Electrified equipment inspection AC electric field imaging method and system

Abstract

The invention relates to the technical field of electric field measurement, in particular to an electrified equipment inspection alternating current electric field imaging method and system. The imaging method comprises the following steps: acquiring a two-dimensional map of an area to be imaged, and discretizing the two-dimensional map into a plurality of discrete measurement points; planning an inspection path, and controlling the inspection robot to reach each discrete measurement point in sequence; when the inspection robot arrives at each discrete measurement point, acquiring electric field intensity data of the current discrete measurement point by using an electric field sensor; and performing signal processing and filtering on the acquired electric field intensity data. According to the invention, the two-dimensional map of the to-be-imaged area is acquired and discretized into a plurality of measurement points, and the electric field intensity of the whole area is comprehensively measured in combination with the path planning of the inspection robot.

Description

Technical Field

[0001] The present invention relates to the technical field of electric field measurement, and in particular to an alternating current electric field imaging method and system for patrol inspection of energized equipment. Background Art

[0002] In the power industry, live working is a high-risk job. Due to the presence of dangerous factors such as high voltage and strong electric field on site, it poses a serious threat to the safety of workers. Traditional electric field measurement methods can often only provide single-point or local electric field strength data, which is difficult to fully reflect the electric field distribution in the entire working area. At the same time, when power equipment is energized, it will generate an electric field around it. The electric field information is closely related to the operating status of the power equipment. When the power equipment fails, such as corona discharge or insulation damage of the insulator, the electric field around the power equipment will be distorted. By measuring the electric field strength around the power equipment and imaging it, the operating status of the equipment can be intuitively understood.

[0003] Based on the above introduction, the problem faced by traditional electric field measurement is that it is difficult to fully and intuitively reflect the electric field distribution in the entire operating area, including around the power equipment. Summary of the invention

[0004] The present invention provides an alternating current electric field imaging method and system for patrol inspection of live equipment to solve the technical problem that traditional electric field measurement methods are difficult to fully and intuitively reflect the electric field distribution.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] On the one hand, a method for AC electric field imaging of live equipment inspection is provided, the steps of the imaging method comprising:

[0007] Acquire a two-dimensional map of the area to be imaged, and discretize the two-dimensional map into a plurality of discrete measurement points;

[0008] Planning an inspection path and controlling the inspection robot to reach each of the discrete measurement points in sequence;

[0009] When the inspection robot reaches each discrete measurement point, the electric field intensity data of the current discrete measurement point is collected by using an electric field sensor;

[0010] Performing signal processing and filtering on the collected electric field strength data;

[0011] Converting the processed electric field intensity data into corresponding grayscale values ​​or color values;

[0012] An electric field intensity distribution image is generated according to the position information of each discrete measurement point and the corresponding grayscale value or color value.

[0013] On the other hand, an AC electric field imaging system for live equipment inspection is provided, the imaging system comprising:

[0014] A map acquisition module, used to acquire a two-dimensional map of the area to be imaged, and discretize the two-dimensional map into a plurality of discrete measurement points;

[0015] Path planning module, used to plan the inspection path of the inspection robot;

[0016] The electric field measurement module includes an electric field sensor for collecting electric field strength data at each discrete measurement point when the inspection robot reaches the location;

[0017] A signal processing module, used for performing signal processing and filtering on the collected electric field strength data;

[0018] An image generation module, used to convert the processed electric field intensity data into corresponding grayscale values ​​or color values, and generate an electric field intensity distribution image;

[0019] A communication module, used for transmitting the electric field intensity distribution image to a remote monitoring center or a mobile device;

[0020] Positioning module, used to track the location of inspection robots and operators in real time;

[0021] A warning module for issuing a warning signal when a potential hazard is detected;

[0022] A data storage module for storing historical measurement data and generated images;

[0023] The human-computer interaction module is used to display the electric field intensity distribution image and receive operation instructions.

[0024] The beneficial effects of the present invention are:

[0025] The present invention obtains a two-dimensional map of the area to be imaged and discretizes it into multiple measurement points, combined with the path planning of the inspection robot, to achieve a comprehensive measurement of the electric field strength in the entire area. This systematic measurement method overcomes the limitations of traditional single-point measurement and can fully reflect the electric field distribution of the entire operating area.

[0026] Furthermore, by collecting electric field strength data at each discrete measurement point and performing signal processing and filtering on the data, the method improves the accuracy of electric field measurement. The processed data is converted into grayscale values ​​or color values, and an electric field strength distribution image is generated, so that complex electric field distribution conditions can be presented in an intuitive and visual manner. The readability of electric field distribution information is improved, which helps staff to quickly and accurately grasp the on-site electric field distribution conditions.

[0027] Furthermore, the AC electric field imaging method for live equipment inspection of the present invention realizes comprehensive imaging of the electric field around live equipment, which can intuitively reflect the equipment operation status and potential faults. By analyzing the abnormal conditions of the electric field distribution image, equipment hidden dangers can be discovered in time, providing a basis for preventive maintenance. At the same time, the electric field distribution image also provides more intuitive safety guidance for operators, which helps to plan safe working paths and areas, thereby significantly improving the safety of live working.

[0028] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of an AC electric field imaging method for inspection of live equipment in one embodiment of the present invention;

[0030] Figure 2 A structural block diagram of a signal processing circuit in one embodiment of the present invention;

[0031] Figure 3 A grayscale conversion flow chart in one embodiment of the present invention;

[0032] Figure 4 A filtering flow chart in one embodiment of the present invention;

[0033] Figure 5 This is a diagram showing the matrix imaging effect in one embodiment of the present invention;

[0034] Figure 6 A flowchart of discretizing a two-dimensional map into multiple discrete measurement points in one embodiment of the present invention;

[0035] Figure 7 A schematic diagram of an inspection path in an embodiment of the present invention;

[0036] Figure 8 A flowchart of planning an inspection route in one embodiment of the present invention;

[0037] Fig. 9 A flowchart of signal processing and filtering in one embodiment of the present invention;

[0038] Fig.10 The figure is a flow chart of real-time transmission of electric field intensity distribution image in one embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention provides the following preferred embodiments:

[0040] Embodiment 1

[0041] In order to solve the problem that the traditional electric field measurement method is difficult to fully reflect the electric field distribution in the entire operating area, this embodiment proposes an AC electric field imaging method for live equipment inspection.

[0042] like Figures 1 to 5 As shown, the steps of the imaging method include:

[0043] S101 , obtaining a two-dimensional map of the area to be imaged, and discretizing the two-dimensional map into a plurality of discrete measurement points.

[0044] S102: Plan an inspection route and control the inspection robot to reach each discrete measurement point in sequence.

[0045] S103. When the inspection robot reaches each discrete measurement point, the electric field intensity data of the current discrete measurement point is collected using an electric field sensor.

[0046] S104: performing signal processing and filtering on the collected electric field strength data.

[0047] S105, converting the processed electric field intensity data into corresponding grayscale values ​​or color values.

[0048] S106 , generating an electric field intensity distribution image according to the position information of each discrete measurement point and the corresponding grayscale value or color value.

[0049] Specifically, in this embodiment, a two-dimensional map of the area to be imaged is first obtained and discretized into a plurality of measurement points, which lays a foundation for subsequent electric field intensity acquisition.

