A power equipment remote multi-target temperature measurement early warning method, system and terminal device

Abstract

The application relates to the technical field of power equipment monitoring, and discloses a power equipment remote multi-target temperature measurement early warning method, system and terminal equipment, multi-spectral sensing data of each target monitoring area is acquired; each monitoring target is positioned based on near-infrared laser echo data, error correction is performed on temperature monitoring values contained in the multi-spectral sensing data according to the positioning information of each monitoring target, and corrected temperature monitoring data is obtained; according to the temperature monitoring data, the temperature early warning level of each monitoring target is determined, and early warning information corresponding to the temperature early warning level is output and displayed. Through positioning and temperature monitoring of each monitoring target, the temperature monitoring values are corrected based on the positioning information and the environmental temperature, the accurate monitoring of the temperature values of the power equipment under complex meteorological environments can be realized, the accuracy of remote temperature monitoring is effectively improved, periodical manual calibration is not needed, and the operation and maintenance cost of the equipment is reduced.

Description

Technical Field

[0001] This application relates to the field of power equipment monitoring technology, and in particular to a method, system and terminal equipment for long-distance multi-target temperature measurement and early warning of power equipment. Background Technology

[0002] During long-term high-load operation, power equipment is prone to localized abnormal temperature rises due to increased contact resistance, insulation aging, or overload current effects. If this temperature rise is not detected and addressed in time, it will accelerate the deterioration of equipment insulation and may even lead to thermal breakdown, short-circuit explosion, or fire, seriously threatening the overall operational safety of the power grid and the safety of life and property of people in the surrounding area. Therefore, remote real-time monitoring of the operating temperature of power equipment is a crucial aspect of power system safety defense.

[0003] The mainstream method for measuring the temperature of power equipment in the current technology is to use a single infrared lens or a dual-light fusion scheme of mid- and far-infrared and visible light. This scheme can only achieve temperature acquisition over short distances. To ensure the accuracy of temperature measurement, the temperature measuring equipment needs to be installed in an area close to the power equipment being measured. This makes the temperature measuring equipment susceptible to the electromagnetic environment and working space limitations of the substation. Moreover, the distance of the measured target in this type of system depends on manual preset or manual input, and it is impossible to automatically obtain the accurate spatial position parameters of the measured target. Temperature measurement correction can only rely on fixed offset settings. In scenarios with long distances and variable weather conditions, the temperature measurement accuracy fluctuates greatly, making it difficult to meet the full-area temperature measurement needs of large-area substations.

[0004] Therefore, existing technologies need to be improved. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide users with a method, system and terminal equipment for long-distance multi-target temperature measurement and early warning of power equipment, overcoming the defects of the existing power equipment temperature measurement and early warning system that cannot achieve long-distance and large-area substation full-area temperature measurement.

[0006] The technical solution adopted by this invention to solve the technical problem is as follows: In a first aspect, the present invention provides a method for long-distance multi-target temperature measurement and early warning of power equipment, comprising: A CNC gimbal equipped with multiple sensing components is set, and preset point positions and inspection paths are set; wherein, each of the sensing components is an infrared focal plane detector, a visible light imaging sensor, and an active near-infrared ranging module. Acquire multispectral sensing data for each target monitoring area; wherein, the multispectral sensing data includes: near-infrared laser echo data, infrared thermal imaging data and / or visible light image data; Based on the near-infrared laser echo data, the monitoring targets within each target monitoring area are located to obtain the three-dimensional spatial coordinates of each monitoring target; Based on the three-dimensional spatial coordinates of each monitoring target, the temperature monitoring values ​​contained in the multispectral sensing data are corrected for distance attenuation error, ambient temperature interference error, and / or emissivity error to obtain the corrected temperature monitoring data of each target monitoring area. Based on the temperature monitoring data of each target monitoring area, determine the temperature warning level of each monitoring target, and output and display the warning information corresponding to the temperature warning level; Distortion correction is performed on the infrared thermal imaging data. The pre-stored infrared intrinsic distortion coefficients are called to correct the infrared pixel coordinates pixel by pixel. The corrected infrared pixel coordinates are mapped to the visible light camera coordinate system to eliminate assembly fixation deviations. The dynamic compensation matrix corresponding to the current gimbal attitude is superimposed to correct the real-time field of view offset, so as to obtain the pixel coordinates of each infrared temperature sampling point on the visible light image. After the coordinate mapping is completed, the temperature monitoring data after temperature correction is bound to the corresponding pixel position one by one with the high-definition visible light real scene as the bottom canvas.

[0007] Optionally, before the step of acquiring multispectral sensing data for each target monitoring area, the method further includes: A CNC gimbal equipped with multiple sensing components is set, and preset point positions and inspection paths are set; wherein, each of the sensing components is an infrared focal plane detector, a visible light imaging sensor, and an active near-infrared ranging module. Adjust the field of view and focal length of each sensing component, and control the optical axis parallelism deviation to be less than the preset threshold, so as to meet the condition of covering the entire area of ​​the power equipment in the substation during a single inspection.

[0008] Optionally, the step of locating the monitoring targets within each target monitoring area based on the near-infrared laser echo data to obtain the three-dimensional spatial coordinates of each monitoring target includes: Based on the principle of active laser time of flight, the radial distance of the monitored targets identified within each target monitoring area is calculated according to the emission and echo time difference of the near-infrared laser contained in the near-infrared laser echo data. The three-dimensional spatial coordinates of each monitoring target are calculated based on the radial distance of each monitoring target and the horizontal polarization angle and pitch angle of each acquisition component in the CNC gimbal when acquiring the near-infrared laser echo data.

[0009] Optionally, the step of calculating the radial distance of the detected targets within each target monitoring area based on the active laser time-of-flight principle and the emission and echo time difference of the near-infrared laser contained in the near-infrared laser echo data includes: Real-time monitoring of ambient temperature, humidity, and air pressure parameters is used to correct for atmospheric refractive index. The radial distance of each monitored target is then calculated using the following formula: ; in, This represents the radial distance between each monitored target. This represents the speed of light under the current conditions. This represents the time difference between the emission and reception of near-infrared laser light. This represents the selection coefficient for effective multi-echo signals. This represents the current atmospheric refractive index correction factor. This represents the compensation coefficient for the coaxiality error between the gimbal axis and the laser emission axis. This represents the correction factor for atmospheric turbulence disturbance.

[0010] Optionally, the step of correcting the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error, and / or emissivity error based on the three-dimensional spatial coordinates of each monitoring target to obtain the corrected temperature monitoring data for each target monitoring area includes: To address distance attenuation error, ambient temperature interference error, and emissivity error during long-distance temperature measurement, the temperature monitoring values ​​contained in the multispectral sensing data are corrected. The corrected temperature monitoring data for each monitoring target is calculated using the following formula: ; in, This represents the temperature monitoring data of the target after correction. This indicates the temperature monitoring value contained in the multispectral sensing data. This represents the distance-temperature compensation coefficient. This indicates the straight-line distance between the monitored target and the temperature measuring radar. This represents the ambient temperature compensation coefficient. This indicates the current real-time ambient temperature. Indicates the calibration reference ambient temperature. This represents the emissivity compensation coefficient. Indicates the calibrated reference emissivity. This indicates the actual emissivity of the monitored target.

[0011] Optionally, the step of correcting the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error, and / or emissivity error based on the three-dimensional spatial coordinates of each monitoring target to obtain the corrected temperature monitoring data for each target monitoring area further includes: Temperature calibration data collected by a temperature reference calibration device fixed within the target monitoring area and radiation calibration data collected by a radiation reference source are acquired at preset intervals. Using the temperature calibration data and radiation calibration data, the distance temperature compensation coefficient, ambient temperature compensation coefficient, and emissivity compensation coefficient are corrected.

[0012] Optionally, the step of determining the temperature warning level for each monitoring target based on the temperature monitoring data of each target monitoring area, and outputting and displaying warning information corresponding to the temperature warning level, includes: The temperature monitoring data of each monitoring target in each target monitoring area is compared with its corresponding temperature threshold to determine whether the temperature monitoring data exceeds the preset temperature threshold, or whether the temperature change rate of the temperature monitoring data within a preset time exceeds the preset rate threshold. If the preset temperature threshold is exceeded, or the rate of temperature change exceeds the preset speed threshold, then the magnitude of the temperature exceeding the threshold and the magnitude of the rate exceeding the threshold are calculated. The warning level is determined based on the magnitude of the temperature and rate exceeding the limit, as well as the voltage level and equipment type corresponding to the monitored target.

[0013] Optionally, the method further includes: Acquire historical temperature monitoring data for each monitoring target; Historical temperature monitoring data of each of the aforementioned monitoring targets are analyzed to predict temperature change trends, and equipment operation and maintenance suggestions are generated based on the temperature change trends. Based on the historical temperature monitoring data, a temperature benchmark model for the same type of power equipment in the same area is constructed. The real-time temperature of the power equipment of the same type as the monitored target is compared with the benchmark model to identify abnormal power equipment whose temperature deviates from the benchmark, determine that there is a potential fault, and issue an early warning signal.

