A method, device and system for intelligent inspection of power data

Abstract

The present invention relates to the field of data processing technology, and in particular to an intelligent inspection method, device and system for power data, wherein the current phase difference degree at the contact position is obtained by the phase difference between first current data and voltage data at the contact position between a high-voltage line and a transmission tower; then, the power abnormal amplitude at the contact position is obtained by combining the amplitude difference between second current data and third current data at a predetermined distance before and after the contact position and the fluctuation factor of the first current data; then, the power abnormal amplitude is corrected in combination with the temperature data at the contact position, so as to obtain the final power abnormal amplitude at the contact position; one-dimensional clustering is performed on the final power abnormal amplitude corresponding to each contact position, so as to obtain the power abnormal index at each contact position, so as to realize abnormality detection at the contact position, thereby improving the accuracy of intelligent inspection of power data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent audit method, device and system for power data. Background Art

[0002] With the development of urbanization and industrialization, the demand for electricity continues to grow. High-voltage lines can effectively support the high-power demands of large industrial facilities, cities, and large commercial areas, ensuring large-scale power supply while maintaining power supply quality. In the process of transmitting electric energy over long distances, transmission towers with different spacings are often used to support high-voltage lines, thereby ensuring that the height of the high-voltage lines can meet the working standards of the power system. The part where the high-voltage line contacts the transmission tower is prone to aging and damage of the insulation shell due to years of contact wear and interference from the complex external environment, which in turn leads to a decrease in the insulation performance of the high-voltage line and increases the risk of electric shock.

[0003] At present, the busbar differential protection method is often used to detect abnormal current data of the part where the high-voltage line contacts the transmission tower. When the insulating shell of the high-voltage line is damaged or aged, it will cause a certain amount of current leakage. Therefore, the busbar differential protection method often compares the difference between the current data before and after the part where the high-voltage line contacts the transmission tower, and uses the absolute value of the difference to reflect the abnormal current data of the part where the high-voltage line contacts the transmission tower. However, since the current data of different parts of the high-voltage line itself has certain changes, the error of the detection result obtained by only relying on the difference in current amplitude at different parts is large, resulting in insufficient accuracy in the inspection of power data. Summary of the invention

[0004] In order to solve the technical problem of low audit accuracy of power data, the purpose of the present invention is to provide an intelligent audit method, device and system for power data. The technical solution adopted is as follows:

[0005] An intelligent audit method for power data, the method comprising:

[0006] Collecting in real time within a predetermined time the first current data, voltage data and temperature data at the contact position between the high-voltage line and the transmission tower, as well as the second current data and the third current data at a predetermined distance before and after the contact position;

[0007] Obtaining a current phase difference degree at the contact position according to a phase difference between the first current data and the voltage data;

[0008] Obtaining a current amplitude difference degree at the contact position according to a current phase difference degree and an amplitude difference between the second current data and the third current data;

[0009] Obtaining the power abnormality amplitude at the contact position according to the current amplitude difference degree and the fluctuation factor of the first current data;

[0010] Correcting the power abnormality amplitude according to the temperature data to obtain a final power abnormality amplitude;

[0011] Performing one-dimensional clustering on the final power abnormality amplitude corresponding to each contact position to obtain a power abnormality index at each contact position;

[0012] The high-voltage line is inspected and repaired according to the power abnormality index.

[0013] Optionally, the absolute value of the phase difference between the first current data and the voltage data at the same time is superimposed to obtain the current phase difference degree.

[0014] Optionally, the absolute value of the difference between the amplitude mean of all data in the second current data and the amplitude mean of all data in the third current data is obtained, and the current phase difference degree is multiplied by the absolute value of the difference to obtain the current amplitude difference degree.

[0015] Optionally, multiple local extreme value data are selected from the first current data, and the absolute value of the difference between the amplitude of each local extreme value data and the amplitude mean of all data in the first current data is superimposed to obtain the fluctuation factor of the first current data.

[0016] Optionally, the current amplitude difference degree is multiplied by a fluctuation factor of the first current data to obtain the power abnormality amplitude.

[0017] Optionally, the ratio of the temperature mean of all the temperature data to the maximum temperature value of all the temperature data is used as a correction factor to correct the power abnormality amplitude to obtain the final power abnormality amplitude.

