A method for intelligent monitoring of the operating status of charging cables

Abstract

This invention relates to the field of power equipment technology, and more particularly to an intelligent monitoring method for the operating status of charging cables. The method includes: conducting a torsion test on a sample of the charging cable to obtain its structural stability; determining the distribution of monitoring points to implement a monitoring strategy for the operating parameters of the charging cable; acquiring fluctuations in current, voltage, and resistance at several monitoring points during operation to determine if the fluctuations are abnormal; determining an anomaly index based on the average of several historical fluctuation data that could cause abnormal charging temperatures; determining a temperature monitoring mode for the charging cable based on the anomaly index and establishing a correlation prediction model for the charging cable temperature; adjusting the distribution of monitoring points based on the historical accuracy of the temperature prediction model; and determining an optimization strategy for the temperature monitoring mode based on the changes in the adjusted accuracy. This invention solves the problem of inaccurate temperature monitoring of charging cables.

Description

Technical Field

[0001] This invention relates to the field of power equipment technology, and in particular to an intelligent monitoring method for the operating status of charging cables. Background Technology

[0002] With the widespread adoption of electronic devices and the rapid development of new energy vehicles and other fields, the frequency and importance of charging cables are becoming increasingly prominent. As a key transmission medium connecting power sources and equipment, the operating status of charging cables directly affects charging efficiency, equipment safety, and lifespan, among other things. However, in existing charging cable usage scenarios, many problems exist that urgently need to be addressed.

[0003] Chinese Patent Application Publication No. CN113406539A discloses a method and device for monitoring the operation of an intelligent cable joint. The technical solution provided in this application involves a data acquisition device activating its own operation monitoring module via wireless charging. The operation monitoring module monitors the operational status data of the corresponding cable segment. It establishes a communication connection with the data acquisition device, extracts the real-time operational status data monitored by the monitoring module, and sends the operational status data to the data acquisition device. It then receives a response from the data acquisition device based on the operational status data and shuts down the operation monitoring module according to the response. By employing the above technical means, the intelligent cable joint is driven by the power provided by the data acquisition device for operation monitoring, ensuring real-time monitoring of the cable's operational status and saving energy consumption in cable operation monitoring operations.

[0004] Therefore, the aforementioned intelligent cable connector operation monitoring method and device have the following problems:

[0005] 1. The failure to properly arrange monitoring points based on the structural stability of the charging cable during the monitoring process resulted in substandard monitoring accuracy of the charging cable's operating parameters.

[0006] 2. The temperature monitoring of the charging cable failed to comprehensively consider the influence of current, voltage and resistance on the temperature of the charging cable, and the importance of the influence of current, voltage and resistance on the temperature of the charging cable was not distinguished, resulting in inaccurate temperature monitoring of the charging cable. Summary of the Invention

[0007] Therefore, the present invention provides an intelligent monitoring method for the operating status of charging cables, which overcomes the problem in the prior art that the temperature monitoring process of the charging cable fails to comprehensively consider the influence of current, voltage and resistance in the charging cable on the temperature of the charging cable.

[0008] To achieve the above objectives, the present invention provides an intelligent monitoring method for the operating status of charging cables, comprising:

[0009] Torsion tests were conducted on samples of the charging cable to obtain the structural stability coefficient of the charging cable and to determine the distribution of monitoring points in order to monitor the operating parameters of the charging cable.

[0010] The current fluctuation, voltage fluctuation, and resistance fluctuation of the charging cable at several monitoring points during operation are obtained to determine whether the fluctuation is abnormal.

[0011] An anomaly index was determined based on several historical fluctuation data that caused abnormal charging temperatures.

[0012] Based on the anomaly index, a preset temperature monitoring cycle is used to monitor the temperature of the charging cable and a correlation prediction model for the temperature of the charging cable is established.

[0013] The accuracy of the correlation prediction model is determined based on the predicted temperature of the charging cable and the actual temperature.

[0014] The distribution of the monitoring points is adjusted based on the historical accuracy of the temperature prediction of the charging cable by the correlation prediction model.

[0015] The optimization of the adjusted temperature monitoring cycle is determined based on the change in the adjusted accuracy.

[0016] Furthermore, the process of obtaining the structural stability coefficient of the charging cable by conducting a torsion test on a sample of the charging cable includes the following steps:

[0017] A sample of the charging cable was selected and a torsion test was performed.

[0018] The number of consecutive torsions of the sample was obtained from the torsion test;

[0019] The ratio of the number of consecutive torsions to the preset number of consecutive torsions is used as the structural stability coefficient of the charging cable.

[0020] Furthermore, the process of determining the distribution of monitoring points based on the stability coefficient of the charging cable to monitor the operating parameters of the charging cable includes,

[0021] Based on the structural stability coefficient being less than or equal to the preset structural stability coefficient, monitoring points are determined and monitored for the operating parameters of the charging cable in an irregular distribution.

[0022] The irregular distribution refers to the arrangement of monitoring points in a ring along the connection area and in a continuous manner along the bending area, based on the connection area and bending area on the charging cable.

