Integrated Energy Processing Methods and Systems

Abstract

This invention provides a comprehensive energy processing method and system, relating to data processing technology. The method includes: dividing a target heating network based on its pipeline distribution; generating an inspection movement path covering the target heating network based on the divided pipelines corresponding to different laying methods; controlling an inspection device to move along the inspection movement path; acquiring images of the target heating network based on an image acquisition method determined by environmental information of the target heating network to obtain an inspection image sequence; identifying abnormal areas within the target heating network based on the inspection image sequence; and extracting features from each abnormal area to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal area. This invention improves the effectiveness of data acquisition under different environmental conditions and increases the coverage and efficiency of inspections.

Description

Technical Field

[0001] This invention relates to data processing technology, and more particularly to a comprehensive energy processing method and system. Background Technology

[0002] Currently, in the operation and maintenance management of heating pipe networks, the detection of faults such as pipe leaks usually relies on manual inspections or simple equipment monitoring. Manual inspections are inefficient, greatly affected by environmental factors such as terrain and weather, and are difficult to cover complex pipe network areas, resulting in significant safety blind spots.

[0003] Existing automated monitoring methods often use fixed monitoring points, which lack adaptability to different pipeline laying methods. This results in data collection that lacks specificity and cannot provide accurate quantitative data support for subsequent maintenance. Therefore, there is an urgent need for a comprehensive energy management method that can adaptively plan inspection paths. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a comprehensive energy processing method and system that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a comprehensive energy processing method is provided, comprising the following steps: The target heating network is divided based on the pipeline distribution of the corresponding target heating network, and an inspection movement path covering the target heating network is generated based on the divided pipelines corresponding to different laying methods. The inspection equipment is controlled to move along the inspection path, and the image acquisition method is determined based on the environmental information of the corresponding target heating network to acquire the target heating network and obtain the inspection image sequence. The inspection image sequence is identified to determine the abnormal areas included in the target heating network; Feature extraction is performed on each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region.

[0006] Optionally, in the method according to the present invention, generating an inspection movement path covering the target heating network based on the obtained division of pipelines corresponding to different laying methods includes: A baseline moving path is generated along the extension direction of the corresponding target heating network, and the division path corresponding to each division pipeline is determined based on the baseline moving path. If the laying method of any pipeline segment is the burial depth method, obtain the laying depth of the corresponding pipeline segment; If the laying depth is greater than the burial depth acquisition threshold, the preset inspection height is determined as the first inspection height; otherwise, the first inspection height is determined based on the burial depth difference corresponding to the laying depth acquisition threshold. The first inspection height is determined as the matching height corresponding to the divided path; If any pipeline is laid in an overhead manner, the laying height of the corresponding pipeline is obtained, and the second inspection height determined based on the laying height is set as the matching height of the corresponding pipeline path. The baseline movement path is updated based on the matching height of each corresponding partition path to obtain the inspection movement path.

[0007] Optionally, in the method according to the invention, the method further includes: If any segment of the path has a corresponding curved segment with a curvature angle less than a preset angle, obtain the corner point of the corresponding curved segment. The corresponding buffer length is obtained based on the bending angle, and the path is divided based on the corner point to obtain the first segment and the second segment. Starting from the corner point, determine the first buffer point and the second buffer point located at the corresponding buffer length of the first split segment and the second split segment, and determine the replacement path segment composed of the first buffer point and the second buffer point based on the divided path; Connect the first buffer point and the second buffer point to obtain the buffer diameter. Generate a buffer arc segment with the corresponding buffer diameter towards the corner point, and determine the center distance between the center of the buffer arc segment and the center of the corner point. The inspection height corresponding to the divided path is adjusted based on the center distance. The obtained third inspection height is determined as the matching height of the corresponding buffer arc segment. The replacement path segment is then replaced based on the buffer arc segment to obtain the updated divided path.

[0008] Optionally, in the method according to the present invention, the step of acquiring images of the target heating network based on the image acquisition method determined by the environmental information of the corresponding target heating network includes: The response determines that the current environment of the target heating network is a non-snow scene based on environmental information, determines that the image acquisition method is infrared acquisition, and acquires images of the target heating network based on infrared acquisition. The response determines that the current environment of the target heating network is a snow scene based on environmental information, determines that the image acquisition method is white light acquisition method, and acquires images of the target heating network based on the white light acquisition method.

[0009] Optionally, in the method according to the present invention, the step of identifying the abnormal areas included in the inspection image sequence to determine the target heating network includes: The response image acquisition method is infrared acquisition, which determines the infrared image frames included in the inspection image sequence; Each hot spot image located in each infrared image frame, indicating the temperature status of the target heating network, is determined, and each hot spot image is stitched together based on the inspection movement path to obtain the first stitched image. The response determines that there are areas of temperature difference in the target heating network based on the first stitched image, and controls the inspection equipment to collect images of the areas of difference at intervals corresponding to the preset number of monitoring times, thereby obtaining images of each difference. If each acquired image shows a region of difference, that region is identified as an abnormal region.

[0010] Optionally, in the method according to the present invention, the step of extracting features from each anomalous region to obtain quantitative data characterizing the leakage attributes corresponding to the anomalous region includes: Obtain the anomaly center point and anomaly contour of the corresponding anomaly region, and connect the contour points obtained by arraying the anomaly contour with the anomaly center point to obtain the contour connection lines. The variance is calculated based on the connection length of each contour line, and the first evaluation value is determined based on the obtained rule characterization value. The abnormal area is divided into arrays, and the point temperature value of each point in the obtained area is determined; The highest point temperature value is defined as the maximum temperature value, and the first distribution coefficient is determined based on the maximum number of all regional points corresponding to the maximum temperature value. The abnormal region is radially divided based on the anomaly center point, and the mean value of the temperature values ​​of all points in each radial sub-region is calculated to obtain the value of each radial level. The difference between adjacent radiation levels is calculated, and the second distribution coefficient is determined based on the largest difference between levels obtained. The second evaluation value is determined based on the first distribution coefficient and the second distribution coefficient, and the comprehensive evaluation value is determined based on the first evaluation value and the second evaluation value. If the overall response evaluation value is less than or equal to the attribute classification value, the leakage attribute is determined as a liquid leakage, and the corresponding quantitative data of the liquid leakage is determined; otherwise, the leakage attribute is determined as a gas leakage, and the corresponding quantitative data of the gas leakage is determined.

[0011] Optionally, in the method according to the invention, determining the leakage attribute as a liquid leakage and determining the corresponding quantitative data of the liquid leakage includes: The first stitched image is processed into coordinates, and the leakage coordinates of the corresponding liquid leak are determined based on the coordinates of the points corresponding to the maximum temperature values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding liquid leakage is determined. Obtain the ambient temperature value corresponding to the current environment, determine the background temperature difference value corresponding to the maximum temperature value, and sum all the differences at all levels to obtain the total value at each level. The leakage efficiency of the corresponding liquid leak is obtained by summing the background temperature difference and the total value of the layers. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding liquid leakage.

[0012] Optionally, in the method according to the invention, determining the leakage attribute as a gas leak and determining the corresponding quantitative data of the gas leak includes: The first stitched image is processed into coordinates, and the leakage coordinates of the corresponding gas leak are determined based on the coordinates of the points corresponding to the maximum temperature values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding gas leak is determined. Obtain the diffusion area of ​​the corresponding abnormal contour, and calculate the average temperature value of all points located in the abnormal area to obtain the average temperature of the entire region. Obtain the ambient temperature value corresponding to the current environment, determine the background temperature difference between the ambient temperature value and the mean temperature value of the whole area, and calculate the leakage efficiency of the corresponding gas leak by multiplying the background temperature difference and the mean temperature value of the whole area. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding gas leakage.