[0018] Optionally, the absolute value of the difference between the final power anomaly amplitude and each final power anomaly amplitude of the suspected normal cluster in the clustering result is superimposed and averaged to obtain the power anomaly index.

[0019] Optionally, when the power anomaly index is greater than a predetermined value, it is determined that an abnormality exists at the contact position of the high-voltage line, and the contact position of the high-voltage line is inspected and repaired.

[0020] The present invention also provides an intelligent power data inspection device, comprising:

[0021] A sampling module, used to collect, in real time within a predetermined time, first current data, voltage data and temperature data at a contact position between the high-voltage line and the transmission tower, as well as second current data and third current data at a predetermined distance before and after the contact position;

[0022] A phase difference degree acquisition module, used for obtaining the current phase difference degree at the contact position according to the phase difference between the first current data and the voltage data;

[0023] an amplitude difference degree acquisition module, configured to obtain the current amplitude difference degree at the contact position according to the current phase difference degree and the amplitude difference between the second current data and the third current data;

[0024] A power abnormal amplitude acquisition module, used for obtaining the power abnormal amplitude at the contact position according to the current amplitude difference degree and the fluctuation factor of the first current data;

[0025] A correction module, used for correcting the power abnormality amplitude according to the temperature data to obtain a final power abnormality amplitude;

[0026] The clustering module is used to perform one-dimensional clustering on the final power anomaly amplitude corresponding to each contact position to obtain a power anomaly index at each contact position, and the power anomaly index is used to inspect and repair the high-voltage line.

[0027] The present invention also provides an intelligent power data audit system, including the intelligent power data audit device.

[0028] The present invention has the following beneficial effects:

[0029] The present invention can obtain the current phase difference degree at the contact position between the high-voltage line and the transmission tower through the phase difference between the first current data and the voltage data at the contact position; then obtain the power abnormality amplitude at the contact position in combination with the amplitude difference between the second current data and the third current data at a predetermined distance before and after the contact position and the fluctuation factor of the first current data; then correct the power abnormality amplitude in combination with the temperature data at the contact position, and then obtain the final power abnormality amplitude at the contact position; perform one-dimensional clustering on the final power abnormality amplitude corresponding to each contact position, obtain the power abnormality index at each contact position, and then realize abnormality detection at the contact position, thereby improving the accuracy of intelligent inspection of power data. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flow chart of an intelligent power data audit method provided by an embodiment of the present invention;

[0032] Figure 2 A data reversal diagram of an intelligent power data audit method provided by an embodiment of the present invention;

[0033] Figure 3 A structural block diagram of an intelligent power data audit device provided by an embodiment of the present invention;

[0034] Wherein, the accompanying drawings are marked as follows:

[0035] 10-sampling module; 20-phase difference degree acquisition module; 30-amplitude difference degree acquisition module; 40-power abnormality amplitude acquisition module; 50-correction module; 60-clustering module. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent audit method, device and system of power data proposed by the present invention, its specific implementation method, structure, characteristics and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0038] The specific scheme of the intelligent power data audit method, device and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0039] See also Figure 1 and Figure 2 , Figure 1 A flowchart of an intelligent power data audit method provided by an embodiment of the present invention is shown. Figure 2 A data reversal diagram of an intelligent audit method for power data provided by an embodiment of the present invention is shown. The intelligent audit method for power data comprises:

[0040] Step S1: within a predetermined time, real-time collection of first current data, voltage data and temperature data at a contact position between the high-voltage line and the transmission tower, as well as second current data and third current data at a predetermined distance before and after the contact position;

[0041] Step S2: obtaining a current phase difference degree at the contact position according to a phase difference between the first current data and the voltage data;

[0042] Step S3: obtaining the current amplitude difference degree at the contact position according to the current phase difference degree and the amplitude difference between the second current data and the third current data;

[0043] Step S4: obtaining the power abnormality amplitude at the contact position according to the current amplitude difference degree and the fluctuation factor of the first current data;

[0044] Step S5: Correcting the power abnormality amplitude according to the temperature data to obtain a final power abnormality amplitude;

[0045] Step S6: performing one-dimensional clustering on the final power abnormality amplitude corresponding to each contact position to obtain a power abnormality index at each contact position;

[0046] Step S7: Inspect and repair the high-voltage line according to the power abnormality index.

