method of this application are judged according to the mode value to determine the insulation unit state category, including emergency alarm state, warning state and safety state, and specific maintenance suggestions and preventive measures are proposed based on the insulation state category.

[0131] Step B12: obtaining the insulation status category of the insulation unit based on the judgment result, and triggering the intelligent management mechanism according to the insulation status category to implement insulation control of the electric vehicle.

[0132] It should be noted that the insulation state category reflects the characteristics of the mode corresponding to the insulation unit state.

[0133] For ease of understanding, the following description is made by taking obtaining a judgment result as an example, wherein the information collection device is an information collection module, the storage device is a memory, and the execution device is an execution module.

[0134] The information acquisition module obtains the judgment result, obtains the insulation status category of the insulation unit based on the judgment result, and triggers the intelligent management mechanism according to the insulation status category.

[0135] If the judgment result indicates that the system enters mode 1, it determines that there is a single-end insulation fault and identifies it as an emergency alarm state. The system immediately activates the insulation fault alarm, automatically disconnects the power equipment from the power battery, and takes effective protective measures.

[0136] If the judgment result indicates that the system enters mode 2, the system determines that a single-end insulation fault occurs and identifies it as an emergency alarm state. The system immediately activates the insulation fault alarm, automatically disconnects the power equipment from the power battery, and takes effective protective measures.

[0137] If the judgment result indicates that the system enters mode 3, it will determine that there is a single-end insulation fault and identify it as an emergency alarm state. The system will immediately activate the insulation fault alarm, automatically disconnect the power equipment from the power battery, and take effective protective measures.

[0138] If the judgment result indicates that the system enters mode 4, it determines that there is a single-end insulation fault and identifies it as an emergency alarm state. The system immediately activates the insulation fault alarm, automatically disconnects the power equipment from the power battery, and takes effective protective measures.

[0139] If the judgment result indicates that the system enters mode 5, the system determines that there is a double-terminal insulation fault and identifies it as an emergency alarm state. The system immediately activates the insulation fault alarm, automatically disconnects the power equipment from the power battery, and takes effective protective measures.

[0140] If the judgment result determines that the system enters mode 6, the system determines that the insulation performance is qualified and performs a dynamic monitoring switching mechanism to change the status of the positive control switch S1 and the negative control switch S2, combine the equations, and re-check the calculated insulation unit. If the judgment result still determines that the system enters mode 6 at this time, it is identified as a warning state, and the system starts an insulation fault warning, prompting the need for real-time monitoring of the insulation status and possible maintenance to prevent deterioration of the insulation performance.

[0141] If the judgment result determines to enter mode 7, the system determines that the insulation performance is qualified and identifies it as a warning state. The system starts the insulation fault warning, prompting the need for real-time monitoring of the insulation status and possible maintenance to prevent deterioration of the insulation performance.

[0142] If the judgment result determines to enter mode 8, the system determines that the insulation performance is qualified and identifies it as a warning state. The system starts the insulation fault warning, prompting the need for real-time monitoring of the insulation status and possible maintenance to prevent deterioration of the insulation performance.

[0143] If the judgment result determines that it enters mode 9, the system determines that the insulation performance is good and identifies it as a safe state. The electrical system can continue to operate normally, but the system will still regularly check the insulation unit to ensure continued safety.

[0144] The insulation unit monitoring method proposed in this embodiment obtains an insulation unit parameter set, obtains insulation unit information through an insulation unit monitoring circuit, determines an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set; constructs an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set; monitors the insulation unit state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and triggers an intelligent management mechanism to achieve electric vehicle insulation control. This solves the technical problem of how to accurately and reliably monitor insulation units. Compared with the existing technology, this application obtains insulation state information of the electric vehicle power battery system in real time through an insulation unit monitoring circuit, thereby constructing an insulation unit state identification and diagnosis model, accurately identifying and predicting the insulation state. When insulation performance degradation or failure is detected, the intelligent management mechanism responds immediately to ensure the safe operation of the vehicle, thereby improving the safety and reliability of electric vehicles, protecting the personal safety of drivers and passengers, and reducing maintenance costs.

[0145] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated later.

[0146] In this embodiment, refer to Figure 5 , Figure 5 This is a flow chart of the second embodiment of the insulation unit monitoring method of the present application, wherein step S20 specifically includes steps S201 to S204:

[0147] Step S201, obtaining a model training cycle;

[0148] It should be noted that the model training cycle reflects the manually preset number of model training times.

[0149] It can be understood that the model training cycle includes the number of training rounds, the loop iterations in each round, and the learning rate adjustment strategy in each iteration, thereby adjusting the convergence speed and final performance of the model.

