Power transmission line inspection method, system and device and nonvolatile storage medium

Abstract

The invention discloses a power transmission line inspection method, system and device and a nonvolatile storage medium. The method comprises the following steps: acquiring line parameters and historical operation data of a target power transmission line; constructing a three-dimensional model of the target power transmission line based on the line parameters; based on the historical operation data, determining an inspection node in the target power transmission line in the three-dimensional model; the target power transmission line is scanned based on the inspection nodes through an NV color center sensor, fluorescence signals corresponding to the inspection nodes are obtained, and the NV color center sensor comprises a plurality of NV color center sensor probes; determining the magnetic field intensity corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node; and determining an inspection result based on the magnetic field intensity corresponding to the inspection node. According to the invention, the technical problems that the partial discharge detection is easily interfered by environmental noise in the current power transmission line inspection, and the inspection result is not accurate enough due to the fact that the overheat detection can only be found when the temperature of the defect is obviously increased are solved.

Description

Technical Field

[0001] This invention relates to the field of transmission line inspection in power systems, and more specifically, to a method, system, device, and non-volatile storage medium for transmission line inspection. Background Technology

[0002] In the operation and maintenance of power systems, transmission lines, as critical channels for power transmission, are crucial for ensuring the normal operation of the power grid. Currently, the condition monitoring of transmission lines, especially the detection of partial discharge and overheating, mainly relies on traditional electromagnetic detection and infrared thermal imagers. Partial discharge detection is typically based on changes in the electromagnetic field, determining the presence of partial discharge phenomena by detecting electromagnetic radiation; while overheating detection primarily relies on measuring the rise in surface temperature of the equipment. However, these traditional methods have significant limitations in practical applications:

[0003] 1. Partial discharge detection is susceptible to environmental noise interference: The electromagnetic environment around transmission lines is complex, including the power equipment itself, changes in weather conditions, and the influence of nearby electrical equipment. These factors may lead to false alarms or missed alarms in electromagnetic detection, reducing the accuracy and reliability of the detection.

[0004] 2. Delay in overheat detection: When infrared thermal imagers detect overheating, the temperature of the equipment has often already risen significantly. This delayed detection method cannot provide early warning in the early stages of overheating, thus missing the best time to deal with it. This may lead to the expansion of equipment failure, increase maintenance costs, and the risk of power grid outage.

[0005] 3. Lack of high-precision positioning capability: Whether it is partial discharge or overheating detection, existing technologies often have difficulty accurately locating the specific location of the fault, especially in complex power transmission networks composed of long distances and multiple lines, where the difficulty of fault location is further increased.

[0006] 4. Maintenance cost and efficiency issues: Frequent manual inspections and equipment checks not only consume a lot of manpower and resources, but may also fail to detect potential safety hazards in a timely manner due to limited inspection scope and insufficient frequency.

[0007] There is currently no effective solution to the above problems. Summary of the Invention

[0008] This invention provides a method, system, device, and non-volatile storage medium for power transmission line inspection, which at least solves the technical problems of partial discharge detection being easily affected by environmental noise and overheating detection only being able to detect defects after a significant temperature rise, resulting in inaccurate inspection results.

[0009] According to one aspect of the present invention, a method for inspecting a power transmission line is provided, comprising: acquiring line parameters and historical operating data of a target power transmission line; constructing a three-dimensional model of the target power transmission line based on the line parameters; determining inspection nodes in the target power transmission line in the three-dimensional model based on the historical operating data; scanning the target power transmission line based on the inspection nodes using an NV color center sensor to obtain fluorescence signals corresponding to the inspection nodes, wherein the NV color center sensor includes multiple NV color center sensor probes; determining the magnetic field strength corresponding to the inspection nodes based on the fluorescence signals corresponding to the inspection nodes; and determining the inspection result of the target power transmission line based on the magnetic field strength corresponding to the inspection nodes.

[0010] Optionally, based on historical operating data, the inspection nodes in the target transmission line are determined according to the three-dimensional model, including: importing historical operating data into the three-dimensional model, performing weight analysis on multiple nodes in the three-dimensional model, and determining the anomaly weights corresponding to each of the multiple nodes; and determining the inspection nodes based on the anomaly weights corresponding to each of the multiple nodes.

[0011] Optionally, the magnetic field strength corresponding to the inspection node is determined based on the fluorescence signal corresponding to the inspection node, including: determining the dataset of fluorescence signal variation of the inspection node with microwave frequency; determining the spin resonance frequency point of the NV color center in the NV color center sensor based on the variation dataset; and calculating the magnetic field strength corresponding to the inspection node based on the spin resonance frequency point according to the Zeeman effect.

[0012] Optionally, the inspection result of the target transmission line is determined based on the magnetic field strength corresponding to the inspection node. When there are multiple inspection nodes, this includes: determining the effective pulse count for each of the multiple inspection nodes based on their respective magnetic field strengths; identifying inspection nodes with an effective pulse count exceeding a preset pulse count threshold as abnormal inspection nodes, where abnormal inspection nodes represent those exhibiting partial discharge phenomena; determining the magnetic field distribution map corresponding to the target transmission line based on the magnetic field strengths corresponding to the multiple inspection nodes; comparing the magnetic field distribution map corresponding to the target transmission line with a preset reference magnetic field distribution map to determine abnormal magnetic field regions in the target transmission line; and determining the inspection result based on the abnormal inspection nodes and abnormal magnetic field regions.

[0013] Optionally, the inspection results are determined based on the abnormal inspection nodes and abnormal magnetic field regions, including: determining the magnetic field vector direction corresponding to each of the multiple NV color center sensor probes by detecting the same pulse signal based on multiple NV color center sensor probes; determining the discharge source based on the magnetic field vector direction corresponding to each of the multiple NV color center sensor probes; determining the target discharge region based on the discharge source and the abnormal inspection nodes; and determining the inspection results based on the target discharge region and the abnormal magnetic field region.