[0013] Optionally, in the method according to the present invention, the step of identifying the abnormal areas included in the inspection image sequence to determine the target heating network includes: The response image acquisition method is white light acquisition method, and the white light image frames included in the inspection image sequence are determined; Each melting image located in each white light image frame, indicating the melting state of the target heating network, is determined, and each melting image is stitched together based on the inspection movement path to obtain a second stitched image. The response determines, based on the second stitched image, that there are areas of difference in melting changes in the target heating network, and controls the inspection equipment to collect images of these areas at preset monitoring intervals and for a preset number of monitoring times, thereby obtaining images of each difference. If each acquired image shows a region of difference, that region is identified as an abnormal region.

[0014] Optionally, in the method according to the present invention, the step of extracting features from each anomalous region to obtain quantitative data characterizing the leakage attributes corresponding to the anomalous region includes: Obtain the anomaly center point and anomaly contour of the corresponding anomaly region, and connect the contour points obtained by arraying the anomaly contour with the anomaly center point to obtain the contour connection lines. The variance is calculated based on the connection length of each contour line, and the first evaluation value is determined based on the obtained rule characterization value. The abnormal region is divided into arrays, and the gray value of each point in the obtained region is determined; the smallest gray value is determined as the minimum gray value, and the first distribution coefficient is determined based on the minimum number of all points in the region corresponding to the minimum gray value. The abnormal region is radially divided based on the anomaly center point, and the mean value of the gray value of all regional points included in each radial sub-region is calculated to obtain the value of each radial level. The difference between adjacent radiation levels is calculated, and the second distribution coefficient is determined based on the largest difference between levels obtained. The second evaluation value is determined based on the first distribution coefficient and the second distribution coefficient, and the comprehensive evaluation value is determined based on the first evaluation value and the second evaluation value. If the overall response evaluation value is less than or equal to the attribute classification value, the leakage attribute is determined as a liquid leakage, and the corresponding quantitative data of the liquid leakage is determined; otherwise, the leakage attribute is determined as a gas leakage, and the corresponding quantitative data of the gas leakage is determined.

[0015] Optionally, in the method according to the invention, determining the leakage attribute as a liquid leakage and determining the corresponding quantitative data of the liquid leakage includes: The second stitched image is processed into coordinates, and the leakage coordinates of the corresponding liquid leak are determined based on the coordinates of the points with the corresponding minimum gray values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding liquid leakage is determined. Obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference value corresponding to the minimum grayscale value, and sum all the differences at all levels to obtain the total value at each level. The leakage efficiency of the corresponding liquid leak is obtained by summing the background grayscale difference and the total layer value. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding liquid leakage.

[0016] Optionally, in the method according to the invention, determining the leakage attribute as a gas leak and determining the corresponding quantitative data of the gas leak includes: The second stitched image is processed into coordinates, and the leakage coordinates of the corresponding gas leak are determined based on the coordinates of the points with the corresponding minimum gray values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding gas leak is determined. Obtain the diffusion area of ​​the corresponding abnormal contour, and calculate the average gray value of all points located in the abnormal region to obtain the global average gray value. Obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference between the ambient grayscale value and the mean grayscale value of the entire area, and calculate the leakage efficiency of the corresponding gas leak by multiplying the background grayscale difference and the mean grayscale value of the entire area. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding gas leakage.

[0017] According to another aspect of the present invention, an integrated energy processing system is provided, comprising: The segmentation module is configured to segment the target heating network based on the pipeline distribution of the corresponding target heating network, and generate an inspection movement path covering the target heating network based on each segmented pipeline with different laying methods. The acquisition module is configured to control the inspection equipment to move along the inspection path and acquire images of the target heating network based on the image acquisition method determined by the environmental information of the corresponding target heating network, thereby obtaining an inspection image sequence. The identification module is configured to identify the inspection image sequence and determine the abnormal areas included in the target heating network; The extraction module is configured to extract features from each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region.

[0018] According to the present invention, firstly, by generating an inspection movement path based on the pipeline distribution and laying method of the heating network, the inspection equipment can adapt to different pipeline characteristics, improving the coverage and efficiency of the inspection; secondly, by adaptively determining the image acquisition method based on environmental information, it ensures that image data reflecting the network status can be effectively acquired under different environmental conditions, improving the accuracy of data acquisition; finally, by identifying abnormal areas in the inspection image sequence and extracting features from the abnormal areas to quantify leakage attributes, the data is detailed and has a certain degree of accuracy. Attached Figure Description

[0019] Figure 1A flowchart of an integrated energy processing method according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a buffer arc segment according to an embodiment of the present invention is shown; Figure 3 A structural block diagram of an integrated energy processing system according to another embodiment of the present invention is shown. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] To address the problems existing in the aforementioned background art, the inventors have proposed the solution of this invention. One embodiment of this invention provides a comprehensive energy processing method that can be executed in a computing device.

[0022] Figure 1 A flowchart of an integrated energy processing method according to an embodiment of the present invention is shown, the method being adapted to be executed in a computing device.

[0023] like Figure 1 As shown, the integrated energy processing method proposed in this embodiment begins with step S1, which includes the following: The target heating network is divided based on the pipeline distribution of the corresponding target heating network, and an inspection movement path covering the target heating network is generated based on the divided pipelines corresponding to different laying methods.

[0024] For example, in this embodiment, the target heating network can be understood as a heating network that needs to be inspected. It is usually composed of a large number of pipe sections, valves and joints, and is distributed underground or above ground in the city. The server will first divide the target heating network according to the distribution of the pipelines. The basis for the division can be the laying method of the pipelines, such as dividing the pipelines for buried laying and overhead laying respectively. For different types of pipelines, the server will generate an appropriate inspection path. For example, for pipelines laid at a deep burial depth, that is, pipelines laid below the ground and covered by soil or gravel, heat needs to be conducted to the ground through the soil layer. Therefore, when the inspection equipment is inspected, the inspection height needs to be adjusted according to the burial depth to ensure that the infrared thermal signal can be effectively collected. The path may need to be closer to the ground to capture the thermal radiation signal. For example, for overhead deep-laid segmented pipelines, which are heating pipelines exposed above ground through structures such as supports and hangers, these segmented pipelines are not covered by soil and are in direct contact with the air. During inspection, it is only necessary to set a safe and appropriate inspection height according to the laying height of the pipeline to clearly collect the appearance and temperature status of the pipeline. Finally, an inspection movement path covering the target heating network is generated.

[0025] It can be noted that the inspection equipment involved in this embodiment may be, for example, a drone.

[0026] Furthermore, the aforementioned "generating an inspection movement path covering the target heating network based on the obtained pipeline divisions corresponding to different laying methods" also includes the following steps: A baseline moving path is generated along the extension direction of the corresponding target heating network, and the division path corresponding to each division pipeline is determined based on the baseline moving path. If the laying method of any pipeline segment is the burial depth method, obtain the laying depth of the corresponding pipeline segment; If the laying depth is greater than the burial depth acquisition threshold, the preset inspection height is determined as the first inspection height; otherwise, the first inspection height is determined based on the burial depth difference corresponding to the laying depth acquisition threshold. The first inspection height is determined as the matching height corresponding to the divided path; If any pipeline is laid in an overhead manner, the laying height of the corresponding pipeline is obtained, and the second inspection height determined based on the laying height is set as the matching height of the corresponding pipeline path. The baseline movement path is updated based on the matching height of each corresponding partition path to obtain the inspection movement path.