[0150] Step S202: converting the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set into a standardized form, and obtaining an insulation unit sample set;

[0151] It can be understood that in order to obtain the insulation unit sample set, the collected insulation unit information, insulation unit parameter set and insulation unit fault status parameter set can be preprocessed. The preprocessing includes data cleaning, normalization, removal of outliers and data enhancement. The preprocessed data is divided into training set, validation set and test set. The training set is used for model training, the validation set is used for model tuning and preventing overfitting, and the test set is used to finally evaluate the performance of the model.

[0152] Step S203: training the insulation unit sample set to determine the category confidence and obtaining an insulation unit state monitoring model;

[0153] It should be noted that the insulation unit condition monitoring model reflects the characteristics of the model constructed by the basic classifier trained based on the tree regression algorithm.

[0154] It is understood that the selected tree-based regression algorithm is used to train the processed sample set. During the training process, the performance of the model is optimized by adjusting the model parameters, such as the learning rate, the depth of the tree, and the regularization parameter, and using cross-validation and other techniques to evaluate the generalization ability of the model on unseen data.

[0155] For ease of understanding, the construction of an insulation unit state monitoring model is taken as an example for explanation, wherein the information acquisition device is an information acquisition module, the storage device is a memory, and the processing device is a processing module.

[0156] The information acquisition module obtains the insulation unit sample set, obtains the standardized insulation unit information R+′ and R-′, and obtains the standardized power battery rated voltage V′ cell , obtain the standardized insulation unit critical safety value R′ Saf,C , obtain the standardized insulation unit safety insulation value R′ Saf,S , thus obtaining the insulation unit sample set.

[0157] The scoring function is used to calculate the scoring feature Q of the insulation unit sample set, and the fourth corresponding relationship is obtained:

[0158] Q=(w + *R+′)+(w - *R-′)+w v *V′ cell +w c *R′ Saf，C+ w s *R′ Saf，S

[0159] Among them, w + 、w - 、w v 、w c 、w s It is the characteristic coefficient corresponding to the information of each unit in the insulation unit sample set.

[0160] The insulation unit sample set is processed using the tree regression algorithm to obtain multiple insulation unit sample subsets, and the weight starting value of each training sample is obtained, and the weight starting value w of the kth sample subset is calculated. k , we get the fifth corresponding relation:

[0161]

[0162] Where t represents the loop iteration, j c is the separation vector, h r is the truncation error.

[0163] Calculate the weighted fusion value ρ of the weight s , we get the sixth corresponding relationship:

[0164]

[0165] Among them, a i represents the gradient factor, t0 represents the training time, and g0 represents the expansion step size.

[0166] Use the Gini coefficient to calculate the initial tree cycle value G k , we get the seventh corresponding relationship:

[0167]

[0168] Among them, k f represents the sample volatility, l p represents the empirical constant, h c Represents the Gini coefficient, which is set according to the specific situation of the tree regression algorithm.

[0169] Initialize the initial tree level to control the growth process of the tree regression algorithm, prevent overfitting, and help improve the generalization ability of the model. Calculate the initial tree level e c , we get the eighth corresponding relationship:

[0170]

[0171] Among them, a0 represents the necessary sample size of each node, g u represents the integrated learning rate, n p Represents a linear interpolation function.

[0172] Taking the nodes of the tree regression algorithm as hyperplanes, we calculate the maximum distance L(i,j) and get the ninth corresponding relationship:

[0173] L(i,j)=r c ×u f ×s i

[0174] Among them, u f represents the Lagrange multiplier, s i Indicates a restriction.

[0175] Thus, the insulation unit basic classifier based on the tree regression algorithm is constructed, and the tenth corresponding relationship is obtained:

[0176]

[0177] in, represents the average particle size, K c is related to category c A collection of .

[0178] Thus, the category confidence is determined to build the insulation unit condition monitoring model.

[0179] In a feasible implementation, step S203 may include steps C11 to C16:

[0180] Step C11, obtaining a scoring function, a tree regression algorithm, loop iterations, a separation vector, a truncation error, a gradient factor, a training time, and an expansion step size;

[0181] It should be noted that the scoring function reflects the characteristics of the contribution of each feature in the insulation unit sample set to the insulation state classification. It can be a linear or nonlinear function, such as the scoring function. The tree regression algorithm reflects the characteristics of the importance of features in the insulation unit sample set and can process nonlinear relationships, such as random forests, decision trees or gradient boosting trees. The loop iteration reflects the characteristics of the loop iterative update of the model, the separation vector reflects the characteristics of the discrimination of the model category, the truncation error reflects the characteristics of the deviation between the model prediction and the actual, the gradient factor reflects the characteristics of the model training efficiency, the training time reflects the characteristics of the cumulative time of the model training, and the expansion step reflects the characteristics of the amplitude of the parameter update of the model during the training process.