[0014] According to another aspect of the present invention, a power transmission line inspection system is also provided, comprising: a mobile inspection module for moving along a target power transmission line; a positioning module connected to the mobile inspection module for recording line parameters of the target power transmission line and constructing a three-dimensional model of the target power transmission line; an NV color center sensor module connected to the mobile inspection module for scanning the target power transmission line based on inspection nodes to obtain fluorescence signals corresponding to the inspection nodes, wherein the NV color center sensor includes multiple NV color center sensor probes; and an analysis module connected to the mobile inspection module and the NV color center sensor module for determining inspection nodes in the target power transmission line in the three-dimensional model based on historical operating data, determining the magnetic field strength corresponding to the inspection nodes based on the fluorescence signals corresponding to the inspection nodes, and determining the inspection result of the target power transmission line based on the magnetic field strength corresponding to the inspection nodes.

[0015] Optionally, the system also includes an early warning module connected to the analysis module, used to make anomaly judgments and issue early warnings based on the inspection results.

[0016] According to another aspect of the present invention, a transmission line inspection device is also provided, comprising: an acquisition module for acquiring line parameters and historical operating data of a target transmission line; a construction module for constructing a three-dimensional model of the target transmission line based on the line parameters; a first determination module for determining inspection nodes in the target transmission line in the three-dimensional model based on the historical operating data; a scanning module for scanning the target transmission line based on the inspection nodes using an NV color center sensor to obtain fluorescence signals corresponding to the inspection nodes, wherein the NV color center sensor includes multiple NV color center sensor probes; a second determination module for determining the magnetic field strength corresponding to the inspection nodes based on the fluorescence signals corresponding to the inspection nodes; and a third determination module for determining the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection nodes.

[0017] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute any of the above-described transmission line inspection methods.

[0018] According to another aspect of the present invention, a computer device is also provided, the computer device including a memory and a processor, the memory storing a computer program; the processor is used to execute the computer program stored in the memory, the computer program causing the processor to execute any of the above-described power transmission line inspection methods when it runs.

[0019] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described transmission line inspection methods.

[0020] In this embodiment of the invention, a transmission line inspection method is adopted. This method involves acquiring the line parameters and historical operating data of the target transmission line; constructing a three-dimensional model of the target transmission line based on the line parameters; determining inspection nodes within the target transmission line in the three-dimensional model based on the historical operating data; scanning the target transmission line based on the inspection nodes using an NV color center sensor (NV color center sensor, which includes multiple NV color center sensor probes); determining the magnetic field strength corresponding to the inspection node based on the fluorescence signal; and determining the inspection result of the target transmission line based on the magnetic field strength. This achieves the goal of using NV color center sensors to detect anomalies in advance, thereby improving the accuracy of inspection results. It also solves the current technical problems in transmission line inspection, such as the susceptibility of partial discharge detection to environmental noise interference and the limitation of overheat detection to only detecting defects after a significant temperature rise, leading to inaccurate inspection results. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 A hardware structure block diagram of a computer terminal for implementing a transmission line inspection method is shown.

[0023] Figure 2 This is a flowchart illustrating the transmission line inspection method provided according to an embodiment of the present invention;

[0024] Figure 3 This is a flowchart illustrating a transmission line inspection method provided by an optional embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of a power transmission line inspection system provided according to an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of a power transmission line inspection system provided in an optional embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of a diamond NV color center sensing unit provided according to an optional embodiment of the present invention;

[0028] Figure 7 This is a structural block diagram of a power transmission line inspection device provided according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] According to an embodiment of the present invention, a method embodiment of a transmission line inspection method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a transmission line inspection method is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1The different configurations shown.

[0033] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0034] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the transmission line inspection method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned transmission line inspection method of the application program. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0035] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0036] Figure 2 This is a flowchart illustrating the transmission line inspection method provided by an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0037] Step S202: Obtain the line parameters and historical operation data of the target transmission line.

[0038] This step involves gathering detailed information about the target transmission line. This includes key physical parameters such as length, diameter, material properties, insulation type, and wiring layout, which form the basis for building an accurate model. Furthermore, historical operational data is indispensable, covering the line's operating status over a past period, including but not limited to current, voltage, and temperature records, as well as any known faults or maintenance activities. This data helps identify potentially high-risk areas, guides inspection work, and ensures effective resource allocation.

[0039] In practice, line parameters and historical operating data can be obtained from the power company's database or on-site inspection records. The quality and completeness of the data are crucial for subsequent steps; therefore, special attention must be paid to ensuring the accuracy and timeliness of the data during collection. Historical operating data is particularly important because it can reveal line operating patterns and any abnormal trends, providing early warning signals for potential future problems.

[0040] Step S204: Based on the line parameters, construct a three-dimensional model of the target transmission line.

[0041] In this step, after obtaining sufficient line parameters, the next step is to construct a 3D model of the target transmission line. The goal of this step is to create a virtual environment that reflects the actual line layout and physical characteristics, enabling more accurate analysis and testing later. The construction of the 3D model typically utilizes specialized software, such as CAD (Computer-Aided Design) tools. These tools can transform the collected line parameters into a visual 3D model, facilitating understanding and operation.

[0042] The 3D model should be as detailed as possible, including not only the main structure of the transmission line but also surrounding environmental factors such as terrain features, vegetation distribution, and nearby buildings or power facilities, as these factors may affect the electromagnetic field distribution of the line. The accuracy of the model directly affects the reliability of the inspection results; therefore, during the modeling process, it is essential to ensure that the parameters are input correctly and that the model's details are handled appropriately.

[0043] Step S206: Based on historical operation data, determine the inspection nodes in the target transmission line in the three-dimensional model.