[0050] Furthermore, this embodiment plans the inspection path and controls the inspection robot to reach each discrete measurement point in turn. This method can achieve systematic coverage of the entire area and ensure the comprehensiveness and representativeness of the electric field strength data. It should be understood that the planning of the inspection path can take into account a variety of factors, such as the distribution density of the measurement points, the mobility of the inspection robot, etc., to optimize the inspection efficiency.

[0051] Furthermore, when the inspection robot reaches each discrete measurement point, the electric field intensity data at the current position is collected using an electric field sensor. It is understandable that the selection of the electric field sensor affects the measurement accuracy. This embodiment can use a capacitive electric field sensor, which has high sensitivity and stability.

[0052] Furthermore, the signal processing and filtering of the collected electric field strength data is to ensure the data quality. In this embodiment, a low-pass filter can be used to remove high-frequency noise, and a precision rectifier circuit can be used to convert the AC signal into a DC signal for subsequent processing. In addition, a digital filtering algorithm, such as Kalman filtering, can be applied to further improve the reliability of the data.

[0053] Furthermore, the processed electric field intensity data is converted into corresponding grayscale values ​​or color values ​​in order to achieve visualization. In this embodiment, a linear mapping or nonlinear mapping method can be used to map the electric field intensity value to a grayscale range of 0-255 or a predefined color space. This conversion method can make full use of the human eye's perception characteristics of brightness and color and enhance the visualization effect of the electric field distribution.

[0054] Further, an electric field intensity distribution image is generated according to the position information of each discrete measurement point and the corresponding grayscale value or color value. This embodiment can use an interpolation algorithm, such as bilinear interpolation or cubic spline interpolation, to generate a smoothly transitioned electric field intensity distribution between discrete measurement points. At the same time, image enhancement technology, such as histogram equalization, can be applied to further improve the contrast and clarity of the image.

[0055] The benefit of this embodiment is that, through systematic electric field strength measurement and visualization, a comprehensive and intuitive presentation of the electric field distribution around live equipment is achieved. This method not only provides more accurate and comprehensive electric field strength information, but also improves the readability of the information through visualization. Workers can quickly identify high electric field strength areas through the generated electric field strength distribution image and effectively plan safe working paths and areas. In addition, this method also provides a new perspective for power equipment status monitoring. By analyzing the abnormal electric field distribution, potential equipment failures can be discovered in time and preventive maintenance can be performed.

[0056] Embodiment 2

[0057] In order to solve the problem that it is difficult to accurately divide the area to be measured in the traditional electric field measurement method, this embodiment further optimizes the method of discretizing the two-dimensional map into multiple discrete measurement points. Through systematic steps, the continuous two-dimensional space is converted into a discrete grid structure, providing an accurate spatial reference for subsequent electric field strength measurement.

[0058] like Figure 6 As shown, the steps of discretizing a two-dimensional map into multiple discrete measurement points include:

[0059] S201, obtaining the actual size and proportion of the area to be imaged.

[0060] S202: Determine the discretization grid size according to a preset resolution requirement.

[0061] S203, dividing the two-dimensional map into rectangular grid units with m rows and n columns, each grid unit being a discrete measurement point.

[0062] S204 . Allocate unique coordinates (i, j) to each discrete measurement point, where i represents a row index and j represents a column index.

[0063] S205: Calculate the physical coordinates (x, y) of each discrete measurement point in the actual space.

[0064] S206: Establish a coordinate mapping relationship to correspond the grid coordinates (i, j) to the physical coordinates (x, y).

[0065] S207. Assign a measurement number to each grid unit.

[0066] Specifically, the present embodiment first obtains the actual size and proportion of the area to be imaged. Step S201 ensures the consistency of the subsequent discretization process with the actual physical space. It should be understood that the acquisition of the actual size can be completed by a measuring instrument or existing geographic information system (GIS) data.

[0067] Furthermore, the discretized grid size is determined according to the preset resolution requirement. It is understandable that the choice of resolution affects the accuracy and computational complexity of the final imaging. Higher resolution can provide more detailed electric field distribution information, but it also increases the burden of data processing. Therefore, a trade-off needs to be made between accuracy and efficiency.

[0068] Furthermore, the two-dimensional map is divided into rectangular grid cells with m rows and n columns, and each grid cell is used as a discrete measurement point. This uniform division method simplifies the subsequent data processing and analysis process. It should be noted that the selection of m and n should take into account the shape characteristics of the area to be measured to ensure effective coverage of the entire area.

[0069] Furthermore, each discrete measurement point is assigned a unique coordinate (i, j). This step establishes a two-dimensional indexing system to facilitate subsequent data management and processing. i represents the row index and j represents the column index, which together determine the position of each measurement point in the grid.

[0070] Furthermore, the physical coordinates (x, y) of each discrete measurement point in the actual space are calculated. This step establishes a correspondence between the grid coordinate system and the actual physical space. This mapping can be achieved by linear interpolation or other appropriate space transformation methods.

[0071] Furthermore, a coordinate mapping relationship is established to correspond the grid coordinates (i, j) to the physical coordinates (x, y). This bidirectional mapping relationship allows the system to flexibly convert between the grid representation and the actual physical location, which is helpful for subsequent data analysis and visualization.

[0072] Further, a measurement number is assigned to each grid unit. Step S207 provides a sequential reference for the electric field strength measurement process. The numbering method can adopt a serpentine path starting from the upper left corner or other methods suitable for the motion characteristics of the inspection robot.

[0073] The benefit of this embodiment is that, through a systematic discretization method, a continuous two-dimensional space is converted into a structured grid system. Not only does it provide an accurate spatial reference frame, but it also simplifies the subsequent data acquisition, processing and analysis process. By establishing a mapping relationship between grid coordinates and physical coordinates, the system can flexibly convert between abstract mathematical representations and actual physical spaces. This structured method lays the foundation for high-precision measurement and visualization of electric field strength, and helps to improve the accuracy and efficiency of the entire electric field imaging system.

[0074] Embodiment 3

[0075] In order to solve the problem that the traditional electric field measurement method lacks systematicness and efficiency in the grid unit number assignment, this embodiment further optimizes the method of assigning measurement numbers to grid units. The method adopts a serpentine path traversal strategy and combines different numbering rules for odd and even rows to achieve efficient numbering of two-dimensional grids.

[0076] like Figure 7 As shown, the steps to assign a measurement number to each grid cell are as follows:

[0077] Define the starting number as 1; start from any corner and traverse all grids in a serpentine path; for even-numbered rows, number them from left to right; for odd-numbered rows, number them from right to left; when i is an even number, the grid cell is numbered i*n+j+1; when i is an odd number, the grid cell is numbered i*n+(nj); store the calculated number and the corresponding grid coordinate (i, j) in a lookup table for quick access later.

[0078] Specifically, this embodiment first defines the starting number as 1. This step establishes a reference point for the entire numbering process, which helps the continuity of subsequent numbering. It should be understood that although the starting number can be any value, choosing 1 as the starting point can simplify the calculation process and match the convention that array indexes start from 0 in most programming languages.

[0079] Furthermore, starting from any corner, all grids are traversed in a serpentine path. This traversal method simulates the motion trajectory of a robot or drone during actual measurement, which can minimize the moving distance of the measuring device and improve the measurement efficiency. It is understandable that the starting angle of the serpentine path can be flexibly selected according to the characteristics of the actual measurement area and the initial position of the measuring device.