[0182] As you can understand, the scoring function maps feature values ​​to a score, representing the importance of that feature in determining insulation status. The tree regression algorithm processes a sample set of insulation units and characterizes the impact of different features on insulation status. Iterations are used to repeatedly traverse the training data and optimize model parameters. Setting the number of iterations ensures the model has sufficient time to learn patterns in the data while avoiding overfitting. The separating vector is used to define the threshold for classifying different categories in the tree model. Choosing an appropriate separating vector can maximize the distinction between categories and improve the model's classification accuracy. The truncation error is used to adjust model parameters or training strategies when necessary. The gradient factor adjusts the model's learning rate during training to prevent overfitting or underfitting during optimization. Properly setting the gradient factor can accelerate convergence and improve model training efficiency. Setting an appropriate training duration ensures the model has sufficient time to learn data features while avoiding wasted resources due to excessive training time. The dilation step size allows for rapid convergence in the early stages of training and allows for fine-tuning in the later stages of training to achieve better performance.

[0183] Step C12, combining the features in the isolation unit sample set into a scoring feature through a scoring function;

[0184] It should be noted that the scoring feature is a feature value synthesized after weighting using a scoring function, which integrates the weight values ​​of multiple features. The synthesis method may include arithmetic synthesis, geometric synthesis or other more complex synthesis strategies.

[0185] It is understood that before a scoring feature is actually applied, it needs to be validated to ensure its effectiveness and reliability. Validation can be accomplished through cross-validation, independent test sets, or real-world application cases. The validation process can assess the performance and stability of the scoring feature under different circumstances. Based on the validation results, the scoring feature needs to be optimized to improve its predictive accuracy. Optimization can include adjusting weights, reselecting features, or introducing new features to simplify the model input while retaining key information, thereby improving model processing speed and accuracy.

[0186] Step C13, using a tree regression algorithm to train the insulation unit sample set to obtain an insulation unit sample subset;

[0187] It should be noted that the insulation unit sample subset reflects the characteristics of a specific insulation state.

[0188] It is understandable that during the training process, adjusting the parameters of the tree regression algorithm, such as the depth of the tree, the number of branches, and the minimum sample split, can optimize the performance and prediction accuracy of the model, while the sample subset contains a specific combination of features to identify the failure mode or performance change of the insulation unit.

[0189] Step C14, calculating the initial weight value based on the isolation unit sample subset, the scoring feature, the loop iteration, the separation vector, and the truncation error;

[0190] It should be noted that the starting weight value reflects the characteristics of the initial weight assigned to each training sample at the beginning of model training.

[0191] It can be understood that by adjusting the starting value of the weight, the learning speed of the model in the early stage of training can be controlled to avoid too fast or too slow learning progress. The appropriate starting value of the weight helps the model converge to the optimal solution faster, improves training efficiency and model performance. During the model training process, the starting value of the weight is used to initialize the leaf node weights of the tree regression algorithm. Based on the starting value of the weight, the weight can be dynamically adjusted according to the changes in the loss function during the iterative training process of the model, and the weight can be fine-tuned according to the performance of the model on the validation set to improve the generalization ability of the model.

[0192] Step C15, calculating the weighted fusion value based on the weight starting value, gradient factor, training time, and expansion step size;

[0193] It should be noted that the weighted fusion value reflects the characteristics of the insulation unit's impact state and is obtained by weighted summation of each feature.

[0194] It can be understood that during the model training process, the weighted fusion value is used to optimize the performance of the classifier. By adjusting the weights, the model can pay more attention to the features that have a significant impact on the classification results, thereby improving the accuracy of the model.

[0195] Step C16: obtaining an insulation unit basic classifier based on the weighted fusion value, and determining an insulation unit state monitoring model based on the insulation unit basic classifier.

[0196] It can be understood that multiple insulation unit base classifiers are constructed based on weighted fusion values. The insulation unit base classifier is a simple classification model, such as a decision tree stump, which performs classification on a single feature. Although the performance of a single insulation unit base classifier is limited, the performance of the overall model can be greatly improved through integrated learning methods.