[0027] For example, in this embodiment, the server will first generate a reference movement path along the extension direction of the corresponding target heating pipeline. The reference movement path is a virtual guide line planned along the pipeline. The server will determine the division path corresponding to each division pipeline according to the reference movement path, that is, the reference movement path is divided to obtain a division path that corresponds to each division pipeline. If a certain pipeline is laid in a burial method, the server will first obtain the laying depth of the corresponding pipeline, and then compare the laying depth with the burial depth acquisition threshold. The burial depth acquisition threshold can be an empirical value set based on the detection capability of the inspection equipment and the thermal conductivity of the soil. When the laying depth is greater than the burial depth acquisition threshold, it means that the pipeline is buried deep and the surface thermal radiation characteristics will be less obvious. Simply reducing the inspection height has limited effect on signal enhancement. Therefore, the server will directly use the preset inspection height as the first inspection height. When the laying depth is less than or equal to the burial depth acquisition threshold, it indicates that the pipeline is buried relatively shallowly, and the surface thermal radiation characteristics are relatively obvious. The leakage heat signal of shallow buried pipelines is more likely to be conducted to the surface, but it is also more likely to be interfered with by surface clutter. Therefore, the server will calculate the difference between the burial depth acquisition threshold and the laying depth to obtain the burial depth difference, and then calculate the difference between the preset inspection height and the burial depth difference to obtain the adjusted first inspection height. That is, the greater the difference in burial depth, the shallower the pipeline is buried and the more obvious the surface heat signal is. Therefore, the corresponding first inspection height is lower to reduce the imaging range and facilitate more accurate determination of the thermal anomaly area. Finally, the server will determine the first inspection height as the matching height corresponding to the divided path, so that subsequent inspection devices can move according to the matching height of different divided paths; There may also be a situation where a certain branch pipeline is laid in an overhead manner, that is, the branch pipeline is directly exposed to the air and is not covered by soil. In this case, the inspection height is mainly limited by the laying height of the branch pipeline and the safe distance for movement. First, the server will obtain the laying height of the corresponding pipeline, and then add a moving safety distance to the laying height. The moving safety distance can be determined according to the scale of the inspection equipment. For example, the moving safety distance can be determined to be 1 meter, thus obtaining the second inspection height. The second inspection height is then determined as the matching height of the corresponding pipeline path. This ensures that the inspection equipment will not collide with the pipeline and can clearly collect data on the pipeline and its surrounding environment. Finally, the server updates the baseline movement path based on the matching height of each segmented path to obtain the inspection movement path. This allows the inspection equipment to adjust the inspection height for different laying methods of the segmented pipelines when performing tasks, so that the inspection equipment can be located in a suitable data acquisition position to obtain high-quality inspection images.

[0028] Furthermore, the above method also includes the following steps: If any segment of the path has a corresponding curved segment with a curvature angle less than a preset angle, obtain the corner point of the corresponding curved segment. The corresponding buffer length is obtained based on the bending angle, and the path is divided based on the corner point to obtain the first segment and the second segment. Starting from the corner point, determine the first buffer point and the second buffer point located at the corresponding buffer length of the first split segment and the second split segment, and determine the replacement path segment composed of the first buffer point and the second buffer point based on the divided path; Connect the first buffer point and the second buffer point to obtain the buffer diameter. Generate a buffer arc segment with the corresponding buffer diameter towards the corner point, and determine the center distance between the center of the buffer arc segment and the center of the corner point. The inspection height corresponding to the divided path is adjusted based on the center distance. The obtained third inspection height is determined as the matching height of the corresponding buffer arc segment. The replacement path segment is then replaced based on the buffer arc segment to obtain the updated divided path.

[0029] For example, in this embodiment, the preset angle is the critical value for judging the sharpness of the turn of the divided path. For example, it is set to 60 degrees. The smaller the bending angle, the sharper the turn. When any divided path has a bending segment with a corresponding bending angle less than the preset angle, it means that the turn of the bending segment is sharp, which will lead to a greater risk of movement. At this time, the server will first obtain the corner point of the corresponding curved section, and then obtain the corresponding buffer length based on the bending angle. The smaller the bending angle, the longer the buffer length needs to be to provide sufficient transition space. That is, the bending angle and the buffer length are inversely proportional. The bending angle and the reciprocal of the preset buffer coefficient can be multiplied to calculate the buffer length. The preset buffer coefficient can be adaptively set according to the turning performance of the inspection equipment. The server will split the path based on the corner point as the split point, thus obtaining the first split segment and the second split segment. Then, taking the corner point as the starting point, the server will determine the corresponding buffer length points on the first split segment and the second split segment, namely the first buffer point and the second buffer point. Then, the server will determine the line segment between the first buffer point and the second buffer point in the divided path, that is, replace the path segment, and then connect the first buffer point and the second buffer point to obtain the buffer diameter. Next, the server will generate a buffer arc segment with a corresponding buffer diameter towards the corner point, such as... Figure 2 As shown, the buffer arc segment can smoothly connect the first buffer point and the second buffer point. The server will further determine the center distance of the buffer arc segment corresponding to the corner point. The smaller the center distance, the closer the buffer arc segment's center is to the corner point, and the smaller the radius of the buffer arc segment, meaning the sharper the turn. To address potential sharp turns, increasing the inspection height expands the field of view of the inspection equipment, ensuring that the collection area still covers the pipeline area during turns. A higher inspection height also provides the inspection equipment with more safe maneuverability, avoiding the risk of collisions. Therefore, the server will adaptively increase the inspection height based on the center distance to obtain the third inspection height. For example, when the center distance is small, a smaller height adjustment compensation value is matched to only slightly raise the inspection height. When the center distance increases, a larger height adjustment compensation value is matched to raise the inspection height more significantly, thereby expanding the field of vision coverage during turning and reserving more maneuvering space to avoid the risk of collision during turning inspection. Ultimately, the server will determine the third inspection height as the matching height of the corresponding buffer arc segment, and then replace the replacement path segment with the buffer arc segment to obtain the updated partitioned path, thereby improving inspection security.

[0030] Step S2 includes the following: The inspection equipment is controlled to move along the inspection path and to collect images of the target heating network based on the image acquisition method determined by the environmental information of the corresponding target heating network, thus obtaining an inspection image sequence.

[0031] For example, in this embodiment, the inspection equipment serves as a carrier for the data acquisition equipment, and the server controls the inspection equipment to move along the inspection movement path. The server will preload environmental information of the target heating network area, such as real-time weather and surface cover status, and determine the appropriate image acquisition method based on this environmental information. The inspection equipment will continuously collect images of the target heating network according to a determined image acquisition method, generating an inspection image sequence; This embodiment can adaptively switch the image acquisition mode based on environmental information, ensuring that high-quality inspection image sequences can be obtained under different environmental conditions, thereby improving the image acquisition effect.

[0032] Furthermore, the aforementioned "collecting images of the target heating network based on the image acquisition method determined by the environmental information of the corresponding target heating network" also includes the following steps: The response determines that the current environment of the target heating network is a non-snow scene based on environmental information, determines that the image acquisition method is infrared acquisition, and acquires images of the target heating network based on infrared acquisition. The response determines that the current environment of the target heating network is a snow scene based on environmental information, determines that the image acquisition method is white light acquisition method, and acquires images of the target heating network based on the white light acquisition method.

[0033] For example, in this embodiment, the target heating network often spans different geographical areas, and the inspection work may be carried out in different seasons and under different weather conditions. If a single image acquisition method is used, it may be difficult to adapt to different inspection environments. When the current environment of the target heating network is a non-snowy scene, if there is a leak in the exposed soil, grass or asphalt road, or heating network, the heat-carrying medium will be conducted to the ground surface or directly radiated into the air, such as hot water, steam, etc., which will cause the ambient temperature around the leak point to be significantly higher than the background temperature. At this time, the infrared thermal imaging camera can accurately capture this thermal radiation difference and convert the temperature distribution into a visualized hot spot image. Therefore, when the current environment of the target heating network is determined to be a non-snowy scene based on environmental information, the server will determine the image acquisition method to be infrared acquisition and collect data on the target heating network based on infrared acquisition, thereby improving the accuracy of monitoring. In snowy scenes, the ground is covered with white snow, the temperature of the background environment will be low, and infrared thermal imaging is easily interfered with by the high reflectivity of the snow layer. Therefore, small temperature differences may be drowned out by background noise. However, the leaked heat will melt the snow covering the pipeline, forming melt holes, wet spots or icicles. These changes in physical form are manifested as obvious grayscale differences and texture features under visible light, which is also known as white light. Therefore, when the environmental information determines that the current environment of the target heating network is a snow scene, the server will determine that the image acquisition method is white light acquisition method, and acquire the target heating network based on the white light acquisition method, so as to use a high-resolution visible light camera to capture the melting traces on the snow surface, thereby accurately identifying abnormal areas.