[0023] Furthermore, the process of determining the distribution of monitoring points based on the stability coefficient of the charging cable to monitor the operating parameters of the charging cable includes,

[0024] Based on the structural stability coefficient being greater than the preset structural stability coefficient, monitoring points are determined and monitored for the operating parameters of the charging cable in a regular distribution.

[0025] The rule distribution involves setting several monitoring points every 30cm along the charging cable.

[0026] Furthermore, the process of determining the temperature monitoring of the charging cable according to the preset temperature monitoring adjustment cycle based on the anomaly index and establishing a correlation prediction model for the temperature of the charging cable includes,

[0027] Acquire several historical fluctuation data points that caused abnormal charging temperatures and assign different weighting coefficients to these fluctuation data points;

[0028] The volatility of several historical fluctuation data points is calculated, and the anomaly index of the charging cable is determined by combining the weighting coefficients of the fluctuation data.

[0029] The anomaly index is compared with a preset anomaly index;

[0030] The temperature monitoring adjustment cycle is determined based on the anomaly index being less than or equal to a preset anomaly index in order to monitor the temperature of the charging cable.

[0031] A correlation prediction model for the temperature of the charging cable is established based on the monitored temperature changes of the charging cable with fluctuations.

[0032] The temperature monitoring adjustment cycle is to increase the temperature monitoring cycle of the charging cable.

[0033] Furthermore, the process of determining the temperature monitoring of the charging cable according to the preset temperature monitoring cycle based on the anomaly index and establishing a correlation prediction model for the temperature of the charging cable includes,

[0034] The temperature monitoring adjustment cycle is determined based on the anomaly index being greater than the preset anomaly index in order to monitor the temperature of the charging cable.

[0035] The temperature monitoring adjustment cycle is to reduce the temperature monitoring cycle of the charging cable.

[0036] Furthermore, the process of determining whether the accuracy of the correlation prediction model meets the standard based on the predicted value and actual temperature of the charging cable according to the correlation prediction model includes:

[0037] Obtain the temperature prediction value of the charging cable from the correlation prediction model and the current actual temperature value of the charging cable;

[0038] Calculate the first difference between the predicted temperature value and the actual temperature value;

[0039] Compare the first difference with a preset first difference;

[0040] If the first difference is greater than a preset first difference, then the accuracy of the correlation prediction model is determined to be substandard.

[0041] Furthermore, the process of determining whether to adjust the distribution of monitoring points for the charging cable based on the historical accuracy of the temperature prediction of the charging cable using the correlation prediction model includes:

[0042] Obtain the predicted and actual temperatures of the charging cable from historical data for several times.

[0043] Calculate the second difference between the predicted temperature value and the actual temperature value for a single instance;

[0044] The historical accuracy rate is calculated as the ratio of the number of times the second difference falls within the allowable error range to the total number of times.

[0045] The historical accuracy rate is compared with the preset historical accuracy rate;

[0046] If the historical accuracy rate is less than or equal to the preset historical accuracy rate, then it is determined that the distribution of monitoring points for the charging cable should be adjusted.

[0047] Furthermore, the process of determining the optimization of the adjusted temperature monitoring cycle based on the adjusted accuracy change includes,

[0048] Obtain the accuracy of the adjusted correlation prediction model;

[0049] Calculate the third difference between the adjusted accuracy and the historical accuracy;

[0050] The third difference is used as the adjusted accuracy change and compared with the preset change.

[0051] If the change is less than or equal to the preset change, then a first optimization strategy is determined.

[0052] The first optimization strategy is to increase the temperature monitoring cycle of the first monitoring mode and the temperature monitoring cycle of the second monitoring mode.

[0053] Furthermore, the process of determining the optimization of the adjusted temperature monitoring cycle based on the adjusted accuracy change includes,

[0054] If the change is greater than the preset change, then a second optimization strategy is determined;

[0055] The second optimization strategy is to reduce the temperature monitoring cycle of the first monitoring mode and reduce the temperature monitoring cycle of the second monitoring mode.

[0056] Compared with the prior art, the beneficial effects of the present invention are that by obtaining the structural stability of the charging cable through a torsion test and setting the distribution of monitoring points according to the structural stability, appropriate monitoring points can be selected for charging cables with different structural stability to implement a monitoring strategy for the operating parameters of the charging cable. For charging cables with poor structural stability, irregularly distributed monitoring points are used to focus on monitoring areas prone to continuity failure or damage. For cables with good structural stability, regularly distributed monitoring points are used with reasonable intervals, thereby comprehensively and accurately monitoring the operating parameters of the charging cable. This effectively avoids missing key operating status information due to unreasonable monitoring point settings, improves the accuracy and reliability of monitoring the operating status of the charging cable, and provides more accurate data support for subsequent fault warning and maintenance.

