Intelligent helmet with self-supervised patrol

Abstract

The application discloses an intelligent helmet with self-supervision and inspection, which comprises a supervision platform, a cloud server and an inspection helmet, wherein the inspection helmet comprises a positioning module, a first video acquisition module, a second video acquisition module and a sensor assembly; the first video acquisition module is connected with the supervision platform and is used for acquiring supervision images; the second video acquisition module is connected with the cloud server and is used for acquiring environment images; the supervision images are video data for recording operation actions of a body of an inspector in an inspection process; and the environment images are video data for recording surrounding environment equipment of the inspector in the inspection process; the sensor assembly comprises a temperature sensor and an infrared thermal imaging sensor; the temperature sensor acquires real-time temperature data every other acquisition cycle and uploads the real-time temperature data to the cloud server; after receiving an abnormal signal, the infrared thermal imaging sensor acquires infrared thermal images of the surroundings of the helmet and uploads the infrared thermal images to the cloud server; and the positioning module is used for acquiring real-time position information of the inspection helmet.

Description

Technical Field

[0001] This invention relates to the field of power line inspection technology, and in particular to a smart helmet with built-in monitoring and inspection capabilities. Background Technology

[0002] In the traditional substation inspection model, substation inspectors carry paper-based standardized operating procedures and equipment inspection devices to conduct on-site inspections. This model is somewhat arbitrary and unpredictable, failing to ensure that inspectors complete their tasks on time, at designated locations, and in the required quantities. Furthermore, substation managers lack effective management and supervision of the inspectors' work, often leading to missed or incorrect inspections. During inspections, various types of faults or defects are discovered, requiring inspectors to respond correctly and promptly. This demands a high level of experience from the inspectors; those without extensive experience often struggle to handle issues immediately, delaying appropriate action and rendering them incapable of fulfilling their inspection duties. Summary of the Invention

[0003] The purpose of this invention is to provide a user-friendly method that effectively manages and supervises the inspection process, avoiding missed inspections and incorrect inspections.

[0004] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0005] A smart helmet with built-in monitoring and inspection capabilities includes: a monitoring platform, a cloud server, and an inspection helmet.

[0006] The inspection helmet includes a positioning module, a first video acquisition module, a second video acquisition module, and sensor components. The first video acquisition module is connected to the monitoring platform to acquire monitoring images, and the second video acquisition module is connected to the cloud server to acquire environmental images. The monitoring images are video data recording the body movements of the inspection personnel during the inspection process, and the environmental images are video data recording the surrounding environment and equipment of the inspection personnel during the inspection process. The sensor components include a temperature sensor and an infrared thermal imaging sensor. The temperature sensor acquires real-time temperature data at regular intervals and uploads it to the cloud server. The infrared thermal imaging sensor acquires infrared thermal images of the area around the inspection helmet after receiving an abnormal signal and uploads them to the cloud server. The positioning module is used to obtain the real-time location information of the inspection helmet.

[0007] The cloud server includes a device storage module, a temperature storage and analysis module, an anomaly analysis module, and an alert module. The device storage module pre-stores inspection point information, including the device name, image, location, and alert distance for each piece of equipment to be inspected. The alert module includes a correction submodule and a calculation submodule. If the correction submodule receives heat source information, it generates a corrected alert distance based on the heat source information and the alert distance. The calculation submodule determines whether the inspection personnel are within the corrected alert distance based on real-time location information, equipment location information, and the corrected alert distance. If the correction submodule does not receive heat source information, the calculation submodule determines whether the inspection personnel are within the alert distance based on real-time location information, equipment location information, and the alert distance. If the personnel are within the alert distance or the corrected alert distance, an alert signal is sent to the inspection helmet to remind them to move away from the equipment.

[0008] The temperature storage and analysis module includes an analysis submodule and a storage submodule. The storage submodule stores historical inspection data, which consists of all real-time temperature data collected by the temperature sensor during the current inspection. The analysis submodule determines whether there is a temperature anomaly based on the historical inspection data and the received real-time temperature data. If an anomaly is found, an anomaly signal is sent to the infrared thermal imaging sensor. If no anomaly is found, the real-time temperature data is stored in the historical inspection data. The anomaly analysis module receives the infrared thermal image and performs image analysis processing to obtain heat source information, which includes the location, name, and degree of anomaly of the field equipment with temperature anomalies.