[0080] Furthermore, for even-numbered rows, they are numbered from left to right; for odd-numbered rows, they are numbered from right to left. This alternating numbering method corresponds to the serpentine path traversal to ensure the continuity of the numbering. It should be noted that this numbering method needs to consider the parity of the row index in implementation to ensure the correct numbering order.

[0081] Furthermore, when i is an even number, the grid cell is numbered as in+j+1; when i is an odd number, the grid cell is numbered as in+(nj). This numbering calculation method cleverly combines the row index i and the column index j to achieve the mapping from two-dimensional coordinates to one-dimensional numbering. It can be understood that this mapping relationship takes into account the different numbering directions of odd and even rows, ensuring the uniqueness and continuity of the numbering.

[0082] Furthermore, the calculated number and the corresponding grid coordinates (i, j) are stored in a lookup table for quick access later. This step establishes a bidirectional mapping relationship between the number and the grid coordinates, which facilitates subsequent data processing and analysis. It should be noted that the lookup table can be implemented using a hash table or other efficient data structures to optimize query performance.

[0083] The benefit of this embodiment is that by designing a systematic grid numbering method, efficient numbering and rapid access to two-dimensional space are achieved. This method not only adapts to the measurement strategy of the serpentine path, but also minimizes the moving distance of the measuring device by alternating the numbering of odd and even rows. By establishing a mapping relationship between the numbering and the grid coordinates, this method facilitates subsequent data processing and analysis, and helps to improve the efficiency and performance of the entire electric field imaging system. In addition, the flexibility of this numbering method allows it to adapt to measurement areas of different shapes and sizes.

[0084] Embodiment 4

[0085] In order to solve the problem that the traditional inspection path planning method is inefficient and difficult to adapt to complex environments, this embodiment further optimizes the inspection path planning steps in the AC electric field imaging method for live equipment inspection. The method comprehensively considers the spatial distribution of measurement points, environmental obstacles and time factors, and generates an efficient and executable inspection path plan through a multi-step optimization process.

[0086] like Figure 8 As shown in the figure, the steps of planning the inspection route include:

[0087] S401. Generate an initial path point sequence according to the order of measurement numbers.

[0088] S402, calculating the distance between adjacent discrete measurement points using the following formula:

[0089]

[0090] S403: Apply ant colony algorithm or genetic algorithm to optimize the path.

[0091] S404: Consider obstacles in the actual environment and adjust the path.

[0092] S405. Add arrival time estimation for each waypoint.

[0093] S406: Generate a detailed path plan including coordinates, time and action instructions.

[0094] Specifically, this embodiment first generates an initial path point sequence according to the order of the measurement numbers. Step S401 provides a basic framework for path planning to ensure that all measurement points are included in the inspection range. It should be understood that the initial sequence may not be optimal, but it provides a starting point for subsequent optimization.

[0095] It is understood that calculating the distance between adjacent discrete measurement points is the basis for optimizing the path. This embodiment uses the Euclidean distance formula to calculate the straight-line distance between two points. It is understood that this distance calculation method is suitable for an ideal barrier-free environment and provides a preliminary evaluation criterion for subsequent path optimization.

[0096] Furthermore, the ant colony algorithm or genetic algorithm is applied to optimize the path. The ant colony algorithm simulates the behavior of ants in the process of searching for food, and finds the optimal path through the accumulation and volatilization of pheromones. The genetic algorithm simulates the biological evolution process, and continuously improves the path plan through selection, crossover and mutation operations. Both algorithms have strong global search capabilities and can find near-optimal solutions in complex solution spaces.

[0097] Furthermore, considering the obstacles in the actual environment, the path is adjusted to ensure that the path is executable. This involves the application of obstacle detection and obstacle avoidance algorithms. It should be noted that the obstacle information can come from pre-environment modeling or real-time sensor data.

[0098] Further, the arrival time estimate is added for each path point, and step S405 takes into account the motion characteristics and environmental factors of the inspection equipment. The time estimate can be calculated based on the path length, the speed of the equipment, and possible acceleration and deceleration processes. This time information is conducive to the scheduling and management of the entire inspection process.

[0099] Furthermore, a detailed path plan including coordinates, time and action instructions is generated. This plan includes not only spatial information (coordinates), but also time information and specific action instructions. Action instructions may include moving, turning, stopping, etc. The precise definition of these instructions is conducive to the actual control of the inspection equipment.

[0100] The benefit of this embodiment is that, through a series of systematic steps, the transition from the initial path to the optimized path and then to the executable plan is achieved. This method not only takes into account the spatial optimization of the path, but also takes into account the time factor and environmental constraints, thereby generating an efficient inspection path plan. By applying algorithm optimization, this method can find a near-optimal solution in a complex environment. At the same time, by considering the obstacles and equipment motion characteristics in the actual environment, the executable nature of the path is ensured. This comprehensive path planning method provides a reliable execution basis for electric field strength measurement, which helps to improve the efficiency and accuracy of the entire electric field imaging process.

[0101] Embodiment 5

[0102] In order to solve the problem of unstable data and large errors in the process of electric field strength measurement, this embodiment further optimizes the method of collecting electric field strength data of discrete measurement points using electric field sensors. This method improves the accuracy and reliability of measurement through a series of sophisticated operation steps and data processing technology.

[0103] Specifically, the steps of collecting electric field strength data of the current discrete measurement points using the electric field sensor include:

[0104] After the inspection robot reaches the designated discrete measurement point, it maintains a stable hovering state.

[0105] Turn on the E-field sensor to warm up and self-calibrate.

[0106] At each discrete measurement point, k electric field data samples are collected, where k ≥ 5.

[0107] Record the acquisition timestamp for each sample.

[0108] Calculate the average value of k electric field data samples as the electric field strength value of the discrete measurement point using the following formula:

[0109] Among them, E avg is the average value of the electric field strength.

[0110] The standard deviation of the samples is also calculated to assess the stability of the measurement.

[0111] If the standard deviation exceeds a preset threshold, the discrete measurement point is marked as needing to be re-collected.

[0112] Information such as mean value, standard deviation and time stamp is stored in association with the coordinates of the measurement points.

[0113] Furthermore, this embodiment first requires that the inspection robot maintain a stable hovering state after reaching the designated discrete measurement point. It should be understood that stable hovering can minimize the impact of the robot's own motion on the electric field measurement. It is possible to consider using a high-precision positioning system and attitude control algorithm, such as a combined navigation system of RTK-GPS combined with an inertial measurement unit (IMU), to achieve centimeter-level positioning accuracy and sub-degree-level attitude stability.

[0114] Further, the electric field sensor is turned on for preheating and self-calibration. This step involves the working characteristics of the sensor. It is understandable that many high-precision sensors require a certain warm-up time to reach a stable working state. The self-calibration process may include zero drift correction and sensitivity adjustment, which helps to eliminate the influence of environmental factors (such as temperature and humidity) on the measurement results.

[0115] Further, k electric field data samples are collected at each discrete measurement point, where k ≥ 5. It can be understood that the multiple sampling method helps to reduce the impact of random errors. It should be noted that the selection of the number of samples k needs to balance the measurement accuracy and time efficiency. In practical applications, the k value can be dynamically adjusted according to the field conditions and accuracy requirements.