[0057] Furthermore, by setting up several current sensors and several voltage sensors at several monitoring points, the current fluctuations, voltage fluctuations, and resistance fluctuations on the charging cable can be monitored in real time. Combined with preset current fluctuations, preset voltage fluctuations, and preset resistance fluctuations, it can be determined whether the fluctuation data on the charging cable is in an abnormal fluctuation state. This allows the charging cable to be detected and warned in a timely manner if any potential faults occur during operation, such as abnormal current fluctuations or abnormal voltage fluctuations, thus preventing the abnormal state from deteriorating further and causing equipment damage or safety accidents, and ensuring the safe operation of the charging cable and related equipment.

[0058] Furthermore, by establishing a correlation prediction model for the temperature of the charging cable based on the changes in temperature with current, voltage, and resistance fluctuations, and comparing the historical accuracy of the correlation prediction model with the preset historical accuracy, it is determined whether the distribution of monitoring points for the charging cable needs to be adjusted. This can continuously improve the accuracy of the correlation prediction model, and by comprehensively considering the influence of current, voltage, and resistance operating parameters on temperature, it can accurately predict the temperature change trend of the charging cable, providing a scientific basis for taking preventive measures in advance. This enhances the adaptability of the charging cable operation status monitoring system to different operating conditions, enabling it to better cope with complex and ever-changing charging scenarios.

[0059] Furthermore, by using several historical fluctuation data points that cause abnormal charging temperatures and assigning different weighting coefficients to these fluctuation data points, an anomaly index is calculated. This reflects the different importance of different fluctuation data points in causing abnormal charging temperatures. The impact of current fluctuations, voltage fluctuations, and resistance fluctuations on the temperature of the charging cable is comprehensively evaluated, avoiding misjudgments that may occur if only a single parameter is relied upon to determine anomalies. This makes the assessment of abnormal states of the charging cable more comprehensive, accurate, and reliable, providing a stronger basis for subsequent fault diagnosis and handling.

[0060] Furthermore, the temperature monitoring mode for the charging cable is determined by comparing the abnormal index with the preset abnormal index. When the abnormal index is less than the preset abnormal index, a monitoring mode with an increased temperature monitoring cycle is adopted to monitor the temperature of the charging cable. When the abnormal index is greater than the preset abnormal index, a monitoring mode with a decreased temperature monitoring cycle is adopted to monitor the temperature of the charging cable. This ensures that the monitoring cost of the charging cable is reduced when the abnormal index is small, and that the temperature monitoring cycle is reduced to capture temperature changes in a timely manner when the abnormal index is large. This improves the efficiency and pertinence of temperature monitoring and provides more accurate temperature protection for the safe operation of the charging cable.

[0061] Furthermore, by comparing the adjusted accuracy change with the preset change, an optimization strategy for optimizing the temperature monitoring mode of the charging cable is determined. When the change is less than the preset change, the first temperature monitoring cycle and the second temperature monitoring cycle are increased; when the change is greater than the preset change, the first temperature monitoring cycle and the second temperature monitoring cycle are decreased. This allows for timely adjustment of the temperature prediction value of the associated prediction model, ensuring that the accuracy remains dynamically within a reasonable range and increasing the accuracy of temperature prediction for the charging cable. Attached Figure Description

[0062] Figure 1 This is a schematic diagram illustrating the steps of the intelligent monitoring method for the operating status of charging cables according to an embodiment of the present invention;

[0063] Figure 2 In this embodiment of the invention, the distribution of monitoring points is determined based on structural stability to implement a monitoring strategy for the operating parameters of the charging cable;

[0064] Figure 3 In this embodiment of the invention, the abnormality of fluctuations is determined based on current fluctuations, voltage fluctuations, and resistance fluctuations.

[0065] Figure 4 In this embodiment of the invention, the temperature monitoring mode for the charging cable is determined based on an anomaly index;

[0066] Figure 5 In this embodiment of the invention, the accuracy of the correlation prediction model is determined based on the first difference between the predicted value of the charging cable temperature by the correlation prediction model and the actual temperature.

[0067] Figure 6 In this embodiment of the invention, the distribution of the charging cable monitoring points is adjusted based on the historical accuracy of the temperature prediction of the associated prediction model.

[0068] Figure 7 This invention provides an optimization strategy for determining the temperature monitoring mode of the charging cable based on the adjusted accuracy change. Detailed Implementation

[0069] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0070] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0071] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0072] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0073] Please see Figures 1 to 7 As shown, Figure 1 This is a schematic diagram illustrating the steps of the intelligent monitoring method for the operating status of charging cables according to an embodiment of the present invention; Figure 2 In this embodiment of the invention, the distribution of monitoring points is determined based on structural stability to implement a monitoring strategy for the operating parameters of the charging cable; Figure 3In this embodiment of the invention, the abnormality of fluctuations is determined based on current fluctuations, voltage fluctuations, and resistance fluctuations. Figure 4 In this embodiment of the invention, the temperature monitoring mode for the charging cable is determined based on an anomaly index; Figure 5 In this embodiment of the invention, the accuracy of the correlation prediction model is determined based on the first difference between the predicted value of the charging cable temperature by the correlation prediction model and the actual temperature. Figure 6 In this embodiment of the invention, the distribution of the charging cable monitoring points is adjusted based on the historical accuracy of the temperature prediction of the associated prediction model. Figure 7 This invention provides an optimization strategy for determining the temperature monitoring mode of the charging cable based on the adjusted accuracy change.