[0197] In a feasible implementation, step C16 may include steps C161 to C165:

[0198] Step C161, obtaining sample volatility, empirical constant, Gini coefficient, necessary sample size, ensemble learning rate, linear interpolation function, Lagrange multiplier, constraint condition, average granularity, insulation unit fault state basic classification set, and category set;

[0199] It should be noted that sample volatility reflects the degree of fluctuation of sample data within its value range, the empirical constant reflects the characteristics of the generalization ability of the model, the Gini coefficient reflects the characteristics of determining the expected segmentation point during model training, the necessary sample size reflects the characteristics of the minimum number of samples required to build an effective tree regression algorithm, the integrated learning rate reflects the characteristics of the contribution of each tree to the final result, the linear interpolation function reflects the characteristics of processing missing data or predicting continuous values, the Lagrange multiplier reflects the characteristics of processing model constraint problems, the constraint conditions reflect the characteristics of the complexity of the model, the average granularity reflects the characteristics of the fineness of data segmentation during model construction, the basic classification set of insulation unit fault status reflects the characteristics of classifying the model, and the category set reflects the characteristics of the state category of the model.

[0200] It is understandable that sample volatility characterizes the stability and consistency of the data. When building a model, low sample volatility indicates that the data is stable, and high sample volatility indicates that there are noise or outliers in the data. The empirical constant is set based on historical data and experience, which can help the model maintain a certain generalization ability when facing new data. The Gini coefficient is used to measure the impurity of a node, that is, the uncertainty of the sample category under the node. The smaller the Gini coefficient, the lower the impurity of the node and the better the classification effect. The necessary sample size can ensure that each tree regression algorithm node has enough sample size, which helps to improve the stability and accuracy of the model. The ensemble learning rate is in the ensemble learning method, such as Boosting or Bagging, which determines the contribution of each tree to the final result. The contribution of the model to the result can be determined by selecting an appropriate learning rate to balance the training speed of the model and the final generalization ability. In model training, linear interpolation functions can be used to handle missing data or predict continuous values. Lagrange multipliers can help the model find the optimal solution while meeting the constraints. The constraints are used to control the complexity of the model and prevent overfitting. For example, the maximum depth of the tree regression algorithm or the minimum number of samples required for node splitting can be limited. The average granularity characterizes the depth of the tree and the number of leaf nodes, affecting the generalization ability and complexity of the model. The basic classification set of insulation unit fault status can classify the features in the model. The category set is all insulation unit status categories that the model needs to identify.

[0201] Step C162, calculating the initial tree cycle value based on the weighted fusion value, sample volatility, empirical constant, and Gini coefficient;

[0202] It should be noted that the initial tree cycle value reflects the characteristics of the initial state during the construction process of the tree regression algorithm.

[0203] It is understandable that the initial tree loop value is used in the construction process of the tree regression algorithm to help determine the depth of the tree and the splitting conditions of the leaf nodes, thereby affecting the complexity of the model and the final classification performance. By reasonably setting the initial tree loop value, we can avoid model overfitting and improve the model's generalization ability on unknown data.

[0204] Step C163 , calculating an initial tree level based on the initial tree cycle value, the required sample size, the ensemble learning rate, and the linear interpolation function;

[0205] It should be noted that the initial tree level reflects the characteristics of the basic depth when the tree regression algorithm starts to grow when building the tree regression algorithm model, and determines the starting point of the tree and the initial direction of the construction process.

[0206] It is understandable that a reasonable initial tree level can help the model quickly learn the basic features of the data in the early stages of training while avoiding falling into overfitting too quickly.

[0207] Step C164 , calculating the maximum distance based on the initial tree level, the Lagrange multiplier, and the constraint conditions;

[0208] It should be noted that maximizing the distance reflects the characteristic that the model seeks the maximum degree of discrimination between different categories or categories and features.

[0209] It is understood that the maximized distance can be calculated by different methods, such as using Euclidean distance, Manhattan distance or other distance metrics.

[0210] Step C165 : calculating an insulation unit basic classifier based on the maximized distance, the average granularity, the insulation unit fault state basic classification set, and the category set, and determining an insulation unit state monitoring model based on the insulation unit basic classifier.

[0211] As can be understood, the insulation unit basic classifier for identifying insulation unit status is calculated by comprehensively considering the maximum distance, average granularity, weak classification set of insulation unit fault status, and category set. The insulation unit basic classifier utilizes key features and category information extracted from the data and fuses multiple insulation unit basic classifiers into a more powerful monitoring model through ensemble learning. This model accurately monitors and classifies the status of insulation units in electric vehicle power battery systems, ensuring their safety and reliability.

[0212] Step S204 : training the pre-classification result of the insulation unit state monitoring model and the insulation unit sample set based on the model training cycle to determine an insulation unit state recognition and diagnosis model.

[0213] It can be understood that the insulation unit status recognition and diagnosis model is constructed based on machine learning or deep learning algorithms, and can identify the current status of the insulation unit based on the input feature data.