[0047] First, step S1 is performed to collect the current, voltage and temperature at the contact position (support position of the transmission tower) between the high-voltage line and each transmission tower in real time within a predetermined time to obtain first current data, voltage data and temperature data. At the same time, the current at a predetermined distance before and after the contact position is also collected in real time to obtain second current data and third current data.

[0048] Specifically, a current sensor, a voltage sensor and a temperature sensor can be used to collect the first current data, voltage data and temperature data in real time, and a current sensor can be used to collect the second current data and the third current data in real time. In this embodiment, the predetermined time can be an empirical value of 3 days, the collection frequency is once per minute, and the predetermined distance can be an empirical value of 25m, and all collected data are uploaded to a computer for subsequent analysis.

[0049] It is understandable that, compared with direct current, alternating current can be converted to voltage more simply and effectively by a transformer. Therefore, in the process of long-distance high-voltage line current transportation, alternating current is often transmitted, so the high-voltage line also usually transmits alternating current. If there is no abnormal condition, the current and voltage of the alternating current in the high-voltage line should have good synchronization, that is, the phase difference between the alternating current and the alternating voltage should tend to 0. When the insulating shell of the voltage line ages and breaks, it will cause a certain fluctuation in the current in the high-voltage line, and then cause the voltage to fluctuate. The fluctuation will destroy the phase synchronization between the current and the voltage. Therefore, the greater the phase difference between the current and the voltage at a certain contact position, the greater the possibility that the current at the contact position is abnormal.

[0050] Based on this, step S2 is executed, and the current phase difference degree at the contact position can be obtained according to the phase difference between the first current data and the voltage data. The current phase difference degree can preliminarily reflect the abnormality of the corresponding first current data from the phase difference between the first current data and the voltage data. In this embodiment, the absolute value of the phase difference value of the first current data and the voltage data at the same time can be superimposed to obtain the current phase difference degree.

[0051] Specifically, the current phase difference degree can be obtained according to the following formula:

[0052]

[0053] Among them, α k represents the current phase difference degree at the kth contact position; N k,t represents the data amount of the first current data at the kth contact position; |Δθ k,t | represents the absolute value of the phase difference between the t-th data in the first current data and the t-th data in the voltage data at the k-th contact position; It means that the absolute value of the phase difference between the first current data at the kth contact position and the data at the same time in the voltage data is superimposed. The larger the value is, the greater the possibility that the first current data at the kth contact position is abnormal; +1 is used to prevent the current phase difference from being equal to 0.

[0054] It can be understood that when the insulating shell of the high-voltage line ages and is damaged at the contact position, a certain degree of current leakage will occur, resulting in the current amplitude of the high-voltage line before the contact position being lower than the current amplitude after the contact position. The more serious the aging and damage of the insulating shell, the more significant the current leakage phenomenon. The greater the difference between the current amplitude of the high-voltage line before the contact position and the current amplitude after the contact position, the higher the possibility that there is an abnormality in the current at the contact position.

[0055] Based on this, the current amplitude difference degree at the contact position can be obtained according to the current phase difference degree and the amplitude difference between the second current data and the third current data. Specifically, the absolute value of the difference between the amplitude mean of all data in the second current data and the amplitude mean of all data in the third current data is first obtained, and then the current phase difference degree is multiplied by the absolute value of the difference to obtain the current amplitude difference degree.

[0056] Specifically, the current amplitude difference degree can be obtained according to the following formula:

[0057]

[0058] Among them, β k Indicates the degree of difference in the current amplitude at the kth contact position; represents the amplitude mean of all data in the second current data at a predetermined distance before the kth contact position; represents the amplitude mean of all data in the third current data at a predetermined distance after the kth contact position; express and The greater the absolute value of the difference, the greater the difference between the second current data at a predetermined distance before the k-th contact position and the third current data at a predetermined distance after the k-th contact position, and the higher the possibility of current abnormality at the k-th contact position; norm() represents a normalization function; α k represents the current phase difference degree at the kth contact position, α k As the confidence parameter, α k The larger the value, the higher the reliability of the calculated current amplitude difference.