[0074] The intelligent monitoring method for the operating status of charging cables according to embodiments of the present invention includes:

[0075] Step S1: Based on the torsion test of the charging cable sample, the structural stability coefficient of the charging cable is obtained, and the distribution of monitoring points is determined to implement a monitoring strategy for the operating parameters of the charging cable.

[0076] Step S2: Under the condition of determining the corresponding monitoring strategy, obtain the fluctuations of current, voltage and resistance at several monitoring points during the operation of the charging cable to determine whether the fluctuation is abnormal.

[0077] Step S3: Under the condition that the fluctuation situation is in an abnormal fluctuation state, determine the abnormal index based on several historical fluctuation data that caused the abnormal charging temperature.

[0078] Step S4: Determine the temperature monitoring mode of the charging cable with a preset temperature monitoring adjustment cycle based on the anomaly index and establish a correlation prediction model for the temperature of the charging cable.

[0079] Step S5: Adjust the distribution of monitoring points based on the historical accuracy of the temperature prediction of the charging cable by the correlation prediction model.

[0080] Step S6: Determine the optimization strategy for the adjusted temperature monitoring cycle based on the adjusted accuracy change;

[0081] Specifically, the distribution of monitoring points is determined based on the comparison between the structural stability coefficient of the charging cable obtained from a torsion test on a sample and a preset structural stability coefficient, in order to implement a monitoring strategy for the operating parameters of the charging cable.

[0082] If the structural stability coefficient is less than or equal to the preset structural stability coefficient, then the monitoring points are determined to be irregularly distributed to implement the monitoring strategy for the operating parameters of the charging cable.

[0083] If the structural stability coefficient is greater than the preset structural stability coefficient, then the monitoring points are determined to be distributed in a regular manner to implement the monitoring strategy for the operating parameters of the charging cable.

[0084] The preset structural stability coefficient is the ratio of the number of consecutive torsions of the sample to the preset number of consecutive torsions. The preset structural stability coefficient ranges from 0.8 to 1.3, and the preferred value in this invention is 1. The range and preferred range of the preset structural stability coefficient can be determined according to the actual situation, and are not specifically limited here.

[0085] The preset number of continuous twists ranges from 23,000 to 28,000 times, and the preferred value in this invention is 25,000 times. The preferred range and value of the preset number of continuous twists can be determined according to the actual situation, and no specific limitation is made here.

[0086] Specifically, the structural stability coefficient is determined by conducting a torsion test on the charging cable. The ratio of the number of consecutive torsions in the torsion test to a preset number of consecutive torsions is used as the structural stability coefficient of the charging cable. In practical applications, a sample of the charging cable is taken, with a length ranging from 0.8 to 1.2 m. In this invention, the preferred length is 1 m. The sheath and insulation layer are stripped from both ends of the sample to expose the conductors in the charging cable sample, so as to connect to the terminals on the continuity detection device for torsion testing. After pretreatment, one end of the sample is fixed, and the other end is connected to the rotating clamp on the detection device. Each core in the sample is connected to the terminal of the continuity detection device to form a closed loop. At this time, the detection device is started, and the sample is repeatedly torsiond at a preset angle and preset speed. The continuity status of the cores is monitored in real time, and the number of consecutive torsions in the continuity status is recorded. The ratio of the number of consecutive torsions to the preset number of torsions is used as the structural stability coefficient of the charging cable.

[0087] In this embodiment of the invention, the structural stability coefficient of the charging cable sample reflects the structural stability of the charging cable.

[0088] Wherein, the preset angle is the continuous torsion angle of the sample when performing the torsion test, and the value is ±90°, ±180°, ±720°. The preferred angle of the present invention is ±180°. The preset speed is the torsion speed of the sample when performing the torsion test, and the value ranges from 1° to 90° / s. The preferred value of the present invention is 45° / s.

[0089] In this embodiment of the invention, the preset structural stability coefficient is set to 1. During implementation, the distribution of monitoring points is determined by comparing the structural stability coefficient of the charging cable with the preset structural stability coefficient obtained from a torsion test on a sample of the charging cable. This comparison is used to implement a monitoring strategy for the operating parameters of the charging cable. For example, if the structural stability coefficient is 0.8, it indicates that the structural stability of the charging cable is poor, and the charging cable fails to meet the standard in the torsion test, resulting in continuity failure. Therefore, the monitoring points are determined to be irregularly distributed to implement a monitoring strategy for the operating parameters of the charging cable. (Based on a length of one meter...) The monitoring points are distributed at points prone to continuity failure or damage on the charging cable sample to monitor the operating parameters of the charging cable. For example, if the structural stability coefficient is 1.2, which is greater than the preset structural stability coefficient, it indicates that the structural stability of the charging cable is good. If the charging cable meets the standard and does not experience continuity failure in the torsion test, then the monitoring points are determined to be regularly distributed to implement the monitoring strategy for the operating parameters of the charging cable. Based on the structural stability of a one-meter-long charging cable sample, monitoring points are set at 30cm intervals to monitor the operating parameters of the charging cable.