[0116] Furthermore, the acquisition timestamp of each sample is recorded. This step provides information of the time dimension for subsequent data analysis and processing. The accurate recording of timestamps can help identify and analyze the time variation characteristics of electric field strength, especially in AC electric field measurement.

[0117] Furthermore, the average value of k electric field data samples is calculated as the electric field strength value of the discrete measurement point. The use of the arithmetic mean method can effectively reduce the influence of random errors.

[0118] Furthermore, the standard deviation of the sample is calculated at the same time to evaluate the stability of the measurement. The standard deviation is an important indicator to measure the degree of dispersion of data. It can be understood that a smaller standard deviation indicates better consistency of the measurement results, while a larger standard deviation may mean that there is interference or abnormality in the measurement process.

[0119] Furthermore, it is determined whether the standard deviation exceeds the preset threshold. If the standard deviation exceeds the preset threshold, the discrete measurement point is marked as needing to be re-collected. This step introduces a quality control mechanism that helps to identify and process abnormal data. The setting of the threshold should be based on the actual application scenario and accuracy requirements, and the appropriate value can be determined through experiments and statistical analysis.

[0120] Furthermore, information such as the mean value, standard deviation, and timestamp are stored in association with the coordinates of the measurement points. This comprehensive data storage method provides a rich information basis for subsequent data analysis and electric field imaging. It is possible to consider using a structured data format (such as JSON or XML) to organize this information to facilitate data transmission and processing.

[0121] The benefit of this embodiment is that the accuracy of electric field strength measurement is improved through systematic data acquisition and processing methods. Through multiple sampling and statistical analysis, the influence of random errors is effectively reduced. The introduction of standard deviation analysis and quality control mechanism enhances the credibility of the measurement results. At the same time, combining time information with spatial information provides the possibility for subsequent dynamic analysis of the electric field. This method is not only applicable to static electric field measurement, but also provides a basis for the study of the characteristics of AC electric fields. Through this embodiment, the application scope of electric field imaging technology is expanded, providing more reliable data support for fields such as power system monitoring and environmental electromagnetic field assessment.

[0122] Embodiment 6

[0123] In order to solve the problem of insufficient measurement accuracy of electric field sensors and significant influence of environmental factors, this embodiment further optimizes the calibration and use method of capacitive electric field sensors. The method improves the accuracy of electric field strength measurement through a systematic calibration process and compensation mechanism.

[0124] Specifically, the electric field sensor is a capacitive electric field sensor, and the relationship between the output voltage U of the electric field sensor and the electric field strength E at its location is expressed as:

[0125] U = ε × d × E, where ε is the sensor sensitivity coefficient and d is the distance between the sensor plates;

[0126] The steps to turn on the electric field sensor, warm up and self-calibrate include:

[0127] Calibrate the sensor before use to determine the precise values ​​of ε and d;

[0128] Establish a calibration curve of voltage U and electric field strength E;

[0129] Perform zero point calibration before each measurement;

[0130] Regularly calibrate using a standard electric field source of known strength;

[0131] The measurement results are compensated and corrected according to factors such as ambient temperature and humidity.

[0132] Furthermore, this embodiment uses a capacitive electric field sensor as a measuring device. The working principle of this sensor is based on the linear relationship between the electric field strength and the sensor output voltage. It should be understood that this linear relationship provides a theoretical basis for the accurate measurement of the electric field strength.

[0133] Furthermore, the sensor is calibrated before use to determine the exact values ​​of ε and d. This step is the basis for ensuring the accuracy of the measurement. It is understandable that the values ​​of ε and d may deviate from the nominal values ​​due to production errors or changes during use. The calibration process can use a standard electric field source of known strength to determine the actual values ​​of these two parameters through multi-point measurement and regression analysis. It is recommended to use a high-precision standard electric field source, such as a parallel plate capacitor or a Hall effect probe calibration system to ensure the accuracy of the calibration.

[0134] Furthermore, a calibration curve of voltage U and electric field strength E is established. This step further refines the characteristic description of the sensor. Although the relationship between U and E is linear in theory, there may be nonlinear factors in practice. By measuring at multiple points of known electric field strength, a more accurate calibration curve can be drawn. It is possible to consider using methods such as polynomial fitting or piecewise linear fitting to describe this relationship in order to improve the accuracy of the measurement.

[0135] Furthermore, a zero calibration is performed before each measurement. This step helps eliminate the effects of the ambient background electric field and the zero drift of the sensor itself. It is understood that zero drift may be caused by factors such as temperature changes, humidity changes, or sensor aging. Zero calibration can be performed by placing the sensor in a known zero electric field environment (such as a Faraday cage) or by differential measurement techniques.

[0136] Furthermore, calibration is performed regularly using a standard electric field source of known strength. This regular calibration mechanism helps to detect and correct any changes in sensor performance in a timely manner. Consider establishing a calibration schedule that determines the calibration period based on the frequency of use and environmental conditions. The calibration results should be recorded and analyzed to track the long-term trend of sensor performance.

[0137] Furthermore, the measurement results are compensated and corrected according to factors such as ambient temperature and humidity. This step aims to eliminate the influence of environmental factors on the measurement results. It should be noted that temperature and humidity not only affect the performance of the sensor itself, but may also change the characteristics of the measured electric field. A model of the influence of temperature and humidity on the measurement results can be established through experiments, and then compensation can be performed according to real-time environmental parameters in actual measurements. It is recommended to use a high-precision temperature and humidity sensor integrated with the electric field sensor to achieve real-time compensation.

[0138] The benefit of this embodiment is that, through a comprehensive calibration and compensation mechanism, the accuracy and reliability of the capacitive electric field sensor measurement are significantly improved. By accurately calibrating the sensor parameters and establishing a detailed calibration curve, the system error is reduced. The regular calibration and zero calibration mechanism ensures the stability of long-term measurements. Environmental factor compensation further improves the consistency of measurement results under different conditions. This method is not only applicable to static electric field measurements, but also lays the foundation for the accurate measurement of AC electric fields. Through this embodiment, the application scope of electric field measurement technology is expanded, providing more reliable and accurate data support for fields such as power system monitoring and environmental electromagnetic field assessment.

[0139] Embodiment 7

[0140] In order to solve the problems of weak signal, serious noise interference and insufficient signal processing accuracy during the electric field strength measurement, this embodiment further optimizes the method of signal processing and filtering the collected electric field strength data. The method improves the signal-to-noise ratio and accuracy of the electric field strength measurement through multi-level signal processing and filtering technology.

[0141] like Fig. 9 As shown, the steps of performing signal processing and filtering on the collected electric field strength data include:

[0142] S701. A low noise amplifier is used to amplify the weak sensor output signal. The gain of the low noise amplifier is adjustable.

[0143] S702. Use a bandpass filter to remove power frequency interference and high-frequency noise. The center frequency of the filter is set to 50 Hz or 60 Hz of the power frequency.

[0144] S703: Apply an adaptive notch filter to further suppress power frequency interference.

[0145] S704: Use wavelet transform to perform denoising on the signal.

[0146] S705: Use Kalman filter algorithm to smooth the signal.

[0147] S706: Convert the filtered signal into a DC signal through a precision rectifier circuit.

[0148] S707. Use a 16-bit high-precision analog-to-digital converter to convert the analog signal into a digital signal.