[0034] Step S3 includes the following: The inspection image sequence is identified to determine the abnormal areas included in the target heating network.

[0035] For example, in this embodiment, the inspection image sequence contains a large amount of background information in addition to pipeline information, such as soil, vegetation, and buildings. Therefore, the server will identify the abnormal areas included in the target heating pipeline, which is highly targeted.

[0036] Furthermore, the aforementioned "identifying the inspection image sequence and determining the abnormal areas included in the target heating network" also includes the following steps: The response image acquisition method is infrared acquisition, which determines the infrared image frames included in the inspection image sequence; Each hot spot image located in each infrared image frame, indicating the temperature status of the target heating network, is determined, and each hot spot image is stitched together based on the inspection movement path to obtain the first stitched image. The response determines that there are areas of temperature difference in the target heating network based on the first stitched image, and controls the inspection equipment to collect images of the areas of difference at intervals corresponding to the preset number of monitoring times, thereby obtaining images of each difference. If each acquired image shows a region of difference, that region is identified as an abnormal region.

[0037] For example, in this embodiment, when the image acquisition method is infrared acquisition, the infrared thermal imager on the inspection equipment will continuously shoot according to the set frame rate to generate various infrared image frames containing temperature information. Each infrared image frame records the thermal radiation distribution of the ground surface or pipeline surface at the corresponding shooting time and shooting location. The infrared image frames form an inspection image sequence. Since there may be other background areas besides the target heating network in each infrared image frame, in order to improve the accuracy of subsequent identification of abnormal areas, the server will extract hot spot images that represent the temperature state of the network from each infrared image frame through temperature threshold segmentation. That is, the hot spot images only indicate the temperature state of the area corresponding to the target heating network. Next, the server will stitch together each hot spot image according to the inspection movement path to obtain the first stitched image. If the first stitched image determines that there is a corresponding temperature rise difference area in the target heating network, in order to rule out whether the temperature rise is caused by instantaneous interference, such as whether it is caused by a moving vehicle or a passing pedestrian, the server will control the inspection equipment to collect data at intervals of a corresponding preset number of monitoring times for the difference area to obtain each difference collection image. If the difference area is caused by a momentary disturbance from a moving vehicle, the hot spot in the difference area will not remain stable after the vehicle leaves. However, if it is a real leak in the target heating network, the hot spot in the abnormal area will remain stable or even expand for a long time due to the continuous supply of underground heat sources. Therefore, the server will compare and analyze the differential images collected from multiple acquisitions, and only when a differential region exists in all differential acquisition images will the server identify the differential region as an abnormal region, so as to improve the accuracy and reliability of anomaly identification and reduce the false alarm rate.

[0038] Step S4 includes the following: Feature extraction is performed on each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region.

[0039] For example, in this embodiment, after identifying the abnormal area, the server will further extract features from the abnormal area to obtain quantitative data that characterizes the leakage attributes corresponding to the abnormal area. The quantitative data can characterize the severity of the leakage. Leakage attributes refer to the specific type of leak, such as liquid leak or gas leak. Different types of leaks exhibit different morphological characteristics in images. For example, liquid leaks usually form a relatively regular diffusion outline, while gas leaks show irregular large-area diffusion. This example, by determining the quantitative data representing the leakage attributes corresponding to the abnormal area, can facilitate maintenance personnel to quickly locate the abnormal area and intervene in a timely manner, thereby improving the operation and maintenance efficiency of the heating network.

[0040] Furthermore, the aforementioned "extracting features from each anomalous region to obtain quantitative data characterizing the leakage attributes corresponding to the anomalous region" also includes the following steps: Obtain the anomaly center point and anomaly contour of the corresponding anomaly region, and connect the contour points obtained by arraying the anomaly contour with the anomaly center point to obtain the contour connection lines. The variance is calculated based on the connection length of each contour line, and the first evaluation value is determined based on the obtained rule characterization value. The abnormal area is divided into arrays, and the point temperature value of each point in the obtained area is determined; The highest point temperature value is defined as the maximum temperature value, and the first distribution coefficient is determined based on the maximum number of all regional points corresponding to the maximum temperature value. The abnormal region is radially divided based on the anomaly center point, and the mean value of the temperature values ​​of all points in each radial sub-region is calculated to obtain the value of each radial level. The difference between adjacent radiation levels is calculated, and the second distribution coefficient is determined based on the largest difference between levels obtained. The second evaluation value is determined based on the first distribution coefficient and the second distribution coefficient, and the comprehensive evaluation value is determined based on the first evaluation value and the second evaluation value. If the overall response evaluation value is less than or equal to the attribute classification value, the leakage attribute is determined as a liquid leakage, and the corresponding quantitative data of the liquid leakage is determined; otherwise, the leakage attribute is determined as a gas leakage, and the corresponding quantitative data of the gas leakage is determined.

[0041] For example, in this embodiment, the server will obtain the abnormal center point and abnormal contour of the corresponding abnormal area, then divide the abnormal contour into an array to obtain each contour point, and connect each contour point with the abnormal center point to obtain each contour connection line. Next, the server will calculate the variance of the connection length of each contour line to obtain the rule representation value. Then, the rule representation value will be multiplied with the preset rule coefficient to obtain the first evaluation value. The first evaluation value is mainly used to represent the regularity of the contour of the abnormal area. The smaller the rule representation value, the more regular the contour, and the lower the first evaluation value. Since liquid leaks are usually affected by gravity and flow or accumulate along the ground, the shape of the infrared hot spot formed is often relatively regular, close to a circle or ellipse. Therefore, the calculated regularity characterization value is small, and the corresponding first evaluation value is also low. Conversely, gas leaks are affected by airflow and spread rapidly and irregularly, forming an extremely irregular shape of infrared hot spot. Therefore, the corresponding first evaluation value is high. Next, the server will divide the abnormal area into arrays and determine the point temperature value of each area. The maximum value among all point temperature values ​​can represent the highest temperature of the leaking heat source. Therefore, the server will determine the maximum point temperature value as the maximum temperature value. Next, the server will determine the maximum number of all regional points corresponding to the maximum temperature value, and then normalize the maximum number to obtain the first distribution coefficient. The first distribution coefficient can characterize the concentration of high temperature areas. If the first distribution coefficient is large, it means that the high temperature areas are widely distributed, and there may be multiple leaks or gas diffusion. If the first distribution coefficient is small, it means that the high temperature areas are concentrated, which is consistent with the point source characteristics of liquid leakage. Next, the server will divide the area into radial regions with the anomaly center point as the center, forming multiple radial sub-regions with different radial level values. Then, the average value of the temperature values ​​of all points in each radial sub-region will be calculated to obtain the value of each radial level. Because the heat source of liquid leakage is concentrated, the heat is conducted outward from the center and decays rapidly. Therefore, the temperature difference between adjacent radiation levels is large, that is, the level difference is large, indicating a steep temperature gradient. Conversely, gas leakage has a wide diffusion range, the temperature distribution is relatively uniform, and the level difference is small. The server will calculate the difference between adjacent radiation level values, and then normalize the largest level difference to obtain the second distribution coefficient. Liquid leaks have a concentrated heat source, and the heat is conducted outwards and attenuates rapidly. Therefore, the difference between adjacent radiation levels is large, and the second distribution coefficient is large. In contrast, gas leaks have a wide diffusion range, relatively uniform temperature distribution, smaller difference between levels, and a smaller second distribution coefficient. Then, the server will perform a weighted summation of the first distribution coefficient and the second distribution coefficient to obtain the second evaluation value. The second evaluation value can be used to characterize the gradient characteristics of the temperature distribution. Then, the first evaluation value and the second evaluation value will be weighted summation to obtain the comprehensive evaluation value. Finally, the server compares the comprehensive evaluation value with the attribute classification value. The attribute classification value is a critical threshold obtained based on a large amount of historical leakage sample data. When the comprehensive evaluation value is less than or equal to the attribute classification value, the server will determine the leakage attribute as a liquid leakage and determine the corresponding quantitative data of the liquid leakage. Otherwise, the leakage attribute will be determined as a gas leakage and the corresponding quantitative data of the gas leakage will be determined.