[0009] Further configuration: The anomaly analysis module includes a comparison and acquisition submodule, a heat source analysis submodule, a comparison analysis submodule, an image analysis submodule, a degree analysis submodule, and a sending submodule.

[0010] Upon receiving the infrared thermal image, the comparison acquisition submodule uses the second video acquisition module to acquire an environmental image at the same moment as the comparison image.

[0011] The heat source analysis submodule extracts features from the infrared thermal image to obtain a first gradient region, a second gradient region, and a third gradient region, with the temperature range of the first gradient region, the second gradient region, and the third gradient region decreasing sequentially.

[0012] The comparison analysis submodule extracts the coordinate position information of the first gradient region, the second gradient region, and the third echelon region, and locates the first comparison region on the comparison image based on the coordinate position information.

[0013] The image analysis submodule performs image analysis processing based on the comparison image, the first comparison area and the inspection point information to determine the equipment name information and equipment location information of the field equipment corresponding to the location of the first comparison area;

[0014] The degree analysis submodule extracts temperature information from the first gradient region, the second gradient region, and the third gradient region, and calculates the degree of abnormality information based on the extracted temperature information and the first gradient region, the second gradient region, and the third gradient region.

[0015] The sending submodule generates heat source information based on the anomaly level information, the equipment name information and equipment location information of the field equipment, and sends it to the warning module.

[0016] Further configuration: The heat source analysis submodule extracts features from the infrared thermal image to obtain the first gradient region, the second gradient region, and the third gradient region, specifically including the following steps:

[0017] The pixel with the highest temperature in the infrared thermal image is extracted as the high temperature source point. Starting from the high temperature source point, the region formed by the pixels whose temperature difference with the high temperature source point is within the first temperature difference range is the first gradient region.

[0018] The second temperature range and the third temperature range are calculated based on the high temperature source point temperature and the preset second temperature difference range and third temperature difference range. The second gradient region is the region formed by pixels whose temperature is within the second temperature range, and the third gradient region is the region formed by pixels whose temperature is within the third temperature range.

[0019] Further configuration: The degree analysis submodule includes a range comparison unit, a temperature comparison unit, and a degree calculation unit.

[0020] The range comparison unit obtains the areas of the first gradient region, the second gradient region, and the third gradient region to obtain the first area, the second area, and the third area, and calculates the first range parameter based on the ratio of the first area and the second area, and calculates the second range parameter based on the ratio of the second area and the third area.

[0021] The temperature comparison unit calculates the average temperatures of the first gradient region, the second gradient region, and the third gradient region based on the extracted temperature information, respectively, as the first average temperature, the second average temperature, and the third average temperature. It then calculates the first temperature parameter based on the ratio of the first average temperature to the second average temperature, and the second temperature parameter based on the ratio of the second average temperature to the third average temperature.

[0022] The degree calculation unit generates anomaly degree information based on the first range parameter, the second range parameter, the first temperature parameter, and the second temperature parameter.

[0023] Further configuration: The correction submodule includes a correction unit, a distance extraction unit, and an output unit.

[0024] The correction unit has a preset temperature correction weight parameter, and the correction unit generates a correction distance based on the temperature correction weight parameter and the anomaly information in the heat source information.

[0025] The distance extraction unit extracts the warning distance of the corresponding field device from the device storage module based on the device name and location information of the field device in the heat source information.

[0026] The output unit outputs the corrected warning distance based on the corrected distance and the warning distance.

[0027] Further configuration: The inspection helmet also includes a humidity detection module. When the correction submodule receives heat source information, the humidity detection module starts collecting humidity information and sends it to the correction submodule.

[0028] The correction submodule also includes a humidity correction unit, which generates humidity correction parameters based on the humidity information and generates a correction distance based on the temperature correction weight parameters, the humidity correction parameters, and the anomaly information in the heat source information.