[0149] S708: Perform mean filtering on the digital signal to remove sudden interference.

[0150] Furthermore, this embodiment first uses a low noise amplifier to amplify the weak sensor output signal. It should be understood that the original output signal of the electric field sensor is usually very weak and easily affected by external noise. The use of a low noise amplifier, such as AD8429 or LT1028, can amplify the signal without introducing additional noise. The design of adjustable amplification allows the gain to be flexibly adjusted according to different measurement scenarios to adapt to electric fields of different intensities.

[0151] Furthermore, a bandpass filter is used to remove power frequency interference and high frequency noise. It is understandable that in the power system environment, 50Hz or 60Hz power frequency interference is one of the main noise sources. When designing a bandpass filter, an active filter circuit can be used, such as a Sallen-Key topology, with the center frequency set to the power frequency of 50Hz or 60Hz, and the bandwidth can be set to about ±1Hz. This narrowband design can effectively suppress noise other than the power frequency while retaining useful signal components.

[0152] Furthermore, an adaptive notch filter is applied to further suppress the power frequency interference. It should be noted that the frequency of the power frequency interference may have slight fluctuations. The adaptive notch filter can dynamically adjust its center frequency to accurately track and suppress the power frequency interference. It is possible to consider using a digital signal processor (DSP) to implement an adaptive algorithm, such as the least mean square (LMS) algorithm or the recursive least squares (RLS) algorithm.

[0153] Furthermore, the signal is denoised using wavelet transform. Wavelet transform has good localization characteristics in the time and frequency domain and is suitable for processing non-stationary signals. Discrete wavelet transform (DWT) can be used to select appropriate wavelet basis functions (such as Daubechies wavelet) and decomposition layers to perform multi-scale analysis on the signal. By removing high-frequency noise components through threshold processing and reconstructing the signal, broadband noise can be effectively removed.

[0154] Furthermore, the Kalman filter algorithm is used to smooth the signal. Kalman filtering is a recursive optimal estimation algorithm, which is particularly suitable for processing dynamic systems with random errors. In the implementation process, it is necessary to establish appropriate system state equations and observation equations, and reasonably set the process noise covariance and measurement noise covariance. Kalman filtering can not only smooth the signal, but also predict the future signal value, which helps to improve the real-time performance of the measurement.

[0155] Further, the filtered signal is converted into a DC signal through a precision rectifier circuit. It is understandable that in AC electric field measurement, we usually focus on the effective value of the electric field strength. The precision rectifier circuit can use a full-wave rectifier circuit composed of an operational amplifier and a diode, such as an integrated circuit such as AD736 or AD637, which can provide accurate RMS-to-DC conversion.

[0156] Furthermore, a 16-bit high-precision analog-to-digital converter is used to convert the analog signal into a digital signal. The selection of a high-precision ADC is critical to maintaining the overall accuracy of the signal processing chain. Consider using ADCs such as ADS1115 or AD7606, which not only provide 16-bit resolution but also have low noise and high linearity. At the same time, attention should be paid to the ADC sampling rate setting to ensure that the requirements of the Nyquist sampling theorem are met.

[0157] Furthermore, the digital signal is mean filtered to remove sudden interference. This is the last step of digital domain processing, which aims to further improve the stability of the signal. Mean filtering can be implemented by the sliding window method, and the selection of the window size needs to balance the signal smoothness and response speed. Consider using a weighted moving average to give a higher weight to the latest data sample to improve the system's response speed to changes in the electric field.

[0158] The benefit of this embodiment is that, through multi-stage signal processing and filtering technology, a complete signal processing chain is constructed, from amplification of weak signals to final digital signal output. This systematic approach not only effectively improves the signal-to-noise ratio and accuracy of electric field strength measurement, but also enhances the adaptability of the measurement system to different electric field environments.

[0159] Embodiment 8

[0160] In order to solve the problems of insufficient data security, low transmission efficiency and inconvenient remote access during the real-time transmission of the electric field intensity distribution image, this embodiment further optimizes the transmission, storage and access method of the electric field intensity distribution image. The method realizes the safe and efficient transmission and real-time remote access of the electric field intensity distribution image through data processing, encryption and transmission technology.

[0161] like Fig.10 As shown, the steps of the AC electric field imaging method for live equipment inspection also include transmitting the electric field intensity distribution image to a remote monitoring center or a mobile device in real time, the steps include:

[0162] S801, compressing the generated electric field intensity distribution image using JPEG2000 algorithm.

[0163] S802: Encrypt the compressed electric field intensity distribution image data using the AES-256 encryption algorithm.

[0164] S803, packetize the encrypted data, and add a timestamp and a sequence number.

[0165] S804: Transmit the data packet to the remote server using wired or wireless communication.

[0166] S805: The remote server receives the data packet and performs decryption and data integrity verification.

[0167] S806: The server reorganizes and decompresses the data of the electric field intensity distribution image to reconstruct the electric field intensity distribution image.

[0168] S807: Store the reconstructed electric field intensity distribution image in a database, and record related metadata information.

[0169] S808. Push the latest electric field intensity distribution image through the Web interface or the mobile application API to achieve real-time image update and history backtracking functions.

[0170] Further, the present embodiment first uses the JPEG2000 algorithm to compress the generated electric field intensity distribution image. The JPEG2000 algorithm is an advanced image compression standard that provides a higher compression rate and better image quality than the traditional JPEG algorithm. It should be understood that JPEG2000 supports lossless and lossy compression, and a suitable compression mode can be selected according to actual needs. In the implementation process, it can be considered to use the open source OpenJPEG library or the commercial Kakadu software to implement JPEG2000 compression. The selection of compression parameters requires a balance between image quality and file size, and it is recommended to determine the optimal parameter settings through experiments.

[0171] Furthermore, the compressed electric field intensity distribution image data is encrypted using the AES-256 encryption algorithm. AES-256 is a widely used symmetric encryption algorithm that provides a high degree of security. It is understandable that the encryption process not only protects the confidentiality of the image data, but also prevents the data from being tampered with during transmission. When implementing, the OpenSSL library or a dedicated hardware encryption module can be used to perform AES-256 encryption. Key management is the key to the encryption system, and it is recommended to use a secure key exchange protocol (such as Diffie-Hellman key exchange) to protect the encryption key.

[0172] Furthermore, the encrypted data is packetized and timestamps and sequence numbers are added. This step is intended to improve the reliability and traceability of data transmission. The UDP protocol can be considered for data transmission because it is more suitable for real-time data streams than TCP. Each data packet should contain a unique sequence number, timestamp, and data integrity checksum. This design allows the receiver to detect packet loss and disorder and request retransmission of missing data packets.

[0173] Furthermore, the data packets are transmitted to the remote server using wired or wireless communication. According to the actual application scenario, an appropriate communication method can be selected. For monitoring in fixed places, wired Ethernet or fiber optic communication can be used; for mobile scenarios, 4G / 5G mobile networks or satellite communications can be considered. It should be noted that no matter which communication method is selected, appropriate network security measures should be implemented, such as VPN tunnels or IPsec protocols, to provide additional transport layer security.