[0042] Furthermore, the aforementioned "identifying the leak attribute as a liquid leak and determining the corresponding quantitative data of the liquid leak" also includes the following steps: The first stitched image is processed into coordinates, and the leakage coordinates of the corresponding liquid leak are determined based on the coordinates of the points corresponding to the maximum temperature values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding liquid leakage is determined. Obtain the ambient temperature value corresponding to the current environment, determine the background temperature difference value corresponding to the maximum temperature value, and sum all the differences at all levels to obtain the total value at each level. The leakage efficiency of the corresponding liquid leak is obtained by summing the background temperature difference and the total value of the layers. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding liquid leakage.

[0043] For example, in this embodiment, since the heat source of liquid leakage is usually concentrated at the leakage point, the maximum temperature value point can accurately indicate the location of the leakage center. Therefore, the server will perform coordinate processing on the first stitched image, and then determine the leakage coordinates of the corresponding liquid leakage based on the coordinates of the point corresponding to the maximum temperature value. Next, the server will determine the leakage level of the corresponding liquid leak based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline. The leakage level reflects the severity of the leak. The lower the first evaluation value, the more regular the leak profile, which usually means that the leak point is single and continuous, and the severity is high. The larger the second distribution coefficient, the larger the corresponding layer difference, indicating that the gradient of heat conduction from the center to the surrounding area is steeper. This means that the heat carried by the leaking medium is released in a large amount in a short period of time, which is more serious. The shallower the laying depth, the more direct the impact of the leak on the surface environment, and the higher the risk level. Therefore, the server can first multiply the first evaluation value with the laying depth to obtain the product result, then divide the second distribution coefficient by the product result to obtain the intermediate calculation value, and finally multiply the retrieved comprehensive correction coefficient with the intermediate calculation value to obtain the leakage value corresponding to the liquid leakage. Different leakage values ​​correspond to different leakage levels. For example, the server is configured with four leakage levels: minor leakage, moderate leakage, severe leakage, and critical leakage. When the calculated leakage value is less than 1.5, the leakage level is determined to be minor leakage; when the leakage value is greater than or equal to 1.5 and less than 2.5, it is determined to be moderate leakage; when the leakage value is greater than or equal to 2.5 and less than 3.5, it is determined to be severe leakage; and when the leakage value is greater than or equal to 3.5, it is determined to be critical leakage. Subsequently, the server will obtain the ambient temperature value corresponding to the current environment, calculate the difference between the maximum temperature value and the ambient temperature value to obtain the background temperature difference. The background temperature difference reflects the temperature difference between the leak center and the environment. The larger the temperature difference, the higher the heat energy density carried by the leaking medium. Next, the server will sum up all the differences between the layers to obtain the total value of the layers. The total value of the layers is the sum of the differences in the average temperature of each radiation layer, which reflects the total potential energy of heat spreading from the center to the surrounding areas. Subsequently, the server will sum the background temperature difference and the total layer value to obtain the leakage efficiency of the corresponding liquid leak. The leakage efficiency is a quantitative indicator that can characterize the intensity of leakage energy release. For liquid leaks, the heat source is concentrated and the diffusion path is relatively fixed. The background temperature difference characterizes the heat source intensity, and the total layer value characterizes the heat attenuation gradient. Adding the background temperature difference and the total layer value can comprehensively reflect the total amount of heat energy released at the leak point. The larger this value is, the greater the leakage amount or heat loss per unit time, and the higher the urgency of maintenance. Finally, the server determines the three-dimensional spatial coordinates based on the leak coordinates and laying depth, and defines the leak level, leak efficiency, and three-dimensional spatial coordinates as quantitative data of the corresponding liquid leak. It expands the two-dimensional geographical latitude and longitude coordinates into three-dimensional spatial coordinates that include depth information, which can realize the three-dimensional positioning of the leak point in the underground pipeline network. This allows maintenance personnel to directly obtain the precise three-dimensional location, severity level, and energy loss efficiency of the leak point, thereby rationally allocating excavation equipment and maintenance resources to achieve precise emergency repairs.

[0044] Furthermore, the aforementioned "identifying the leak as a gas leak and determining the corresponding quantitative data of the gas leak" also includes the following steps: The first stitched image is processed into coordinates, and the leakage coordinates of the corresponding gas leak are determined based on the coordinates of the points corresponding to the maximum temperature values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding gas leak is determined. Obtain the diffusion area of ​​the corresponding abnormal contour, and calculate the average temperature value of all points located in the abnormal area to obtain the average temperature of the entire region. Obtain the ambient temperature value corresponding to the current environment, determine the background temperature difference between the ambient temperature value and the mean temperature value of the whole area, and calculate the leakage efficiency of the corresponding gas leak by multiplying the background temperature difference and the mean temperature value of the whole area. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding gas leakage.

[0045] For example, in this embodiment, the server will perform coordinate processing on the first stitched image, and determine the leakage coordinates of the corresponding gas leak based on the coordinates of the point corresponding to the maximum temperature value. Then, based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding gas leak will be determined. Subsequently, the server will obtain the diffusion area of ​​the corresponding abnormal contour. The diffusion area directly reflects the impact range of the gas leak. The server will also calculate the average temperature value of all points in the abnormal area to obtain the average temperature of the entire region, so as to truly reflect the overall average thermal energy density. For gas leaks, the background temperature difference represents the thermal potential energy of the gas cloud relative to the environment, while the average temperature over the entire region represents the energy level of the gas cloud itself. The product of the two can comprehensively reflect the total heat energy release intensity of the gas leak. Therefore, the server will obtain the ambient temperature value corresponding to the current environment, then calculate the difference between the average temperature of the whole area and the ambient temperature value, that is, the background temperature difference, and multiply the background temperature difference and the average temperature of the whole area to obtain the leakage efficiency of the corresponding gas leak. Since the gas leak has a wide range of impact, multiplying the background temperature difference and the average temperature of the whole area can comprehensively reflect the overall scale and intensity of heat energy diffusion. Finally, the server will determine the three-dimensional spatial coordinates based on the leak coordinates and the laying depth, and determine the leak level, leak efficiency, and three-dimensional spatial coordinates as the corresponding quantitative data of the gas leak. This will enable subsequent maintenance personnel to determine whether the gas leak is a minor gasket leak or a pipe rupture based on the quantitative data, and take corresponding venting, depressurization, or sealing measures.

[0046] Furthermore, the aforementioned "identifying the inspection image sequence and determining the abnormal areas included in the target heating pipeline network" also includes the following steps: The response image acquisition method is white light acquisition method, and the white light image frames included in the inspection image sequence are determined; Each melting image located in each white light image frame, indicating the melting state of the target heating network, is determined, and each melting image is stitched together based on the inspection movement path to obtain a second stitched image. The response determines, based on the second stitched image, that there are areas of difference in melting changes in the target heating network, and controls the inspection equipment to collect images of these areas at preset monitoring intervals and for a preset number of monitoring times, thereby obtaining images of each difference. If each acquired image shows a region of difference, that region is identified as an abnormal region.

