Insulation residual life prediction algorithm and device based on real-time temperature acquisition and medium

Abstract

The invention discloses an insulation residual life prediction algorithm and device based on real-time temperature acquisition and a medium, and relates to the technical field of new energy automobiles, and the method mainly comprises the steps: constructing an offline insulation failure database through an accelerated thermal aging experiment of a twisted pair sample, and recording the PDIV of a target motor at a brand new state room temperature as an initial PDIV0; based on the off-line insulation failure database, obtaining the aging slope of the PDIV decaying along with the aging time at each motor winding temperature and a corresponding aging curve; according to the mapping relation between the PDIV and the motor winding temperature, obtaining an insulation threshold voltage corresponding to the current motor winding temperature under the correction of a temperature correction coefficient; and based on the current PDIV0 and the insulation threshold voltage, substituting the aging curve corresponding to the current motor winding temperature to obtain and output the difference value of the corresponding insulation failure time of the two as the insulation residual life. The method can accurately capture the initial characteristics of insulation aging, the early warning time is advanced, and the timeliness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to an insulation remaining life prediction algorithm, device and medium based on real-time temperature acquisition. Background Art

[0002] As the DC bus voltage of electric vehicles continues to rise, the reliability of the insulation system of automotive variable-frequency motors faces severe challenges. Partial discharge is one of the main causes of insulation failure in variable-frequency motors. For variable-frequency motors with Type I insulation, the occurrence of partial discharge typically indicates a significant decline in insulation performance and may even lead to insulation failure. During electric vehicle operation, the temperature of the motor windings gradually rises, accelerating the aging process of the insulation material, further increasing the risk of insulation failure. Therefore, real-time monitoring of the motor insulation status and accurate prediction of its remaining life are crucial to ensuring the safe and stable operation of electric vehicles.

[0003] Partial discharge monitoring is an effective means of assessing insulation condition. However, in practice, online partial discharge monitoring is extremely difficult due to the strong electromagnetic interference generated by the frequent switching on and off of the inverter switching devices in the electric drive system. Currently, the more common method for online insulation condition monitoring is leakage current monitoring, which predicts the remaining insulation life by detecting changes in leakage current at different stages of insulation aging. However, this method requires the installation of additional leakage current sensors, and due to practical cost constraints, many automakers may not choose to equip such sensors. In contrast, winding temperature monitoring is a more common method for motor condition monitoring, and many motors are already equipped with dedicated temperature sensors. However, changes in motor temperature do not directly reflect the remaining life of the winding insulation. Summary of the Invention

[0004] To solve the above-mentioned problem of monitoring the remaining insulation life of the motor, the present invention proposes an insulation remaining life prediction algorithm based on real-time temperature acquisition, comprising the following steps: S1: By measuring the PDIV of twisted pair samples after pulse voltage excitation at different temperatures, a linear function is used to fit the mapping relationship between PDIV and motor winding temperature; S2: Through accelerated thermal aging experiments on twisted pair samples, an offline insulation failure database is constructed, which contains the relationship between PDIV and aging time at different aging temperatures. The PDIV of the target motor in its new state at room temperature is recorded as the initial PDIV0. S3: Based on the offline insulation failure database, obtain the aging slope of PDIV decay with aging time at each motor winding temperature and the corresponding aging curve; S4: Collect the motor winding temperature of the current sampling period during the actual operation of the target motor. According to the mapping relationship between PDIV and the motor winding temperature, obtain the insulation threshold voltage corresponding to the current motor winding temperature under the correction of the temperature correction coefficient. ; S5: Based on the PDIV aging slope corresponding to the current motor winding temperature, calculate the change in PDIV in the current sampling period, and update PDIV0 based on the change; S6: Based on PDIV0 before the update and , substitute the aging curve corresponding to the current motor winding temperature to obtain the corresponding insulation failure time of the two, output the difference between the two corresponding insulation failure times as the remaining insulation life, enter the next sampling cycle, and return to step S4.

[0005] Furthermore, the twisted pair wire sample adopts enameled wire with the same insulation heat resistance grade, insulation material and insulation thickness as the target motor.

[0006] Furthermore, the aging temperature range of the accelerated thermal aging experiment is within a preset multiple range of the motor winding temperature range during the actual operation of the target motor, and the PDIV needs to be measured after each aging cycle when the temperature is restored to room temperature.