[0174] Furthermore, the remote server receives the data packet, performs decryption and data integrity verification. The server side should design a high-concurrency data reception and processing architecture to cope with the situation where multiple monitoring points upload data at the same time. Asynchronous I / O and multi-threading technology can be used to improve the processing power of the server. Data integrity verification should include packet sequence integrity check and packet content verification and verification.

[0175] Furthermore, the server reassembles and decompresses the data of the electric field intensity distribution image to reconstruct the electric field intensity distribution image. This process needs to take into account the possibility that the data packets may be out of order or lost. A buffer mechanism can be designed to allow the data packets to be collected and sorted within a certain time window. The decompression process should use the JPEG2000 decoder corresponding to the compression. The quality of the reconstructed image should be consistent with the original image, which can be evaluated by indicators such as peak signal-to-noise ratio (PSNR).

[0176] Furthermore, the reconstructed electric field intensity distribution image is stored in a database, and the relevant metadata information is recorded. It is possible to consider using a distributed file system (such as HDFS) to store a large amount of image data, and using a relational database (such as PostgreSQL) or a document database (such as MongoDB) to store metadata. The metadata should include image acquisition time, location coordinates, equipment information, environmental parameters, etc., for subsequent data analysis and retrieval.

[0177] Furthermore, the latest electric field intensity distribution images are pushed through the Web interface or mobile application API to achieve real-time image updates and historical backtracking functions. A RESTful API can be designed to provide image data access services and support queries by time, location, and other conditions. For real-time updates, the WebSocket protocol or server push technology (SSE) can be considered to achieve low-latency data push. Mobile applications can adopt responsive design to adapt to different device screen sizes and resolutions.

[0178] The benefits of this embodiment are that through a comprehensive data processing, encryption, transmission, and storage solution, the secure, efficient transmission and real-time remote access of the electric field strength distribution image are achieved. The efficient compression algorithm and reliable transmission mechanism significantly improve the data transmission efficiency. The encryption and secure transmission measures ensure the security of the data during transmission and storage. The flexible data storage and access interface facilitate subsequent data analysis and visualization.

[0179] Embodiment Nine

[0180] To address the problem of insufficient safety risk management for operators during the inspection of energized equipment, this embodiment further optimizes the real-time personnel safety warning system based on the electric field strength distribution. This system realizes the precise monitoring and timely warning of operators approaching high electric field strength areas by combining electric field strength analysis, computer vision technology, and a multi-level warning mechanism.

[0181] Specifically, the steps of the AC electric field imaging method for the inspection of energized equipment further include issuing a warning to the operator when it is detected that the operator is approaching an area with a relatively high electric field strength. The steps include:

[0182] Define the electric field strength threshold E th , and mark the area where the electric field strength is greater than E th as a high-risk area;

[0183] Use computer vision technology to real-time track the position coordinates (x p , y p ) of the operator;

[0184] Calculate the distance d between the operator and the boundary of the nearest high-risk area:

[0185] where (x b , y b ) is a point on the boundary of the high-risk area;

[0186] Set multi-level warning distances d1 > d2 > d3;

[0187] When d < d1, trigger a first-level warning and give an audible and visual prompt;

[0188] When d < d2, trigger a second-level warning and give a strong audible and visual alarm and a vibration reminder;

[0189] When d < d3, trigger a third-level warning, make an emergency contact with the monitoring center and start an emergency plan;

[0190] Record all warning events, including the time, location, and warning level;

[0191] Generate a warning event report for safety analysis.

[0192] Further, in this embodiment, the electric field strength threshold E is first defined. th , and the area where the electric field strength is greater than E th is marked as a high-risk area. It should be understood that the selection of E th should be based on relevant safety standards and the actual working environment. The guidelines of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) can be considered as a reference. For a 50 Hz power frequency electric field, the public exposure limit is 5 kV / m, and the occupational exposure limit is 10 kV / m. The system can set multiple thresholds to adapt to different safety requirements and operation types.

[0193] Further, computer vision technology is used to real-time track the position coordinates (x p , y p ) of the operator. It can be understood that accurate personnel positioning is the basis for effective early warning. Object detection and tracking algorithms based on deep learning, such as YOLOv5 or Faster R-CNN, can be adopted, combined with a multi-camera system to achieve full coverage monitoring of the operation area. To improve the positioning accuracy, 3D cameras or lidar (LiDAR) technology can be considered to obtain more accurate depth information. At the same time, the Kalman filter algorithm can be used to smooth the tracking results and improve the stability of positioning.

[0194] Further, the distance d between the operator and the boundary of the nearest high-risk area is calculated. This step involves complex spatial analysis. Spatial indexing techniques, such as R-trees or quadtrees, can be adopted to accelerate the nearest point query. For high-risk areas with irregular shapes, polygon approximation or contour description algorithms can be used to represent the boundary. In the distance calculation formula, optimization algorithms, such as KD-trees, can be considered to improve the calculation efficiency, especially in large-scale scenarios.

[0195] Further, multiple warning distances d1 > d2 > d3 are set. This hierarchical early warning mechanism allows the system to take different levels of responses according to the degree of danger. The setting of the warning distances should consider factors such as the attenuation characteristics of the electric field strength, the moving speed of the operator, and the reaction time. These parameters can be optimized through simulation analysis and on-site tests. At the same time, the system should have the ability to dynamically adjust the warning distances according to the actual situation to adapt to different working environments and task requirements.

[0196] Further, when d < d1, a first-level warning is triggered, and audible and visual alerts are given. This is the lowest level of early warning, aiming to attract the attention of the operator. Low-power LED indicators and low-decibel buzzers can be used to achieve this. The audible and visual alerts should be designed to be easily recognizable but not cause alarm, and pulse-type flashing and intermittent sound alerts can be considered.

[0197] Further, when d < d2, a secondary warning is triggered, and strong audible and visual alarms and vibration reminders are given. The purpose of this level of warning is to strongly prompt the operator to immediately pay attention to the surrounding environment. High-brightness flashing LED light strips and high-decibel alarms can be used. The vibration reminder can be achieved through intelligent devices worn by the operator (such as smart bracelets or safety helmets). The warning signal should be designed to have distinct features and be easily distinguishable from other environmental signals.

[0198] Further, when d < d3, a tertiary warning is triggered, and the monitoring center is emergently contacted and the emergency plan is activated. This is the highest level of warning, indicating that the operator is in an emergency and dangerous state. The system should automatically send an emergency notice to the monitoring center, including the precise location of the operator and the surrounding environment information. At the same time, the preset emergency plan can be activated, such as automatically cutting off the power of relevant equipment and dispatching the nearest rescue personnel. The use of dedicated emergency communication channels, such as satellite communication or dedicated wireless networks, can be considered to ensure reliable communication.

[0199] Further, all warning events are recorded, including the time, location, and warning level. These records are used for subsequent safety analysis and system optimization. A distributed database system, such as Apache Cassandra or InfluxDB, can be used to store a large amount of time-series data. Each record should contain detailed context information, such as environmental parameters, operation type, personnel information, etc., for comprehensive analysis.

[0200] Further, a warning event report is generated for safety analysis. The report generation system should be able to automatically analyze the warning event data and identify potential safety hazards and risk patterns. Machine learning algorithms, such as clustering analysis or anomaly detection, can be used to discover the patterns and anomalies in the data. The report should contain visual statistical charts, risk heat maps, and detailed event analysis to provide a basis for safety management decisions.