[0044] In this step, inspection nodes refer to points or areas in the 3D model that require key monitoring. These can be marked based on anomaly records in historical operational data. By analyzing historical operational data, areas that have experienced past failures, frequent maintenance activities, or large data fluctuations can be identified. These areas are likely to experience problems again, and therefore are set as inspection nodes in the model.

[0045] Specifically, a three-dimensional digital model of the target transmission line is constructed, and key inspection nodes are preset in the system based on equipment failure probabilities and historical data. These nodes include, but are not limited to, insulator strings, equipotential rings, vibration dampers, and electrical connection points between conductors and fittings. Setting inspection nodes in the 3D model allows the use of data mining techniques, such as time series analysis, clustering algorithms, or machine learning models, to identify patterns or anomalies in the data, thereby determining which locations are the focus of inspection. Furthermore, line design specifications and expert experience can be combined to assist in node selection, ensuring coverage of all possible risk points.

[0046] Step S208: The target transmission line is scanned based on the inspection node using the NV color center sensor to obtain the fluorescence signal corresponding to the inspection node. The NV color center sensor includes multiple NV color center sensor probes.

[0047] In this step, the NV center sensor is a quantum sensor known for its extremely high magnetic field sensitivity and anti-interference capability. In step S208, the sensor is deployed on the inspection node to scan and collect magnetic field information near the node. The sensor works based on the quantum physical phenomenon that NV centers (Nitrogen-Vacancy Centers) in diamond emit fluorescent signals under the influence of a magnetic field. When an NV center is affected by an external magnetic field, its fluorescence intensity changes. By accurately measuring these changes, the strength and direction of the magnetic field can be calculated.

[0048] The mobile inspection robot equipped with a diamond NV color center sensor is controlled to move to the preset inspection starting point. Relying on a high-precision navigation system, it can achieve autonomous cruising and precise positioning navigation along the ground line or a designated path, ensuring that the sensor probe can reach and cover all preset high-risk areas.

[0049] In practice, multiple NV color center sensor probes can be used simultaneously to improve detection coverage and data reliability. Multiple NV color center sensor probes can form a small array. When a discharge occurs, by analyzing the time difference or the difference in magnetic field vector direction of the same magnetic pulse signal arriving at different probes, time-difference positioning or vector intersection positioning methods can be used to achieve precise three-dimensional positioning of the discharge source in a single measurement, resulting in higher accuracy. The sensor probes should be carefully arranged to ensure coverage of all important inspection nodes, while also considering signal reception and processing capabilities. During data acquisition, environmental variables such as temperature and humidity also need to be monitored to eliminate potential interference factors.

[0050] By utilizing the nanotesla-level magnetic field sensitivity of NV color centers, early, weak partial discharges and current distribution anomalies that are undetectable by traditional methods can be detected. Furthermore, NV color center sensors are insensitive to electric fields, and interference from low-frequency environmental magnetic noise can be effectively suppressed through specific quantum manipulation techniques.

[0051] Step S210: Determine the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node.

[0052] In this step, the collected fluorescence signals need to be analyzed and processed to convert them into meaningful magnetic field strength data. This typically involves signal filtering, amplification, and digitization to remove noise and quantify signal strength. Next, a specialized algorithm is used to calculate the magnetic field strength at each inspection node based on the relationship between changes in the fluorescence signal and the magnetic field strength. This calculation process may rely on complex physical models, such as the Zeeman effect model, which requires a deep understanding of the sensor's characteristics.

[0053] To improve measurement accuracy, a strategy of multiple sampling and data averaging can be employed to reduce the impact of random errors. Furthermore, real-time calibration of the sensor's sensitivity and response curve is also crucial, ensuring data accuracy and consistency.

[0054] Step S212: Determine the inspection results of the target transmission line based on the magnetic field strength corresponding to the inspection node.

[0055] Finally, based on the collected and processed magnetic field strength data, the inspection results of the target transmission line can be comprehensively analyzed in this step. This step may involve data visualization, such as generating a magnetic field strength distribution map, and statistical analysis of the data, such as calculating outliers or trends.

[0056] The determination of inspection results may be achieved by comparing them with historical data or with predetermined normal ranges. Abnormal magnetic field strength may indicate the presence of problems such as partial discharge or overheating. In addition, pattern recognition algorithms, such as support vector machines (SVM) and neural networks, can be used to automatically identify and classify detected anomalies, improving the level of intelligence in detection.

[0057] Through the detailed implementation of the above steps, the transmission line inspection method provided by this invention can effectively monitor the health status of transmission lines and provide early warnings of potential faults by utilizing advanced sensing technology and data analysis methods, thereby significantly improving the safety and reliability of the power system. This method not only reduces the impact of environmental noise on partial discharge detection but also overcomes the lag in overheat detection, achieving accurate assessment of the transmission line condition.

[0058] As an optional embodiment, the inspection nodes in the target transmission line are determined based on historical operating data and a three-dimensional model, including: importing historical operating data into the three-dimensional model, performing weight analysis on multiple nodes in the three-dimensional model, and determining the abnormal weights corresponding to each of the multiple nodes; and determining the inspection nodes based on the abnormal weights corresponding to each of the multiple nodes.

[0059] Optionally, in order to accurately determine the inspection nodes in the three-dimensional model of the transmission line, it is necessary to perform weight analysis on each node in the model based on historical operating data to identify those nodes with potential anomalies or fault risks.

[0060] Historical operational data can be imported into the 3D model. This data may include, but is not limited to, the load status of transmission lines, past fault records, maintenance history, and environmental factors such as changes in humidity and temperature. Through in-depth mining and analysis of this data, weights can be assigned to multiple nodes in the 3D model. These weights reflect the likelihood of each node experiencing abnormal conditions in its past operation.

