Multi-type power equipment temperature rise integrated mobile detection device

Abstract

The invention, which relates to the technical field of electrical equipment temperature rise detection, discloses a multi-type electrical equipment temperature rise integrated mobile detection apparatus comprising a mobile carrier, a multi-dimensional environment sensing module, an equipment operation parameter acquisition module, a temperature rise detection module, a data processing module and a power supply module. According to the invention, the temperature rise detection module adapted to multiple types of power equipment is additionally arranged, a distributed layout and multi-sensing type combined acquisition mode is adopted, comprehensive coverage acquisition of temperature rise original data of each key part of the equipment is realized, and a dynamic compensation model of the parameter acquisition and data processing module of the multi-dimensional environment sensing module is combined, so that the temperature rise of the power equipment is acquired. According to the method and the system, the problems of large temperature measurement error and insufficient reliability caused by the fact that only a single environment parameter is considered, the temperature rise acquisition adaptability is poor and the operation state of the equipment is not coordinated to be corrected in the prior art are solved, good adaptation to a complex detection scene is realized, and the accuracy and the reliability of temperature rise detection data are improved.

Description

Technical Field

[0001] This invention relates to the field of temperature rise detection technology for power equipment, specifically to an integrated mobile detection device for temperature rise of multiple types of power equipment. Background Technology

[0002] Power equipment is a core component of the power system, encompassing various equipment such as transformers, high-voltage switchgear, cable joints, and power modules. It undertakes the critical tasks of power generation, transmission, distribution, and conversion. Its stable operation is the foundation for the safe and reliable power supply of the power system, directly affecting the continuous operation of industrial production, the normal life of residents, and the stable development of the social economy. It holds irreplaceable importance. Power equipment temperature rise detection is a core technical means of monitoring temperature changes during equipment operation to determine whether there are potential problems such as overload, poor contact, insulation aging, and abnormal component wear. Effective temperature rise detection can identify potential equipment failures in advance, preventing large-scale power outages caused by equipment overheating, ensuring the continuous and stable operation of the power system, reducing equipment maintenance costs, and extending equipment lifespan. It is of great significance to the safe and efficient operation of the power system.

[0003] However, existing power equipment temperature rise detection devices still have certain shortcomings. Most of them only consider the impact of a single environmental parameter on the detection results, failing to comprehensively take into account multiple environmental factors such as temperature, humidity, wind speed, and electromagnetic interference intensity. Furthermore, the layout of environmental parameter acquisition lacks rationality, the temperature rise detection module has poor adaptability to the environment, and it does not coordinate with the actual operating status of the equipment for correction. As a result, in complex scenarios such as outdoor, indoor, and high-voltage power distribution rooms, the temperature measurement results are easily affected by environmental interference, making it difficult to accurately reflect the true temperature rise status of the equipment. The reliability of the detection data is insufficient, which in turn affects the accuracy of subsequent fault diagnosis and operation and maintenance decisions. Therefore, it is of great significance to develop an integrated mobile detection device for temperature rise of multiple types of power equipment. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an integrated mobile temperature rise detection device for multiple types of power equipment. By adding a temperature rise detection module adapted to multiple types of power equipment, and adopting a distributed layout and a multi-sensor combination acquisition method, it can achieve comprehensive coverage of raw temperature rise data of key parts of the equipment. Combined with the parameter acquisition of the multi-dimensional environmental sensing module and the dynamic compensation model of the data processing module, it can coordinate with the equipment operating parameters for comprehensive correction, achieve good adaptation to complex detection scenarios, and improve the accuracy and reliability of temperature rise detection data.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an integrated mobile detection device for temperature rise of multiple types of power equipment, the device comprising: a mobile carrier, a multi-dimensional environmental sensing module, an equipment operating parameter acquisition module, a temperature rise detection module, a data processing module, and a power supply module; The multi-dimensional environmental sensing module adopts a distributed layout and integrates temperature sensors, humidity sensors, wind speed sensors, and electromagnetic interference sensors to collect multi-dimensional environmental parameters of different areas of the power equipment and transmit them to the data processing module. The equipment operation parameter acquisition module collects the current and voltage operation status parameters of the power equipment and transmits them to the data processing module; The temperature rise detection module adopts a distributed layout, integrating an infrared thermal imaging sensor, a contact temperature sensor, and a non-contact laser temperature sensor to acquire raw temperature rise data of key parts of the power equipment and transmit it to the data processing module. The data processing module has a built-in improved Gaussian process regression dynamic compensation model. It receives raw temperature rise data, multi-dimensional environmental parameters and operating status parameters. The trained Gaussian process regression algorithm fits the nonlinear relationship between environmental interference and temperature measurement error and outputs the corrected real temperature rise data. The mobile carrier provides installation support and mobility foundation for each module, and the power supply module is electrically connected to each module to provide power to each module.