[0014] Secondly, the present invention also provides a long-distance multi-target temperature measurement and early warning system for power equipment, comprising: The data acquisition module is used to set up a CNC pan-tilt unit equipped with multiple sensing components and to set preset point positions and inspection paths; to acquire multispectral sensing data of each target monitoring area; wherein each of the sensing components is an infrared focal plane detector, a visible light imaging sensor, and an active near-infrared ranging module; the multispectral sensing data includes: near-infrared laser echo data, as well as infrared thermal imaging data and / or visible light image data; The multi-target positioning module is used to locate the monitoring targets within each target monitoring area based on the near-infrared laser echo data, obtain the three-dimensional spatial coordinates of each monitoring target, and perform distortion correction on the infrared thermal imaging data. It calls the pre-stored infrared intrinsic distortion coefficients to correct the infrared pixel coordinates pixel by pixel; maps the corrected infrared pixel coordinates to the visible light camera coordinate system to eliminate assembly fixation deviations; superimposes the dynamic compensation matrix corresponding to the current gimbal attitude to correct the real-time field of view offset, and obtains the pixel coordinates corresponding to each infrared temperature sampling point on the visible light image; after the coordinate mapping is completed, using the high-definition visible light real-scene image as the bottom canvas, the temperature monitoring data after temperature correction is bound to the corresponding pixel position one by one. The temperature correction module is used to correct the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error and / or emissivity error according to the three-dimensional spatial coordinates of each monitoring target, so as to obtain the corrected temperature monitoring data of each target monitoring area. The early warning analysis module is used to determine the temperature early warning level of each monitoring target based on the temperature monitoring data of each target monitoring area, and output and display the early warning information corresponding to the temperature early warning level.

[0015] Thirdly, the present invention also provides a terminal device, comprising: a processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory to execute the steps of the described method for long-distance multi-target temperature measurement and early warning of power equipment.

[0016] Beneficial effects: This invention discloses a method, system, and terminal device for long-distance multi-target temperature monitoring and early warning of power equipment. It acquires multispectral sensing data of each target monitoring area and locates the monitoring targets within each area. Based on the location information, the multispectral sensing data is corrected to obtain corrected temperature monitoring data for each target monitoring area. Based on this temperature monitoring data, early warning information corresponding to each monitoring target is determined. This invention utilizes active laser measurement to achieve precise location of each monitoring target and corrects the temperature monitoring data of the monitoring area based on the location information. This not only avoids the tedious manual input of distance information for monitoring targets but also improves the temperature measurement accuracy of each monitoring target in long-distance scenarios. Furthermore, this invention can achieve full-area temperature monitoring over long distances and large areas, reducing equipment maintenance costs while improving the safety of power equipment. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the steps of the long-distance multi-target temperature measurement and early warning method for power equipment provided by this invention; Figure 2This is a schematic diagram illustrating the steps and principles of multi-target localization in the method of this embodiment of the invention; Figure 3 This is a schematic diagram illustrating the principle of temperature monitoring data correction and output in the method of this embodiment of the invention; Figure 4 This is a schematic diagram of the early warning classification step in the method of this embodiment of the invention; Figure 5 This is a flowchart of the data analysis and edge linkage control in the method of this embodiment of the invention; Figure 6 This is a schematic diagram illustrating the implementation principle of the long-distance multi-target temperature measurement and early warning method for power equipment in the embodiments of the present invention; Figure 7 This invention provides a schematic diagram of the principle of a long-distance multi-target temperature measurement and early warning system for power equipment. Figure 8 A structural principle block diagram of the terminal device provided by the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] As a critical node in the power transmission network, the operating status of the internal electrical equipment of a substation directly affects the reliability of the regional power grid. Abnormal overheating of equipment is a typical early symptom of most power faults, such as insulation degradation and poor contact. Therefore, continuous and accurate temperature monitoring of all electrical equipment in the substation is one of the fundamental aspects of substation operation and maintenance.

[0020] In the early days, temperature measurement of power equipment relied mainly on maintenance personnel using handheld infrared thermometers for regular inspections. However, due to limitations such as shift scheduling, personnel experience, and environmental conditions, there were many blind spots in the inspections, insufficient real-time temperature measurement, and difficulty in achieving continuous monitoring around the clock. It was difficult to detect hidden heating defects in a timely manner. Especially during special periods such as extreme weather and power supply for major events, the operational risks of manual inspections increased significantly, and the coverage efficiency could not meet the actual operation and maintenance needs.

[0021] Most current power equipment temperature measurement methods employ single infrared lenses or dual-light fusion solutions combining mid- and far-infrared and visible light. These methods have relatively short effective measurement distances, requiring the equipment to be installed close to the monitored equipment to ensure accuracy. This makes them susceptible to limitations imposed by the electromagnetic environment and working space within substations. Furthermore, these systems typically rely on manual preset or input of the target distance, failing to automatically acquire precise spatial location parameters. Temperature calibration is limited to fixed offset settings, leading to significant fluctuations in accuracy at long distances and under varying weather conditions, making them unsuitable for the comprehensive temperature measurement needs of large-area substations. Even some temperature measurement devices with laser ranging capabilities can only measure single-point targets, unable to perform pixel-level spatial positioning of all power equipment within the field of view. The number of configurable measurement targets is limited by hardware resources, hindering the flexible configuration of unlimited custom measurement areas. Additionally, periodic manual disassembly and calibration are required, resulting in high maintenance costs.

[0022] Furthermore, existing temperature monitoring systems often employ simple, fixed threshold judgment strategies for their early warning functions. This makes it difficult to implement tiered early warnings based on equipment voltage levels, importance, and temperature change trends, resulting in a high false alarm rate and increasing the workload for maintenance personnel. Moreover, temperature monitoring and perimeter intrusion detection functions are typically independent, requiring the deployment of two separate sets of data acquisition equipment, which increases the equipment investment and subsequent maintenance burden of substations. In multi-device network scenarios, existing systems can only achieve basic data aggregation, lacking the ability to perform integrated processing such as coverage area stitching and cross-validation of measured values, making it difficult to meet the differentiated adaptation needs of substations of different sizes for full-area coverage.

[0023] In summary, existing substation temperature measurement systems have significant shortcomings in terms of automation, measurement accuracy, and intelligent early warning capabilities, necessitating the development of a more comprehensive solution.

[0024] To overcome the aforementioned problems in the prior art, this invention provides a method, system, and terminal device for long-distance multi-target temperature measurement and early warning of power equipment. By locating multiple monitored targets and correcting the target monitoring temperatures obtained from infrared monitoring based on the location information, a more accurate actual temperature of each monitored target is obtained. This invention can effectively improve the accuracy of long-distance temperature measurement, reduce the workload of maintenance personnel, adapt to the needs of single-device or multi-device network deployment in substations of different sizes, effectively improve the digitalization level of power operation and maintenance, and ensure the stability of power grid supply.

[0025] The following description, in conjunction with the accompanying drawings, provides a more detailed account of a method, system, and terminal device for long-distance multi-target temperature measurement and early warning of power equipment disclosed in this invention.

[0026] In a first aspect, the present invention provides a method for long-distance multi-target temperature measurement and early warning of power equipment, such as... Figure 1 As shown, it includes: Step S1: Acquire multispectral sensing data for each target monitoring area; wherein, the multispectral sensing data includes: near-infrared laser echo data, infrared thermal imaging data and / or visible light image data.

[0027] In this invention, infrared detection equipment and image acquisition equipment are used to acquire detection information and image information of each power device within the target monitoring area, and temperature information of each circuit device is obtained based on the detection information and image information. In this embodiment, the target monitoring area is the area where the substation is located, and each power device is arranged within its respective target monitoring area, with each power device serving as the monitoring target. Each target monitoring area in this step can be a sub-area of ​​the substation area, and these target monitoring areas together constitute the entire substation area. Multispectral sensing data refers to detection data acquired using different detection methods.

[0028] Specifically, the multispectral sensing data in this step includes near-infrared laser echo data, infrared thermal imaging data, and visible light image data. Near-infrared laser echo data is obtained by emitting near-infrared light of a specific wavelength using a near-infrared spectrometer, fiber optic probe, or lidar system, and collecting the light reflected back from each monitored target using a receiving device. Infrared thermal imaging data corresponds to a passive detection technology, which uses an infrared thermal imager or a thermal infrared camera mounted on a UAV to receive the infrared radiation emitted by each monitored target due to its temperature, converting it into temperature values ​​and thermal images. Visible light image data is obtained by receiving natural or ambient light reflected from each monitored target, forming a color or grayscale image similar to human vision.