[0029] Further features: The inspection helmet is also equipped with a voice output module, and the supervision platform also includes a voice input module. The voice input module is used for supervisors to input prompt audio and send it to the inspection helmet, and the voice output module is used to play the prompt audio.

[0030] In summary, this invention has the following beneficial effects: The monitoring images captured by the first video acquisition module are uploaded to the monitoring platform, enabling substation managers to effectively manage and supervise the work process of inspection personnel. Furthermore, by observing the improper or non-standard operations performed by inspection personnel during fault handling or emergency situations, improper or non-standard operations can be promptly detected and corrected to prevent accidents. The environmental images captured by the second video acquisition module record the inspected field equipment in real time during the inspection process, avoiding false positives and false negatives. In the event of an accident, the recorded environmental images can be reviewed, increasing the likelihood of identifying the cause of the accident. Since leakage is often difficult for inspection personnel to detect, and leakage locations cause temperature increases, the temperature storage and analysis module and the anomaly analysis module can detect areas of excessively high temperature during the inspection process. Infrared thermal images obtained by an infrared thermal imaging sensor determine the location of the heat source, thereby identifying the leakage location of the field equipment and correcting the warning distance. This prevents inspection personnel from entering the corrected warning distance, ensuring their personal safety and preventing accidents. Attached Figure Description

[0031] Figure 1 This is an overall structural block diagram of the embodiment. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to the accompanying drawings.

[0033] Example:

[0034] like Figure 1 As shown, a smart helmet with built-in monitoring and inspection capabilities includes: a monitoring platform, a cloud server, and an inspection helmet.

[0035] The inspection helmet includes a positioning module, a first video acquisition module, a second video acquisition module, and sensor components. The first video acquisition module is connected to the monitoring platform to acquire monitoring images, and the second video acquisition module is connected to the cloud server to acquire environmental images. The monitoring images are video data recording the body movements of the inspection personnel during the inspection process, and the environmental images are video data recording the surrounding environment and equipment of the inspection personnel during the inspection process. The monitoring images are used by supervisors to effectively supervise and manage the work of the inspection personnel, and can also promptly identify situations where the inspection personnel handle faults or unexpected situations improperly or where there are potential safety hazards, so as to correct them in a timely manner.

[0036] The sensor assembly includes a temperature sensor and an infrared thermal imaging sensor. The temperature sensor collects real-time temperature data every acquisition cycle and uploads it to the cloud server. After receiving an abnormal signal, the infrared thermal imaging sensor acquires infrared thermal images of the area around the inspection helmet and uploads them to the cloud server. The positioning module is used to obtain the real-time location information of the inspection helmet.

[0037] The cloud server includes a device storage module, a temperature storage and analysis module, an anomaly analysis module, and an alert module. The device storage module pre-stores inspection point information, including the device name, image, location, and alert distance for each piece of equipment to be inspected. The alert module includes a correction submodule and a calculation submodule. If the correction submodule receives heat source information, it generates a corrected alert distance based on the heat source information and the alert distance. The calculation submodule determines whether the inspection personnel are within the corrected alert distance based on real-time location information, equipment location information, and the corrected alert distance. If the correction submodule does not receive heat source information, the calculation submodule determines whether the inspection personnel are within the alert distance based on real-time location information, equipment location information, and the alert distance. If the personnel are within the alert distance or the corrected alert distance, an alert signal is sent to the inspection helmet to remind them to move away from the equipment.

[0038] The temperature storage and analysis module includes an analysis submodule and a storage submodule. The storage submodule stores historical inspection data, which consists of all real-time temperature data collected by the temperature sensor during the current inspection. The analysis submodule determines whether there is a temperature anomaly based on the historical inspection data and the received real-time temperature data. If an anomaly is found, an anomaly signal is sent to the infrared thermal imaging sensor. If no anomaly is found, the real-time temperature data is stored in the historical inspection data. The anomaly analysis module receives the infrared thermal image and performs image analysis processing to obtain heat source information, which includes the location, name, and degree of anomaly of the field equipment with temperature anomalies.