[0090] The irregular distribution is achieved by arranging several current sensors and several voltage sensors in a ring at 15cm intervals along the connection area and several current sensors and several voltage sensors continuously at 10cm intervals along the bending area, based on the connection area and bending area on the charging cable.

[0091] The rule distribution involves installing several current sensors and several voltage sensors at monitoring points spaced 30cm apart on the charging cable to monitor the operating parameters of the charging cable.

[0092] Specifically, given a determined distribution of monitoring points to implement a monitoring strategy for the operating parameters of the charging cable, current fluctuations, voltage fluctuations, and resistance fluctuations at several monitoring points during the operation of the charging cable are acquired to determine whether the fluctuations are abnormal.

[0093] If the current fluctuation is greater than the preset current fluctuation, then the current fluctuation is determined to be abnormal;

[0094] If the voltage fluctuation is greater than the preset voltage fluctuation, then the voltage fluctuation is determined to be abnormal;

[0095] If the resistance fluctuation is greater than the preset resistance fluctuation, then the resistance fluctuation is determined to be abnormal;

[0096] Wherein, the preset current fluctuation is the change in the magnitude of the current in the circuit over time, and the value range of the preset current fluctuation is ±1% of the rated current, preferably 1% of the rated current in this invention; the preset voltage fluctuation is the change in the magnitude of the voltage in the circuit over time, and the value range of the preset voltage fluctuation is ±15% of the rated voltage, preferably 10% of the rated voltage in this invention; the preset resistance fluctuation is the change in the magnitude of the resistance in the circuit over time, and the value range of the preset resistance fluctuation is ±5%, preferably 3% in this invention. The preferred value range and preferred value of the preset current fluctuation, preset voltage fluctuation and preset resistance fluctuation can be determined according to the actual situation, and are not specifically limited here.

[0097] The current fluctuation is obtained in real time by current transformers at several monitoring points on the charging cable, the voltage fluctuation is obtained in real time by voltmeters at several monitoring points on the charging cable, and the resistance fluctuation is obtained by using Ohm's law to calculate the ratio of the voltage fluctuation to the current fluctuation as the resistance fluctuation of the charging cable based on the real-time obtained current fluctuation and voltage fluctuation.

[0098] In this embodiment of the invention, the preset current fluctuation range is 1% of the rated current. In practice, for example, if the current fluctuation is 1.5% of the rated current, it indicates that the current fluctuation in the charging cable is large, and the current fluctuation is determined to be abnormal. The preset voltage fluctuation is 10% of the rated voltage. In practice, if the voltage fluctuation is 20% of the rated voltage, it indicates that the voltage fluctuation in the charging cable is large, and the voltage fluctuation is determined to be abnormal. The preset resistance fluctuation is 3%. In practice, for example, if the resistance fluctuation is 8%, it indicates that the resistance fluctuation in the charging cable is large, and the resistance fluctuation is determined to be abnormal.

[0099] In this embodiment of the invention, based on the abnormal current fluctuations, voltage fluctuations, and resistance fluctuations, an abnormality index of the charging cable is determined according to several historical fluctuation data that caused the charging temperature abnormality. In practical applications, current fluctuation, voltage fluctuation, and resistance fluctuation data corresponding to temperature abnormalities during the past 30 charging processes are selected. The current fluctuation rate is obtained by the ratio of the current fluctuation to the rated current, the voltage fluctuation rate is obtained by the ratio of the voltage fluctuation to the rated voltage, and the resistance fluctuation rate is obtained by the ratio of the resistance fluctuation to the nominal resistance. Different weighting coefficients are assigned to the fluctuation data that caused the charging temperature abnormality. The current fluctuation rate is assigned a weighting coefficient of 0.5, the voltage fluctuation rate is assigned a weighting coefficient of 0.3, and the resistance fluctuation rate is assigned a weighting coefficient of 0.2. The weighted comprehensive fluctuation value during a single charging process is calculated by summing the products of the weighting coefficients of the current fluctuation rate and the current fluctuation rate, the weighting coefficients of the voltage fluctuation rate and the voltage fluctuation rate, and the weighting coefficients of the resistance fluctuation rate and the resistance fluctuation rate. Finally, the average value of the weighted comprehensive fluctuation values ​​during 30 charging processes is taken to obtain the abnormality index.

[0100] Specifically, given the anomaly index, the temperature monitoring mode for the charging cable is determined based on the comparison between the anomaly index and a preset anomaly index, and a correlation prediction model for the temperature of the charging cable is established.

[0101] If the abnormality index is less than or equal to the preset abnormality index, then the temperature monitoring mode for the charging cable is determined to be the first monitoring mode.