[0029] In one implementation, an integrated silicon oxide uncooled infrared focal plane detector, a 2-megapixel visible light imaging camera, and an 830nm near-infrared laser emitter and receiver are used to simultaneously acquire infrared thermal imaging data, visible light image data, and near-infrared laser echo data of the target monitoring area. After acquiring the aforementioned infrared thermal imaging data, visible light image data, and near-infrared laser echo data, the field of view of the acquired data is calibrated to ensure pixel-level alignment of different modal data.

[0030] The field-of-view calibration in this invention primarily utilizes a calibration system combining a collimator and a two-dimensional precision turntable. The hardware components of this calibration system include: a collimator, a band-matched detector, a two-dimensional electrically controlled precision turntable, signal acquisition equipment, and an extinction device. The collimator (collimator) generates a parallel beam to simulate an infinitely distant target. The band-matched detector is a blackbody source for visible / near-infrared / long-wave infrared light. The two-dimensional electrically controlled precision turntable (azimuth + elevation) has an angular accuracy ≤0.01°. The signal acquisition equipment includes a lock-in amplifier or a high-precision source meter (for measuring photocurrent); weak light detection requires modulation of the light source and phase locking to suppress noise. An anechoic chamber and extinction structure are also included (to eliminate stray ambient light).

[0031] Detailed, standard calibration procedures include: Warm-up and baseline: The detector is powered on and warmed up for 30 minutes; dark current is collected when there is no light, and the dark baseline is subtracted from subsequent measurements.

[0032] Optical axis alignment: Adjust the angle of the turntable so that the parallel light is incident normally (0°), and record the peak response Rmax.

[0033] Angle scanning measurement: Horizontal direction: with 0° as the center, scan at a constant speed for both positive and negative angles; step size 0.05°~0.2°.

[0034] After each step, wait for the turntable to stabilize and record the angle and normalized response. Repeat the scan in the vertical direction.

[0035] Data processing: Plot the angle response curve, interpolate to find the positive and negative angles corresponding to the response = 0.5\(R_\text{max}\); the difference between the two is the full field of view angle.

[0036] The above calibration steps may also include the following steps: two-dimensional grid scanning to generate a field of view contour map to check whether the field of view is symmetrical and whether there is any eccentric occlusion.

[0037] Furthermore, before the step of acquiring multispectral sensing data for each target monitoring area, the following steps are also included: Step S01: Set up a CNC gimbal equipped with multiple sensing components, and set preset point positions and inspection paths; wherein, each of the sensing components is an infrared focal plane detector, a visible light imaging sensor, and an active near-infrared ranging module.

[0038] This step provides a CNC gimbal with horizontal and pitch rotation degrees of freedom, and mounts an infrared focal plane detector, a visible light imaging sensor, and an active near-infrared ranging module on the gimbal's support platform. All sensing components are installed using a parallel optical axis configuration to ensure consistent optical axis pointing, enabling multimodal synchronous acquisition of the same target area.

[0039] Preset points represent the spatial position of the gimbal when rotated to specific horizontal and pitch angles. Each preset point corresponds to a set of gimbal attitude parameters. The setting method is as follows: Drive the gimbal to rotate via the gimbal control interface, aligning the field of view centers of the visible light imaging sensor, infrared focal plane detector, and active near-infrared ranging module with the key observation positions of the target area. Record the horizontal and pitch angles corresponding to the optimal observation state of each sensor sequentially, and store these as preset point parameters in the storage unit of the gimbal control system. Multiple preset points can be stored as the same preset point group; when recalled, the gimbal automatically selects the corresponding attitude parameters based on the currently operating sensor.

[0040] In addition, the inspection path is an automatic movement trajectory of the PTZ (pan-tilt unit) sequentially passing through multiple preset points and performing data acquisition. The setup method is as follows: create an inspection path through the PTZ control system, select several preset points from the stored preset point list, arrange them according to the spatial distribution order of the target area, set the movement speed and dwell time of the PTZ between each preset point, and after saving, the PTZ can automatically complete the multi-sensor data acquisition task along the path.

[0041] Step S02: Adjust the acquisition field of view and focal length of each sensing component, and control the optical axis parallelism deviation to be less than the preset threshold, so as to meet the condition of covering the entire area of ​​the power equipment in the substation during a single inspection.

[0042] In this step, the field of view and focal length parameters of the infrared focal plane detector, visible light imaging sensor, and active near-infrared ranging module are determined based on the spatial distribution range of the power equipment in the entire substation and the preset inspection distance.

[0043] Specifically, by adjusting the zoom and compensation groups of the optical lenses of each sensing component, the actual field of view of each sensor reaches its respective preset field of view threshold, and each sensor can cover its corresponding target area at the optimal imaging distance. The focal length adjustment of the visible light imaging sensor is based on clear imaging, the focal length adjustment of the infrared focal plane detector is based on clear focusing of the thermal radiation signal, and the focal length adjustment of the active near-infrared ranging module is based on maximizing the echo signal intensity.

[0044] The optical axes of the infrared focal plane detector, visible light imaging sensor, and active near-infrared ranging module are adjusted to be parallel to each other, and the parallelism deviation between any two optical axes is controlled to be less than a preset threshold. Specifically, a front collimator is used as a reference light source to simultaneously project a parallel beam containing a crosshair onto each sensing component; the positional offset of the center of the crosshair relative to the image center in the image acquired by each sensing component is read; based on the positional offset, the installation angle of each sensing component is adjusted or an optical wedge compensation element is used to make the center of each crosshair converge to within a preset error range of its respective image center, thus ensuring that the parallelism deviation of each optical axis is less than the preset threshold.

[0045] When the field of view, focal length, and optical axis parallelism of each sensing component meet preset conditions, the pan-tilt unit sequentially passes through each preset point along a preset path during a single inspection. The data collected by each sensing component covers the entire area of ​​the substation's power equipment. Preferably, the field of view coverage between adjacent preset points has a preset overlap ratio to ensure that no area is missed in the data collection.

[0046] Step S2: Based on the near-infrared laser echo data, locate the monitoring targets within each target monitoring area to obtain the three-dimensional spatial coordinates of each monitoring target.

[0047] This step is based on the active laser time-of-flight principle, processes the time difference between the emission and echo of near-infrared laser, calculates the distance, azimuth, and elevation angle parameters of all identified power equipment targets within the field of view, and outputs the three-dimensional spatial coordinates of each target, achieving pixel-level positioning of each monitoring target.

[0048] In detail, the steps of locating the monitoring targets within each monitoring area based on the near-infrared laser echo data and obtaining the three-dimensional spatial coordinates of each monitoring target include: Step S21: Based on the active laser time-of-flight principle, calculate the radial distance of the monitored targets identified within each target monitoring area according to the emission and echo time difference of the near-infrared laser contained in the near-infrared laser echo data.

[0049] Radial distance refers to the straight-line distance from the sensor or gimbal position to the target monitoring point. This step relies on the 830nm near-infrared laser TOF time-of-flight principle. First, the time difference Δt between laser emission and echo is collected; then, parameters such as temperature, humidity, air pressure, and gimbal coaxial deviation are used to correct the ranging error, and the straight-line distance to the target is calculated.

[0050] In detail, the step of calculating the radial distance of the detected targets within each target monitoring area based on the active laser time-of-flight principle and the emission and echo time difference of the near-infrared laser contained in the near-infrared laser echo data includes: Real-time monitoring of ambient temperature, humidity, and air pressure parameters is used to correct for atmospheric refractive index. The radial distance of each monitored target is then calculated using the following formula: ; in, This represents the radial distance between each monitored target. This represents the speed of light under the current conditions. This represents the time difference between the emission and reception of near-infrared laser light. This represents the selection coefficient for effective multi-echo signals. This represents the current atmospheric refractive index correction factor. This represents the compensation coefficient for the coaxiality error between the gimbal axis and the laser emission axis. This represents the atmospheric turbulence disturbance correction coefficient. The above-mentioned multi-echo effective signal screening coefficient, ambient atmospheric refractive index correction coefficient, gimbal rotation axis and laser emission axis coaxiality error compensation coefficient, and atmospheric turbulence disturbance correction coefficient can all be customized based on different scenarios.

[0051] For example, in a real-world scenario, the environmental parameters are as follows: 1. Ambient temperature E=25℃, air pressure 101.325kPa, relative humidity 45%, no strong convection or turbulence.

[0052] 2. The laser is emitted to the target of the bus joint. There are no tree branches or dust covers to block it, and only a single effective echo is emitted.

[0053] 3. The equipment has been coaxially calibrated at the factory, and the mechanical clearance of the gimbal is extremely small.

[0054] 4. The reference speed of light is c = 299702547 m / s (the standard atmospheric speed of light at 25℃).

[0055] 5. Actual measured round-trip flight time difference of the laser t = 1.000667 × 10⁻⁶ s.

[0056] The meanings, values, and basis for the symbols of each parameter in the radial distance of each monitoring target are shown in Table 1 below.