[0007] Furthermore, in step S3, the PDIV aging slope corresponding to the motor winding temperature is obtained by the following formula: Where, is the motor winding temperature The linear slope of PDIV changes with aging time t under the condition of , Y is the constant related to the accelerated aging test, e is the natural constant, is the reaction activation energy, is the known Boltzmann constant.

[0008] Furthermore, in step S4, the mapping relationship between the PDIV and the motor winding temperature is expressed as the following formula: Where, is the motor winding temperature Lower partial discharge inception voltage, is the linear slope of PDIV changing with motor winding temperature, is the PDIV of the theoretical zero point, Determined by the least squares method; The temperature correction coefficient is obtained by the following formula: Where, is the motor winding temperature The corresponding temperature correction coefficient is, PDIV is the PDIV of the target motor measured at room temperature in a brand new state; The current failure voltage threshold is obtained by the following formula: Where, Current motor winding temperature The insulation threshold voltage after temperature correction factor correction is: is the maximum voltage between turns of the winding of the target motor, is the partial discharge safety factor.

[0009] Furthermore, in the step S5, the calculation of the change in the current sampling period PDIV and the updating of PDIV0 based on the change are expressed as the following formula: Where, is the motor winding temperature Next, the change in PDIV within a sampling period is: Current motor winding temperature The corresponding PDIV aging slope, is the sampling period.

[0010] Furthermore, in the step S4, after collecting the motor winding temperature of the current sampling period during the actual operation of the target motor, the step of suppressing noise on the motor winding temperature collected in real time by a sliding average filter or a median filter algorithm is also included.

[0011] Furthermore, the step S4 further includes a step of dynamically adjusting the sampling period according to the rate of change of the motor winding temperature. The formula for dynamically adjusting the sampling period is expressed as: Where, is the sampling period, To preset the minimum sampling period, is the preset maximum sampling period, is the rate of change of the motor winding temperature, is the motor winding temperature change rate threshold.

[0012] The present invention also includes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the insulation remaining life prediction algorithm based on real-time temperature acquisition.

[0013] Also included is a device for processing data, comprising: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the insulation remaining life prediction algorithm based on real-time temperature acquisition.

[0014] Compared with the prior art, the present invention has at least the following beneficial effects: (1) The present invention proposes an insulation remaining life prediction algorithm based on real-time temperature acquisition. By utilizing the existing winding temperature sensors of electric vehicle motors, it effectively avoids the reliance of traditional leakage current monitoring methods on additional hardware, significantly reducing system cost and complexity. At the same time, it avoids signal distortion caused by electromagnetic interference and improves overall monitoring reliability. Secondly, the partial discharge inception voltage (PDIV) is used as the core criterion for insulation failure, which is more in line with the failure characteristics of variable frequency motor insulation under the background of high voltage. Combined with the offline data constructed by accelerated thermal aging tests, the inference results can accurately capture the initial characteristics of insulation aging. Compared with the traditional prediction method with insulation short circuit as the end point, the warning time is advanced, which significantly improves the timeliness of fault prevention.

[0015] (2) At the same time, based on the mapping relationship between PDIV and motor winding temperature, the insulation failure threshold can be dynamically adjusted according to the change of the motor's real-time operating temperature and the temperature correction coefficient. The PDIV can be updated in real time in combination with the aging slope, which is more in line with the characteristics of electric vehicles with variable operating conditions. (3) In addition, by dynamically adjusting the sampling period, the algorithm can automatically optimize the data acquisition frequency according to the temperature change rate. In drastically changing working conditions, the sampling interval can be shortened to improve accuracy, while in stable working conditions, the interval can be extended to reduce the computational load, thereby balancing efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a step diagram of an insulation remaining life prediction algorithm based on real-time temperature acquisition; Figure 2 Schematic diagram of the relationship between PDIV and aging time at different temperatures. DETAILED DESCRIPTION

[0017] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.