[0201] The benefits of this embodiment are that a comprehensive operation safety guarantee system is constructed through electric field analysis, real-time personnel positioning, and a multi-level warning mechanism. This system can not only promptly warn of potential dangers but also continuously optimize the safety management strategy through data analysis. Through this embodiment, the safety of power equipment inspection operations is improved, providing more reliable protection for the operators. In addition, this safety monitoring method based on multi-source data fusion also provides new ideas for the safety management of other high-risk operation environments, helping to improve the safety management level in related fields.

[0202] Embodiment Ten

[0203] In order to solve the problems of insufficient electric field distribution measurement accuracy, low data processing efficiency, and imperfect safety risk management during the inspection of live equipment, this embodiment proposes an AC electric field imaging system for live equipment inspection. The system integrates multiple functional modules to achieve functions such as high-precision electric field measurement, real-time data processing, intelligent path planning, and multi-dimensional safety warning, which significantly improves the efficiency and safety of live equipment inspection.

[0204] Specifically, the AC electric field imaging system for live equipment inspection includes:

[0205] The map acquisition module is used to acquire a two-dimensional map of the area to be imaged and discretize the two-dimensional map into a plurality of discrete measurement points.

[0206] The path planning module is used to plan the inspection path of the inspection robot.

[0207] The electric field measurement module includes an electric field sensor, which is used to collect electric field strength data at each discrete measurement point when the inspection robot reaches the location.

[0208] The signal processing module is used to perform signal processing and filtering on the collected electric field strength data.

[0209] The image generation module is used to convert the processed electric field intensity data into corresponding grayscale values ​​or color values ​​and generate an electric field intensity distribution image.

[0210] The communication module is used to transmit the electric field intensity distribution image to a remote monitoring center or a mobile device.

[0211] The positioning module is used to track the location of the inspection robot and operators in real time.

[0212] A warning module is used to issue a warning signal when a potential hazard is detected.

[0213] Data storage module, used to save historical measurement data and generated images.

[0214] The human-computer interaction module is used to display the electric field intensity distribution image and receive operation instructions.

[0215] Furthermore, the map acquisition module of this system uses high-resolution laser scanning technology combined with a differential GPS positioning system to generate accurate three-dimensional point cloud data. It should be understood that this method can not only obtain the planar information of the area to be imaged, but also capture the elevation changes of the environment, providing more comprehensive spatial information for subsequent path planning and electric field analysis. The point cloud data is spatially divided and compressed using the octree algorithm, and then projected onto a two-dimensional plane and rasterized to form a high-precision discrete measurement point grid. The grid density can be dynamically adjusted according to actual needs to balance measurement accuracy and efficiency.

[0216] Furthermore, the path planning module uses an improved algorithm combined with a dynamic obstacle avoidance strategy. It is understandable that traditional algorithms may fall into local optimality in complex environments. This module introduces adaptive heuristic functions and dynamic weight adjustment mechanisms to improve the efficiency and flexibility of path search. At the same time, real-time obstacle detection and trajectory replanning functions are integrated to cope with dynamic changes in the environment. Path planning also takes into account the electric field intensity gradient, and prioritizes the inspection robot to move along areas with large electric field changes to obtain more valuable measurement data.

[0217] Furthermore, the electric field measurement module uses a highly sensitive electric field sensor array combined with multi-sensor data fusion technology. The sensor array consists of multiple orthogonally arranged capacitive field strength probes, each of which is equipped with a low-noise preamplifier and a 24-bit high-precision ADC. Multi-sensor data is fused through the Kalman filter algorithm, which effectively suppresses the measurement noise and fluctuations of a single sensor. In addition, the system also integrates temperature, humidity and air pressure sensors for environmental parameter compensation, further improving the accuracy of electric field strength measurement.

[0218] Furthermore, the signal processing module uses a real-time digital signal processing system based on FPGA. This module implements complex algorithms such as adaptive filtering, wavelet denoising and harmonic analysis. In particular, for power frequency interference, a hybrid algorithm based on notch filter and adaptive linear prediction is developed, which can effectively suppress the 50Hz or 60Hz power frequency components and their harmonics. At the same time, the parallel processing capability of FPGA is used to realize real-time spectrum analysis and transient event capture, which provides a basis for electric field anomaly detection.

[0219] Furthermore, the image generation module uses an improved bilinear interpolation algorithm combined with edge-preserving filtering technology. This method can better preserve the boundary characteristics of the electric field distribution while ensuring interpolation smoothness. The color mapping uses a rainbow color spectrum that has been processed by perceptual homogenization to improve the visualization of changes in electric field intensity. In addition, the module also integrates automatic contrast enhancement and local detail enhancement algorithms to highlight small changes in the electric field distribution.

[0220] Furthermore, the communication module adopts a multi-layer secure communication protocol stack, including spectrum diffusion technology at the physical layer, forward error correction coding at the link layer, and end-to-end encryption at the application layer. Data transmission adopts a hybrid network architecture, combining 5G wireless communication and optical fiber network to ensure high bandwidth and low latency for data transmission. In order to cope with communication interference in complex electromagnetic environments, adaptive channel selection and dynamic power control algorithms are also integrated.

[0221] Furthermore, the positioning module uses multi-sensor fusion technology, combined with inertial measurement unit (IMU), visual odometer and UWB positioning system. Through the extended Kalman filter algorithm, centimeter-level positioning accuracy is achieved. For the positioning of operators, a computer vision algorithm based on deep learning is used, combined with the RFID tag of wearable devices, to achieve all-round personnel tracking.

[0222] Furthermore, the warning module adopts a multi-level early warning mechanism, combining sound and light alarms, vibration reminders and remote notifications. The system calculates the risk level in real time according to the electric field strength and the location of the personnel, and dynamically adjusts the warning threshold through a fuzzy logic controller. In particular, for the potential risk of electric shock, a prediction algorithm based on the electric field gradient and human body capacitance model has been developed, which can identify dangerous situations that may lead to electric shock in advance.

[0223] Furthermore, the data storage module uses a distributed time series database combined with cloud storage technology. High-performance SSDs are used locally to achieve fast data writing and real-time query, while historical data is uploaded to cloud storage through incremental backup. The database schema uses a columnar storage format to optimize the compression and query efficiency of time series data. In addition, a distributed data analysis framework based on Apache Spark is implemented to support offline analysis and mining of large-scale historical data.

[0224] Furthermore, the human-computer interaction module adopts responsive Web design and combines WebGL technology to achieve high-performance 3D electric field distribution visualization. The interface supports multi-touch and gesture operations, and is adapted to display devices of different sizes. The system also integrates a natural language processing engine, supports voice commands and intelligent dialogue, and improves the convenience of operation. In order to meet the needs of remote operation, a real-time video streaming function based on WebRTC was developed to achieve low-latency remote monitoring and control.

[0225] The benefit of this embodiment is that, by integrating multiple advanced technologies, a comprehensive, efficient and safe AC electric field imaging system for live equipment inspection is constructed. This system not only improves the accuracy and efficiency of electric field measurement, but also provides operators with intuitive and accurate electric field distribution information through intelligent data processing and visualization technology. The multi-dimensional safety warning mechanism significantly improves operational safety, while the distributed data storage and analysis framework lays the foundation for long-term equipment status monitoring and predictive maintenance.