[0214] For ease of understanding, we take the construction of insulation unit state recognition and diagnosis model as an example to illustrate, obtain the model training cycle and the pre-classification results of the insulation unit state monitoring model and the insulation unit sample set. It can be understood that the insulation unit sample set includes R+′, R-′, V′ cell , R′ Saf,C , R′ Saf,S, which can be flexibly adjusted according to actual conditions, and there is no specific limit on this. The pre-classification results of the insulation unit state monitoring model include the output of the insulation unit basic classifier and the corresponding state. The number of hidden layers and neurons is set, which can be flexibly adjusted according to actual conditions, and there is no specific limit on this. The number of hidden layers can be set to 2, corresponding to 7 neurons in each hidden layer, and there is no specific limit on this. The hidden layer activation function, loss function, and output layer activation function are set, which can be flexibly adjusted according to actual conditions, and there is no specific limit on this. ReLU can be used as the hidden layer activation function, cross entropy can be used as the loss function, and softmax can be used as the output layer activation function, and there is no specific limit on this. Thus, the insulation unit state monitoring model is constructed.

[0215] In a feasible implementation, step S204 may include steps D11 to D13:

[0216] Step D11, obtaining the number of hidden layers, the number of neurons in the hidden layers, and the neural network function, where the neural network function includes a ReLU activation function, a softmax activation function, and a cross entropy loss function;

[0217] It should be noted that the number of hidden layers reflects the depth of the model and is the number of layers in the neural network excluding the input layer and the output layer. The number of neurons in the hidden layer reflects the number of neurons in each hidden layer and determines the number of parameters in each layer of the network. The neural network function reflects the performance of the model and includes the ReLU activation function, the softmax activation function, and the cross entropy loss function.

[0218] It is understandable that the hidden layer allows the neural network to learn and capture complex patterns and relationships in the data. The larger the number of hidden layers, the corresponding network has stronger learning ability, but it is also more prone to overfitting. The number of neurons in the hidden layer determines the number of parameters in each layer of the network, which affects the capacity and complexity of the model. The more neurons in the hidden layer, the higher the expressive power of the model, but it also leads to increased demand for computing resources and longer training time. Neural network functions include ReLU activation function, softmax activation function and cross-entropy loss function. The ReLU activation function can help solve the gradient disappearance problem and accelerate the training process of the neural network. ReLU increases the nonlinear characteristics of the model by keeping the positive input value unchanged and outputting zero for the negative input value. The softmax activation function is used in the output layer of the neural network to convert the output into a probability distribution, which can output the probability of each category. The cross-entropy loss function measures the difference between the probability distribution predicted by the model and the probability distribution of the true label. Using cross-entropy loss can help the model better learn how to accurately classify.

[0219] Step D12: training the insulation unit condition monitoring model based on the model training cycle, the number of hidden layers, the number of hidden layer neurons, and the neural network function to determine an insulation unit optimization classifier;

[0220] It should be noted that the insulation unit optimization classifier reflects the characteristics of the model classifier based on neural network training.

[0221] It is understandable that by setting the model training cycle, it is ensured that the model has sufficient loop iterations to carry out deep learning, so that the model can achieve better performance during the training process. By adjusting various parameters and strategies in the model training process, such as learning rate adjustment and regularization, the generalization ability of the model is improved. The insulation unit optimization classifier characterization model already has strong classification performance and can accurately identify and distinguish different insulation unit states. The output of the insulation unit optimization classifier can directly trigger the intelligent management mechanism, thereby improving the system's response speed and processing efficiency.

[0222] Step D13: obtaining an insulation unit state recognition and diagnosis model based on the insulation unit optimization classifier.

[0223] It is understandable that the insulation unit optimization classifier constructed by integrating multiple classifiers has significantly improved the recognition accuracy and diagnostic ability of the insulation unit status in the electric vehicle power battery system, enhanced the model's ability to capture complex data patterns, and improved the model's generalization ability and real-time monitoring accuracy by reducing prediction errors and reducing overfitting risks. The efficient performance of the insulation unit optimization classifier can quickly respond to changes in insulation performance, trigger the intelligent management mechanism in time, and ensure the safe operation of the vehicle. At the same time, through precise fault warnings and maintenance suggestions, it effectively reduces maintenance costs and optimizes the user's driving experience.

[0224] The insulation unit monitoring method proposed in this embodiment obtains a model training cycle; converts the insulation unit information, insulation unit parameter set, and insulation unit fault state parameter set into a standardized form, and obtains an insulation unit sample set; trains the insulation unit sample set to determine the category confidence and obtains an insulation unit state monitoring model; and trains the pre-classification results of the insulation unit state monitoring model and the insulation unit sample set based on the model training cycle to determine the insulation unit state recognition and diagnosis model. This solves the technical problem of how to train the insulation unit state recognition and diagnosis model to accurately and reliably monitor the state of the insulation unit. Compared with the existing technology, this application obtains a model training cycle, standardizes the insulation unit information, parameter set, and fault state parameter set, and constructs a sample set to train an insulation unit state recognition and diagnosis model. Insulation faults are identified based on the insulation unit state recognition and diagnosis model, thereby improving the safety and reliability of the system, ensuring the personal safety of drivers and passengers, and reducing maintenance costs.