[0006] Furthermore, the temperature rise detection module performs the following operations when collecting raw temperature rise data of the power equipment: Analyze the type of electrical equipment to be tested and the structural characteristics of each part to be tested, and match the corresponding temperature sensor. Infrared thermal imaging sensors are used for the outer surface and open areas of large equipment, contact temperature sensors are used for precision parts and accessible areas, and non-contact laser temperature sensors are used for exposed key nodes of enclosed equipment. According to the distributed layout specification, each type of temperature sensor is placed near the corresponding detection part of the power equipment so that the sensor detection range covers the target area without obstruction. Simultaneously collect data from various types of temperature sensors, record temperature changes at each detection point, and use formulas... The temperature data collected by various sensors is combined to form raw temperature rise data, which is then sent to the data processing module. This is the raw temperature rise data for a certain testing area. The number of sensors adapted for this part. For the first The fusion weights of individual sensors, For the first Data collected by each sensor Based on the detection accuracy of each sensor and the influence weight of the detection location on equipment operation, the parameters are determined by training samples from historical detection data of similar power equipment.

[0007] Furthermore, the multi-dimensional environmental sensing module performs the following operations when collecting environmental parameters: The installation area of ​​the power equipment is divided, and multiple environmental parameter collection points are set according to the equipment structure distribution and environmental impact differences. Temperature, humidity, wind speed, and electromagnetic interference sensors are installed at each data collection point, with the sensors pointing in the direction that the environmental parameters have the dominant influence. Each sensor is activated and operates at a preset acquisition interval, continuously collecting temperature, humidity, wind speed, and electromagnetic interference intensity data at each location. The collected raw environmental parameters undergo preliminary filtering to remove obvious abnormal data, and then the data is processed using a formula. The multi-dimensional environmental parameters of a single data collection point are integrated and sent to the data processing module. These are the fused environmental parameters for a single data collection point. These are the fusion coefficients for temperature, humidity, wind speed, and electromagnetic interference intensity, respectively. These are the collected data for the corresponding environmental parameters. The sensitivity of power equipment temperature rise detection to interference from different environmental parameters was determined after training with comparative test samples in multiple scenarios.

[0008] Furthermore, the data processing module performs the following operations when correcting the temperature rise data: The system receives raw temperature rise data from the temperature rise detection module, filtered environmental parameters from the multi-dimensional environmental sensing module, and operating status parameters from the equipment operating parameter acquisition module, and performs timestamp alignment processing on the three types of data. The aligned raw temperature rise data is subjected to noise removal processing, and a smoothing filtering algorithm is used to eliminate fluctuations caused by random interference. The built-in improved Gaussian process regression dynamic compensation model is invoked, and the processed environmental parameters and operating state parameters are used as input variables. These are substituted into the algorithm model trained with a small sample size to fit the nonlinear mapping relationship between environmental disturbances and temperature measurement errors under this operating condition. This relationship is then expressed using the formula... Calculate the corrected true temperature rise data, where This is the corrected, actual temperature rise data. This is the interference error function corresponding to the environmental parameters. The disturbance error function corresponding to the operating state parameters. This represents the environmental disturbance impact coefficient. The disturbance impact coefficient during operation. and By training samples of temperature rise detection data of power equipment under different environmental conditions and operating loads, and combining the least squares method for fitting, the calculated corrected actual temperature rise of the equipment is stored.

[0009] Furthermore, the mobile carrier includes a foldable frame structure, moving rollers, and a positioning component. The foldable frame structure is made of lightweight, high-strength alloy material, and multiple modular installation interfaces are provided on the frame. Each functional module is detachably connected to the frame through the installation interfaces. The moving rollers are installed at the four corners of the bottom of the frame and are equipped with a braking locking mechanism. The positioning component integrates a satellite positioning unit and a distance measurement unit. Both the satellite positioning unit and the distance measurement unit are signal-connected to the data processing module to collect the device's location information and relative distance data to the power equipment in real time and transmit them to the data processing module.

[0010] Furthermore, the power supply module includes a solar panel, a lithium battery pack, a charging management unit, and a power distribution unit. The solar panel is mounted on the top of the mobile carrier via an adjustable bracket, which is hinged to the frame of the mobile carrier. The lithium battery pack uses high-capacity lithium iron phosphate batteries and is electrically connected to the charging management unit. The charging management unit is electrically connected to the solar panel, the external AC power interface, and the lithium battery pack. The charging management unit has built-in overcharge protection circuit and over-discharge protection circuit. The power distribution unit is electrically connected to the lithium battery pack and has multiple voltage output interfaces. Each voltage output interface is electrically connected to the mobile carrier, a multi-dimensional environmental sensing module, an equipment operating parameter acquisition module, a temperature rise detection module, and a data processing module.