[0039] The anomaly analysis module includes a comparison acquisition submodule, a heat source analysis submodule, a comparison analysis submodule, an image analysis submodule, a degree analysis submodule, and a transmission submodule.

[0040] Upon receiving the infrared thermal image, the comparison acquisition submodule uses the second video acquisition module to acquire an environmental image at the same moment as the comparison image.

[0041] The heat source analysis submodule extracts features from the infrared thermal image to obtain a first gradient region, a second gradient region, and a third gradient region, with the temperature range of the first gradient region, the second gradient region, and the third gradient region decreasing sequentially.

[0042] The comparison analysis submodule extracts the coordinate position information of the first gradient region, the second gradient region, and the third echelon region, and locates the first comparison region on the comparison image based on the coordinate position information.

[0043] The image analysis submodule performs image analysis processing based on the comparison image, the first comparison area and the inspection point information to determine the equipment name information and equipment location information of the field equipment corresponding to the location of the first comparison area;

[0044] The degree analysis submodule extracts temperature information from the first gradient region, the second gradient region, and the third gradient region, and calculates the degree of abnormality information based on the extracted temperature information and the first gradient region, the second gradient region, and the third gradient region.

[0045] The sending submodule generates heat source information based on the anomaly level information, the equipment name information and equipment location information of the field equipment, and sends it to the warning module.

[0046] The heat source analysis submodule extracts features from the infrared thermal image to obtain the first gradient region, the second gradient region, and the third gradient region, specifically including the following steps:

[0047] The pixel with the highest temperature in the infrared thermal image is extracted as the high temperature source point. Starting from the high temperature source point, the region formed by the pixels whose temperature difference with the high temperature source point is within the first temperature difference range is the first gradient region.

[0048] The second temperature range and the third temperature range are calculated based on the high temperature source point temperature and the preset second temperature difference range and third temperature difference range. The second gradient region is the region formed by pixels whose temperature is within the second temperature range, and the third gradient region is the region formed by pixels whose temperature is within the third temperature range.

[0049] The degree analysis submodule includes a range comparison unit, a temperature comparison unit, and a degree calculation unit.

[0050] The range comparison unit obtains the areas of the first gradient region, the second gradient region, and the third gradient region to obtain the first area, the second area, and the third area, and calculates the first range parameter based on the ratio of the first area and the second area, and calculates the second range parameter based on the ratio of the second area and the third area.

[0051] The temperature comparison unit calculates the average temperatures of the first gradient region, the second gradient region, and the third gradient region based on the extracted temperature information, respectively, as the first average temperature, the second average temperature, and the third average temperature. It then calculates the first temperature parameter based on the ratio of the first average temperature to the second average temperature, and the second temperature parameter based on the ratio of the second average temperature to the third average temperature.

[0052] The degree calculation unit generates anomaly degree information based on the first range parameter, the second range parameter, the first temperature parameter, and the second temperature parameter.

[0053] The correction submodule includes a correction unit, a distance extraction unit, and an output unit.

[0054] The correction unit has a preset temperature correction weight parameter, and the correction unit generates a correction distance based on the temperature correction weight parameter and the anomaly information in the heat source information.

[0055] The distance extraction unit extracts the warning distance of the corresponding field device from the device storage module based on the device name and location information of the field device in the heat source information.

[0056] The output unit outputs the corrected warning distance based on the corrected distance and the warning distance.

[0057] The occurrence of electrical leakage can be inferred by detecting abnormal temperatures, as leaking high-voltage electricity generates high temperatures at the leakage point, and this heat diffuses to the surrounding environment, affecting its temperature. In the event of a leakage, due to the leakage of high-voltage electricity, a corrected distance should be calculated based on the leakage situation and the degree of abnormality to generate a corrected warning distance, thereby increasing the warning area and improving the safety of inspection personnel.

[0058] The inspection helmet also includes a humidity detection module. When the correction submodule receives heat source information, the humidity detection module starts collecting humidity information and sends it to the correction submodule.