[0018] The present invention aims to make full use of existing temperature sensors and propose an online insulation remaining life prediction method that does not require additional sensors. Based on the background of high voltage of variable frequency motors in electric vehicles, the present invention intends to use partial discharge inception voltage (PDIV) as the key judgment basis for insulation failure. Since the partial discharge inception voltage will gradually decrease with the aggravation of insulation aging, it can be used as an important parameter to characterize the insulation state. To this end, the present invention plans to construct an insulation failure database through insulation thermal aging experiments and offline partial discharge inception voltage measurements, and propose an insulation remaining life prediction algorithm based on real-time temperature acquisition to achieve accurate prediction of insulation remaining life. Specifically, if Figure 1 As shown, the present invention proposes an insulation remaining life prediction algorithm based on real-time temperature acquisition, comprising the steps of:

[0019] S1: By measuring the PDIV of twisted pair samples after pulse voltage excitation at different temperatures, a linear function is used to fit the mapping relationship between PDIV and motor winding temperature; S2: Through accelerated thermal aging experiments on twisted pair samples, an offline insulation failure database is constructed, which contains the relationship between PDIV and aging time at different aging temperatures. The PDIV of the target motor in its new state at room temperature is recorded as the initial PDIV0. S3: Based on the offline insulation failure database, obtain the aging slope of PDIV decay with aging time at each motor winding temperature and the corresponding aging curve; S4: Collect the motor winding temperature of the current sampling period during the actual operation of the target motor. According to the mapping relationship between PDIV and the motor winding temperature, obtain the insulation threshold voltage corresponding to the current motor winding temperature under the correction of the temperature correction coefficient. ; S5: Based on the PDIV aging slope corresponding to the current motor winding temperature, calculate the change in PDIV in the current sampling period, and update PDIV0 based on the change; S6: Based on PDIV0 before the update and , substitute the aging curve corresponding to the current motor winding temperature to obtain the corresponding insulation failure time of the two, output the difference between the two corresponding insulation failure times as the remaining insulation life, enter the next sampling cycle, and return to step S4.

[0020] During the specific implementation process, a standardized twisted-pair wire sample must first be prepared. This twisted-pair wire sample must be made of enameled wire with the same insulation heat resistance grade, the same insulation material, and the same insulation thickness as the actual target motor winding to ensure consistency between the experimental data and the actual application scenario. To cover the possible operating temperature range of the motor, in this embodiment, the test temperature is selected according to a gradient, for example, from room temperature to the maximum allowable operating temperature, with a set of test points set at each temperature interval to obtain several sets of temperature conditions. At each temperature set, a square wave pulse voltage excitation device is used to apply voltage to the test sample, with the starting voltage gradually increasing from zero voltage until the partial discharge exceeds a preset threshold (here, set to 5pC according to the IEC60270 standard in this embodiment). At this time, the corresponding PDIV is recorded. At each temperature set, the measurement is repeated at least three times to eliminate accidental errors, and the average value is finally taken as the PDIV at that temperature point.

[0021] After the experiment is completed, a linear regression model is used to fit the PDIV and temperature data. Since PDIV shows an approximately linear downward trend with increasing temperature, the initial fitting function form is:

[0022] in, is the motor winding temperature (Unit: Kelvin) Partial discharge inception voltage, It is a negative slope, reflecting the linear slope of PDIV changing with the motor winding temperature. is the intercept (unit: V), representing the PDIV at the theoretical zero temperature point (0 Kelvin). and The least squares method is used to determine the minimum sum of squares of the residuals between the fitted curve and the experimental data. To verify the reliability of the model, the goodness of fit is calculated. If the goodness of fit is ≥ 0.95, the linear relationship is considered to be significant. Otherwise, the experimental data should be checked for anomalies or the temperature test range should be expanded.

[0023] To further understand the relationship between PDIV and aging time, the present invention extracts relevant information through accelerated aging experiments. Specifically, at least three accelerated aging temperature points are selected based on the actual maximum allowable operating temperature of the motor. The aging temperature range is a preset multiple of the actual maximum allowable operating temperature to accelerate the aging process of the insulation material. For example, for a twisted pair with an insulation heat resistance rating of 180°C, the maximum aging temperature is 250°C and the minimum aging temperature is 200°C, with a multiple of 1.11-1.38. Converted to Kelvin, for a twisted pair with an insulation heat resistance rating of 453.15K, the maximum aging temperature is 523.15K and the minimum aging temperature is 473.15K, with a multiple of 1.04-1.11. The aging cycle is divided into time gradients (for example, one cycle is 24 hours) and continues until the PDIV value drops to 50% or less of the initial value (deemed to be the critical failure point). Parallel experimental groups are set up for each temperature group to ensure data statistical reliability.