[0011] Furthermore, the equipment operating parameter acquisition module includes a current acquisition unit, a voltage acquisition unit, and a data transmission unit. The current acquisition unit uses a snap-on current sensor, which is connected to the power transmission line of the power equipment via a snap-on structure. The voltage acquisition unit uses an inductive voltage sensor, which is installed near the voltage output terminal of the power equipment. The data transmission unit includes a wired transmission interface and a wireless transmission module. The wired transmission interface is connected to the data processing module via a shielded cable. The wireless transmission module uses an industrial-grade wireless communication module and is signal-connected to the data processing module. The current and voltage data acquired by the current and voltage acquisition units are transmitted to the data processing module via the data transmission unit.

[0012] Furthermore, each environmental sensor in the multi-dimensional environmental sensing module is equipped with a protective housing. The protective housing is made of a waterproof, dustproof, and electromagnetic interference resistant composite material. The surface of the housing is provided with vent holes and signal transmission windows. The vent holes are evenly distributed on the side of the housing. The signal transmission windows are set corresponding to the detection end of the electromagnetic interference sensor. Each environmental sensor is mounted on a mobile carrier through an adjustable angle bracket. The adjustable angle bracket includes a horizontal adjustment joint and a vertical adjustment joint. Both the horizontal and vertical adjustment joints are equipped with locking bolts.

[0013] Furthermore, each temperature sensor in the temperature rise detection module integrates a data calibration unit. The data calibration unit has a built-in preset calibration parameter library, which stores zero-point calibration parameters and gain calibration parameters at different ambient temperatures. The data calibration unit is signal-connected to the data processing module. Before the sensor starts acquiring data, the data calibration unit acquires the current ambient temperature data and transmits it to the data processing module. The data processing module calls the corresponding parameters in the calibration parameter library and feeds them back to the data calibration unit. The data calibration unit performs zero-point calibration and gain calibration on the sensor. During the acquisition process, the data calibration unit acquires ambient temperature data once at preset time intervals, repeating the above calibration process.

[0014] Compared with existing technologies, this integrated mobile detection device for temperature rise of various types of power equipment has the following advantages: This invention adds a temperature rise detection module adapted to various types of power equipment. It employs a distributed layout and a multi-sensor combination acquisition method to achieve comprehensive coverage of raw temperature rise data for key components of the equipment. Combined with parameter acquisition from a multi-dimensional environmental sensing module and a dynamic compensation model from a data processing module, it coordinates with equipment operating parameters for comprehensive correction. This addresses the problems of existing technologies that only consider single environmental parameters, have poor temperature rise acquisition adaptability, and suffer from large temperature measurement errors and insufficient reliability due to a lack of coordination with equipment operating status correction. It achieves good adaptability to complex detection scenarios, improves the accuracy and reliability of temperature rise detection data, provides precise data support for power equipment fault diagnosis and maintenance decisions, and effectively ensures the stable and efficient operation of the power system.

[0015] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 This is a schematic diagram of a mobile integrated temperature rise detection device for multiple types of power equipment. Figure 2 A flowchart illustrating the workflow of a mobile integrated temperature rise detection device for multiple types of power equipment. Figure 3 A flowchart illustrating the process of collecting raw temperature rise data from power equipment for the temperature rise detection module. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] This invention provides an integrated mobile temperature rise detection device that is compatible with various types of power equipment. It aims to solve the problems of large temperature measurement errors and insufficient reliability in existing detection technologies and is applicable to the field of power equipment temperature rise detection technology.

[0020] See Figure 1 and Figure 2 The device consists of a mobile carrier, a multi-dimensional environmental sensing module, an equipment operating parameter acquisition module, a temperature rise detection module, a data processing module, and a power supply module. All modules work together to achieve comprehensive and accurate detection.

[0021] The mobile carrier provides installation support and a mobile foundation for each module. It adopts a foldable, lightweight, high-strength alloy frame and is equipped with modular installation interfaces to facilitate the detachable connection of each module. The bottom four corners are equipped with mobile rollers with braking and locking mechanisms. It also integrates a satellite positioning unit and a distance measurement unit, which can collect real-time data on the device's location and relative distance to power equipment and transmit it to the data processing module.

[0022] The multi-dimensional environmental sensing module adopts a distributed layout, integrating four types of sensors: temperature, humidity, wind speed, and electromagnetic interference. During data collection, the installation area of ​​the power equipment is first divided and multiple collection points are set. The sensors are installed with their detection direction facing the direction that has the dominant influence of the environmental parameters. Data is collected at preset intervals. After preliminary filtering to remove abnormal data, the multi-dimensional parameters are fused and then transmitted to the data processing module to comprehensively capture environmental interference factors.

[0023] The equipment operation parameter acquisition module collects parameters such as current and voltage through a snap-on current sensor (connected to the power transmission line) and an inductive voltage sensor (installed near the voltage output terminal). The data is then transmitted to the data processing module via a data transmission unit consisting of a wired transmission interface (connected by a shielded cable) and an industrial-grade wireless communication module.