[0059] Furthermore, due to the aging and damage of the insulating casing of the high-voltage line, local insulation failure may occur, resulting in uneven current distribution in this area of ​​the high-voltage line. In the area where the insulating casing of the high-voltage line is aged and damaged, the current may be unstable due to local discharge or electrical breakdown, resulting in large fluctuations in the current. The more obvious the fluctuation of the current, the greater the possibility of abnormality.

[0060] Based on this, step S4 is executed, and the power abnormal amplitude at the contact position can be obtained according to the current amplitude difference degree and the fluctuation factor of the first current data. In this embodiment, multiple local extreme value data can be selected from the first current data, and the absolute value of the difference between the amplitude of each local extreme value data and the amplitude mean of all data in the first current data is superimposed to obtain the fluctuation factor of the first current data, and then the current amplitude difference degree is multiplied by the fluctuation factor of the first current data to obtain the power abnormal amplitude. The power abnormal amplitude can further reflect the abnormal situation of the current at the contact position.

[0061] Specifically, for the first current data at the kth contact position, 1000 (empirical value) local extreme value data can be randomly selected. The greater the difference between the local extreme value data and the amplitude mean of all data in the first current data, the greater the possibility that the current at the kth contact position has obvious fluctuation characteristics, and the greater the possibility that the current at the kth contact position is abnormal.

[0062] Specifically, the power abnormality amplitude can be obtained according to the following formula:

[0063]

[0064] In the formula, γ k M represents the power abnormality amplitude at the kth contact position; k represents the total number of local extreme value data (1000 in this embodiment); k,i represents the amplitude of the i-th local extreme value data selected from the first current data at the k-th contact position; represents the amplitude mean of all data in the first current data at the kth contact position; represents the fluctuation factor of the first current data at the kth contact position. The larger the value is, the greater the possibility that the current at the kth contact position has obvious fluctuation characteristics, and the greater the possibility that the current at the kth contact position is abnormal; β kIt represents the degree of difference of the current amplitude at the kth contact position. This value is used as a confidence parameter, that is, the larger the value is, the higher the trustworthiness of the calculated power anomaly amplitude is.

[0065] Furthermore, the damaged insulating casing may cause the environmental medium (such as air or surrounding supports) around the high-voltage line to directly contact the high-voltage line. This may increase the local resistance of the high-voltage line and cause additional heat to be generated when the current flows through the corresponding part of the high-voltage line, causing the temperature of the high-voltage line to rise at the aging and damaged part. Therefore, the higher the temperature at the contact position, the higher the possibility that the insulation of the high-voltage line is aged and damaged at the contact position, and the higher the possibility that the current at the contact position is abnormal. The abnormal amplitude of the power at the contact position can be adjusted to increase, otherwise it can be adjusted to decrease.

[0066] Based on this, step S5 is executed to correct the power abnormal amplitude according to the temperature data to obtain the final power abnormal amplitude. In this embodiment, the ratio of the temperature mean value of all data in the temperature data to the maximum temperature value is used as a correction factor to correct the power abnormal amplitude, thereby obtaining the final power abnormal amplitude.

[0067] Specifically, the final power abnormality amplitude can be obtained according to the following formula:

[0068]

[0069] In the formula, γ k ′ represents the final power abnormality amplitude at the kth contact position; γ k represents the power abnormality amplitude at the kth contact position; T max Indicates the maximum temperature value of all the temperature data; N k,t represents the data volume of the temperature data at the kth contact position, which value is also equal to the data volume of the first current data at the kth contact position; T k,t represents the tth data among the temperature data at the kth contact position; It represents the correction factor, that is, the larger the value is, the higher the temperature at the kth contact position is, the higher the possibility of insulation aging and damage at the kth contact position is, the higher the possibility of abnormal current at the kth contact position is, and it is necessary to increase the abnormal amplitude of the electric power at the kth contact position, otherwise it should be reduced.

[0070] It can be understood that the final power abnormal amplitude at each of the contact positions is obtained through the above steps, and aging and damage of the high-voltage line insulating casing is a low-probability event after all. Therefore, the final power abnormal amplitudes at the contact positions where most of the insulating casings are aged and damaged should have similar characteristics, and only the final power abnormal amplitudes at the contact positions where a very small number of insulating casings are aged and damaged do not meet the similarity characteristics. Therefore, the final power abnormal amplitudes at each of the contact positions can be subjected to one-dimensional density clustering, and the distribution discrete characteristics of each cluster in the clustering results can reflect the abnormal conditions of the current at each of the contact positions.