[0047] For example, in this embodiment, when the image acquisition method is white light acquisition, the high-resolution visible light camera on the inspection equipment will continuously take pictures during the movement to generate white light image frames that reflect the state of snow on the ground. Unlike infrared image frames, white light image frames record the intensity of reflected light on the surface of the object, which can clearly present the color and texture details of the snow. Each white light image frame forms an inspection image sequence. Next, the server will perform image recognition on each white light image frame to identify areas in the snow of the corresponding target heating pipe network that have abnormal color or texture, such as honeycomb or water stain textures. These areas are the melting images located in each white light image frame that indicate the melting state of the target heating pipe network. The server will stitch together each melting image according to the inspection movement path to obtain a second stitched image. This second stitched image can intuitively show the spatial distribution of snow melting traces, helping maintenance personnel to quickly locate potential leaking pipe sections. In snowy scenes, the main sources of interference include shadows caused by changes in the angle of illumination, vehicle tracks, or temporary accumulations. In order to eliminate these interferences, the server will identify the areas of difference in the target heating network with corresponding melting changes in the second stitched image, and control the inspection equipment to collect images of the areas of difference at preset monitoring intervals and corresponding preset monitoring times to obtain images of each difference. Unlike the rapid interval acquisition in infrared scenes, snow melting is a relatively slow physical process. Therefore, the preset monitoring period is usually set to be relatively long, such as once every 30 minutes. The preset number of monitoring times can be set to 3 times to observe the evolution of the difference area over a longer time span. If it is an artifact caused by light and shadow, the shadow will move or disappear as the sun's position changes. If it is real snow melting due to leakage, the melting area will gradually expand due to the continuous action of the heat source, and the gray difference will persist or deepen. When all the images acquired have corresponding discrepancies, the server will identify the discrepancy area as an abnormal area to effectively eliminate the influence of changes in ambient lighting and temporary interference, thereby improving the accuracy of abnormal area identification in snowy scenes.

[0048] Furthermore, the aforementioned "extracting features from each anomalous region to obtain quantitative data characterizing the leakage attributes corresponding to the anomalous region" also includes the following steps: Obtain the anomaly center point and anomaly contour of the corresponding anomaly region, and connect the contour points obtained by arraying the anomaly contour with the anomaly center point to obtain the contour connection lines. The variance is calculated based on the connection length of each contour line, and the first evaluation value is determined based on the obtained rule characterization value. The abnormal region is divided into arrays, and the gray value of each point in the obtained region is determined; the smallest gray value is determined as the minimum gray value, and the first distribution coefficient is determined based on the minimum number of all points in the region corresponding to the minimum gray value. The abnormal region is radially divided based on the anomaly center point, and the mean value of the gray value of all regional points included in each radial sub-region is calculated to obtain the value of each radial level. The difference between adjacent radiation levels is calculated, and the second distribution coefficient is determined based on the largest difference between levels obtained. The second evaluation value is determined based on the first distribution coefficient and the second distribution coefficient, and the comprehensive evaluation value is determined based on the first evaluation value and the second evaluation value. If the overall response evaluation value is less than or equal to the attribute classification value, the leakage attribute is determined as a liquid leakage, and the corresponding quantitative data of the liquid leakage is determined; otherwise, the leakage attribute is determined as a gas leakage, and the corresponding quantitative data of the gas leakage is determined.

[0049] For example, in this embodiment, the server will obtain the abnormal center point and abnormal contour of the corresponding abnormal area, and connect each contour point obtained by arraying the abnormal contour with the abnormal center point to obtain each contour connection line. Next, the server will calculate the variance based on the connection length of each contour line to obtain the rule representation value, and then multiply the rule representation value with the preset rule coefficient to obtain the first evaluation value. Next, the server will divide the abnormal area into arrays and determine the grayscale value of each point in the area. The grayscale value reflects the degree of snow melting. The more thoroughly the snow melts, the darker the color of the exposed ground or water, and the lower the grayscale value. The server will determine the smallest gray value of a point as the minimum gray value. The points with the minimum gray value represent the areas where snow melting is most thorough and heat sources are most intense. Then, the minimum number of all points corresponding to the minimum gray value will be determined, and the minimum number will be normalized to obtain the first distribution coefficient. If the number of the first distribution coefficient is large, it indicates that the heat source of leakage is widely distributed or there are multiple leakage points. Subsequently, the server will divide the abnormal area radially based on the abnormal center point, and calculate the mean value of the gray values ​​of all the points included in each radial sub-region to obtain the value of each radial level. Then, the difference between adjacent radial level values ​​will be calculated, and the largest level difference will be normalized to obtain the second distribution coefficient. Because the heat from liquid leakage is concentrated, the snow melts more thoroughly in the central area, resulting in a lower gray level. As the snow melts outward, it rapidly decays into unmelted snow, resulting in a higher gray level. Therefore, the difference in the mean gray level between adjacent layers is large, which means the difference between layers is large. This indicates a steep gray level gradient, dispersed heat from gas leakage, and relatively uniform snow melting over a large area, with a small difference between layers. The second distribution coefficient is used to characterize the steepness of this gray level gradient. Next, the server will perform a weighted summation of the first distribution coefficient and the second distribution coefficient to obtain the second evaluation value. Then, it will perform a weighted summation of the first evaluation value and the second evaluation value to obtain the comprehensive evaluation value. The lower the first evaluation value and the larger the second distribution coefficient, the lower the corresponding second evaluation value will be configured, and the lower the comprehensive evaluation value will be. The attribute classification value is a critical threshold calibrated based on a large amount of historical snow melting sample data. If the response comprehensive evaluation value is less than or equal to the attribute classification value, that is, the abnormal area meets the characteristics of concentrated snow melting, the server will determine the leakage attribute as liquid leakage and determine the corresponding quantitative data of liquid leakage. If the overall response evaluation value is greater than or equal to the attribute division value, that is, if the abnormal area meets the characteristics of snow melting diffusion, the server will determine the leakage attribute as a gas leak and determine the corresponding quantitative data of the gas leak.

[0050] Furthermore, the aforementioned "identifying the leak attribute as a liquid leak and determining the corresponding quantitative data of the liquid leak" also includes the following steps: The second stitched image is processed into coordinates, and the leakage coordinates of the corresponding liquid leak are determined based on the coordinates of the points with the corresponding minimum gray values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding liquid leakage is determined. Obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference value corresponding to the minimum grayscale value, and sum all the differences at all levels to obtain the total value at each level. The leakage efficiency of the corresponding liquid leak is obtained by summing the background grayscale difference and the total layer value. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding liquid leakage.