[0057] Table 1

[0058] Numerator: ; Denominator term: ; Radial distance R: R = 299.903 / 2.00090 ≈ 149.88m; The measured straight-line distance between the busbar connector and the radar was approximately 149.88m. The distance was verified to be 149.87m using a manual laser rangefinder. The ranging error was only 0.01m, which meets the ranging accuracy requirements of ±1cm within 65m and ≤±5cm at long distances.

[0059] Step S22: Calculate the three-dimensional spatial coordinates of each monitoring target based on the radial distance of each monitoring target and the horizontal polarization angle and pitch angle of each acquisition component in the CNC gimbal when acquiring the near-infrared laser echo data.

[0060] By combining the mapping relationship between the pan-tilt encoder angle and the field of view calibration, the azimuth and pitch angles of the target are calculated. The three-dimensional spatial coordinates of the power equipment target are output through spherical coordinate transformation. Relying on the pixel-angle mapping matrix formed by the unified field of view calibration of the multispectral unit, each three-dimensional coordinate is back-projected onto the infrared and visible light images, achieving pixel-level binding and positioning of all devices within the field of view. Precise spatial distance parameters are automatically matched to each temperature-measuring pixel, eliminating the need for manual distance input and providing spatial data support for subsequent infrared temperature measurement distance attenuation correction and accurate anomaly point labeling.

[0061] This step combines the coaxiality error compensation coefficient obtained from the one-click calibration after installation, the atmospheric turbulence disturbance correction coefficient calculated from real-time meteorological data, and the effective signal coefficient determined by the multi-echo signal filtering logic. It can automatically filter out invalid echoes from nearby obstructions such as tree branches, dust covers, and spider webs, and eliminate the impact of installation deviation, gimbal wear and offset, and complex meteorological environment on ranging accuracy. It controls the ranging accuracy within 65 meters to within 1 centimeter and supports the synchronous output of target azimuth and elevation angle calibration data, providing high-precision spatial parameter support for subsequent temperature correction and anomaly point location.

[0062] In practical implementation, dynamic field-of-view offset correction can be achieved through hardware-level synchronization and real-time parameter retrieval during the gimbal's rotation along the inspection path. The gimbal encoder, infrared imaging device, and visible light imaging device share the same hardware synchronization pulse trigger for acquisition, and the timestamp deviation of the three data acquisition channels is controlled within milliseconds, avoiding pixel shift caused by misalignment of acquisition time when the gimbal rotates continuously. When the gimbal rotates to the target preset point or during the inspection process, the system reads the high-precision azimuth and pitch angle parameters output by the gimbal encoder in real time. Based on the current attitude parameters, it retrieves the pre-calibrated offset mapping library and obtains the corresponding dynamic pixel compensation matrix at the current angle through interpolation calculation. This matrix is ​​used to offset the real-time field-of-view offset caused by factors such as gimbal shaft clearance, mechanical transmission tolerance, and minor optical path vibrations. For the slow drift caused by gimbal wear and environmental temperature deformation after long-term operation, the system also extracts stable feature points such as the outline and corners of the power equipment in the two images in real time, performs online feature matching and deviation calculation, and performs small-amplitude adaptive fine-tuning of the dynamic compensation matrix. This maintains registration accuracy over a long period without the need for manual disassembly and calibration.

[0063] After coordinate correction, the system achieves precise binding and image fusion of infrared temperature data and visible light pixels through layer-by-layer coordinate transformation. First, distortion correction is performed on the original infrared temperature image, calling pre-stored infrared intrinsic parameters and distortion coefficients to correct the coordinates pixel by pixel, eliminating the influence of optical distortion from the infrared lens itself. Then, a static extrinsic parameter matrix is ​​substituted to complete the cross-optical path coordinate transformation, mapping the infrared pixel coordinates to the visible light camera coordinate system, eliminating assembly fixation deviations. Finally, a dynamic compensation matrix corresponding to the current gimbal attitude is superimposed to correct the real-time field of view offset, ultimately obtaining the precise pixel coordinates of each infrared temperature sampling point on the visible light image. After coordinate mapping is completed, the system uses a high-definition visible light real-world image as the underlying canvas, and binds the accurate temperature values ​​after three levels of temperature correction to the corresponding pixel positions one by one. At the same time, combined with the equipment outline and temperature measurement area configuration output by multispectral fusion recognition, temperature value labels and temperature measurement area boxes are superimposed on key temperature measurement points such as switch contacts, bus joints, and bushings. It can also highlight high-temperature areas according to the corresponding color mark mapped by the temperature range, and finally generate a high-definition visible light monitoring image with temperature labels, which is output to local display, storage and remote operation and maintenance terminals.

[0064] This multi-level registration mechanism ensures that the PTZ maintains pixel-level alignment accuracy throughout its full rotation range, avoiding misalignment between temperature labels and actual equipment. Maintenance personnel do not need to switch between infrared and visible light images for verification; they can directly read the real-time temperature of any point in the real-world image. Simultaneously, it can be linked with equipment ledger information for synchronous display, reducing the identification difficulty and operational costs for maintenance personnel. It also fundamentally reduces temperature target matching errors caused by pixel misalignment, providing a reliable visual foundation for subsequent temperature anomaly judgment and accurate alarms.

[0065] like Figure 2 As shown, this step involves emitting near-infrared laser light and receiving the echoes to calculate the time difference, thereby locating the monitored target. To eliminate environmental interference, the system uses real-time environmental sensor data to correct for atmospheric refractive index and turbulence effects. Simultaneously, combined with coaxiality error compensation calibrated during installation, the system automatically filters echoes from ineffective obstructions such as tree branches. Finally, it outputs the precise three-dimensional spatial coordinates of the power equipment target, providing a high-precision position reference for subsequent pixel-level temperature overlay and anomaly point marking.

[0066] Step S3: Based on the three-dimensional spatial coordinates of each monitoring target, the temperature monitoring values ​​contained in the multispectral sensing data are corrected for distance attenuation error, ambient temperature interference error, and / or emissivity error to obtain the corrected temperature monitoring data of each target monitoring area.

[0067] Because infrared radiation is absorbed and scattered by the atmosphere during transmission, and the spatial resolution of the instrument itself is limited, temperature monitoring data suffers from distance attenuation errors.

[0068] Infrared thermal imagers measure the infrared radiation emitted by an object and then deduce its temperature. However, in high ambient temperatures, the infrared radiation emitted from the surrounding environment is stronger. This radiation is superimposed on the target's own radiation, causing the infrared thermal imager to read an inflated value. Conversely, in cold environments, if the target reflects radiation from the low-temperature sky, the imager will read a lower value. Therefore, temperature monitoring is affected by ambient temperature.

[0069] On the other hand, in the field of infrared thermometry, emissivity is the ability of a target object's surface to convert its own heat into infrared radiation, with a value ranging from 0 to 1. The total radiant energy received by an infrared thermal imager equals the sum of the target's own radiation and the reflected environmental radiation. If the emissivity is not set accurately, the temperature inversion results will have systematic biases.

[0070] In detail, this step corrects the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error, and emissivity error during long-distance temperature measurement. The corrected temperature monitoring data for each monitoring target is calculated using the following formula: ; in, This represents the temperature monitoring data of the target after correction. This indicates the temperature monitoring value contained in the multispectral sensing data. This represents the distance-temperature compensation coefficient. This indicates the straight-line distance between the monitored target and the temperature measuring radar. This represents the ambient temperature compensation coefficient. This indicates the current real-time ambient temperature. Indicates the calibration reference ambient temperature. This represents the emissivity compensation coefficient. Indicates the calibrated reference emissivity. This indicates the actual emissivity of the monitored target.

[0071] In one implementation, a standard blackbody source within the field of view is used for dynamic self-calibration to solidify the benchmark values ​​of compensation coefficients for the main power equipment categories in the substation, as shown in Table 2 below: Table 2

[0072] The calculation for a 110kV busbar joint (exposed metal) under standard sunny conditions is as follows: Operating parameters include: Equipment type: copper busbar connector; from the table, A=0.032, B=0.86, C=28; target distance obtained by laser ranging: S=50.00m; infrared raw temperature measurement: Tr=62.3℃; real-time ambient temperature: E=25℃, reference temperature: E0=25℃; reference emissivity: F=0.95, actual emissivity of the busbar: Fm=0.35.

[0073] Itemized calculations: Distance compensation AS = 0.032 × 50.00 = 1.60℃; Ambient temperature compensation: B × (E - E0) = 0.86 × (25 - 25) = 0℃; Emissivity compensation: C × (F - Fm) = 28.5 × (0.95 - 0.35) = 17.10℃; Corrected actual temperature: TC = 62.3 + 1.60 + 0 + 17.10 = 81.00℃.