[0024] The temperature rise detection module also adopts a distributed layout, integrating three types of temperature sensors: infrared thermal imaging, contact, and non-contact laser. Before data acquisition, the device type and structural characteristics of the detection area are analyzed, and sensors are matched accordingly. After being arranged according to specifications, data is collected synchronously, fused, and processed to form raw temperature rise data, which is then transmitted. Each sensor also integrates a data calibration unit, which performs zero-point and gain calibration based on ambient temperature before startup and during data acquisition intervals to ensure data acquisition accuracy.

[0025] The data processing module has a built-in improved Gaussian process regression dynamic compensation model. After receiving data transmitted from the three types of modules, it first performs timestamp alignment and noise removal processing, then substitutes environmental parameters and operating status parameters into the model, fits the nonlinear relationship between environmental interference and temperature measurement error, calculates the corrected real temperature rise data, and stores it.

[0026] The power supply module provides stable power to each module, including an adjustable bracket solar panel mounted on top of the mobile carrier, a high-capacity lithium iron phosphate battery pack, a charging management unit with overcharge and over-discharge protection circuits (compatible with solar and AC charging), and a power distribution unit with multiple voltage output interfaces, ensuring that the device can work continuously in different scenarios.

[0027] This device, through multi-module collaboration and dynamic compensation correction, adapts to complex detection scenarios, effectively improving the accuracy and reliability of temperature rise detection data, and providing precise data support for power equipment fault diagnosis and operation and maintenance decisions.

[0028] Example 1 This embodiment is applied to a temperature rise detection scenario for various types of power equipment within a comprehensive substation. This substation includes transformers, high-voltage switchgear, cable joints, power modules, and other power equipment distributed in different areas such as indoor high-voltage distribution rooms and outdoor equipment areas. It faces complex environmental conditions, including significant indoor electromagnetic interference and large fluctuations in outdoor temperature, humidity, and wind speed. Furthermore, the structural characteristics of different equipment vary considerably. Traditional detection devices suffer from poor adaptability and large temperature measurement errors. This embodiment utilizes an integrated mobile temperature rise detection device for various types of power equipment to achieve accurate temperature rise detection of key components of various equipment, providing reliable data support for equipment operation and maintenance. (See also...) Figure 1 and Figure 2 The specific content of this embodiment is as follows: The first step is to deploy and debug the device. The mobile carrier uses a foldable, lightweight, high-strength alloy frame. The frame is unfolded and pushed to the preset testing area in the substation using the four corner rollers at the bottom. The device is then secured using the braking and locking mechanism on the rollers to prevent displacement during testing. Multiple modular installation interfaces are provided on the frame, allowing for detachable connections of the multi-dimensional environmental sensing module, equipment operating parameter acquisition module, temperature rise detection module, and data processing module to the frame, ensuring stable installation of each module. After the satellite positioning unit and distance measurement unit integrated into the positioning component are activated, they collect the device's location information and relative distance data to each piece of electrical equipment under test in real time, and transmit this data to the data processing module to complete the device's positioning calibration.

[0029] During power supply module deployment, the solar panels are mounted on top of the mobile carrier using adjustable brackets. The bracket angle is adjusted to ensure the solar panels face the direction of sufficient sunlight. The brackets are hinged to the carrier frame to ensure flexible adjustment. The lithium battery pack uses high-capacity lithium iron phosphate batteries and is electrically connected to the charging management unit. The charging management unit is simultaneously connected to the solar panels and an external AC power interface. Built-in overcharge and over-discharge protection circuits ensure power supply safety. After the power distribution unit is connected to the lithium battery pack, it provides appropriate power to the mobile carrier, various sensing modules, and data processing modules through multiple voltage output interfaces. After starting the power supply module, the stability of the power supply to each module is checked to ensure normal operation of the device.

[0030] When deploying the multi-dimensional environmental sensing module, the installation area is first divided according to the structural distribution of power equipment and the differences in environmental impact within the substation, and multiple environmental parameter collection points are set. Temperature sensors, humidity sensors, wind speed sensors, and electromagnetic interference sensors are installed at each collection point, and the detection direction of the sensors is adjusted to face the direction of the dominant environmental parameter to ensure accurate detection. Each environmental sensor is equipped with a waterproof, dustproof, and electromagnetic interference-resistant composite material protective shell. Ventilation holes are evenly distributed on the sides of the shell, and a signal transmission window is set at the detection end of the electromagnetic interference sensor to protect the sensor from environmental corrosion without affecting the transmission of detection signals. The sensor is mounted on a mobile carrier using an adjustable angle bracket. After adjusting the horizontal and vertical adjustment joints of the bracket to achieve the optimal sensor detection angle, the locking bolts are tightened for fixation.