[0024] After each aging cycle, the twisted-pair cable samples were cooled to room temperature to eliminate transient temperature interference with the PDIV measurement. Simultaneously, a square-wave pulse voltage excitation device was used to gradually increase the voltage. PDIV was recorded when the partial discharge level exceeded a preset threshold. Each data set was measured three times, and the average value was calculated after removing outliers.

[0025] Based on all test results, a matrix-based offline insulation failure database was constructed, storing the corresponding aging time, PDIV, and fitting parameters by temperature gradient. The PDIV of the target motor at room temperature in its new state was recorded as the initial PDIV0. Based on the constructed insulation failure database, the relationship between PDIV and aging time was extracted as follows:

[0026] like Figure 2 As shown (abscissa time t, ordinate PDIV, the figure shows the current motor winding temperature Corresponding When the aging temperature is The time corresponding to the ), is a schematic diagram of the trend of PDIV changing with aging time at different aging temperatures in the offline insulation failure database constructed by the present invention. It can be seen from the figure that at the aging temperature The rate of change of PDIV over time can be expressed as express: in, Aging temperature Next, the PDIV change within a sampling period is: is a sampling period.

[0027] Similarly, at the aging temperature Under these conditions, the rate of change of PDIV over time can be expressed as Then, according to the Arrhenius law, the insulation aging rate at different aging temperatures can be described by the following formula:

[0028] Take the logarithm of both sides to get the motor winding temperature The corresponding PDIV aging slope expression is: in, is the motor winding temperature The linear slope of PDIV changes with aging time t under the condition of , Y is the constant related to the accelerated aging test, e is the natural constant, is the reaction activation energy, is the known Boltzmann constant.

[0029] Based on this, relying on the data in the offline insulation failure database, and and The functional relationship between them can be obtained through linear fitting and Y, and obtain the arbitrary motor winding temperature The curve below: in, is the motor winding temperature Under the condition of aging, the PDIV of the twisted pair cable sample is measured after aging for time t and cooling to room temperature.

[0030] With the help of this aging curve, the motor winding temperature can be The PDIV variation is calculated within one sampling period. The reason for calculating the PDIV variation here is that as the motor ages, its insulation PDIV decays over time due to thermal aging. Therefore, the target motor in a brand new state (initial PDIV is PDIV0, that is, ) After being put into formal operation, it is necessary to rely on the existing motor winding temperature sensor to record the motor winding temperature in real time during the current sampling period, calculate the PDIV change during the current sampling period based on the mapping relationship between PDIV and the motor winding temperature, and update PDIV0 (initially the initial PDIV0). The change in PDIV during the current sampling period and the update of PDIV0 are expressed as follows:

[0031] Where, Current motor winding temperature Next, the change in PDIV within one sampling period.

[0032] Furthermore, to ensure the reliability of prediction results, the system incorporates a built-in anomaly detection mechanism: if the updated PDIV0 deviates from the decay trend of historical data by more than 10% (e.g., a sudden increase or decrease in PDIV0), a data review process is automatically triggered, prioritizing recalculation using nearby valid data, or flagging the result as an abnormal condition requiring replenishment of the offline database. Furthermore, each prediction result is compared with the actual measured PDIV value of the next cycle. If the error exceeds 5% for three consecutive times, the database update process described in claim 10 is initiated, adding new temperature point data and refitting the model.

[0033] Based on the above principle, according to the preliminary mapping relationship between PDIV and motor winding temperature obtained in the offline test phase and the offline insulation failure database, during the actual operation of the target motor (initially in a new state), the motor's built-in winding temperature sensor is used to measure the motor's winding temperature with a fixed sampling period (initial preset maximum sampling period). ) to obtain real-time motor winding temperature data. Here, to eliminate transient noise interference, the original motor winding temperature signal is processed using a sliding average filter (or median filter). The window width is consistent with the sampling period to ensure the stability and representativeness of the collected motor winding temperature.