[0102] If the abnormality index is greater than the preset abnormality index, then the temperature monitoring mode for the charging cable is determined to be the second monitoring mode.

[0103] The preset abnormality index ranges from 0.25 to 0.5, with a preferred value of 0.35. The preset abnormality index range and the preferred range can be determined according to the actual situation, and no specific limitation is made here.

[0104] In this embodiment of the invention, the preset anomaly index is 0.35. During implementation, the temperature monitoring mode of the charging cable is determined based on the comparison result between the anomaly index and the preset anomaly index, and a correlation prediction model for the temperature of the charging cable is established. For example, when the anomaly index is 0.25, it meets the condition that the anomaly index is less than the preset anomaly index, indicating that the fluctuation range of the data causing abnormal charging temperature in the charging cable is small. In this case, the temperature monitoring mode of the charging cable is determined to be the first monitoring mode. For example, when the anomaly index is 0.45, it meets the condition that the anomaly index is greater than the preset anomaly index, indicating that the fluctuation range of the data causing abnormal charging temperature in the charging cable is large. In this case, the temperature monitoring mode of the charging cable is determined to be the second monitoring mode.

[0105] The first monitoring mode is to increase the temperature monitoring cycle, and monitor the temperature of the charging cable at intervals of 20 to 30 minutes. Preferably, the temperature of the charging cable is monitored at intervals of 25 minutes. The second monitoring mode is to decrease the temperature monitoring cycle, and monitor the temperature of the charging cable at intervals of 10 to 15 minutes. Preferably, the temperature of the charging cable is monitored at intervals of 12 minutes.

[0106] In this embodiment of the invention, the monitoring period before adjustment can be set to 18 minutes. The specific monitoring period can be determined according to the actual situation and is not limited here.

[0107] In this embodiment of the invention, based on the temperature fluctuations of the charging cable with changes in current, voltage, and resistance, a correlation prediction model for the charging cable temperature is established. In practical applications, current, voltage, resistance, and temperature data are acquired, preprocessed, and low-pass filters are used to remove high-frequency noise and outliers. The processed current, voltage, resistance, and temperature data are then normalized and divided into training, validation, and test sets. Correlation analysis is used to select features with high correlation to temperature changes. The maximum, minimum, and average current values ​​are extracted from the flow data; the maximum, minimum, and average voltage values ​​are extracted from the voltage data; and the maximum, minimum, and average resistance values ​​are extracted from the resistance data. A linear regression model is selected and trained using a training set. The current, voltage, and resistance data are used as inputs to the linear regression model, and the temperature value is used as the output to output the corresponding temperature prediction value. The hyperparameters of the linear regression model are adjusted using a validation set to optimize the linear regression model. Finally, the ability of the linear regression model is verified using a test set to obtain the correlation prediction model for predicting the temperature of the charging cable.

[0108] Specifically, under the condition of establishing a correlation prediction model for the temperature of the charging cable, the accuracy of the correlation prediction model is determined based on the comparison between the predicted value of the charging cable temperature by the correlation prediction model and the first difference between the actual temperature and a preset first difference.

[0109] If the first difference is less than or equal to a preset first difference, then the accuracy of the correlation prediction model is determined to be up to standard.

[0110] If the first difference is greater than the preset first difference, then it is determined that the accuracy of the correlation prediction model is substandard.

[0111] The preset first difference value ranges from 3 to 8°C, and the preferred value in this invention is 5°C. The preset difference value range and the preferred value range can be determined according to the actual situation, and no specific limitation is made here.

[0112] Wherein, the first difference is the absolute difference between the predicted temperature of the charging cable and the actual temperature.

[0113] In this embodiment of the invention, the preset first difference value is 5℃. During implementation, the accuracy of the correlation prediction model is determined by comparing the first difference between the predicted value of the charging cable temperature and the actual temperature by the correlation prediction model and the preset first difference value. For example, if the first difference is 3℃, it meets the condition that the first difference is less than the preset first difference value, indicating that the first difference between the predicted value of the charging cable temperature and the actual temperature by the correlation prediction model is within the error range. In this case, the accuracy of the correlation prediction model is determined to be up to standard. For example, if the first difference is 8℃, it meets the condition that the first difference is greater than the preset first difference value, indicating that the first difference between the predicted value of the charging cable temperature and the actual temperature by the correlation prediction model exceeds the error range. In this case, the accuracy of the correlation prediction model is determined to be down to standard.

[0114] Specifically, if it is determined that the accuracy of the correlation prediction model in predicting the temperature of the charging cable is substandard, a determination is made based on the comparison between the historical accuracy rate of the correlation prediction model in predicting the temperature of the charging cable and the preset historical accuracy rate to determine whether the distribution of the monitoring points for the charging cable should be adjusted.

[0115] If the historical accuracy rate is less than or equal to the preset historical accuracy rate, then it is determined that the distribution of the charging cable monitoring points should be adjusted.