[0031] Each sensor is activated and operates at a preset acquisition interval, continuously collecting temperature, humidity, wind speed, and electromagnetic interference intensity data at each location. The collected raw environmental parameters undergo preliminary filtering to remove obviously abnormal data. In the specific implementation of this embodiment, the formula... It integrates multi-dimensional environmental parameters from a single data collection point, among which... These are the fused environmental parameters for a single data collection point. These are fusion coefficients for temperature, humidity, wind speed, and electromagnetic interference intensity, respectively. These coefficients are determined based on the interference sensitivity of different environmental parameters to the temperature rise detection of power equipment, and were trained using comparative test samples from multiple indoor and outdoor scenarios. The collected data are for the corresponding environmental parameters, and the merged environmental parameters are transmitted to the data processing module.

[0032] During installation of the equipment operating parameter acquisition module, the current acquisition unit uses a snap-fit ​​current sensor, which connects to the power transmission line of the power equipment via a snap-fit ​​structure, ensuring a tight connection without affecting the normal operation of the line. The voltage acquisition unit uses an inductive voltage sensor, installed near the voltage output terminal of the power equipment to ensure accurate voltage signal acquisition. The wired transmission interface of the data transmission unit connects to the data processing module via a shielded cable, while the wireless transmission module uses an industrial-grade wireless communication module and is signal-connected to the data processing module. After the acquisition unit is started, current and voltage data are stably transmitted to the data processing module via wired or wireless transmission.

[0033] When implementing the temperature rise detection module, please refer to... Figure 3 First, the type of power equipment to be tested and the structural characteristics of each testing part are analyzed to match the corresponding temperature sensors. For the outer surface of large equipment such as transformers and open areas, infrared thermal imaging sensors are selected; for precision components inside high-voltage switchgear and accessible areas, contact temperature sensors are selected; for exposed critical nodes of enclosed equipment such as cable joints, non-contact laser temperature sensors are selected. Following distributed layout specifications, each type of temperature sensor is arranged near the corresponding testing part of the power equipment to ensure that the sensor detection range covers the target area without obstruction.

[0034] Each temperature sensor integrates a data calibration unit. Before data acquisition begins, the data calibration unit collects the current ambient temperature data and transmits it to the data processing module. The data processing module then calls upon its built-in calibration parameter library, which stores zero-point calibration parameters and gain calibration parameters for different ambient temperatures. The corresponding parameters are fed back to the data calibration unit to complete the zero-point and gain calibration of the sensors. All types of temperature sensors are then activated to synchronously collect data, recording temperature changes at each detection point. During the acquisition process, the data calibration unit collects ambient temperature data at preset intervals, repeating the above calibration process to ensure acquisition accuracy.

[0035] In the specific implementation process of this embodiment, through formula The temperature data collected by various sensors are combined to form the raw temperature rise data, among which This is the raw temperature rise data for a certain testing area. The number of sensors adapted for this part. For the first The fusion weights of the individual sensors are determined based on the detection accuracy of each sensor and the impact of the detection location on equipment operation. These weights are obtained by training the system with historical detection data from similar power equipment. For the first The data collected by each sensor is fused together, and the raw temperature rise data is sent to the data processing module.

[0036] After the data processing module starts, it first receives the raw temperature rise data transmitted by the temperature rise detection module, the filtered environmental parameters transmitted by the multi-dimensional environmental sensing module, and the operating status parameters transmitted by the equipment operating parameter acquisition module. It then performs timestamp alignment on these three types of data to ensure data time sequence consistency. A smoothing filtering algorithm is then used to remove noise from the aligned raw temperature rise data, eliminating fluctuations caused by random interference.

[0037] The built-in improved Gaussian process regression dynamic compensation model is invoked, and the processed environmental parameters and operating state parameters are used as input variables. These are substituted into the algorithm model trained with a small number of samples to fit the nonlinear mapping relationship between environmental disturbances and temperature measurement errors under this operating condition. In the specific implementation process of this embodiment, the formula is used... Calculate the corrected true temperature rise data, where This is the corrected, actual temperature rise data. This is the interference error function corresponding to the environmental parameters. The disturbance error function corresponding to the operating state parameters. This represents the environmental disturbance impact coefficient. The disturbance impact coefficient during operation. and The corrected actual temperature rise data of the equipment is obtained by training samples from power equipment temperature rise detection data under different environmental conditions and operating loads and fitting them using the least squares method.

[0038] During the testing process, the operating status of each module is monitored in real time to ensure continuous and stable data acquisition, transmission, and processing. After completing one round of testing, the device can be unlocked via the moving wheels of the mobile carrier and pushed to the next testing area, repeating the above testing process to achieve comprehensive testing of various types of power equipment within the substation.