Claims

1. A method for AC electric field imaging for inspection of live equipment, characterized in that: The steps of the imaging method include: Acquire a two-dimensional map of the area to be imaged, and discretize the two-dimensional map into a plurality of discrete measurement points; Planning an inspection path and controlling the inspection robot to reach each of the discrete measurement points in sequence; When the inspection robot reaches each discrete measurement point, the electric field intensity data of the current discrete measurement point is collected by using an electric field sensor; Performing signal processing and filtering on the collected electric field strength data; Converting the processed electric field intensity data into corresponding grayscale values ​​or color values; An electric field intensity distribution image is generated according to the position information of each discrete measurement point and the corresponding grayscale value or color value.

2. The AC electric field imaging method for live equipment inspection according to claim 1, characterized in that: The step of discretizing the two-dimensional map into a plurality of discrete measurement points comprises: Obtaining the actual size and scale of the area to be imaged; Determine the discretization grid size according to the preset resolution requirements; Divide the two-dimensional map into rectangular grid units with m rows and n columns, each of which serves as a discrete measurement point; Assigning a unique coordinate (i, j) to each of the discrete measurement points, where i represents a row index and j represents a column index; Calculating the physical coordinates (x, y) of each of the discrete measurement points in the real space; Establish a coordinate mapping relationship to correspond the grid coordinates (i, j) to the physical coordinates (x, y); A measurement number is assigned to each of the grid cells.

3. The AC electric field imaging method for live equipment inspection according to claim 2, characterized in that: The step of assigning a measurement number to each grid unit comprises: Define the starting number as 1; Start from any corner and traverse all grids in a serpentine path; For even-numbered rows, they are numbered from left to right; For odd-numbered rows, number them from right to left; When i is an even number, the grid unit is numbered i*n+j+1; When i is an odd number, the grid unit is numbered as i*n+(nj); The calculated number and the corresponding grid coordinate (i, j) are stored in a lookup table for quick access later.

4. The AC electric field imaging method for live equipment inspection according to claim 2, characterized in that: The steps of planning the inspection path include: generating an initial path point sequence according to the order of the measurement numbers; The distance between adjacent discrete measurement points is calculated using the following formula: Apply ant colony algorithm or genetic algorithm to optimize the path; Consider obstacles in the actual environment and adjust the path; Add arrival time estimates for each waypoint; Generates a detailed path plan including coordinates, time, and motion instructions.

5. The AC electric field imaging method for live equipment inspection according to claim 1, characterized in that: The step of using an electric field sensor to collect electric field strength data of the current discrete measurement points includes: After the inspection robot reaches the designated discrete measurement point, it maintains a stable hovering state; Turning on the electric field sensor to perform preheating and self-calibration; Collect k electric field data samples at each of the discrete measurement points, where k ≥ 5; Record the acquisition timestamp of each sample; The average value of k electric field data samples is calculated as the electric field strength value of the discrete measurement point, using the following formula: Among them, E avg is the average value of the electric field strength; The standard deviation of the sample is also calculated to assess the stability of the measurement; If the standard deviation exceeds a preset threshold, the discrete measurement point is marked as needing to be re-collected; Information such as mean value, standard deviation and time stamp is stored in association with the coordinates of the measurement points.

6. The AC electric field imaging method for live equipment inspection according to claim 5, characterized in that: The electric field sensor is a capacitive electric field sensor, and the relationship between the output voltage U of the electric field sensor and the electric field strength E at its location is expressed as: U = ε × d × E, where ε is the sensor sensitivity coefficient and d is the distance between the sensor plates; The steps of turning on the electric field sensor for preheating and self - calibration include: Performing sensor calibration before use to determine the exact values of ε and d; Establishing a calibration curve between voltage U and electric field strength E; Performing zero - point calibration before each measurement; Regularly using a standard electric field source with a known intensity for verification; Compensating and correcting the measurement results according to factors such as ambient temperature and humidity.

7. The AC electric field imaging method for live equipment inspection according to claim 1, characterized in that: The steps of signal processing and filtering for the collected electric field strength data include: Using a low - noise amplifier to amplify the weak sensor output signal with an adjustable amplification factor; Using a band - pass filter to remove power - frequency interference and high - frequency noise, and setting the center frequency of the filter to 50 Hz or 60 Hz of the power frequency; Applying an adaptive notch filter to further suppress power - frequency interference; Using wavelet transform to denoise the signal; Using the Kalman filtering algorithm to smooth the signal; Converting the filtered signal into a DC signal through a precision rectifier circuit; Using a 16 - bit high - precision analog - to - digital converter to convert the analog signal into a digital signal; Performing mean filtering on the digital signal to remove burst interference.

8. The AC electric field imaging method for live equipment inspection according to claim 1, characterized in that: The steps of the imaging method further include real - time transmitting the electric field strength distribution image to a remote monitoring center or a mobile device, and the steps include: Using the JPEG2000 algorithm to compress the generated electric field strength distribution image; Using the AES - 256 encryption algorithm to encrypt the compressed electric field strength distribution image data; Dividing the encrypted data into packets and adding a timestamp and a sequence number; Using wired or wireless communication to transmit the data packets to a remote server; The remote server receives the data packets, decrypts them and performs data integrity verification; The server recombines and decompresses the data of the electric field strength distribution image, and reconstructs the electric field strength distribution image; Storing the reconstructed electric field strength distribution image in a database and recording relevant metadata information; Pushing the latest electric field strength distribution image through a Web interface or a mobile application API to achieve real - time update and historical backtracking functions of the image.

9. The AC electric field imaging method for live equipment inspection according to claim 1, characterized in that: The steps of the imaging method further include warning the operator when it is detected that the operator approaches a region with a high electric field strength, and the steps include: Define the electric field strength threshold E th , the electric field strength is greater than E th The areas are marked as high risk areas; Use computer vision technology to track the location coordinates of operators in real time (x p ,y p ); Calculating the distance d between the operator and the boundary of the nearest high - risk area: Where (x b ,y b ) is a point on the boundary of the high-risk area; Setting multi - level warning distances d1 > d2 > d3; When d < d1, triggering a first - level warning for audible and visual prompts; When d < d2, triggering a second - level warning for strong audible and visual alarms and vibration reminders; When d < d3, triggering a third - level warning for urgently contacting the monitoring center and starting an emergency plan; Recording all warning events, including time, location and warning level; Generating a warning event report for safety analysis.

10. An AC electric field imaging system for inspection of live equipment, characterized in that: The imaging system includes: A map acquisition module for acquiring a two - dimensional map of the area to be imaged and discretizing the two - dimensional map into multiple discrete measurement points; A path planning module for planning the inspection path of the inspection robot; The electric field measurement module includes an electric field sensor for collecting electric field strength data at each discrete measurement point when the inspection robot reaches the location; A signal processing module, used for performing signal processing and filtering on the collected electric field strength data; An image generation module, used to convert the processed electric field intensity data into corresponding grayscale values ​​or color values, and generate an electric field intensity distribution image; A communication module, used for transmitting the electric field intensity distribution image to a remote monitoring center or a mobile device; Positioning module, used to track the location of inspection robots and operators in real time; A warning module for issuing a warning signal when a potential hazard is detected; A data storage module for storing historical measurement data and generated images; The human-computer interaction module is used to display the electric field intensity distribution image and receive operation instructions.

Images