[0061] Specifically, the anomaly weights of nodes can be calculated using the following steps: Perform statistical analysis on the historical operational data of each node, including but not limited to fault frequency, maintenance intervals, and abnormal temperature records. Utilize machine learning methods, such as Support Vector Machines (SVM), decision trees, and neural networks, to establish an anomaly weight model and predict the risk of future anomalies based on historical data. Apply the anomaly weight model to calculate the anomaly weight of each node in the 3D model; a higher weight value indicates a greater likelihood of the node exhibiting an anomaly.

[0062] Next, based on the calculated anomaly weights of multiple nodes, inspection nodes are determined. A threshold can be set, and nodes with weight values ​​exceeding this threshold are designated as inspection nodes. These nodes require focused monitoring and inspection to prevent potential failures. Alternatively, a sorting algorithm can be used to select the top N nodes with the highest anomaly weights as inspection nodes, where the specific value of N can be determined based on the availability and priority of inspection resources.

[0063] By following these steps, not only can we identify which nodes are the key points for inspection, but we can also quantify the risk level of each node, providing a scientific basis for subsequent inspection plans and resource allocation. This method makes full use of historical operational data, effectively predicting potential fault points and improving the efficiency and targeting of transmission line inspections. In other embodiments, real-time monitoring data and predictive models can be combined to dynamically update node weights, further enhancing the intelligence level of the inspection.

[0064] By employing the above-described settings and performing anomaly weight analysis on nodes in the 3D model based on historical operational data, it is possible to effectively focus on nodes with potential risks, reduce the randomness of inspections, and improve the efficiency and accuracy of inspection work. In other embodiments, more complex algorithms, such as deep learning technology, can be introduced to further enhance the accuracy of weight analysis, ensuring that the selection of inspection nodes is more scientific and reasonable, which helps prevent transmission line faults and ensures the safe and stable operation of the power system.

[0065] As an optional embodiment, the magnetic field strength corresponding to the inspection node is determined based on the fluorescence signal corresponding to the inspection node, including: determining the data set of fluorescence signal corresponding to the inspection node as a function of microwave frequency; determining the spin resonance frequency point of the NV color center in the NV color center sensor based on the changing data set; and calculating the magnetic field strength corresponding to the inspection node based on the spin resonance frequency point according to the Zeeman effect.

[0066] Optionally, a dataset of fluorescence signal variations with microwave frequency corresponding to the inspection node can be determined first. The working principle of the NV center sensor relies on the interaction between the spin state of its internal NV center and the microwave frequency. When the sensor is close to the transmission line, the NV center is affected by the external magnetic field, thereby changing its spin resonant frequency. By scanning different microwave frequencies, the changes in fluorescence signal can be recorded, forming a dataset reflecting the changes in fluorescence intensity at a specific frequency.

[0067] Next, based on this changing dataset, the spin resonance frequency points of the NV color centers in the NV color center sensor are determined. The spin resonance frequency refers to the specific frequency at which the spin of the NV color center resonates with the microwave frequency under the influence of an external magnetic field. Through data analysis, peak values ​​or inflection points can be found; these characteristic points correspond to the spin resonance frequency points.

[0068] Finally, based on the Zeeman effect, the magnetic field strength corresponding to the inspection node is calculated based on the spin resonance frequency. The Zeeman effect describes the effect of a magnetic field on the splitting of atomic or molecular energy levels; for NV color centers, their spin energy levels undergo similar changes under the influence of a magnetic field. There is a definite relationship between the spin resonance frequency and the magnetic field strength; therefore, the current magnetic field strength can be inferred by measuring the spin resonance frequency.

[0069] Specifically, resonant frequency Magnetic field components along the NV color center axis A linear relationship exists: ,in, It is the spin resonance frequency. For zero-field splitting parameters, It is the electron gyromagnetic ratio.

[0070] It can be measured precisely drift amount Then, based on the ratio of the drift amount to the electron gyromagnetic ratio, the absolute magnetic field strength is calculated.

[0071] Specifically, temperature measurement also involves simultaneously calculating the temperature value by monitoring changes in the zero-field splitting value. Zero-field splitting parameter It is temperature Linear functions:

[0072] ;

[0073] By monitoring Change It can be based on the calibration coefficient. Calculate the absolute temperature value of the measured point. .

[0074] Through the above steps, not only can the magnetic field strength of transmission line inspection nodes be accurately measured, but environmental noise interference can also be largely avoided, improving the accuracy and reliability of partial discharge detection. In other embodiments, the data acquisition and processing algorithms can be further optimized, such as using filters to reduce background noise that may exist during microwave frequency scanning, or employing more complex mathematical models to improve the accuracy of magnetic field strength calculation. In this way, even in complex electromagnetic environments, the stability and accuracy of the detection results can still be ensured, providing more reliable data support for transmission line maintenance and fault early warning.

[0075] As an optional embodiment, the inspection result of the target transmission line is determined based on the magnetic field strength corresponding to the inspection node. When there are multiple inspection nodes, the process includes: determining the effective pulse count for each of the multiple inspection nodes based on their respective magnetic field strengths; identifying inspection nodes with an effective pulse count exceeding a preset pulse count threshold as abnormal inspection nodes, where abnormal inspection nodes represent those exhibiting partial discharge phenomena; determining the magnetic field distribution map corresponding to the target transmission line based on the magnetic field strengths corresponding to the multiple inspection nodes; comparing the magnetic field distribution map of the target transmission line with a preset reference magnetic field distribution map to determine abnormal magnetic field regions in the target transmission line; and determining the inspection result based on the abnormal inspection nodes and abnormal magnetic field regions.

[0076] Optionally, the inspection results can be reflected in two aspects: one is partial discharge detection, which can be based on the number of effective pulses; the other is detecting whether there are potential overheating hazards in the transmission lines.