[0059] The correction submodule also includes a humidity correction unit. This unit generates humidity correction parameters based on the humidity information and generates a correction distance based on temperature correction weight parameters, humidity correction parameters, and anomaly information within the heat source information. Increased humidity in the air may facilitate current diffusion and affect the surrounding environment; therefore, it is necessary to detect the surrounding humidity and appropriately increase the correction distance based on the air humidity to avoid issues for inspection personnel.

[0060] The inspection helmet is also equipped with a voice output module, and the supervision platform also includes a voice input module. The voice input module is used for supervisors to input prompt audio and send it to the inspection helmet, and the voice output module is used to play the prompt audio.

[0061] The first video acquisition module captures monitoring images and uploads them to the monitoring platform, enabling substation managers to effectively manage and supervise the inspection personnel's work process. It also allows for the timely detection and correction of improper or non-standard operations by inspection personnel during fault handling or emergency situations, preventing accidents. The second video acquisition module captures environmental images, recording the inspected equipment in real time, avoiding false positives and false negatives. Furthermore, in the event of an accident, the recorded environmental images can be reviewed to increase the likelihood of identifying the cause. Since leakage is often difficult for inspection personnel to detect, and leakage locations cause temperature increases, the included temperature storage and analysis module and anomaly analysis module can detect areas of excessively high temperature during inspections. Infrared thermal imaging sensors acquire infrared thermal images to pinpoint the heat source location, thereby determining the leakage location on the equipment and correcting the warning distance to prevent inspection personnel from entering the corrected warning distance, ensuring their safety and preventing accidents.

[0062] The embodiments described above do not constitute a limitation on the scope of protection of this technical solution. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the above embodiments should be included within the scope of protection of this technical solution.

Claims

1. A smart helmet with built-in monitoring and inspection capabilities, characterized in that, include: Monitoring platform, cloud server, and inspection helmet. The inspection helmet includes a positioning module, a first video acquisition module, a second video acquisition module, and sensor components. The first video acquisition module is connected to the monitoring platform to acquire monitoring images, and the second video acquisition module is connected to the cloud server to acquire environmental images. The monitoring images are video data recording the body movements of the inspection personnel during the inspection process, and the environmental images are video data recording the surrounding environment and equipment of the inspection personnel during the inspection process. The sensor components include a temperature sensor and an infrared thermal imaging sensor. The temperature sensor acquires real-time temperature data at regular intervals and uploads it to the cloud server. The infrared thermal imaging sensor acquires infrared thermal images of the area around the inspection helmet after receiving an abnormal signal and uploads them to the cloud server. The positioning module is used to obtain the real-time location information of the inspection helmet. The cloud server includes a device storage module, a temperature storage and analysis module, an anomaly analysis module, and an alert module. The device storage module pre-stores inspection point information, which includes the device name, image, and location information of the field equipment to be inspected, as well as the alert distance for each field equipment. The warning module includes a correction submodule and a calculation submodule. If the correction submodule receives heat source information, it generates a corrected warning distance based on the heat source information and the warning distance. The calculation submodule determines whether the inspection personnel are within the corrected warning distance based on the real-time location information, equipment location information, and the corrected warning distance. If the correction submodule does not receive heat source information, the calculation submodule determines whether the inspection personnel are within the warning distance based on the real-time location information, equipment location information, and warning distance. If the equipment is within the warning distance or the corrected warning distance, a warning signal is sent to the inspection helmet to remind the inspection personnel to stay away from the equipment on site. The temperature storage and analysis module includes an analysis submodule and a storage submodule. The storage submodule is used to store historical inspection data, which consists of all real-time temperature data collected by the temperature sensor during the current inspection. The analysis submodule determines whether there is a temperature anomaly based on the historical inspection data and the received real-time temperature data. If an anomaly is found, an anomaly signal is sent to the infrared thermal imaging sensor. If no anomaly is found, the real-time temperature data is stored in the historical inspection data. After receiving the infrared thermal image, the anomaly analysis module performs image analysis and processing to obtain heat source information, which includes the location, name, and degree of anomaly of the field equipment with temperature anomalies.