[0074] The results show that the actual measured temperature of the busbar joint by the on-site contact thermocouple was 80.8℃, and the temperature deviation after correction was only 0.2℃, which meets the system's ±2℃ temperature measurement accuracy requirement. If the three-level compensation is not enabled, the original infrared reading of 62.3℃ will deviate from the true value by as much as 18.5℃, which is completely unusable for fault diagnosis.

[0075] Further, the step of correcting the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error, and / or emissivity error based on the three-dimensional spatial coordinates of each monitoring target to obtain the corrected temperature monitoring data for each target monitoring area also includes: Temperature calibration data collected by a temperature reference calibration device fixed within the target monitoring area and radiation calibration data collected by a radiation reference source are acquired at preset intervals; the distance temperature compensation coefficient, ambient temperature compensation coefficient, and emissivity compensation coefficient are corrected using the temperature calibration data and radiation calibration data.

[0076] In this step, to obtain more accurate temperature monitoring data, a dynamic self-calibration logic is also incorporated, which automatically corrects the readings at fixed intervals by calling a standard blackbody calibration source installed at a fixed position within the radar's field of view. Three types of compensation coefficients eliminate the need for regular manual disassembly and calibration. In practical implementation, dedicated compensation coefficients can be configured for different types of power equipment such as transformers, disconnectors, busbars, and insulators, controlling the temperature measurement accuracy within a 65-meter range to within ±2℃, fully meeting the accuracy requirements for long-distance temperature measurement in substations.

[0077] Furthermore, since visible light images are obtained in this step, the outline, texture, and identification features of each power device can be extracted from the visible light images. These features can then be matched with a pre-stored power device ledger database to identify the type, material, rated parameters, and maintenance history of the target device. The emissivity parameters of the target device can be output by matching the pre-stored device material emissivity database. Simultaneously, dynamic pixel registration logic can be implemented to correct the slight field-of-view shift generated during the pan-tilt-zoom rotation in real time. Infrared temperature data can be accurately superimposed onto the corresponding pixel positions in the visible light image to generate a high-definition visible light monitoring screen with temperature annotations. Maintenance personnel can directly view the real-time temperature and equipment ledger information at any location in the visible light screen without switching between infrared and visible light screens or manually verifying equipment identification information.

[0078] The aforementioned dynamic pixel registration and temperature superposition steps are based on a static reference established by the pre-calibration of multiple sensing units. After the system is factory-tested and installed on-site, single-channel intrinsic parameter calibration is first performed on the infrared focal plane detector and the visible light imaging unit. By acquiring multiple sets of standard checkerboard target images at different angles, the effective focal length, principal point coordinates, and lens distortion coefficient of each imaging unit are solved, generating a corresponding distortion correction lookup table. This first eliminates pixel deformation and coordinate deviation caused by the barrel and pincushion distortion of the lens itself, ensuring the geometric accuracy of the single-channel image itself. On this basis, cross-modal coaxial calibration is performed with the visible light optical path as the reference axis, fine-tuning the installation posture of the infrared lens. Simultaneously, multiple sets of target feature points at different azimuth and pitch angles are acquired. Through feature matching and iterative solution, the static extrinsic parameter matrix of the infrared optical path relative to the visible light optical path is solved, solidifying the rotation and translational fixed deviations generated during the assembly process. This ensures that the pixel overlap error of the two images in the static state of the equipment is controlled at the sub-pixel level. To meet the requirements of full-stroke inspection of the gimbal, the system will also pre-compile the field of view offset calibration of the gimbal across the entire angle range, control the CNC gimbal to traverse the entire working angle range, collect synchronous infrared and visible light images at fixed step sizes, extract stable feature points in the image to calculate the two-way pixel offset under the current attitude, and construct a mapping database of gimbal azimuth angle, pitch angle and pixel offset compensation matrix as the underlying data support for dynamic correction during gimbal rotation.

[0079] In one implementation, such as Figure 3As shown, the main process of this step includes: identifying the type and material of power equipment based on the collected infrared, visible light, or laser data; matching the identification results with information from the material emissivity database to obtain the type and material of the power equipment; and then performing three levels of temperature correction based on the type and material of the power equipment. The three levels of temperature correction include: distance attenuation compensation, ambient temperature interference compensation, and dynamic emissivity steps, and calculating the actual temperature value based on the compensated coefficients. Through dynamic pixel registration logic, the corrected infrared temperature data is accurately superimposed onto the visible light image. This processing method allows maintenance personnel to directly read accurate temperature values ​​from high-definition visible light images, greatly improving identification efficiency.

[0080] Step S4: Based on the temperature monitoring data of each target monitoring area, determine the temperature warning level of each monitoring target, and output and display the warning information corresponding to the temperature warning level.

[0081] In this step, based on the temperature monitoring data corresponding to different targets within each target monitoring area, the temperature warning level for each target is determined, and a warning message is issued based on the temperature warning level. Furthermore, in practice, each monitoring target corresponds to a temperature threshold that triggers an alarm. The temperature monitoring data for each target is compared with its corresponding temperature threshold to determine whether an alarm has been triggered and, if so, the level of the alarm.

[0082] The system monitors the temperature and rate of change of each target in real time. Once the limit is exceeded, the early warning and tiered response unit will assess the situation based on the equipment's voltage level, importance, and load data. A Level 1 warning will trigger a coordinated effort with surrounding equipment to identify and match fault case solutions; a Level 2 warning will be recorded pending review; and a Level 3 warning will generate a log. This tiered mechanism effectively filters false alarms, ensuring that critical potential issues are pushed to maintenance personnel's mobile terminals immediately and assisting them in determining whether the problem is caused by poor contact or overload.

[0083] In detail, such as Figure 4 As shown, the step of determining the temperature warning level for each monitoring target based on the temperature monitoring data of each target monitoring area, and outputting and displaying the warning information corresponding to the temperature warning level, includes: Step S510: Compare the temperature monitoring data of each monitoring target within each target monitoring area with its corresponding temperature threshold.

[0084] Step S520: Determine whether the temperature monitoring data exceeds the preset temperature threshold, or determine whether the temperature change rate of the temperature monitoring data within a preset time exceeds the preset rate threshold.

[0085] Step S530: If the threshold is exceeded, a warning logic determination is performed. Specifically, this includes: if the temperature monitoring data exceeds the preset temperature threshold, or the temperature change rate exceeds the preset speed threshold, calculating the magnitude of the temperature exceedance and the magnitude of the rate exceedance. Based on the magnitude of the temperature exceedance and the magnitude of the rate exceedance, as well as the voltage level and equipment type corresponding to the monitored target, a warning level is determined. Steps S531, S532, and S533 are executed according to the different warning levels.

[0086] Step S531, Level 1 Warning: Immediately push and link to focus, and execute step S540.

[0087] Step S532, Level 2 Warning: Records are stored pending review.

[0088] Step S533, Level 3 Early Warning: Automatically generate operation logs.

[0089] Step S540: Match the fault case library with the handling plan.

[0090] Step S550: Connect to SCADA (Supervisory Control And Data Acquisition) load-assisted diagnosis. This step utilizes real-time power grid operation data provided by the SCADA system and employs intelligent analysis technology to assist dispatchers in diagnosing whether there are abnormalities in the power grid load and to provide decision-making suggestions.

[0091] Step S560: Abnormal closed-loop handling.

[0092] Furthermore, the method also includes: Historical temperature monitoring data of each monitoring target is acquired; the historical temperature monitoring data of each monitoring target is analyzed to predict temperature change trends, and equipment operation and maintenance suggestions are generated based on the temperature change trends; and a temperature benchmark model of the same type of power equipment in the same area is constructed based on the historical temperature monitoring data, the real-time temperature of power equipment of the same type as the monitoring target is compared with the benchmark model, abnormal power equipment whose temperature deviates from the benchmark is identified, potential faults are determined, and a warning signal is issued.

[0093] Combination Figure 5As shown, this step analyzes the historical temperature data of each temperature measurement target, explores the patterns of temperature changes with load, ambient temperature, and season, predicts temperature change trends, anticipates potential temperature anomalies, generates equipment maintenance suggestions and pushes them to maintenance personnel, can build a temperature benchmark model for similar equipment in the same area, compares the temperature of all equipment with the benchmark model in real time, identifies abnormal equipment whose temperature deviates from the benchmark, and can issue early warnings of potential hazards even if the temperature does not exceed the preset threshold. At the same time, it can automatically analyze the temperature differences of equipment in the same circuit, identify potential fault hazards such as poor contact and circuit abnormalities, and assist maintenance personnel in handling them in advance to prevent the fault from escalating.

[0094] The method disclosed in this invention also supports custom inspection cycles and dwell time at each preset point. It can be connected to the power grid load dispatching system to dynamically adjust the inspection strategy. During peak load periods or special power supply guarantee periods, the inspection cycle is shortened to increase the inspection frequency of important equipment. During off-peak load periods, the inspection cycle is extended to reduce equipment power consumption. During the inspection process, the current weather environment is automatically identified. When encountering severe weather such as strong winds, heavy rain, or heavy snow, the gimbal rotation speed is automatically reduced to avoid equipment damage. At the same time, the sensitivity of anomaly detection is improved. When an abnormal temperature occurs at a preset point, the dwell time is automatically extended to increase the sampling frequency. After confirming the anomaly, the subsequent inspection tasks are continued.