[0034] According to the real-time collected motor winding temperature of the current sampling period (the average value of the temperature in the current sampling period), the corresponding insulation threshold voltage is calculated. It should be noted that although the PDIV at any motor winding temperature is related to the current motor winding temperature, it cannot be simply assumed that it can be obtained based on the mapping relationship between PDIV and motor winding temperature. This still requires further correction. The specific correction process steps are as follows:

[0035] (1) PDIV and motor winding temperature obtained based on step S1 The initial mapping relationship between: , calculate the temperature correction coefficient : Here It is the PDIV value of a new sample measured at room temperature.

[0036] (2) For the actual motor to be tested, the maximum value of the motor winding inter-turn voltage is obtained through the winding voltage distribution modeling method such as the high-frequency equivalent circuit model, or through actual measurement. .

[0037] (3) At any motor winding temperature, the insulation threshold voltage that causes insulation failure is: in, Current motor winding temperature The insulation threshold voltage after temperature correction factor correction is: is the partial discharge safety factor.

[0038] Then, according to the current sampling period, the and , substituting the current motor winding temperature constructed previously The corresponding aging curves can be used to obtain the corresponding insulation failure time of the two, and the remaining insulation life of the motor can be obtained by subtracting the two.

[0039] Furthermore, in order to cope with the rapid change of motor winding temperature caused by sudden changes in motor working conditions (such as rapid acceleration or high load scenarios), a threshold buffer zone is introduced. When the motor winding temperature change rate of two adjacent sampling cycles exceeds the motor winding temperature change rate threshold, the sampling cycle is automatically shortened to the preset minimum sampling cycle. , and corrected using weighted interpolation :

[0040] in, To preset the minimum sampling period, is the preset maximum sampling period, is the rate of change of the motor winding temperature, is the motor winding temperature change rate threshold, The corrected motor winding temperature of the current sampling period The corresponding PDIV, is the normalized weight coefficient of the motor winding temperature change rate used to smooth the threshold jump, The motor winding temperature in the current sampling period The corresponding PDIV, The motor winding temperature in the previous sampling period Corresponding PDIV.

[0041] Through the above process, PDIV0 is updated in each sampling cycle, and the latest insulation threshold voltage is obtained. Then, substitute the current motor winding temperature The corresponding aging curve can be used to predict and update the remaining insulation life, thereby achieving full-period prediction of the remaining insulation life throughout the life cycle of the motor.

[0042] The present invention also includes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the insulation remaining life prediction algorithm based on real-time temperature acquisition.

[0043] Also included is a device for processing data, comprising: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the insulation remaining life prediction algorithm based on real-time temperature acquisition.

[0044] In summary, the proposed insulation remaining life prediction algorithm based on real-time temperature acquisition effectively avoids the reliance on additional hardware in traditional leakage current monitoring methods by leveraging existing winding temperature sensors in electric vehicle motors, significantly reducing system cost and complexity while also avoiding signal distortion caused by electromagnetic interference, thereby improving overall monitoring reliability. Secondly, using partial discharge inception voltage (PDIV) as the core criterion for insulation failure better reflects the failure characteristics of variable-frequency motor insulation under high-voltage conditions. Combined with offline data generated from accelerated thermal aging tests, the predicted results accurately capture the initial characteristics of insulation aging. Compared to traditional prediction methods that use insulation short circuits as the endpoint, this method provides earlier warning times, significantly improving the timeliness of fault prevention.

[0045] At the same time, based on the mapping relationship between PDIV and motor winding temperature, the insulation failure threshold can be dynamically adjusted according to the changes in the real-time operating temperature of the motor and the temperature correction coefficient. The PDIV can be updated in real time in combination with the aging slope, which is more in line with the characteristics of variable operating conditions of electric vehicles.

[0046] In addition, by dynamically adjusting the sampling period, the algorithm can automatically optimize the data collection frequency according to the temperature change rate, shortening the sampling interval to improve accuracy under drastically changing working conditions, and extending the interval under stable working conditions to reduce the computing load, thereby balancing efficiency and accuracy.