[0051] For example, in this embodiment, since the area where the snow melts most thoroughly after a liquid leak is usually located directly above the leak point or where the liquid accumulates, and this area is represented by the lowest gray value in the image, the server will perform coordinate processing on the second stitched image and determine the leak coordinates of the corresponding liquid leak based on the coordinates of the point with the corresponding minimum gray value. Next, the server will determine the leakage level of the corresponding liquid leak based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline. The leakage level is a key indicator for assessing the severity of the fault. The lower the first evaluation value, the more regular the outline of the snow melting area, which means that the leak point is single and continuous, and the severity is high. The larger the second distribution coefficient, the greater the gray difference of the corresponding layer, indicating that the snow melting degree decreases more drastically from the center to the periphery, which means that the leakage heat is concentrated and has a strong destructive force on the snow accumulation structure. The shallower the laying depth, the shorter the path of the leaked heat to the ground surface, the higher the snow melting efficiency, and the higher the risk level accordingly; Subsequently, the server will obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference value corresponding to the minimum grayscale value, and sum all the level differences to obtain the total level value. The server will then sum the background grayscale difference value and the total level value to obtain the leakage efficiency of the corresponding liquid leak. Leakage efficiency is a comprehensive indicator that characterizes the intensity of leakage energy and the rate of snow melting. The background gray value difference, calculated based on the difference between the minimum gray value and the ambient gray value, reflects the visual difference between the snow melting center and the surrounding unmelted snow. The greater the difference, the more thorough the snow melting and the higher the leakage energy. The total value of the hierarchy reflects the steepness of the gray-scale gradient. The larger the total value, the faster the heat is conducted from the center to the surrounding area, which is consistent with the characteristics of heat conduction from the liquid leak point source. By adding the background gray-scale difference and the total value of the hierarchy, the visual and thermodynamic effects of liquid leakage in the snow environment can be fully quantified. The greater the leakage efficiency, the greater the leakage volume or the higher the temperature, and the stronger the urgency of maintenance. Finally, the server will determine the three-dimensional spatial coordinates based on the leak coordinates and the laying depth, and will determine the leak level, leak efficiency, and three-dimensional spatial coordinates as quantitative data of the corresponding liquid leak. This will enable maintenance personnel to directly obtain the precise three-dimensional location, severity level, and snow melting efficiency of the leak point through the quantitative data, thereby rationally allocating excavation equipment and maintenance resources to achieve precise emergency repairs.

[0052] Furthermore, the aforementioned "identifying the leak as a gas leak and determining the corresponding quantitative data of the gas leak" also includes the following steps: The second stitched image is processed into coordinates, and the leakage coordinates of the corresponding gas leak are determined based on the coordinates of the points with the corresponding minimum gray values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding gas leak is determined. Obtain the diffusion area of ​​the corresponding abnormal contour, and calculate the average gray value of all points located in the abnormal region to obtain the global average gray value. Obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference between the ambient grayscale value and the mean grayscale value of the entire area, and calculate the leakage efficiency of the corresponding gas leak by multiplying the background grayscale difference and the mean grayscale value of the entire area. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding gas leakage.

[0053] For example, in this embodiment, although the gas leak spreads over a wide area, the leak point is still one of the areas with the most concentrated heat and the most thorough snow melting. The server will perform coordinate processing on the second stitched image and determine the leak coordinates of the corresponding gas leak based on the coordinates of the points with the corresponding minimum gray values. Next, the server will determine the leakage level of the corresponding gas leak based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline. A higher first evaluation value indicates that the outline of the snow melting area is extremely irregular, which is consistent with the gas diffusion characteristics. A smaller second distribution coefficient and a smaller gray value of the corresponding layer indicate that the degree of snow melting decreases slowly from the center to the surrounding area and the gray distribution is uniform. Subsequently, the server will obtain the diffusion area of ​​the corresponding abnormal contour, which can intuitively reflect the impact range of the gas leak, and calculate the average gray value of all points in the abnormal area to obtain the average gray value of the entire region, so as to truly reflect the average snow melting degree caused by the entire gas cloud. Next, the server will obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference between the ambient grayscale value and the mean grayscale value of the entire area, and multiply the background grayscale difference and the mean grayscale value of the entire area to calculate the leakage efficiency of the corresponding gas leak. For gas leaks, the background grayscale difference reflects the average contrast of the entire snow-melting area relative to the background snow, representing the average intensity of the gas's thermal energy, while the overall grayscale mean represents the overall degree of snow melting. The product of the two can comprehensively reflect the total thermal energy release intensity of the gas leak. Finally, the server will determine the three-dimensional spatial coordinates based on the leak coordinates and the laying depth, and determine the leak level, leak efficiency, and three-dimensional spatial coordinates as the corresponding quantitative data of the gas leak. This will enable maintenance personnel to determine whether the gas leak is a minor gasket leak or a pipe rupture based on the quantitative data, and take corresponding venting, depressurization, or sealing measures.

[0054] According to the present invention, firstly, by generating an inspection movement path based on the pipeline distribution and laying method of the heating network, the inspection equipment can adapt to different pipeline characteristics, improving the coverage and efficiency of the inspection; secondly, by adaptively determining the image acquisition method based on environmental information, it ensures that image data reflecting the network status can be effectively acquired under different environmental conditions, improving the accuracy of data acquisition; finally, by identifying abnormal areas in the inspection image sequence and extracting features from the abnormal areas to quantify leakage attributes, the data is detailed and has a certain degree of accuracy.

[0055] Another embodiment of the present invention provides an integrated energy processing system. Figure 3 Its corresponding system block diagram includes: The segmentation module is configured to segment the target heating network based on the pipeline distribution of the corresponding target heating network, and generate an inspection movement path covering the target heating network based on each segmented pipeline with different laying methods. The acquisition module is configured to control the inspection equipment to move along the inspection path and acquire images of the target heating network based on the image acquisition method determined by the environmental information of the corresponding target heating network, thereby obtaining an inspection image sequence. The identification module is configured to identify the inspection image sequence and determine the abnormal areas included in the target heating network; The extraction module is configured to extract features from each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region.

[0056] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of the invention.

[0057] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0058] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof.

[0059] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0060] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components.

[0061] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0062] Furthermore, some of the embodiments described herein are methods or combinations of method elements that can be implemented by a processor of a computer system or by other means of performing the functions. Therefore, a processor having the necessary instructions for implementing the methods or method elements forms means for implementing the methods or method elements. Furthermore, the elements described herein in the apparatus embodiments are examples of means for implementing the functions performed by the objective elements for carrying out the invention.

[0063] As used herein, unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

[0064] Although the invention has been described with respect to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and edibility purposes, and not for the purpose of explaining or limiting the subject matter of the invention.

Claims

1. A comprehensive energy processing method, characterized in that, include: The target heating network is divided based on the pipeline distribution of the corresponding target heating network, and an inspection movement path covering the target heating network is generated based on the divided pipelines corresponding to different laying methods. The inspection equipment is controlled to move along the inspection path, and the image acquisition method is determined based on the environmental information of the corresponding target heating network to acquire the target heating network and obtain the inspection image sequence. The inspection image sequence is identified to determine the abnormal areas included in the target heating network; Feature extraction is performed on each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region.

2. The method according to claim 1, characterized in that, The process of generating an inspection movement path covering the target heating network based on the obtained pipeline divisions corresponding to different laying methods includes: A baseline moving path is generated along the extension direction of the corresponding target heating network, and the division path corresponding to each division pipeline is determined based on the baseline moving path. If the laying method of any pipeline segment is the burial depth method, obtain the laying depth of the corresponding pipeline segment; If the laying depth is greater than the burial depth acquisition threshold, the preset inspection height is determined as the first inspection height; otherwise, the first inspection height is determined based on the burial depth difference corresponding to the laying depth acquisition threshold. The first inspection height is determined as the matching height corresponding to the divided path; If any pipeline is laid in an overhead manner, the laying height of the corresponding pipeline is obtained, and the second inspection height determined based on the laying height is set as the matching height of the corresponding pipeline path. The baseline movement path is updated based on the matching height of each corresponding partition path to obtain the inspection movement path.

3. The method according to claim 2, characterized in that, The method further includes: If any segment of the path has a corresponding curved segment with a curvature angle less than a preset angle, obtain the corner point of the corresponding curved segment. The corresponding buffer length is obtained based on the bending angle, and the path is divided based on the corner point to obtain the first segment and the second segment. Starting from the corner point, determine the first buffer point and the second buffer point located at the corresponding buffer length of the first split segment and the second split segment, and determine the replacement path segment composed of the first buffer point and the second buffer point based on the divided path; Connect the first buffer point and the second buffer point to obtain the buffer diameter. Generate a buffer arc segment with the corresponding buffer diameter towards the corner point, and determine the center distance between the center of the buffer arc segment and the center of the corner point. The inspection height corresponding to the divided path is adjusted based on the center distance. The obtained third inspection height is determined as the matching height of the corresponding buffer arc segment. The replacement path segment is then replaced based on the buffer arc segment to obtain the updated divided path.