[0039] In summary, this embodiment effectively solves the adaptability problem of temperature rise detection for various types of power equipment in the complex environment of integrated substations by deploying and applying an integrated mobile temperature rise detection device for multiple types of power equipment. The device adopts a distributed layout and a multi-sensor combination acquisition method, achieving comprehensive coverage of raw temperature rise data for key parts of the equipment. Combined with the coordinated correction of multi-dimensional environmental parameters and equipment operating parameters, and by fitting the nonlinear relationship between interference and error using an improved Gaussian process regression dynamic compensation model, the impact of environmental interference and equipment operating status on the temperature measurement results is significantly reduced.

[0040] Example 2 This embodiment is applied to the temperature rise detection scenario of a cluster of power equipment in a large industrial park. This park encompasses various power equipment, including production workshops, power distribution rooms, outdoor power control cabinets, production line transformers, and cable trenches. The equipment is distributed across different areas, such as enclosed indoor workshops, open-air areas, and semi-enclosed trenches. It faces a complex environment with high temperature and humidity in the workshops, significant oil and dust pollution, drastic wind speed changes in the open areas, and concentrated electromagnetic interference in the trenches. Simultaneously, the equipment needs to frequently start and stop to keep pace with production, resulting in significant load fluctuations. Traditional detection solutions are ill-suited to the precise temperature measurement requirements under dynamic load changes. This embodiment utilizes an optimized integrated mobile temperature rise detection device for multiple types of power equipment to achieve efficient and accurate temperature rise detection of power equipment under different operating conditions within the park. (See [link to relevant documentation]). Figure 1 and Figure 2 The specific content of this embodiment is as follows: This embodiment optimizes the adaptability and functional expansion of the device based on the previous embodiments. The mobile carrier retains the foldable lightweight high-strength alloy frame and modular installation interface. The bottom moving rollers are upgraded to wear-resistant and non-slip type, and the vibration resistance of the braking locking mechanism is enhanced, making it suitable for rugged roads and complex terrain around the equipment in the park. In addition to the aforementioned satellite positioning unit and distance measurement unit, the positioning component adds a factory area Bluetooth positioning auxiliary module. This module supplements the satellite positioning coverage in open areas with weak satellite signals, such as workshops and ditches, using Bluetooth positioning to supplement these areas. It collects real-time data on the device's location and relative distance to the equipment and transmits this data to the data processing module, ensuring accurate positioning throughout the entire area.

[0041] The power supply module retains the core structure of the aforementioned solar panel lithium battery pack charging management unit and power distribution unit. The adjustable bracket of the solar panel adds an automatic angle adjustment function, adjusting its orientation in real time by sensing light intensity to ensure charging efficiency. For scenarios with insufficient lighting in the workshop, an emergency energy storage module is added, employing a dual lithium battery pack alternating power supply mode. The charging management unit optimizes the charging strategy, dynamically adjusting charging priority according to equipment operating times, prioritizing simultaneous charging of both battery packs during peak production periods and off-peak periods. The power distribution unit adds an overload protection function, automatically cutting off power to that branch when a module's power consumption is abnormal, preventing impact on the overall device operation.

[0042] Based on the aforementioned distributed layout, the multi-dimensional environmental sensing module divides the data collection areas according to equipment operating load. High-load equipment areas have denser data collection points, while ordinary load areas have more points strategically placed. In addition to the aforementioned waterproof, dustproof, and electromagnetic interference-resistant features, the protective housings of each environmental sensor are further enhanced with an oil-resistant coating to adapt to oily workshop environments. The ventilation holes utilize removable dust filters for easy periodic cleaning.

[0043] After the sensor is mounted on an adjustable-angle bracket, the detection direction is dynamically calibrated according to the prevailing wind direction and heat source distribution in the park. After startup, the sensor's data acquisition interval is dynamically adjusted according to the equipment load; the acquisition interval is shortened during high-load operation. After preliminary filtering to remove abnormal data, the acquired data is then processed using the formula in this specific implementation example. Multi-dimensional environmental parameters are integrated, and the integrated parameters are transmitted to the data processing module in real time.

[0044] Building upon the aforementioned current and voltage acquisition functions, the equipment operation parameter acquisition module adds a power factor acquisition unit. This unit employs an inductive power factor sensor, installed near the equipment's inlet terminal to synchronously collect power factor data. The current acquisition unit retains the open-loop current sensor, while the voltage acquisition unit uses an inductive voltage sensor. In addition to the aforementioned wired transmission interface and industrial-grade wireless communication module, the data transmission unit adds a 5G communication module to adapt to the long-distance data transmission needs of large-area equipment coverage within the park. The acquired current, voltage, and power factor data are transmitted to the data processing module via multiple channels, ensuring the stability and real-time performance of data transmission.