2. The smart helmet with built-in monitoring and inspection as described in claim 1, characterized in that, The anomaly analysis module includes a comparison acquisition submodule, a heat source analysis submodule, a comparison analysis submodule, an image analysis submodule, a degree analysis submodule, and a transmission submodule. Upon receiving the infrared thermal image, the comparison acquisition submodule uses the second video acquisition module to acquire an environmental image at the same moment as the comparison image. The heat source analysis submodule extracts features from the infrared thermal image to obtain a first gradient region, a second gradient region, and a third gradient region, with the temperature range of the first gradient region, the second gradient region, and the third gradient region decreasing sequentially. The comparison analysis submodule extracts the coordinate position information of the first gradient region, the second gradient region, and the third echelon region, and locates the first comparison region on the comparison image based on the coordinate position information. The image analysis submodule performs image analysis processing based on the comparison image, the first comparison area and the inspection point information to determine the equipment name information and equipment location information of the field equipment corresponding to the location of the first comparison area; The degree analysis submodule extracts temperature information from the first gradient region, the second gradient region, and the third gradient region, and calculates the degree of abnormality information based on the extracted temperature information and the first gradient region, the second gradient region, and the third gradient region. The sending submodule generates heat source information based on the anomaly level information, the equipment name information and equipment location information of the field equipment, and sends it to the warning module.

3. The smart helmet with built-in monitoring and inspection as described in claim 2, characterized in that, The heat source analysis submodule extracts features from the infrared thermal image to obtain the first gradient region, the second gradient region, and the third gradient region, specifically including the following steps: The pixel with the highest temperature in the infrared thermal image is extracted as the high temperature source point. Starting from the high temperature source point, the region formed by the pixels whose temperature difference with the high temperature source point is within the first temperature difference range is the first gradient region. The second temperature range and the third temperature range are calculated based on the high temperature source point temperature and the preset second temperature difference range and third temperature difference range. The second gradient region is the region formed by pixels whose temperature is within the second temperature range, and the third gradient region is the region formed by pixels whose temperature is within the third temperature range.

4. The smart helmet with built-in monitoring and inspection as described in claim 3, characterized in that, The degree analysis submodule includes a range comparison unit, a temperature comparison unit, and a degree calculation unit. The range comparison unit obtains the areas of the first gradient region, the second gradient region, and the third gradient region to obtain the first area, the second area, and the third area, and calculates the first range parameter based on the ratio of the first area and the second area, and calculates the second range parameter based on the ratio of the second area and the third area. The temperature comparison unit calculates the average temperatures of the first gradient region, the second gradient region, and the third gradient region based on the extracted temperature information, respectively, as the first average temperature, the second average temperature, and the third average temperature. It then calculates the first temperature parameter based on the ratio of the first average temperature to the second average temperature, and the second temperature parameter based on the ratio of the second average temperature to the third average temperature. The degree calculation unit generates anomaly degree information based on the first range parameter, the second range parameter, the first temperature parameter, and the second temperature parameter.

5. The intelligent helmet with built-in monitoring and inspection as described in claim 1, characterized in that, The correction submodule includes a correction unit, a distance extraction unit, and an output unit. The correction unit has a preset temperature correction weight parameter, and the correction unit generates a correction distance based on the temperature correction weight parameter and the anomaly information in the heat source information. The distance extraction unit extracts the warning distance of the corresponding field device from the device storage module based on the device name and location information of the field device in the heat source information. The output unit outputs the corrected warning distance based on the corrected distance and the warning distance.

6. A smart helmet with built-in monitoring and inspection as described in claim 5, characterized in that, The inspection helmet also includes a humidity detection module. When the correction submodule receives heat source information, the humidity detection module starts collecting humidity information and sends it to the correction submodule. The correction submodule also includes a humidity correction unit, which generates humidity correction parameters based on the humidity information and generates a correction distance based on the temperature correction weight parameters, the humidity correction parameters, and the anomaly information in the heat source information.

7. The intelligent helmet with built-in monitoring and inspection as described in claim 1, characterized in that, The inspection helmet is also equipped with a voice output module, and the supervision platform also includes a voice input module. The voice input module is used for supervisors to input prompt audio and send it to the inspection helmet, and the voice output module is used to play the prompt audio.

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