[0077] Specifically, it includes the following steps.

[0078] The number of valid pulses is determined based on the magnetic field strength corresponding to each of the multiple inspection nodes. When detecting the magnetic field strength at each inspection node, the NV color center sensor receives a series of pulse signals. Based on the magnetic field strength data of each inspection node, the number of valid pulses within a certain time interval can be determined. Here, "valid pulses" refer to those pulse signals that exceed a preset magnetic field strength threshold; these are more likely to indicate the presence of partial discharge phenomena.

[0079] A pulse count threshold can be set to distinguish between normal and abnormal inspection nodes. When the effective pulse count of an inspection node exceeds this threshold, the node is identified as an abnormal inspection node, strongly suggesting the possible presence of partial discharge in its vicinity. This pulse counting-based anomaly detection method improves detection sensitivity and promptly captures abnormal signals, even in environments with high noise levels.

[0080] Based on the magnetic field strength corresponding to each of the multiple inspection nodes, a magnetic field distribution map of the target transmission line can be drawn. This distribution map not only reflects the magnetic field strength around the transmission line but also reveals the changing trends and potential irregularities in the magnetic field. The magnetic field distribution map allows for a direct observation of areas with abnormal magnetic field strength, providing clues for fault location. Comparing the magnetic field distribution map of the target transmission line with a pre-set reference magnetic field distribution map aims to identify abnormal magnetic field areas. The reference magnetic field distribution map is established based on historical data or theoretical models under normal operating conditions and represents the magnetic field distribution of the transmission line under ideal conditions. By comparison, areas where the magnetic field strength deviates significantly from the reference value can be identified; these areas are highly likely to be sources of overheating or partial discharge.

[0081] The final inspection results are based on a comprehensive analysis of abnormal inspection nodes and abnormal magnetic field regions. If an abnormal pulse count is detected at an inspection node, and an abnormal magnetic field strength region is found around that node, then it can be considered that there is a risk of partial discharge or overheating in that area. The inspection results will include these risk assessments, as well as recommendations for further investigation and maintenance that may be necessary.

[0082] Specifically, during the scanning process, time-frequency analysis is performed on the real-time calculated magnetic field strength time series signal, and the comprehensive analysis software continuously monitors the calculated magnetic field signal. Partial discharge activity manifests as nanosecond-level transient magnetic pulses superimposed on the static background magnetic field. If the number of effective pulses captured by any channel per unit time exceeds a preset threshold, it is determined that partial discharge exists in that area. By setting an amplitude threshold and using a digital filtering algorithm, these pulse events are effectively extracted from the environmental noise.

[0083] The severity of the discharge is quantitatively assessed based on pulse data. By statistically analyzing characteristic parameters such as pulse count rate per unit time, probability distribution of pulse amplitude, and pulse cumulative energy, a quantitative grading model for the severity of discharge is established to achieve an objective assessment of the state of insulation defects.

[0084] Mild: Sparse, low-amplitude magnetic pulses are present.

[0085] Moderate: There are relatively dense, medium-amplitude magnetic pulse sequences.

[0086] Severe: There is a continuous, high-amplitude magnetic pulse group.

[0087] When scanning the connection between the conductor and the fitting, a "current magnetic field distribution map" for that area can be generated simultaneously. This map visually displays the spatial distribution of the magnetic field generated by the power frequency current. The "current magnetic field distribution map" obtained from the scan is then matched with a "health baseline magnetic field distribution map" pre-stored in the database using high-precision image registration and differential calculation.

[0088] If the differential results show areas of abnormally increased or decreased local magnetic field strength, it indicates a distortion in the current density distribution at that location, a direct magnetic characterization of increased contact resistance. In this case, once the detected magnetic field distortion exceeds the safety threshold, the system can trigger an early warning of "overheating hazard." Simultaneously, combining the temperature data calculated in step three for joint verification can further improve the confidence level of the diagnosis and the warning level.

[0089] It automatically integrates all analysis results to generate a structured comprehensive diagnostic report. The report includes: the identity information and spatial coordinates of the inspected equipment, the detection results of partial discharge, the severity level and location information, the analysis conclusion of magnetic field distribution distortion, temperature measurement values, and a comprehensive health status assessment and operation and maintenance decision recommendations based on the above information (such as "continue to observe", "planned inspection", "emergency maintenance"), providing accurate data support for predictive maintenance.

[0090] Through the above steps, efficient and accurate inspections of transmission lines can be achieved. Even in the early stages of partial discharge or under environmental noise interference, abnormalities can be effectively detected and located, thereby improving system safety and reducing the probability of faults and maintenance costs. Furthermore, this inspection method relies on advanced data analysis algorithms and precise magnetic field strength measurement technology, providing strong support for intelligent operation and maintenance in the power industry.

[0091] The following is a specific example. Figure 3 This is a flowchart illustrating a transmission line inspection method provided by an optional embodiment of the present invention, comprising three stages:

[0092] The first phase (system initialization and path planning) demonstrates the preparations before the inspection begins. This phase starts by building a 3D digital model of the line, identifying key inspection components such as insulators and fittings based on the model, and finally guiding the inspection robot equipped with an NV color center sensor to autonomously navigate to the inspection starting position.

[0093] The second phase (on-site quantum sensing data acquisition): The process begins with a robot scanning the target device at close range, simultaneously triggering the NV sensing core unit. This unit details its quantum sensing workflow: the optical excitation module emits an excitation laser, and the microwave excitation module scans the resonant microwave field, working in tandem to stimulate the diamond NV color center; the excited NV color center then generates fluorescence, which is collected by the photoelectric detection module. Throughout the process, a high-precision positioning system assigns precise spatial coordinates and a timestamp to each set of data.