[0071] Specifically, the final power abnormality amplitude at each contact position is mapped to a one-dimensional coordinate system, and DBSCAN density clustering is performed on the one-dimensional coordinate system, wherein the clustering radius is selected as an empirical value of 6, and the minimum number of sample points is selected as an empirical value of 10, thereby obtaining a density clustering result. Since the final power abnormality amplitudes at the contact positions where most insulating shells are aged and damaged should have similar characteristics, the final power abnormality amplitudes are very likely to exist in the same cluster in the density clustering result. Therefore, in the density clustering result, the cluster with the largest number of samples in the cluster can be selected as the suspected normal cluster. The greater the difference between the final power abnormality amplitude and the final power abnormality amplitudes in the suspected normal cluster, the higher the distribution outlier of the final power abnormality amplitude, and the greater the possibility that the current at the contact position corresponding to the final power abnormality amplitude is abnormal.

[0072] Based on this, step S6 is executed to perform one-dimensional clustering on the final power abnormal amplitude corresponding to each contact position to obtain the power abnormality index at each contact position. In this embodiment, the absolute value of the difference between the final power abnormal amplitude and each final power abnormal amplitude of the suspected normal cluster in the clustering result is superimposed and averaged to obtain the power abnormality index.

[0073] Specifically, the power anomaly index can be obtained according to the following formula:

[0074]

[0075] In the formula, σ k represents the power anomaly index at the kth contact position; U represents the amount of data in the suspected normal cluster in the clustering result; γ k ′ represents the final power abnormality amplitude at the kth contact position; γ x ′ represents the xth final power anomaly amplitude in the suspected normal cluster in the clustering result; σ kThe larger the value of , the farther the final power abnormal amplitude at the k-th contact position has the distribution outlier in the density clustering result, which means that the possibility of abnormality in the current at the k-th contact position is higher.

[0076] Next, step S7 is executed to inspect and repair the high-voltage line according to the power abnormality index obtained in step S6. Specifically, when the power abnormality index is greater than a predetermined value, it is determined that there is an abnormality at the contact position of the high-voltage line, and the contact position of the high-voltage line is inspected and repaired.

[0077] In this embodiment, the predetermined value is an empirical value, for example, it may be 0.88. If a certain power abnormality index is higher than 0.88, it is considered that there is an abnormality at the contact position corresponding to the power abnormality index, and the high-voltage line at the contact position needs to be inspected and repaired.

[0078] See also Figure 3 , which shows a structural block diagram of an intelligent power data audit device provided by an embodiment of the present invention, the intelligent power data audit device comprising:

[0079] The sampling module 10 is used to collect, in real time within a predetermined time, first current data, voltage data and temperature data at a contact position between the high-voltage line and the transmission tower, as well as second current data and third current data at a predetermined distance before and after the contact position;

[0080] A phase difference degree acquisition module 20, configured to obtain a current phase difference degree at the contact position according to a phase difference between the first current data and the voltage data;

[0081] An amplitude difference degree acquisition module 30, configured to obtain a current amplitude difference degree at the contact position according to a current phase difference degree and an amplitude difference between the second current data and the third current data;

[0082] The power abnormal amplitude acquisition module 40 is used to obtain the power abnormal amplitude at the contact position according to the current amplitude difference degree and the fluctuation factor of the first current data;

[0083] A correction module 50, configured to correct the power abnormality amplitude according to the temperature data to obtain a final power abnormality amplitude;

[0084] The clustering module 60 is used to perform one-dimensional clustering on the final power anomaly amplitude corresponding to each contact position to obtain a power anomaly index at each contact position, and the power anomaly index is used to inspect and repair the high-voltage line.

[0085] Furthermore, this embodiment also provides an intelligent audit system for power data, including the intelligent audit device for power data.