[0095] The method of this invention may further include: identifying abnormal events such as personnel intrusion, foreign object hanging, and small animal intrusion within the field of view; supporting integration with the electronic tag system for maintenance personnel to distinguish between authorized and unauthorized personnel; ensuring that authorized personnel entering the monitoring area will not trigger false alarms; identifying personnel entering the work area without wearing safety helmets, insulated shoes, or other safety protective equipment and triggering an alarm; identifying small foreign objects such as hanging dust covers, kite strings, and plastic sheets, providing early warning of potential discharge hazards; automatically triggering audible and visual alarms upon detecting abnormal events; simultaneously capturing and storing on-site images and pushing them to the maintenance terminal; effectively preventing safety hazards caused by unauthorized personnel entering the substation equipment area.

[0096] like Figure 6 The diagram illustrates the process steps of this embodiment. The method begins with step H1, simultaneously acquiring infrared, visible light, and laser echo data. Step H2, through pre-set point inspection by the PTZ (pan-tilt unit), the system achieves full substation coverage. Then, it enters the core processing stage: first, step H3, performing multi-target spatial positioning; then step H4, performing temperature correction calculations using multi-dimensional parameters; step H5, real-time temperature monitoring and rule comparison; step H6, determining if there is a temperature anomaly; if so, proceeding to step H7; otherwise, proceeding to step H8. Step H7, implementing graded early warning handling and pushing it to the terminal; step H8, simultaneously performing data storage and edge computing. Finally, step H9, maintenance personnel obtain the early warning information through the terminal and perform closed-loop processing.

[0097] In practical implementation, the method of this invention also supports the network operation of two to four temperature measuring radars. The data collected by all radars are synchronously uploaded to a unified monitoring platform. The platform can stitch together the coverage areas of multiple radars to achieve blind-spot-free coverage of the entire substation. When a target appears in the overlapping coverage area of ​​multiple radars, the platform automatically selects the temperature measurement data of the radar closest to the target as valid data, and cross-verifies it by comparing it with the measurement values ​​of other radars to further improve the reliability of the temperature measurement data.

[0098] Secondly, the present invention also provides a long-distance multi-target temperature measurement and early warning system for power equipment, such as... Figure 7 As shown, it includes: The data acquisition module 710 is used to acquire multispectral sensing data of each target monitoring area; wherein, the multispectral sensing data includes: near-infrared laser echo data, infrared thermal imaging data and / or visible light image data; its function is as described in step S1.

[0099] The multi-target positioning module 720 is used to locate the monitoring targets in each target monitoring area based on the near-infrared laser echo data, and obtain the three-dimensional spatial coordinates of each monitoring target; its function is as described in step S2.

[0100] The temperature correction module 730 is used to correct the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error and / or emissivity error according to the three-dimensional spatial coordinates of each monitoring target, so as to obtain the corrected temperature monitoring data of each target monitoring area; its function is as described in step S3.

[0101] The early warning analysis module 740 is used to determine the temperature early warning level of each monitoring target based on the temperature monitoring data of each target monitoring area, and output and display the early warning information corresponding to the temperature early warning level, as described in step S4.

[0102] Thirdly, the present invention also provides a terminal device, such as... Figure 8 As shown, the terminal device 200 includes a processor 201 and a memory 202. The memory 202 is used to store computer programs, and the processor 201 is used to call and run the computer programs stored in the memory 202 to execute the steps of the long-distance multi-target temperature measurement and early warning method for power equipment.

[0103] The following two examples further illustrate specific embodiments of the present invention: Example 1: Single equipment deployment scenario of 110kV outdoor medium-sized substation.

[0104] The substation covers an area of ​​approximately 8,000 square meters and is equipped with two main transformers and 12 incoming and outgoing line bays. The original temperature measurement method involved maintenance personnel using handheld infrared thermometers to conduct inspections once a week. Due to the limitations of the on-site working environment and shift scheduling, there were problems such as many blind spots in the inspection, untimely detection of abnormal hazards, and a heavy workload for maintenance personnel.

[0105] The system is installed on a fixed bracket on the top of the substation's main control building, at a height of 6 meters. It employs a multispectral sensing module integrating an uncooled silicon dioxide infrared focal plane detector, a 2-megapixel visible light imaging unit, and an 830nm near-infrared laser emitting and receiving unit. After installation, a one-click calibration function is activated to calibrate the field of view of all acquisition units, ensuring complete overlap of the fields of view of the three acquisition channels and pixel-level alignment of different modal data. Simultaneously, the coaxiality error between the gimbal axis and the laser emission axis is calibrated. The system is equipped with a high-precision CNC gimbal. Maintenance personnel configure 18 preset points according to the substation equipment layout, setting default inspection paths and parameters. A single inspection can cover the entire power equipment area of ​​the substation without manual on-site adjustment of equipment posture. The default inspection cycle is set to complete a full-area inspection every hour. During major events, power supply protection, or peak load periods, the cycle automatically adjusts to every 15 minutes. In severe weather conditions such as strong winds or heavy rain, the gimbal automatically reduces its rotation speed to prevent equipment vibration from affecting acquisition accuracy and to improve anomaly detection sensitivity.

[0106] The system incorporates a multi-target positioning and ranging module. Based on the active laser time-of-flight principle, it processes the emission and echo time difference of near-infrared lasers, calculates the distance, azimuth, and elevation angles of all identified power equipment targets within the field of view, and outputs the three-dimensional spatial coordinates of each target, achieving pixel-level target positioning. The temperature correction and temperature calculation module integrates infrared thermal imaging data, visible light recognition data, target positioning and ranging data, and environmental sensor data to perform multiple error corrections on the raw infrared temperature values, outputting the accurate actual temperature value for each target. The multi-target temperature monitoring configuration module supports various temperature measurement area configurations, including global, single-point, straight-line, rectangular, and custom polygon types. This deployment added 217 temperature measurement targets, covering all heat-prone points such as main transformer bushings, disconnector contacts, busbar joints, and insulator strings. There is no limit to the number of temperature measurement targets that can be added, and it supports independent configuration of temperature alarm thresholds and judgment rules for each target. During system operation, the laser ranging unit collects ambient temperature, humidity, and air pressure parameters in real time to correct atmospheric refractive index, automatically filters invalid echoes from nearby obstructions such as tree branches, dust covers, and spider webs, and outputs accurate distance parameters for each temperature measurement target. The multispectral fusion recognition unit extracts equipment outlines and identification features from the visible light image, matches them with the pre-stored equipment ledger database and emissivity database, outputs the emissivity parameters of the corresponding target, dynamically corrects minor field-of-view shifts during pan-tilt rotation, and accurately overlays infrared temperature data onto the corresponding pixel positions in the visible light image. Maintenance personnel can directly view the real-time temperature and equipment ledger information at any point on the visible light monitoring screen without switching between different modal screens.

[0107] The system compares the actual temperature value of each temperature measurement target with its corresponding threshold in real time. When the temperature exceeds the threshold or the rate of temperature change exceeds a preset limit, an early warning is automatically triggered. Simultaneously, the precise location of the anomaly is marked in both visible light and infrared thermal images, and the warning information is pushed to the maintenance terminal. The warning grading unit classifies warning information based on the magnitude of the temperature exceeding the threshold, the rate of temperature change, and the importance of the equipment. Level 1 warnings are directly pushed to the maintenance personnel's mobile terminals and the monitoring center's large screen, simultaneously matching historical fault case libraries and pushing troubleshooting and handling plans. Level 2 warnings are stored as an anomaly record for review during inspections, while Level 3 warnings only generate log records. The data storage and interaction module has a built-in large-capacity solid-state storage unit, capable of storing at least 3 years of historical temperature data, warning records, and image data. It supports generating temperature change curves for a specified time period, supporting simultaneous comparison of multiple temperature curves on the same screen, and supporting local querying and remote export of all data. The edge computing and linkage control module embeds a lightweight deep learning algorithm, which can identify personnel intrusion and abnormal events such as hanging foreign objects in the field of view in real time, and trigger corresponding alarms simultaneously. It can also perform self-testing of the device's own operating status on a regular basis, generate device health status reports, and connect to the electronic tag system for maintenance personnel to distinguish between authorized and unauthorized personnel. Authorized maintenance personnel entering the monitoring area will not trigger false alarms. When personnel without safety helmets or insulated shoes, or foreign objects such as hanging kite strings or plastic sheets are detected, an audible and visual alarm is automatically triggered and on-site images are captured and pushed to the maintenance terminal. When small animals are detected entering, an early warning is triggered simultaneously. The system supports dual transmission modes of RJ45 Ethernet and 4G. When the network is interrupted, the edge independently stores all operating data, and automatically synchronizes the data at the breakpoint after the network is restored. It supports mainstream protocols such as ONVIF (Open Network Video Interface Forum), GB / T28181, and RTSP (Real Time Streaming Protocol). It can be directly connected to the existing monitoring system of the substation without modifying the original system. After the system is deployed, only an initial calibration is required for long-term stable operation without frequent manual debugging.