[0094] The third stage (quantum data processing and state diagnosis) demonstrates the process of transforming quantum data into operational decisions. First, using a quantum spectrum fitting algorithm, the magnetic field strength and temperature values ​​at the measured point are calculated from the raw fluorescence and microwave data. Then, the process is divided into two parallel and complementary diagnostic paths:

[0095] Partial discharge analysis path: By identifying characteristic pulse sequences in the magnetic field signal, assessing their intensity and frequency to determine the severity level, and using multi-sensor information fusion technology to spatially locate the discharge source.

[0096] Overheating hazard analysis path: By reconstructing the magnetic field distribution map and comparing it with the baseline map of equipment health status, the system diagnoses magnetic field distortion caused by abnormal current distribution, achieving early warning before significant temperature rises. Finally, the system integrates all analysis results to generate a comprehensive diagnostic report and outputs specific operation and maintenance decision recommendations.

[0097] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that the transmission line inspection method according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0099] According to embodiments of the present invention, a power transmission line inspection system is also provided. Figure 4 This is a system schematic diagram of a power transmission line inspection system provided according to an embodiment of the present invention, such as... Figure 4 As shown, it includes:

[0100] The mobile inspection module is used to move along the target power transmission line.

[0101] The positioning module, connected to the mobile inspection module, is used to record the line parameters of the target transmission line and construct a three-dimensional model of the target transmission line.

[0102] The NV color center sensor module, connected to the mobile inspection module, is used to scan the target transmission line based on the inspection node and obtain the fluorescence signal corresponding to the inspection node. The NV color center sensor includes multiple NV color center sensor probes.

[0103] The analysis module, connected to the mobile inspection module and the NV color center sensor module, is used to determine the inspection nodes in the target transmission line in the 3D model based on historical operating data, determine the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node, and determine the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection node.

[0104] As an optional embodiment, the system also includes an early warning module connected to the analysis module, used to make anomaly judgments and issue early warnings based on the inspection results.

[0105] The following is a specific example. Figure 5 This is a system schematic diagram of a power transmission line inspection system provided according to an optional embodiment of the present invention, such as... Figure 5 As shown, the power transmission line inspection system includes a mobile inspection platform and a data acquisition and integrated analysis control terminal to inspect the equipment of the power transmission line under test.

[0106] Mobile Inspection Platform: Utilizing an inspection robot as its mobile carrier, this platform possesses the capability for autonomous / remote movement along the conductor. Before inspection, upper and lower tracks are pre-set on the tower body. The robot is placed on a platform at the lower end of the tracks. The control center fully defines the robot's behavior. After the robot goes online, it autonomously performs inspections according to the pre-set program, sequentially checking the equipment and automatically issuing alarms upon detecting line abnormalities. The robot is equipped with a high-precision positioning module and a diamond NV color center sensor unit to record the precise spatial coordinates of the scanned points.

[0107] The diamond NV color center sensing unit comprises the following components: Figure 6 This is a schematic diagram of a diamond NV color center sensing unit provided according to an optional embodiment of the present invention, as shown below. Figure 6 As shown:

[0108] Diamond NV color center sensor probe: The core sensing element used to measure partial discharge and overheating risks.

[0109] Optical excitation and collection module: integrates a laser diode, objective lens group and filter for exciting NV color centers and collecting fluorescence.

[0110] Microwave excitation module: integrates a programmable microwave source and a microwave antenna to generate a frequency-tunable microwave field to manipulate the spin state of the NV color center.

[0111] Photoelectric detection and signal processing module: A high-sensitivity photodetector is used to convert fluorescence signals into electrical signals, and then pre-amplifies and filters them.

[0112] The data acquisition and comprehensive analysis control terminal integrates the following components:

[0113] Main control computer: runs control software and data analysis algorithms.

[0114] Data acquisition card: controls the emission of lasers and microwaves, and simultaneously acquires signals from photodetectors.

[0115] The comprehensive analysis software scans microwave frequencies and monitors fluorescence intensity to plot photodetector magnetic resonance spectra, thereby calculating the strength and temperature of the measured magnetic field. It fuses robot positioning information with magnetic field / temperature data to generate a "current magnetic field distribution map" and a "temperature distribution map" for the device under test. Multiple algorithms can be applied, including partial discharge identification, positioning, and magnetic field / temperature imaging algorithms, to identify pulse characteristics in the magnetic field signal. Positioning is achieved by analyzing the temporal or spatial characteristics of the pulse signal, and images of the magnetic field and temperature are generated. A warning decision module can be invoked to comprehensively assess the device status and issue warnings based on the degree of magnetic field distortion, partial discharge intensity, and temperature thresholds.

[0116] According to embodiments of the present invention, a transmission line inspection device for implementing the above-described transmission line inspection method is also provided. Figure 7 This is a structural block diagram of a power transmission line inspection device provided according to an embodiment of the present invention, such as... Figure 7 As shown, the transmission line inspection device includes: an acquisition module 702, a construction module 704, a first determination module 706, a scanning module 708, a second determination module 710, and a third determination module 712. The transmission line inspection device will be described below.

[0117] The acquisition module 702 is used to acquire the line parameters and historical operating data of the target transmission line.

[0118] The construction module 704, connected to the acquisition module 702, is used to construct a three-dimensional model of the target transmission line based on the line parameters.

[0119] The first determining module 706, connected to the construction module 704, is used to determine the inspection nodes in the target transmission line in the three-dimensional model based on historical operating data.

[0120] The scanning module 708, connected to the first determining module 706, is used to scan the target transmission line based on the inspection node using the NV color center sensor to obtain the fluorescence signal corresponding to the inspection node. The NV color center sensor includes multiple NV color center sensor probes.

[0121] The second determining module 710, connected to the scanning module 708, is used to determine the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node.

[0122] The third determining module 712, connected to the second determining module 710, is used to determine the inspection results of the target transmission line based on the magnetic field strength corresponding to the inspection node.