[0086] In summary, the present embodiment provides a method, device and system for intelligent inspection of power data, wherein the phase difference between the first current data and the voltage data at the contact position between the high-voltage line and the transmission tower is used to obtain the degree of current phase difference at the contact position; then, the amplitude difference between the second current data and the third current data at a predetermined distance before and after the contact position and the fluctuation factor of the first current data are combined to obtain the power abnormality amplitude at the contact position; then, the power abnormality amplitude is corrected in combination with the temperature data at the contact position, so as to obtain the final power abnormality amplitude at the contact position; one-dimensional clustering is performed on the final power abnormality amplitude corresponding to each contact position, so as to obtain the power abnormality index at each contact position, so as to realize abnormality detection at the contact position, thereby improving the accuracy of intelligent inspection of power data.

[0087] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0088] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent audit method for power data, characterized in that: The method comprises: Collecting in real time within a predetermined time the first current data, voltage data and temperature data at the contact position between the high-voltage line and the transmission tower, as well as the second current data and the third current data at a predetermined distance before and after the contact position; Obtaining a current phase difference degree at the contact position according to a phase difference between the first current data and the voltage data; Obtaining a current amplitude difference degree at the contact position according to a current phase difference degree and an amplitude difference between the second current data and the third current data; Obtaining the power abnormality amplitude at the contact position according to the current amplitude difference degree and the fluctuation factor of the first current data; Correcting the power abnormality amplitude according to the temperature data to obtain a final power abnormality amplitude; Performing one-dimensional clustering on the final power abnormality amplitude corresponding to each contact position to obtain a power abnormality index at each contact position; Inspect and repair the high-voltage line according to the power abnormality index; The acquisition of the fluctuation factor includes: A plurality of local extreme value data are selected from the first current data, and the absolute value of the difference between the amplitude of each of the local extreme value data and the amplitude mean of all data in the first current data is superimposed to obtain a fluctuation factor of the first current data.

2. The intelligent audit method for power data according to claim 1, characterized in that: The absolute value of the phase difference between the first current data and the voltage data at the same time is superimposed to obtain the current phase difference degree.

3. The intelligent audit method for power data according to claim 1, characterized in that: The absolute value of the difference between the amplitude mean of all data in the second current data and the amplitude mean of all data in the third current data is obtained, and the current phase difference degree is multiplied by the absolute value of the difference to obtain the current amplitude difference degree.

4. The intelligent audit method for power data according to claim 1, characterized in that: The current amplitude difference degree is multiplied by the fluctuation factor of the first current data to obtain the power abnormality amplitude.

5. The intelligent audit method for power data according to claim 1, characterized in that: The ratio of the temperature mean of all the temperature data to the maximum temperature value of all the temperature data is used as a correction factor to correct the power abnormality amplitude to obtain the final power abnormality amplitude.

6. The intelligent audit method for power data according to claim 1, characterized in that: The absolute value of the difference between the final power abnormality amplitude and each final power abnormality amplitude of the suspected normal cluster in the clustering result is superimposed and averaged to obtain the power abnormality index.

7. The intelligent audit method for power data according to claim 1, characterized in that: When the power abnormality index is greater than a predetermined value, it is determined that an abnormality exists at the contact position of the high-voltage line, and the contact position of the high-voltage line is inspected and repaired.

8. An intelligent power data inspection device, characterized in that: The device is used to implement the method according to any one of claims 1 to 7, and the device comprises: A sampling module, used to collect, in real time within a predetermined time, first current data, voltage data and temperature data at a contact position between the high-voltage line and the transmission tower, as well as second current data and third current data at a predetermined distance before and after the contact position; A phase difference degree acquisition module, used for obtaining the current phase difference degree at the contact position according to the phase difference between the first current data and the voltage data; an amplitude difference degree acquisition module, configured to obtain the current amplitude difference degree at the contact position according to the current phase difference degree and the amplitude difference between the second current data and the third current data; A power abnormal amplitude acquisition module, used for obtaining the power abnormal amplitude at the contact position according to the current amplitude difference degree and the fluctuation factor of the first current data; A correction module, used for correcting the power abnormality amplitude according to the temperature data to obtain a final power abnormality amplitude; The clustering module is used to perform one-dimensional clustering on the final power anomaly amplitude corresponding to each contact position to obtain a power anomaly index at each contact position, and the power anomaly index is used to inspect and repair the high-voltage line.

9. An intelligent power data audit system, characterized in that: It comprises the intelligent auditing device for power data as described in claim 8.

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