[0108] Table 3: Comparison of Single-device Deployment Scenario Performance with Original Manual Inspection Method

[0109] The data in Table 3 comes from the substation's operational statistics over three consecutive months. The temperature measurement target coverage includes all primary equipment points prone to overheating throughout the station. Previously, manual inspections were limited by inspection routes and operating time, leaving some high-altitude and remote areas uncovered. This system's pre-set inspection routes cover all points in the entire area, eliminating blind spots. The anomaly detection timeliness statistic measures the time interval from abnormal equipment overheating to detection. Previously, manual inspections had a weekly cycle, and some potential problems might remain dormant for several days before being discovered. This system's continuous inspections can trigger warnings immediately upon anomaly occurrence. The false alarm rate statistic measures the proportion of invalid alarms to the total number of alarms. Previously, manual temperature measurement was affected by personnel experience and operating procedures, easily leading to measurement errors and misjudgments. This system's multiple temperature corrections and tiered warning logic effectively filter invalid alarms. The reduced workload related to temperature measurement allows maintenance personnel to devote more energy to fault handling and equipment maintenance, effectively improving overall maintenance efficiency.

[0110] Example 2: Multi-device network deployment scenario for a 220kV large outdoor substation.

[0111] The substation covers an area of ​​approximately 22,000 square meters and is equipped with 4 main transformers and 36 incoming and outgoing line bays. The original temperature measurement solution involved deploying multiple independently set fixed infrared temperature measurement devices, which resulted in numerous coverage blind spots, inconsistent data, and high operation and maintenance costs.

[0112] A total of four long-distance multi-target temperature measurement and early warning systems for power equipment were deployed. They were installed on brackets at the four corners of the substation, each at a height of 8 meters. All devices are connected to a unified monitoring platform via Ethernet. Each device uses a multispectral sensing acquisition module that integrates a silicon oxide uncooled infrared focal plane detector, a 2-megapixel visible light imaging unit, and an 830-nanometer near-infrared laser emitting and receiving unit. After installation, initial calibration of each device was performed to ensure that the field of view of each acquisition unit is completely coincident and that different modal data are aligned at the pixel level. Simultaneously, the coaxiality error calibration of the pan-tilt axis and the laser emission axis of each device was completed. Each device is equipped with a high-precision CNC gimbal and has 22 preset points. A unified inspection path and cycle rule are set. The platform stitches together the coverage areas of the four devices to achieve blind-spot-free coverage of the entire station area. When the temperature measurement target is in the overlapping coverage area of ​​multiple devices, the platform automatically selects the temperature measurement data of the device closest to the target as valid data. At the same time, it cross-verifies the measurement values ​​of other devices to further improve the reliability of the temperature measurement data. The inspection cycle is set to complete a full-area inspection every 2 hours by default, and automatically adjusts to once every 30 minutes during peak load periods.

[0113] Each device has a built-in multi-target positioning and ranging module. Based on the active laser time-of-flight principle, it processes the emission and echo time difference of near-infrared lasers, calculates the distance, azimuth, and elevation angle parameters of all identified power equipment targets within the field of view, and outputs the three-dimensional spatial coordinates of each target, achieving pixel-level target positioning. The temperature correction and temperature calculation module integrates infrared thermal imaging data, visible light recognition data, target positioning and ranging data, and environmental sensor data to perform multiple error corrections on the raw infrared temperature measurement values, outputting the accurate actual temperature value of each target. The multi-target temperature monitoring configuration module supports various temperature measurement area configurations, including global, single-point, straight line, rectangular, and custom polygon types. This network deployment configures a total of 642 temperature measurement targets, covering all heat-prone points in the entire station. There is no limit to the number of temperature measurement targets that can be added. The thresholds and early warning rules for all temperature measurement targets are uniformly configured on the platform, synchronously connecting to the substation's SCADA system to obtain real-time load data of the equipment, assisting in determining whether the cause of overheating is due to typical faults such as overload or poor contact. The temperature correction unit calls a standard blackbody calibration source at a fixed location to automatically correct the temperature compensation coefficient periodically, without the need for manual disassembly and calibration. The temperature measurement accuracy fully meets the long-distance temperature measurement requirements of substations.

[0114] The temperature anomaly detection and early warning module compares the actual temperature value of each temperature measurement target with the corresponding threshold in real time. When the temperature exceeds the threshold or the rate of temperature change exceeds the preset limit, an early warning is automatically triggered. Simultaneously, the precise location of the anomaly point is marked in the visible light image and infrared thermal image, and the early warning information is pushed to the maintenance terminal. The data storage and interaction module has a built-in large-capacity solid-state storage unit. A single device can store at least 3 years of historical temperature data, early warning records, and image data. The platform aggregates the temperature measurement data of all devices, generates a temperature distribution heat map of all devices in the station, supports the generation of temperature change curves within a specified time period, supports the comparison of multiple sets of temperature curves on the same screen, and supports local query and remote export of all data. The temperature data intelligent analysis unit mines the historical temperature data of each temperature measurement target, analyzes the temperature change patterns with load, ambient temperature, and season, predicts temperature change trends, and builds a temperature benchmark model for similar devices in the same area. The temperature of all devices is compared with the benchmark model in real time to identify abnormal devices whose temperature deviates from the benchmark. Even if the temperature does not exceed the threshold, a potential hazard warning can be pushed in advance. At the same time, it automatically analyzes the temperature differences of devices in the same circuit, identifies potential fault hazards such as poor contact and circuit abnormalities, and generates equipment maintenance suggestions on a monthly basis and pushes them to maintenance personnel. The edge computing and linkage control module embeds a lightweight deep learning algorithm, enabling real-time identification of personnel intrusion and foreign object suspension anomalies within the field of view, synchronously triggering corresponding alarms. It periodically performs self-monitoring of the device's operational status, generating device health reports. In multi-device network scenarios, the intrusion detection functions of all devices are unified and linked. When a device detects personnel intrusion or foreign object suspension, surrounding related devices automatically focus on the abnormal point to collect multi-angle data, and the platform uniformly pushes alarm information. The device body uses an aluminum alloy shell, with a protection level conforming to IP67 standards, supporting stable operation in ambient temperatures ranging from -10℃ to 60℃. It has built-in level 3 lightning protection, surge protection, and electromagnetic interference resistance modules, adapting to the strong electromagnetic environment of substations without data errors. It supports remote firmware upgrades, allowing system function iterations to be completed without on-site operation by maintenance personnel. The system supports mainstream protocols such as ONVIF, GB / T28181, and RTSP, directly connecting to the existing monitoring system of the substation without requiring modification of the original system. After deployment, only an initial calibration is needed for long-term stable operation, eliminating the need for frequent manual debugging.

[0115] Table 4: Comparison of the operating effect of multi-device networking scenario with the original distributed temperature measurement solution.

[0116]

[0117] The data in Table 4 comes from the substation's operational statistics over six consecutive months. Coverage integrity statistics represent the coverage ratio of all heat-prone points in the entire station. Existing distributed temperature measurement equipment had multiple coverage blind spots due to installation location limitations. This system's multi-device splicing coverage achieves full coverage without blind spots. Data consistency statistics represent the proportion of deviations in measurements of the same target from different devices within the allowable range. Existing distributed equipment lacked a unified calibration mechanism, resulting in significant deviations in measurements from different devices. This system's unified calibration and cross-validation mechanism ensures the consistency of all data. Anomaly identification accuracy statistics represent the proportion of genuine anomaly alarms to total alarms. Existing distributed equipment only supported fixed threshold judgments, leading to a high false alarm rate. This system's multi-dimensional anomaly judgment and hierarchical logic effectively improves identification accuracy. Annual maintenance costs include the total investment in manual calibration, equipment maintenance, and troubleshooting. This system eliminates the need for regular manual calibration, integrates temperature measurement and perimeter protection functions, and eliminates the need for additional perimeter detection equipment, resulting in significantly lower maintenance costs than the original solution.

[0118] This invention discloses a method, system, and terminal device for long-distance multi-target temperature monitoring and early warning of power equipment. It acquires multispectral sensing data of each target monitoring area and locates the monitoring targets within each area. Based on the location information, the multispectral sensing data is corrected to obtain corrected temperature monitoring data for each target monitoring area. Based on this temperature monitoring data, early warning information corresponding to each monitoring target is determined. This invention utilizes active laser measurement to achieve precise location of each monitoring target and corrects the temperature monitoring data of the monitoring area based on the location information. This not only avoids the tedious manual input of distance information for monitoring targets but also improves the temperature measurement accuracy of each monitoring target in long-distance scenarios. Furthermore, this invention can achieve full-area temperature monitoring over long distances and large areas, reducing equipment maintenance costs while improving the safety of power equipment.