[0045] The temperature rise detection module includes the infrared thermal imaging sensor, contact temperature sensor, and non-contact laser temperature sensor provided in the aforementioned embodiments. For special detection objects such as high-temperature components of large compressors in the industrial park, a high-temperature resistant contact sensor is selected; for enclosed environments with high dust levels in cable trenches, an enhanced non-contact laser temperature sensor is used to improve the laser's ability to penetrate dust. The sensors are arranged according to a distributed layout specification, with the number of sensors increased at key heat-generating parts of high-load equipment to ensure comprehensive coverage.

[0046] In addition to the aforementioned ambient temperature calibration, each sensor's calibration unit adds a calibration mechanism linked to the equipment's operating load. When the equipment load changes beyond a set range, the calibration process is automatically triggered. After the sensors begin synchronously collecting data, in the specific implementation of this embodiment, the calibration is performed using the formula... The temperature data is merged to form raw temperature rise data, which is then transmitted to the data processing module.

[0047] The data processing module receives the raw temperature rise data, filtered environmental parameters, and operating status parameters, and first performs timestamp alignment and noise removal. The improved Gaussian process regression dynamic compensation model, based on the aforementioned model, adds a load mutation interference factor and incorporates the power factor into the operating status parameter input variables. It then uses small-sample training to fit the relationship between interference and error under load mutation conditions. In the specific implementation of this embodiment, the formula... The corrected actual temperature rise data is calculated, and a database of equipment temperature rise safety thresholds is established. The calculated actual temperature rise data is compared with the corresponding equipment safety thresholds in real time.

[0048] During the testing process, the device moves within the park along a preset testing route using a mobile carrier, with the positioning component providing real-time location feedback to ensure no blind spots. The data processing module not only stores actual temperature rise data but also transmits exceeding-limit data and early warning information to the park's operation and maintenance center terminal in real time via the data transmission unit, simultaneously displaying it on the device's local screen. After completing one round of testing, the device automatically generates a temperature rise test report for the park's electrical equipment, marking high-risk equipment and corresponding test data, providing maintenance personnel with accurate maintenance guidance.

[0049] In summary, this embodiment, through scenario optimization for power equipment clusters in industrial parks, expands the functional adaptability and environmental adaptability of the device based on the aforementioned embodiments. Optimized designs, such as load-linked sensor calibration for load-zoned environmental parameter acquisition and the incorporation of power factor into operating parameters, effectively adapt to the characteristics of large load fluctuations and high levels of environmental pollutants in industrial parks. Upgrades to the positioning and power supply modules ensure the continuity and stability of full-area detection, while the newly added real-time early warning function in the data processing module shortens fault response time.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A mobile integrated temperature rise detection device for multiple types of power equipment, characterized in that, The device includes: a mobile carrier, a multi-dimensional environmental sensing module, an equipment operating parameter acquisition module, a temperature rise detection module, a data processing module, and a power supply module; The multi-dimensional environmental sensing module adopts a distributed layout and integrates temperature sensors, humidity sensors, wind speed sensors, and electromagnetic interference sensors to collect multi-dimensional environmental parameters of different areas of the power equipment and transmit them to the data processing module. The equipment operation parameter acquisition module collects the current and voltage operation status parameters of the power equipment and transmits them to the data processing module; The temperature rise detection module adopts a distributed layout, integrating an infrared thermal imaging sensor, a contact temperature sensor, and a non-contact laser temperature sensor to acquire raw temperature rise data of key parts of the power equipment and transmit it to the data processing module. The data processing module has a built-in improved Gaussian process regression dynamic compensation model. It receives raw temperature rise data, multi-dimensional environmental parameters and operating status parameters. The trained Gaussian process regression algorithm fits the nonlinear relationship between environmental interference and temperature measurement error and outputs the corrected real temperature rise data. The mobile carrier provides installation support and mobility foundation for each module, and the power supply module is electrically connected to each module to provide power to each module.

2. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, The temperature rise detection module performs the following operations when collecting raw temperature rise data of power equipment: Analyze the type of electrical equipment to be tested and the structural characteristics of each part to be tested, and match the corresponding temperature sensor. Infrared thermal imaging sensors are used for the outer surface and open areas of large equipment, contact temperature sensors are used for precision parts and accessible areas, and non-contact laser temperature sensors are used for exposed key nodes of enclosed equipment. According to the distributed layout specification, each type of temperature sensor is placed near the corresponding detection part of the power equipment so that the sensor detection range covers the target area without obstruction. Simultaneously collect data from various types of temperature sensors, record temperature changes at each detection point, and use formulas... The temperature data collected by various sensors is combined to form raw temperature rise data, which is then sent to the data processing module. This is the raw temperature rise data for a certain testing area. The number of sensors adapted for this part. For the first The fusion weights of individual sensors, For the first Data collected by each sensor.

3. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, The multi-dimensional environment sensing module performs the following operations when collecting environmental parameters: The installation area of ​​the power equipment is divided, and multiple environmental parameter collection points are set according to the equipment structure distribution and environmental impact differences. Temperature, humidity, wind speed, and electromagnetic interference sensors are installed at each data collection point, with the sensors pointing in the direction that the environmental parameters have the dominant influence. Each sensor is activated and operates at a preset acquisition interval, continuously collecting temperature, humidity, wind speed, and electromagnetic interference intensity data at each location. The raw environmental parameters are then preliminarily filtered to remove obvious abnormal data, and finally processed using a formula. The multi-dimensional environmental parameters of a single data collection point are integrated and sent to the data processing module. These are the fused environmental parameters for a single data collection point. These are the fusion coefficients for temperature, humidity, wind speed, and electromagnetic interference intensity, respectively. These are the collected data for the corresponding environmental parameters.

4. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, When the data processing module corrects the temperature rise data, it performs the following operations: The system receives raw temperature rise data from the temperature rise detection module, filtered environmental parameters from the multi-dimensional environmental sensing module, and operating status parameters from the equipment operating parameter acquisition module, and performs timestamp alignment processing on the three types of data. The aligned raw temperature rise data is subjected to noise removal processing, and a smoothing filtering algorithm is used to eliminate fluctuations caused by random interference. The built-in improved Gaussian process regression dynamic compensation model is invoked, and the processed environmental parameters and operating state parameters are used as input variables. These are substituted into the algorithm model trained with a small sample size to fit the nonlinear mapping relationship between environmental disturbances and temperature measurement errors under this operating condition. This is then expressed using the formula... Calculate the corrected true temperature rise data, where This is the corrected, actual temperature rise data. This is the interference error function corresponding to the environmental parameters. The disturbance error function corresponding to the operating state parameters. This represents the environmental disturbance impact coefficient. The calculated and corrected actual temperature rise of the equipment is stored as the interference impact coefficient for the operating state.

5. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, The mobile carrier includes a foldable frame structure, moving rollers, and a positioning component. The foldable frame is provided with multiple modular installation interfaces, and each functional module is detachably connected to the frame through the installation interfaces. The moving rollers are installed at the four corners of the bottom of the frame. The positioning component integrates a satellite positioning unit and a distance measurement unit. Both the satellite positioning unit and the distance measurement unit are signal-connected to the data processing module to collect the location information of the device and the relative distance data with the power equipment in real time and transmit them to the data processing module.

6. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, The power supply module includes a solar panel, a lithium battery pack, a charging management unit, and a power distribution unit. The solar panel is installed on the top of the mobile carrier. The lithium battery pack is electrically connected to the charging management unit. The charging management unit is electrically connected to the solar panel, an external AC power interface, and the lithium battery pack. The charging management unit has built-in overcharge protection circuit and over-discharge protection circuit. The power distribution unit is electrically connected to the lithium battery pack and has multiple voltage output interfaces. Each voltage output interface is electrically connected to the mobile carrier, a multi-dimensional environmental sensing module, an equipment operating parameter acquisition module, a temperature rise detection module, and a data processing module.

7. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, The equipment operating parameter acquisition module includes a current acquisition unit, a voltage acquisition unit, and a data transmission unit. The current acquisition unit uses a snap-on current sensor, which is connected to the power transmission line of the power equipment via a snap-on structure. The voltage acquisition unit uses an inductive voltage sensor, which is installed near the voltage output terminal of the power equipment. The data transmission unit includes a wired transmission interface and a wireless transmission module. The wired transmission interface is connected to the data processing module via a shielded cable. The wireless transmission module uses an industrial-grade wireless communication module and is signal-connected to the data processing module. The current and voltage data acquired by the current and voltage acquisition units are transmitted to the data processing module via the data transmission unit.

8. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, Each environmental sensor in the multi-dimensional environmental sensing module is equipped with a protective shell. The surface of the shell is provided with ventilation holes and signal transmission windows. The signal transmission windows are set at the detection end of the electromagnetic interference sensor. Each environmental sensor is mounted on a mobile carrier through an adjustable angle bracket.

9. The integrated mobile detection device for temperature rise of multiple types of power equipment according to claim 1, characterized in that, Each temperature sensor in the temperature rise detection module integrates a data calibration unit. The data calibration unit has a built-in preset calibration parameter library, which stores zero-point calibration parameters and gain calibration parameters at different ambient temperatures. The data calibration unit is signal-connected to the data processing module. Before the sensor starts acquiring data, the data calibration unit acquires the current ambient temperature data and transmits it to the data processing module. The data processing module calls the corresponding parameters in the calibration parameter library and feeds them back to the data calibration unit. The data calibration unit performs zero-point calibration and gain calibration on the sensor. During the acquisition process, the data calibration unit acquires ambient temperature data once at preset time intervals, repeating the above calibration process.

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