[0123] It should be noted that the acquisition module 702, construction module 704, first determination module 706, scanning module 708, second determination module 710, and third determination module 712 mentioned above correspond to steps S202 to S212 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0124] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0125] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the transmission line inspection method and device in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned transmission line inspection method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0126] The processor can access information and application programs stored in the memory via a transmission device to perform the following steps: acquire line parameters and historical operating data of the target transmission line; construct a three-dimensional model of the target transmission line based on the line parameters; determine inspection nodes in the target transmission line within the three-dimensional model based on the historical operating data; scan the target transmission line based on the inspection nodes using an NV color center sensor to obtain the fluorescence signal corresponding to the inspection node, wherein the NV color center sensor includes multiple NV color center sensor probes; determine the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node; and determine the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection node.

[0127] Optionally, the processor may also execute program code for the following steps: determining inspection nodes in the target transmission line based on historical operating data and a three-dimensional model, including: importing historical operating data into the three-dimensional model, performing weight analysis on multiple nodes in the three-dimensional model, determining the abnormal weights corresponding to each of the multiple nodes; and determining the inspection nodes based on the abnormal weights corresponding to each of the multiple nodes.

[0128] Optionally, the processor may also execute program code for the following steps: determining the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node, including: determining the dataset of fluorescence signal variation with microwave frequency corresponding to the inspection node; determining the spin resonance frequency point of the NV color center in the NV color center sensor based on the variation dataset; and calculating the magnetic field strength corresponding to the inspection node based on the spin resonance frequency point according to the Zeeman effect.

[0129] Optionally, the processor may also execute program code for the following steps: determining the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection node; when there are multiple inspection nodes, this includes: determining the effective pulse number corresponding to each of the multiple inspection nodes based on the magnetic field strength corresponding to each of the multiple inspection nodes; identifying inspection nodes whose effective pulse number exceeds a preset pulse number threshold as abnormal inspection nodes, wherein abnormal inspection nodes represent inspection nodes exhibiting partial discharge phenomena; determining the magnetic field distribution map corresponding to the target transmission line based on the magnetic field strength corresponding to each of the multiple inspection nodes; comparing the magnetic field distribution map corresponding to the target transmission line with a preset reference magnetic field distribution map to determine abnormal magnetic field regions in the target transmission line; and determining the inspection result based on the abnormal inspection nodes and abnormal magnetic field regions.

[0130] Optionally, the processor may also execute program code for the following steps: determining the inspection result based on the abnormal inspection node and the abnormal magnetic field region, including: detecting the same pulse signal based on multiple NV color center sensor probes and determining the magnetic field vector direction corresponding to each of the multiple NV color center sensor probes; determining the discharge source based on the magnetic field vector direction corresponding to each of the multiple NV color center sensor probes; determining the target discharge region based on the discharge source and the abnormal inspection node; and determining the inspection result based on the target discharge region and the abnormal magnetic field region.

[0131] This invention provides a method for inspecting power transmission lines. The method involves acquiring line parameters and historical operating data of the target power transmission line; constructing a three-dimensional model of the target power transmission line based on the line parameters; identifying inspection nodes within the three-dimensional model based on the historical operating data; scanning the target power transmission line based on the inspection nodes using an NV color center sensor (NVC sensor, which includes multiple NVC sensor probes); determining the magnetic field strength corresponding to the inspection node based on the fluorescence signal; and determining the inspection result of the target power transmission line based on the magnetic field strength. This method achieves the goal of detecting anomalies in advance using an NVC sensor, thereby improving the accuracy of inspection results. It also solves the technical problems of current power transmission line inspections, such as partial discharge detection being susceptible to environmental noise interference and overheating detection only being able to detect defects after a significant temperature rise, leading to inaccurate inspection results.

[0132] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0133] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the transmission line inspection method provided in the above embodiments.

[0134] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0135] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring line parameters and historical operating data of the target transmission line; constructing a three-dimensional model of the target transmission line based on the line parameters; determining inspection nodes in the target transmission line in the three-dimensional model based on the historical operating data; scanning the target transmission line based on the inspection nodes using an NV color center sensor to obtain fluorescence signals corresponding to the inspection nodes, wherein the NV color center sensor includes multiple NV color center sensor probes; determining the magnetic field strength corresponding to the inspection nodes based on the fluorescence signals corresponding to the inspection nodes; and determining the inspection results of the target transmission line based on the magnetic field strength corresponding to the inspection nodes.

[0136] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining inspection nodes in the target transmission line based on historical operating data and a three-dimensional model, including: importing historical operating data into the three-dimensional model, performing weight analysis on multiple nodes in the three-dimensional model, and determining the abnormal weights corresponding to each of the multiple nodes; and determining the inspection nodes based on the abnormal weights corresponding to each of the multiple nodes.

[0137] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node, including: determining the dataset of the fluorescence signal corresponding to the inspection node changing with microwave frequency; determining the spin resonance frequency point of the NV color center in the NV color center sensor based on the changing dataset; and calculating the magnetic field strength corresponding to the inspection node based on the spin resonance frequency point according to the Zeeman effect.

[0138] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection node; when there are multiple inspection nodes, this includes: determining the effective pulse number corresponding to each of the multiple inspection nodes based on the magnetic field strength corresponding to each of the multiple inspection nodes; determining inspection nodes whose effective pulse number exceeds a preset pulse number threshold as abnormal inspection nodes, wherein the abnormal inspection node represents an inspection node with partial discharge phenomenon; determining the magnetic field distribution map corresponding to the target transmission line based on the magnetic field strength corresponding to each of the multiple inspection nodes; comparing the magnetic field distribution map corresponding to the target transmission line with a preset reference magnetic field distribution map to determine the abnormal magnetic field region in the target transmission line; and determining the inspection result based on the abnormal inspection nodes and the abnormal magnetic field region.