[0119] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A method for long-distance multi-target temperature measurement and early warning of power equipment, characterized in that, include: A CNC gimbal equipped with multiple sensing components is set, and preset point positions and inspection paths are set; wherein, each of the sensing components is an infrared focal plane detector, a visible light imaging sensor, and an active near-infrared ranging module. Acquire multispectral sensing data for each target monitoring area; wherein, the multispectral sensing data includes: near-infrared laser echo data, infrared thermal imaging data and / or visible light image data; Based on the near-infrared laser echo data, the monitoring targets within each target monitoring area are located to obtain the three-dimensional spatial coordinates of each monitoring target; Based on the three-dimensional spatial coordinates of each monitoring target, the temperature monitoring values ​​contained in the multispectral sensing data are corrected for distance attenuation error, ambient temperature interference error, and / or emissivity error to obtain the corrected temperature monitoring data of each target monitoring area. Based on the temperature monitoring data of each target monitoring area, determine the temperature warning level of each monitoring target, and output and display the warning information corresponding to the temperature warning level; Distortion correction is performed on the infrared thermal imaging data. The pre-stored infrared intrinsic distortion coefficients are called to correct the infrared pixel coordinates pixel by pixel. The corrected infrared pixel coordinates are mapped to the visible light camera coordinate system to eliminate assembly fixation deviations. The dynamic compensation matrix corresponding to the current CNC gimbal posture is superimposed to correct the real-time field of view offset, and the pixel coordinates of each infrared temperature sampling point on the visible light image are obtained. After the coordinate mapping is completed, the temperature monitoring data after temperature correction is bound to the corresponding pixel position one by one, using the visible light real scene as the bottom canvas.

2. The method for long-distance multi-target temperature measurement and early warning of power equipment according to claim 1, characterized in that, Before acquiring multispectral sensing data for each target monitoring area, the following steps are also included: Adjust the field of view and focal length of each sensing component, and control the optical axis parallelism deviation to be less than the preset threshold, so as to meet the condition of covering the entire area of ​​the power equipment in the substation during a single inspection.

3. The method for long-distance multi-target temperature measurement and early warning of power equipment according to claim 2, characterized in that, The steps for locating monitoring targets within each monitoring area based on the near-infrared laser echo data and obtaining the three-dimensional spatial coordinates of each monitoring target include: Based on the principle of active laser time of flight, the radial distance of the monitored targets identified within each target monitoring area is calculated according to the emission and echo time difference of the near-infrared laser contained in the near-infrared laser echo data. The three-dimensional spatial coordinates of each monitoring target are calculated based on the radial distance of each monitoring target and the horizontal polarization angle and pitch angle of each acquisition component in the CNC gimbal when acquiring the near-infrared laser echo data.

4. The method for long-distance multi-target temperature measurement and early warning of power equipment according to claim 3, characterized in that, The step of calculating the radial distance of the detected targets within each target monitoring area based on the active laser time-of-flight principle and the emission and echo time difference of the near-infrared laser contained in the near-infrared laser echo data includes: Real-time monitoring of ambient temperature, humidity, and air pressure parameters is used to correct for atmospheric refractive index. The radial distance of each monitored target is then calculated using the following formula: ； in, Indicates the radial distance between each monitored target. This represents the speed of light under the current conditions. This represents the time difference between the emission and reception of near-infrared laser light. This represents the selection coefficient for effective multi-echo signals. This represents the current atmospheric refractive index correction factor. This represents the compensation coefficient for the coaxiality error between the gimbal axis and the laser emission axis. This represents the correction factor for atmospheric turbulence disturbance.

5. The method for long-distance multi-target temperature measurement and early warning of power equipment according to claim 1, characterized in that, The step of correcting the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error, and / or emissivity error based on the three-dimensional spatial coordinates of each monitoring target to obtain the corrected temperature monitoring data for each target monitoring area includes: To address distance attenuation error, ambient temperature interference error, and emissivity error during long-distance temperature measurement, the temperature monitoring values ​​contained in the multispectral sensing data are corrected. The corrected temperature monitoring data for each monitoring target is calculated using the following formula: ； in, This represents the temperature monitoring data of the target after correction. This indicates the temperature monitoring value contained in the multispectral sensing data. This represents the distance-temperature compensation coefficient. This indicates the straight-line distance between the monitored target and the temperature measuring radar. This represents the ambient temperature compensation coefficient. This indicates the current real-time ambient temperature. Indicates the calibration reference ambient temperature. This represents the emissivity compensation coefficient. Indicates the calibrated reference emissivity. This indicates the actual emissivity of the monitored target.

6. The method for long-distance multi-target temperature measurement and early warning of power equipment according to claim 5, characterized in that, The step of correcting the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error, and / or emissivity error based on the three-dimensional spatial coordinates of each monitoring target to obtain the corrected temperature monitoring data for each target monitoring area further includes: Temperature calibration data collected by a temperature reference calibration device fixed within the target monitoring area and radiation calibration data collected by a radiation reference source are acquired at preset intervals. Using the temperature calibration data and radiation calibration data, the distance temperature compensation coefficient, ambient temperature compensation coefficient, and emissivity compensation coefficient are corrected.

7. The method for long-distance multi-target temperature measurement and early warning of power equipment according to claim 1, characterized in that, The step of determining the temperature warning level for each monitoring target based on the temperature monitoring data of each target monitoring area, and outputting and displaying the warning information corresponding to the temperature warning level, includes: The temperature monitoring data of each monitoring target in each target monitoring area is compared with its corresponding temperature threshold to determine whether the temperature monitoring data exceeds the preset temperature threshold, or whether the temperature change rate of the temperature monitoring data within a preset time exceeds the preset rate threshold. If the preset temperature threshold is exceeded, or the rate of temperature change exceeds the preset speed threshold, then the magnitude of the temperature exceeding the threshold and the magnitude of the rate exceeding the threshold are calculated. The warning level is determined based on the magnitude of the temperature and rate exceeding the limit, as well as the voltage level and equipment type corresponding to the monitored target.

8. The method for long-distance multi-target temperature measurement and early warning of power equipment according to claim 1, characterized in that, The method further includes: Acquire historical temperature monitoring data for each monitoring target; Historical temperature monitoring data of each of the aforementioned monitoring targets are analyzed to predict temperature change trends, and equipment operation and maintenance suggestions are generated based on the temperature change trends. Based on the historical temperature monitoring data, a temperature benchmark model for the same type of power equipment in the same area is constructed. The real-time temperature of the power equipment of the same type as the monitored target is compared with the benchmark model to identify abnormal power equipment whose temperature deviates from the benchmark, determine that there is a potential fault, and issue an early warning signal.

9. A long-distance multi-target temperature measurement and early warning system for power equipment, characterized in that, include: The data acquisition module is used to set up the CNC gimbal equipped with multiple sensor components and to set the preset point positions and inspection paths; Acquire multispectral sensing data for each target monitoring area; wherein each of the sensing components is an infrared focal plane detector, a visible light imaging sensor, and an active near-infrared ranging module; the multispectral sensing data includes: near-infrared laser echo data, as well as infrared thermal imaging data and / or visible light image data; The multi-target positioning module is used to locate the monitoring targets within each target monitoring area based on the near-infrared laser echo data, obtain the three-dimensional spatial coordinates of each monitoring target, and perform distortion correction on the infrared thermal imaging data. It calls pre-stored infrared intrinsic distortion coefficients to correct the infrared pixel coordinates pixel by pixel; maps the corrected infrared pixel coordinates to the visible light camera coordinate system to eliminate assembly fixation deviations; superimposes the dynamic compensation matrix corresponding to the current CNC gimbal posture to correct the real-time field of view offset, obtaining the pixel coordinates corresponding to each infrared temperature sampling point on the visible light image; after coordinate mapping, using the visible light real-world image as the underlying canvas, it binds the temperature-corrected temperature monitoring data one by one to the corresponding pixel positions. The temperature correction module is used to correct the temperature monitoring values ​​contained in the multispectral sensing data for distance attenuation error, ambient temperature interference error and / or emissivity error according to the three-dimensional spatial coordinates of each monitoring target, so as to obtain the corrected temperature monitoring data of each target monitoring area. The early warning analysis module is used to determine the temperature early warning level of each monitoring target based on the temperature monitoring data of each target monitoring area, and output and display the early warning information corresponding to the temperature early warning level.

10. A terminal device, characterized in that, include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory to perform the steps of the long-distance multi-target temperature measurement and early warning method for power equipment as described in any one of claims 1 to 8.

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