[0047] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0048] In addition, in the present invention, descriptions such as "first," "second," and "one" are for descriptive purposes only and should not be understood to indicate or imply their relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0049] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0050] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. An insulation remaining life prediction algorithm based on real-time temperature acquisition, characterized in that: Including steps: S1: By measuring the PDIV of twisted pair samples after pulse voltage excitation at different temperatures, a linear function is used to fit the mapping relationship between PDIV and motor winding temperature; S2: Through accelerated thermal aging experiments on twisted pair samples, an offline insulation failure database is constructed, which contains the relationship between PDIV and aging time at different aging temperatures. The PDIV of the target motor in its new state at room temperature is recorded as the initial PDIV0. S3: Based on the offline insulation failure database, obtain the aging slope of PDIV decay with aging time at each motor winding temperature and the corresponding aging curve; S4: Collect the motor winding temperature of the current sampling period during the actual operation of the target motor. According to the mapping relationship between PDIV and the motor winding temperature, obtain the insulation threshold voltage corresponding to the current motor winding temperature under the correction of the temperature correction coefficient. ; S5: Based on the PDIV aging slope corresponding to the current motor winding temperature, calculate the change in PDIV in the current sampling period, and update PDIV0 based on the change; S6: Based on PDIV0 before the update and , substitute the aging curve corresponding to the current motor winding temperature to obtain the corresponding insulation failure time of the two, output the difference between the two corresponding insulation failure times as the remaining insulation life, enter the next sampling cycle, and return to step S4.

2. The insulation remaining life prediction algorithm based on real-time temperature acquisition according to claim 1 is characterized in that: The twisted pair wire sample adopts enameled wire with the same insulation heat resistance grade, insulation material and insulation thickness as the target motor.

3. The insulation remaining life prediction algorithm based on real-time temperature acquisition according to claim 1 is characterized in that: In step S2, the aging temperature range of the accelerated thermal aging experiment is within a preset multiple range of the motor winding temperature range during the actual operation of the target motor, and the PDIV needs to be measured after each aging cycle to restore to room temperature.

4. The insulation remaining life prediction algorithm based on real-time temperature acquisition according to claim 1, characterized in that: In step S3, the PDIV aging slope corresponding to the motor winding temperature is obtained by the following formula: Where, is the motor winding temperature The linear slope of PDIV changes with aging time t under the condition of , Y is the constant related to the accelerated aging test, e is the natural constant, is the reaction activation energy, is the known Boltzmann constant.

5. The insulation remaining life prediction algorithm based on real-time temperature acquisition according to claim 1 is characterized in that: In step S4, the mapping relationship between the PDIV and the motor winding temperature is expressed as the following formula: Where, is the motor winding temperature Lower partial discharge inception voltage, is the linear slope of PDIV changing with motor winding temperature, is the PDIV of the theoretical zero point, Determined by the least squares method; The temperature correction coefficient is obtained by the following formula: Where, is the motor winding temperature The corresponding temperature correction coefficient is, PDIV is the PDIV of the target motor measured at room temperature in a brand new state; The insulation threshold voltage is obtained by the following formula: Where, is the current motor winding temperature after correction by the temperature correction coefficient The corresponding insulation threshold voltage, is the maximum voltage between turns of the winding of the target motor, is the partial discharge safety factor.

6. The insulation remaining life prediction algorithm based on real-time temperature acquisition according to claim 1, characterized in that: In the step S5, the change in the current sampling period PDIV is calculated, and PDIV0 is updated based on the change, which is expressed as the following formula: Where, is the change, Current motor winding temperature The corresponding PDIV aging slope, is the sampling period.

7. The insulation remaining life prediction algorithm based on real-time temperature acquisition according to claim 1 is characterized in that: In the step S4, after collecting the motor winding temperature of the current sampling period during the actual operation of the target motor, the method further includes the step of suppressing noise on the motor winding temperature collected in real time by using a sliding average filter or a median filter algorithm.

8. The insulation remaining life prediction algorithm based on real-time temperature acquisition according to claim 1 is characterized in that: The step S4 further includes a step of dynamically adjusting the sampling period according to the rate of change of the motor winding temperature. The formula for dynamically adjusting the sampling period is expressed as: Where, is the sampling period, To preset the minimum sampling period, is the preset maximum sampling period, is the rate of change of the motor winding temperature, is the motor winding temperature change rate threshold.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of an insulation remaining life prediction algorithm based on real-time temperature acquisition as described in any one of claims 1 to 8 are implemented.

10. A device for processing data, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the insulation remaining life prediction algorithm based on real-time temperature acquisition as described in any one of claims 1 to 8.

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