[0225] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the insulation unit monitoring method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0226] This application also provides an insulation unit monitoring device, please refer to Figure 6 , the insulation unit monitoring device includes:

[0227] An acquisition module 10 is configured to acquire an insulation unit parameter set, acquire insulation unit information through an insulation unit monitoring circuit, and determine an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set;

[0228] A processing module 20 is configured to construct an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set;

[0229] The execution module 30 is used to monitor the insulation unit status based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set and the insulation unit status identification and diagnosis model, and trigger the intelligent management mechanism to realize the insulation control of the electric vehicle.

[0230] The insulation unit monitoring device provided in this application, employing the insulation unit monitoring method of the aforementioned embodiment, can solve the technical problem of accurately and reliably monitoring insulation units. Compared to the prior art, the insulation unit monitoring device provided in this application has the same beneficial effects as the insulation unit monitoring method provided in the aforementioned embodiment. Other technical features of the insulation unit monitoring device are the same as those disclosed in the aforementioned embodiment and are not further described here.

[0231] In one embodiment, the acquisition module 10 is further used to obtain the critical safety value of the insulation unit, the safety insulation value of the insulation unit, the rated voltage of the power battery, the positive and negative pole switch information and the insulation fault diagnosis unit information set, where the positive and negative pole switch information includes the positive pole control switch information and the negative pole control switch information; based on the positive pole control switch information and the negative pole control switch information, the insulation unit monitoring circuit is measured to obtain the positive and negative pole voltage information, where the positive and negative pole voltage information includes the positive pole voltage information and the negative pole voltage information; based on the insulation fault diagnosis unit information set, the positive pole voltage information and the negative pole voltage information, the insulation unit information is determined; based on the insulation unit critical safety value and the insulation unit safety insulation value, the insulation unit parameter set is obtained; based on the insulation unit information, the insulation unit parameter set and the rated voltage of the power battery, the insulation unit fault state parameter set is determined.

[0232] In one embodiment, the processing module 20 is further used to obtain a model training cycle; convert the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set into a standardized form, and obtain an insulation unit sample set; train the insulation unit sample set to determine the category confidence, and obtain an insulation unit state monitoring model; train the pre-classification results of the insulation unit state monitoring model and the insulation unit sample set based on the model training cycle to determine the insulation unit state recognition and diagnosis model.

[0233] In one embodiment, the processing module 20 is also used to obtain a scoring function, a tree regression algorithm, a loop iteration, a separation vector, a truncation error, a gradient factor, a training time, and an expansion step; combine the features in the insulation unit sample set into a scoring feature through the scoring function; use the tree regression algorithm to train the insulation unit sample set to obtain an insulation unit sample subset; calculate the weight starting value based on the insulation unit sample subset, the scoring feature, the loop iteration, the separation vector, and the truncation error; calculate the weighted fusion value based on the weight starting value, the gradient factor, the training time, and the expansion step; obtain an insulation unit basic classifier based on the weighted fusion value, and determine the insulation unit state monitoring model based on the insulation unit basic classifier.

[0234] In one embodiment, the processing module 20 is further used to obtain sample volatility, empirical constant, Gini coefficient, necessary sample size, integrated learning rate, linear interpolation function, Lagrange multiplier, constraint condition, average granularity, insulation unit fault state basic classification set and category set; calculate the initial tree loop value based on the weighted fusion value, sample volatility, empirical constant and Gini coefficient; calculate the initial tree hierarchy based on the initial tree loop value, necessary sample size, integrated learning rate and linear interpolation function; calculate the maximized distance based on the initial tree hierarchy, Lagrange multiplier and constraint condition; calculate the insulation unit basic classifier based on the maximized distance, average granularity, insulation unit fault state basic classification set and category set, and determine the insulation unit state monitoring model based on the insulation unit basic classifier.

[0235] In one embodiment, the processing module 20 is also used to obtain the number of hidden layers, the number of hidden layer neurons and the neural network function, the neural network function including the ReLU activation function, the softmax activation function and the cross entropy loss function; based on the model training cycle, the number of hidden layers, the number of hidden layer neurons and the neural network function, the insulation unit state monitoring model is trained to determine the insulation unit optimization classifier; based on the insulation unit optimization classifier, the insulation unit state recognition and diagnosis model is obtained.