[0116] If the historical accuracy rate is greater than the preset historical accuracy rate, then it is determined that the distribution of the charging cable monitoring points will not be adjusted.

[0117] The preset historical accuracy rate ranges from 0.85 to 0.95, with a preferred value of 0.9. The preset historical accuracy rate range and the preferred range can be determined based on actual conditions, and no specific limitation is made here.

[0118] The historical accuracy rate is achieved by obtaining a number of predicted and actual temperature values ​​of the charging cable from historical data. The number of times the predicted and actual temperatures are obtained ranges from 20 to 50, with 30 being the preferred value in this invention. The predicted temperature values ​​of the charging cable are obtained 30 times, and a second difference between the predicted and actual temperature values ​​is calculated. If the second difference is within the allowable error range, it proves that the correlation prediction model accurately predicted the temperature of the charging cable in that instance. If the second difference exceeds the allowable error range, it proves that the correlation prediction model inaccurately predicted the temperature of the charging cable in that instance. The ratio of the number of times the second difference between the predicted and actual temperature values ​​of the charging cable within the 30 times is within the allowable error range to the total number of times is used as the historical accuracy rate of the correlation prediction model.

[0119] In this embodiment of the invention, the preset historical accuracy rate is set to 0.9. During implementation, the distribution of the charging cable monitoring points is determined based on the comparison between the historical accuracy rate of the temperature prediction of the charging cable by the correlation prediction model and the preset historical temperature accuracy rate. For example, if the historical accuracy rate is 0.8, it is consistent with the condition that the historical accuracy rate is less than the preset historical accuracy rate, indicating that the correlation prediction model has repeatedly failed to predict the temperature of the charging cable. In this case, it is determined that the distribution of the charging cable monitoring points should be adjusted. For example, if the historical accuracy rate is 0.95, it is consistent with the condition that the historical accuracy rate is greater than the preset historical accuracy rate, indicating that the correlation prediction model has repeatedly achieved the condition for predicting the temperature of the charging cable. In this case, it is determined that the distribution of the charging cable monitoring points should not be adjusted.

[0120] The monitoring points of the charging cable are adjusted by reducing the interval between adjacent monitoring points under the condition of regular distribution. The reduced interval is 15-25cm, preferably 20cm. This 20cm interval is used to monitor the operating parameters of the charging cable. Under the condition of irregular distribution, the interval between monitoring points in the ring arrangement in the connection area and the interval between continuous arrangements in the bending area are reduced by 3-6cm, preferably 4cm. This 4cm interval is used to increase the number of monitoring points for the charging cable. Monitoring points are also added in the wear area of ​​the charging cable to install current and voltage sensors to monitor the operating parameters of the charging cable, thereby increasing the accuracy of the correlation prediction model in predicting the temperature of the charging cable.

[0121] Specifically, given that the monitoring points of the charging cable need to be adjusted, an optimization strategy for optimizing the temperature monitoring mode of the charging cable is determined based on the comparison between the adjusted accuracy change and the preset change.

[0122] If the change is less than or equal to the preset change, then the first optimization strategy is determined;

[0123] If the change is greater than the preset change, then the second optimization strategy is determined.

[0124] The preset change amount ranges from 3% to 8%, and the preferred value in this invention is 5%. The preset change amount range and the preferred value range can be determined according to the actual situation, and no specific limitation is made here.

[0125] The change is the third difference between the adjusted accuracy and the historical accuracy.

[0126] In this embodiment of the invention, the preset change amount is 5%. During implementation, an optimization strategy for optimizing the temperature monitoring mode of the charging cable is determined based on the adjusted accuracy change amount and the preset change amount. For example, when the change amount is 3%, it meets the condition that the change amount is less than the preset change amount, indicating that the adjusted accuracy change amount is small. In this case, a first optimization strategy for optimizing the temperature monitoring mode of the charging cable is determined. For example, when the change amount is 8%, it meets the condition that the change amount is greater than the preset change amount, indicating that the adjusted accuracy change amount is large. In this case, a second optimization strategy for optimizing the temperature monitoring mode of the charging cable is determined.

[0127] The first optimization strategy involves increasing both the first and second monitoring cycles by 1.5-1.8 times the adjusted temperature monitoring cycle. Preferably, the original temperature monitoring cycle is increased by 1.6 times. The second optimization strategy involves decreasing both the first and second monitoring cycles by 0.6-0.9 times the adjusted temperature monitoring cycle. Preferably, the original temperature monitoring cycle is decreased by 0.8 times.