[0139] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the inspection result based on the abnormal inspection node and the abnormal magnetic field region, including: detecting the same pulse signal based on multiple NV color center sensor probes and determining the magnetic field vector direction corresponding to each of the multiple NV color center sensor probes; determining the discharge source based on the magnetic field vector direction corresponding to each of the multiple NV color center sensor probes; determining the target discharge region based on the discharge source and the abnormal inspection node; and determining the inspection result based on the target discharge region and the abnormal magnetic field region.

[0140] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire line parameters and historical operating data of a target transmission line; construct a three-dimensional model of the target transmission line based on the line parameters; determine inspection nodes in the target transmission line in the three-dimensional model based on the historical operating data; scan the target transmission line based on the inspection nodes using an NV color center sensor to obtain fluorescence signals corresponding to the inspection nodes, wherein the NV color center sensor includes multiple NV color center sensor probes; determine the magnetic field strength corresponding to the inspection nodes based on the fluorescence signals corresponding to the inspection nodes; and determine the inspection results of the target transmission line based on the magnetic field strength corresponding to the inspection nodes.

[0141] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0142] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0147] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for inspecting power transmission lines, characterized in that, include: Obtain the line parameters and historical operating data of the target transmission line; Based on the line parameters, a three-dimensional model of the target transmission line is constructed; Based on the historical operation data, the inspection nodes in the target transmission line are determined in the three-dimensional model; The target transmission line is scanned based on the inspection node using an NV color center sensor to obtain the fluorescence signal corresponding to the inspection node. The NV color center sensor includes multiple NV color center sensor probes. The magnetic field strength corresponding to the inspection node is determined based on the fluorescence signal corresponding to the inspection node. The inspection results of the target transmission line are determined based on the magnetic field strength corresponding to the inspection node.

2. The method according to claim 1, characterized in that, The step of determining the inspection nodes in the target transmission line based on the historical operating data and the three-dimensional model includes: The historical operation data is imported into the three-dimensional model, and weight analysis is performed on multiple nodes in the three-dimensional model to determine the abnormal weights corresponding to each of the multiple nodes. The inspection node is determined based on the anomaly weights corresponding to each of the multiple nodes.

3. The method according to claim 1, characterized in that, The step of determining the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node includes: Determine the dataset of fluorescence signal variations with microwave frequency corresponding to the inspection node; Based on the aforementioned change dataset, the spin resonance frequency points of the NV color centers in the NV color center sensor are determined. Based on the Zeeman effect, the magnetic field strength corresponding to the inspection node is calculated based on the spin resonance frequency point.

4. The method according to claim 1, characterized in that, The step of determining the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection node, when there are multiple inspection nodes, includes: Based on the magnetic field strength corresponding to each of the multiple inspection nodes, the number of effective pulses corresponding to each of the multiple inspection nodes is determined. Inspection nodes whose effective pulse count exceeds a preset pulse count threshold are identified as abnormal inspection nodes, wherein the abnormal inspection node is characterized by the presence of partial discharge phenomenon. Based on the magnetic field strength corresponding to each of the multiple inspection nodes, the magnetic field distribution map corresponding to the target transmission line is determined; The magnetic field distribution map corresponding to the target transmission line is compared with a preset reference magnetic field distribution map to determine the abnormal magnetic field region in the target transmission line. The inspection results are determined based on the abnormal inspection nodes and the abnormal magnetic field regions.

5. The method according to claim 4, characterized in that, The inspection results are determined based on the abnormal inspection nodes and the abnormal magnetic field regions, including: Based on the multiple NV color center sensor probes, the same pulse signal is detected to determine the direction of the magnetic field vector corresponding to each of the multiple NV color center sensor probes. The discharge source is determined based on the magnetic field vector direction corresponding to each of the multiple NV color center sensor probes. Based on the discharge source and the abnormal inspection node, the target discharge area is determined; The inspection results are determined based on the target discharge region and the abnormal magnetic field region.

6. A power transmission line inspection system, characterized in that, include: The mobile inspection module is used to move along the target transmission line; A positioning module, connected to the mobile inspection module, is used to record the line parameters of the target transmission line and construct a three-dimensional model of the target transmission line. An NV color center sensor module, connected to the mobile inspection module, is used to scan the target transmission line based on the inspection node to obtain the fluorescence signal corresponding to the inspection node. The NV color center sensor includes multiple NV color center sensor probes. The analysis module, connected to the mobile inspection module and the NV color center sensor module, is used to determine the inspection nodes in the target transmission line in the three-dimensional model based on historical operating data, determine the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node, and determine the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection node.

7. The system according to claim 6, characterized in that, Also includes: The early warning module, connected to the analysis module, is used to make anomaly judgments and issue early warnings based on the inspection results.

8. A transmission line inspection device, characterized in that, include: The acquisition module is used to acquire the line parameters and historical operating data of the target transmission line; A construction module is used to construct a three-dimensional model of the target transmission line based on the line parameters; The first determining module is used to determine the inspection nodes in the target transmission line in the three-dimensional model based on the historical operation data. The scanning module is used to scan the target transmission line based on the inspection node using an NV color center sensor to obtain the fluorescence signal corresponding to the inspection node. The NV color center sensor includes multiple NV color center sensor probes. The second determining module is used to determine the magnetic field strength corresponding to the inspection node based on the fluorescence signal corresponding to the inspection node. The third determining module is used to determine the inspection result of the target transmission line based on the magnetic field strength corresponding to the inspection node.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the transmission line inspection method according to any one of claims 1 to 5.

10. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the transmission line inspection method according to any one of claims 1 to 5.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the transmission line inspection method according to any one of claims 1 to 5.

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