[0236] In one embodiment, the execution module 30 is further used to judge the insulation unit fault state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set and the insulation unit state identification and diagnosis model, and obtain a judgment result; obtain the insulation state category of the insulation unit based on the judgment result, and trigger the intelligent management mechanism according to the insulation state category to realize the insulation control of the electric vehicle.

[0237] The present application provides an insulation unit monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the insulation unit monitoring method in the above-mentioned embodiment one.

[0238] Reference below Figure 7 , which shows a schematic diagram of the structure of an insulation unit monitoring device suitable for implementing an embodiment of the present application. The insulation unit monitoring device in the embodiment of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The insulation unit monitoring device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0239] like Figure 7As shown, the insulation unit monitoring device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the insulation unit monitoring device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the insulation unit monitoring device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an insulation unit monitoring device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0240] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0241] The insulation unit monitoring device provided in this application, employing the insulation unit monitoring method of the aforementioned embodiment, can solve the technical problem of accurately and reliably monitoring insulation units. Compared to the prior art, the insulation unit monitoring device provided in this application has the same beneficial effects as the insulation unit monitoring method provided in the aforementioned embodiment. Other technical features of the insulation unit monitoring device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0242] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0243] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0244] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the insulation unit monitoring method in the above embodiment.

[0245] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0246] The computer-readable storage medium may be included in the insulation unit monitoring device; or may exist independently without being assembled into the insulation unit monitoring device.

[0247] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the insulation unit monitoring device, the insulation unit monitoring device is enabled to: obtain an insulation unit parameter set, and obtain insulation unit information through the insulation unit monitoring circuit, and determine the insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set; construct an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set and the insulation unit fault state parameter set; monitor the insulation unit state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set and the insulation unit state identification and diagnosis model, and trigger an intelligent management mechanism to realize electric vehicle insulation control.

[0248] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0249] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0250] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0251] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned insulation unit monitoring method. This computer-readable storage medium can address the technical problem of accurately and reliably monitoring insulation units. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the insulation unit monitoring method provided in the aforementioned embodiment, and are not further elaborated here.

[0252] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for monitoring an insulation unit, characterized in that: The insulation unit monitoring method is applied to an insulation unit monitoring circuit, which includes: a power battery, a positive control switch, a start switch, a negative control switch, a positive insulation unit, a negative insulation unit, a first insulation fault diagnosis unit, a second insulation fault diagnosis unit, a third insulation fault diagnosis unit, and a fourth insulation fault diagnosis unit; The positive electrode of the power battery is connected to the first end of the positive control switch and the first end of the positive insulation unit, the second end of the positive control switch is connected to the second end of the first insulation fault diagnostic unit, the first end of the first insulation fault diagnostic unit is connected to the first end of the second insulation fault diagnostic unit, the second end of the second insulation fault diagnostic unit is connected to the second end of the starting switch and the second end of the third insulation fault diagnostic unit, the first end of the starting switch is connected to the second end of the positive insulation unit and the second end of the negative insulation unit, the first end of the third insulation fault diagnostic unit is respectively connected to the first end of the fourth insulation fault diagnostic unit, the second end of the fourth insulation fault diagnostic unit is connected to the second end of the negative control switch, the first end of the negative control switch is connected to the first end of the negative insulation unit and the negative electrode of the power battery, and the method includes: Acquire an insulation unit parameter set, and acquire insulation unit information through the insulation unit monitoring circuit, and determine an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set; Building an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set; Monitoring the insulation unit status based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit status identification and diagnosis model, and triggering an intelligent management mechanism to implement insulation control of the electric vehicle; The steps of obtaining an insulation unit parameter set, obtaining insulation unit information through the insulation unit monitoring circuit, and determining an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set include: Obtaining an insulation unit critical safety value, an insulation unit safety insulation value, a power battery rated voltage, positive and negative pole switch information, and an insulation fault diagnosis unit information set, wherein the positive and negative pole switch information includes positive pole control switch information and negative pole control switch information; Measuring the insulation unit monitoring circuit based on the positive control switch information and the negative control switch information to obtain positive and negative voltage information, wherein the positive and negative voltage information includes positive voltage information and negative voltage information; determining insulation unit information based on the insulation fault diagnosis unit information set, the positive electrode voltage information, and the negative electrode voltage information; Obtaining an insulation unit parameter set based on the insulation unit critical safety value and the insulation unit safety insulation value; An insulation unit fault state parameter set is determined based on the insulation unit information, the insulation unit parameter set, and the power battery rated voltage.

2. The method according to claim 1, wherein The step of constructing an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set includes: Get the model training cycle; Converting the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set into a standardized form, and obtaining an insulation unit sample set; Training the insulation unit sample set to determine category confidence and obtain an insulation unit state monitoring model; The pre-classification result of the insulation unit state monitoring model and the insulation unit sample set are trained based on the model training cycle to determine an insulation unit state recognition and diagnosis model.