[0128] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for intelligently monitoring the running state of a charging cable, characterized in that, include, Torsion tests were conducted on samples of the charging cable to obtain the structural stability coefficient of the charging cable and to determine the distribution of monitoring points in order to monitor the operating parameters of the charging cable. Among them, monitoring points are determined based on the structural stability coefficient being less than or equal to a preset structural stability coefficient, and the monitoring of the operating parameters of the charging cable is performed in an irregular distribution. The irregular distribution is based on the connection area and bending area on the charging cable, with monitoring points arranged in a ring at a first interval distance along the connection area, and monitoring points arranged continuously at a second interval distance along the bending area. The first interval distance is greater than the second interval distance; Based on the fact that the structural stability coefficient is greater than the preset structural stability coefficient, the monitoring points are determined and the monitoring of the operating parameters of the charging cable is carried out in a regular distribution. The rule distribution is that several monitoring points are set every 30cm on the charging cable; The interval distance of the regular distribution is greater than the interval distance of the irregular distribution; The current fluctuation, voltage fluctuation, and resistance fluctuation of the charging cable at several monitoring points during operation are obtained to determine whether the fluctuation is abnormal. An anomaly index was determined based on several historical fluctuation data that caused abnormal charging temperatures. Based on the anomaly index, a preset temperature monitoring and adjustment cycle is determined to monitor the temperature of the charging cable and establish a correlation prediction model for the temperature of the charging cable. Among them, several historical fluctuation data that caused abnormal charging temperature were obtained and different weighting coefficients were assigned to several fluctuation data. The volatility of several historical fluctuation data points is calculated, and the anomaly index of the charging cable is determined by combining the weighting coefficients of the fluctuation data. The anomaly index is compared with a preset anomaly index; The temperature monitoring adjustment cycle is determined based on the anomaly index being less than or equal to a preset anomaly index in order to monitor the temperature of the charging cable. A correlation prediction model for the temperature of the charging cable is established based on the monitored temperature changes of the charging cable with fluctuations. The temperature monitoring adjustment cycle is to increase the temperature monitoring cycle of the charging cable. The temperature monitoring adjustment cycle is determined based on the anomaly index being greater than the preset anomaly index in order to monitor the temperature of the charging cable. The temperature monitoring adjustment cycle is to reduce the temperature monitoring cycle of the charging cable. The accuracy of the correlation prediction model is determined based on the predicted temperature of the charging cable and the actual temperature. The distribution of the monitoring points is adjusted based on the historical accuracy of the temperature prediction of the charging cable by the correlation prediction model. Among them, the predicted and actual temperature values ​​of the charging cable are obtained from historical data for several times. Calculate the second difference between the predicted temperature value and the actual temperature value for a single instance; The historical accuracy rate is calculated as the ratio of the number of times the second difference falls within the allowable error range to the total number of times. The historical accuracy rate is compared with the preset historical accuracy rate; If the historical accuracy rate is less than or equal to the preset historical accuracy rate, then it is determined that the distribution of the monitoring points of the charging cable should be adjusted. The adjustment method is to reduce the interval between adjacent monitoring points under the condition of regular distribution, and to reduce the interval between monitoring points arranged in a ring in the connecting area and the interval between continuous monitoring points arranged in the bending area under the condition of irregular distribution. The optimization of the temperature monitoring adjustment cycle is determined based on the change in the adjusted accuracy.

2. The method of claim 1, wherein, The process of obtaining the structural stability coefficient of a charging cable by conducting a torsion test on a sample of the charging cable includes the following steps: A sample of the charging cable was selected and a torsion test was performed. The number of consecutive torsions of the sample was obtained from the torsion test; The ratio of the number of consecutive torsions to the preset number of consecutive torsions is used as the structural stability coefficient of the charging cable.

3. The method of claim 2, wherein, The process of determining whether the accuracy of the correlation prediction model meets the standard based on the predicted value and actual temperature of the charging cable according to the correlation prediction model includes: Obtain the temperature prediction value of the charging cable from the correlation prediction model and the current actual temperature value of the charging cable; Calculate the first difference between the predicted temperature value and the actual temperature value; Compare the first difference with a preset first difference; If the first difference is greater than a preset first difference, then the accuracy of the correlation prediction model is determined to be substandard.

4. The method of claim 3, wherein, The process of determining the optimization of the temperature monitoring adjustment cycle based on the adjusted accuracy change includes, Obtain the accuracy of the adjusted correlation prediction model; Calculate the third difference between the adjusted accuracy and the historical accuracy; The third difference is used as the adjusted accuracy change and compared with the preset change. If the change in accuracy is less than or equal to the preset change, then a first optimization strategy is determined. The first optimization strategy is to increase the temperature monitoring cycle of the first monitoring mode and increase the temperature monitoring cycle of the second monitoring mode. The first monitoring mode determines the temperature monitoring mode of the charging cable as the first monitoring mode based on the abnormality index being less than or equal to a preset abnormality index. The second monitoring mode determines the temperature monitoring mode for the charging cable as the second monitoring mode based on the fact that the anomaly index is greater than the preset anomaly index.

5. The intelligent monitoring method for the operating status of charging cables according to claim 4, characterized in that, The process of determining the optimization of the temperature monitoring adjustment cycle based on the adjusted accuracy change includes, If the change in accuracy is greater than the preset change, then a second optimization strategy is determined. The second optimization strategy is to reduce the temperature monitoring cycle of the first monitoring mode and reduce the temperature monitoring cycle of the second monitoring mode.

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