3. The method according to claim 2, wherein The step of training the insulation unit sample set to determine the category confidence and obtaining the insulation unit state monitoring model includes: Get the scoring function, tree regression algorithm, loop iterations, separation vector, truncation error, gradient factor, training time, and dilation step size; Combining the features in the isolation unit sample set into scoring features through the scoring function; Using the tree regression algorithm to train the insulation unit sample set to obtain an insulation unit sample subset; Calculating a weight starting value based on the isolation unit sample subset, the scoring feature, the loop iteration, the separation vector, and the truncation error; Calculating a weighted fusion value based on the weight starting value, the gradient factor, the training time, and the expansion step size; An insulation unit basic classifier is obtained based on the weighted fusion value, and an insulation unit state monitoring model is determined based on the insulation unit basic classifier.

4. The method according to claim 3, wherein The steps of obtaining an insulation unit basic classifier based on the weighted fusion value and determining an insulation unit state monitoring model based on the insulation unit basic classifier include: Obtain sample volatility, empirical constant, Gini coefficient, necessary sample size, ensemble learning rate, linear interpolation function, Lagrange multiplier, constraint conditions, average granularity, insulation unit fault state basic classification set, and category set; Calculating an initial tree cycle value based on the weighted fusion value, the sample volatility, the empirical constant, and the Gini coefficient; calculating an initial tree level based on the initial tree cycle value, the necessary sample size, the ensemble learning rate, and the linear interpolation function; calculating a maximized distance based on the initial tree level, the Lagrange multiplier, and the constraint; An insulation unit basic classifier is calculated based on the maximized distance, the average granularity, the insulation unit fault state basic classification set, and the category set, and an insulation unit state monitoring model is determined based on the insulation unit basic classifier.

5. The method according to claim 2, wherein The step of training the pre-classification result of the insulation unit state monitoring model and the insulation unit sample set based on the model training cycle to determine the insulation unit state recognition and diagnosis model includes: Obtaining the number of hidden layers, the number of neurons in the hidden layers, and a neural network function, wherein the neural network function includes a ReLU activation function, a softmax activation function, and a cross entropy loss function; Training the insulation unit state monitoring model based on the model training cycle, the number of hidden layers, the number of neurons in the hidden layer, and the neural network function to determine an insulation unit optimization classifier; An insulation unit state recognition and diagnosis model is obtained based on the insulation unit optimization classifier.

6. The method according to any one of claims 1 to 5, characterized in that The step of monitoring the insulation unit state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and triggering an intelligent management mechanism to implement insulation control of the electric vehicle includes: Determine the insulation unit fault state based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit state identification and diagnosis model, and obtain a determination result; The insulation state category of the insulation unit is obtained based on the judgment result, and an intelligent management mechanism is triggered according to the insulation state category to realize insulation control of the electric vehicle.

7. The method according to claim 1, wherein The method is applied to an insulation unit monitoring device, the device comprising: an acquisition module, configured to acquire an insulation unit parameter set, acquire insulation unit information through the insulation unit monitoring circuit, and determine an insulation unit fault state parameter set based on the insulation unit information and the insulation unit parameter set; a processing module, configured to construct an insulation unit state identification and diagnosis model based on the insulation unit information, the insulation unit parameter set, and the insulation unit fault state parameter set; an execution module, configured to monitor the insulation unit status based on the insulation unit information, the insulation unit parameter set, the insulation unit fault state parameter set, and the insulation unit status identification and diagnosis model, and trigger an intelligent management mechanism to implement insulation control of the electric vehicle; The acquisition module is further configured to acquire an insulation unit critical safety value, an insulation unit safety insulation value, a power battery rated voltage, positive and negative pole switch information, and an insulation fault diagnosis unit information set, wherein the positive and negative pole switch information includes positive pole control switch information and negative pole control switch information; Measuring the insulation unit monitoring circuit based on the positive control switch information and the negative control switch information to obtain positive and negative voltage information, wherein the positive and negative voltage information includes positive voltage information and negative voltage information; determining insulation unit information based on the insulation fault diagnosis unit information set, the positive electrode voltage information, and the negative electrode voltage information; Obtaining an insulation unit parameter set based on the insulation unit critical safety value and the insulation unit safety insulation value; An insulation unit fault state parameter set is determined based on the insulation unit information, the insulation unit parameter set, and the power battery rated voltage.

8. An insulation unit monitoring device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the insulation unit monitoring method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the insulation unit monitoring method according to any one of claims 1 to 6 are implemented.

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