4. The method according to claim 1, characterized in that, The image acquisition method based on the environmental information of the corresponding target heating network is used to acquire images of the target heating network, including: The response determines that the current environment of the target heating network is a non-snow scene based on environmental information, determines that the image acquisition method is infrared acquisition, and acquires images of the target heating network based on infrared acquisition. The response determines that the current environment of the target heating network is a snow scene based on environmental information, determines that the image acquisition method is white light acquisition method, and acquires images of the target heating network based on the white light acquisition method.

5. The method according to claim 4, characterized in that, The step of identifying the abnormal areas in the inspection image sequence to determine the target heating network includes: The response image acquisition method is infrared acquisition, which determines the infrared image frames included in the inspection image sequence; Determine each hot spot image located in each infrared image frame that indicates the temperature status of the target heating network, and stitch each hot spot image together based on the inspection movement path to obtain the first stitched image. The response determines that there are areas of temperature difference in the target heating network based on the first stitched image, and controls the inspection equipment to collect images of the areas of difference at intervals corresponding to the preset number of monitoring times, thereby obtaining images of each difference. If each acquired image shows a region of difference, that region is identified as an abnormal region.

6. The method according to claim 5, characterized in that, The step of extracting features from each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region includes: Obtain the anomaly center point and anomaly contour of the corresponding anomaly region, and connect the contour points obtained by arraying the anomaly contour with the anomaly center point to obtain the contour connection lines. The variance is calculated based on the connection length of each contour line, and the first evaluation value is determined based on the obtained rule characterization value. The abnormal area is divided into arrays, and the point temperature value of each point in the obtained area is determined; The highest point temperature value is defined as the maximum temperature value, and the first distribution coefficient is determined based on the maximum number of all regional points corresponding to the maximum temperature value. The abnormal region is radially divided based on the anomaly center point, and the mean value of the temperature of all points in each radial sub-region is calculated to obtain the value of each radial level. The difference between adjacent radiation levels is calculated, and the second distribution coefficient is determined based on the largest difference between levels obtained. The second evaluation value is determined based on the first distribution coefficient and the second distribution coefficient, and the comprehensive evaluation value is determined based on the first evaluation value and the second evaluation value. If the overall response evaluation value is less than or equal to the attribute classification value, the leakage attribute is determined as a liquid leakage, and the corresponding quantitative data of the liquid leakage is determined; otherwise, the leakage attribute is determined as a gas leakage, and the corresponding quantitative data of the gas leakage is determined.

7. The method according to claim 6, characterized in that, The step of determining the leakage attribute as a liquid leakage and determining the corresponding quantitative data of the liquid leakage includes: The first stitched image is processed into coordinates, and the leakage coordinates of the corresponding liquid leak are determined based on the coordinates of the points corresponding to the maximum temperature values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding liquid leakage is determined. Obtain the ambient temperature value corresponding to the current environment, determine the background temperature difference value corresponding to the maximum temperature value, and sum all the differences at all levels to obtain the total value at each level. The leakage efficiency of the corresponding liquid leak is obtained by summing the background temperature difference and the total value of the layers. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding liquid leakage.

8. The method according to claim 6, characterized in that, The step of determining the leakage attribute as a gas leak and determining the corresponding quantitative data of the gas leak includes: The first stitched image is processed into coordinates, and the leakage coordinates of the corresponding gas leak are determined based on the coordinates of the points corresponding to the maximum temperature values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding gas leak is determined. Obtain the diffusion area of ​​the corresponding abnormal contour, and calculate the average temperature value of all points located in the abnormal area to obtain the average temperature of the entire region. Obtain the ambient temperature value corresponding to the current environment, determine the background temperature difference between the ambient temperature value and the mean temperature value of the whole area, and calculate the leakage efficiency of the corresponding gas leak by multiplying the background temperature difference and the mean temperature value of the whole area. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding gas leakage.

9. The method according to claim 4, characterized in that, The step of identifying the abnormal areas in the inspection image sequence to determine the target heating network includes: The response image acquisition method is white light acquisition method, and the white light image frames included in the inspection image sequence are determined; Each melting image located in each white light image frame, indicating the melting state of the target heating network, is determined, and each melting image is stitched together based on the inspection movement path to obtain a second stitched image. The response determines, based on the second stitched image, that there are areas of difference in melting changes in the target heating network, and controls the inspection equipment to collect images of these areas at preset monitoring intervals and for a preset number of monitoring times, thereby obtaining images of each difference. If each acquired image shows a region of difference, that region is identified as an abnormal region.

10. The method according to claim 9, characterized in that, The step of extracting features from each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region includes: Obtain the anomaly center point and anomaly contour of the corresponding anomaly region, and connect the contour points obtained by arraying the anomaly contour with the anomaly center point to obtain the contour connection lines. The variance is calculated based on the connection length of each contour line, and the first evaluation value is determined based on the obtained rule characterization value. The abnormal region is divided into arrays, and the gray value of each point in the obtained region is determined; the smallest gray value is determined as the minimum gray value, and the first distribution coefficient is determined based on the minimum number of all points in the region corresponding to the minimum gray value. The abnormal region is radially divided based on the anomaly center point, and the mean value of the gray value of all regional points included in each radial sub-region is calculated to obtain the value of each radial level. The difference between adjacent radiation levels is calculated, and the second distribution coefficient is determined based on the largest difference between levels obtained. The second evaluation value is determined based on the first distribution coefficient and the second distribution coefficient, and the comprehensive evaluation value is determined based on the first evaluation value and the second evaluation value. If the overall response evaluation value is less than or equal to the attribute classification value, the leakage attribute is determined as a liquid leakage, and the corresponding quantitative data of the liquid leakage is determined; otherwise, the leakage attribute is determined as a gas leakage, and the corresponding quantitative data of the gas leakage is determined.

11. The method according to claim 10, characterized in that, The step of determining the leakage attribute as a liquid leakage and determining the corresponding quantitative data of the liquid leakage includes: The second stitched image is processed into coordinates, and the leakage coordinates of the corresponding liquid leak are determined based on the coordinates of the points with the corresponding minimum gray values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding liquid leakage is determined. Obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference value corresponding to the minimum grayscale value, and sum all the differences at all levels to obtain the total value at each level. The leakage efficiency of the corresponding liquid leak is obtained by summing the background grayscale difference and the total layer value. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding liquid leakage.

12. The method according to claim 10, characterized in that, The step of determining the leakage attribute as a gas leak and determining the corresponding quantitative data of the gas leak includes: The second stitched image is processed into coordinates, and the leakage coordinates of the corresponding gas leak are determined based on the coordinates of the points with the corresponding minimum gray values. Based on the first evaluation value, the second distribution coefficient, and the laying depth of the corresponding pipeline, the leakage level of the corresponding gas leak is determined. Obtain the diffusion area of ​​the corresponding abnormal contour, and calculate the average gray value of all points located in the abnormal region to obtain the global average gray value. Obtain the ambient grayscale value corresponding to the current environment, determine the background grayscale difference between the ambient grayscale value and the mean grayscale value of the entire area, and calculate the leakage efficiency of the corresponding gas leak by multiplying the background grayscale difference and the mean grayscale value of the entire area. The three-dimensional spatial coordinates are determined based on the leakage coordinates and the laying depth, and the leakage level, leakage efficiency, and three-dimensional spatial coordinates are determined as the quantitative data of the corresponding gas leakage.

13. A comprehensive energy processing system, characterized in that, include: The segmentation module is configured to segment the target heating network based on the pipeline distribution of the corresponding target heating network, and generate an inspection movement path covering the target heating network based on each segmented pipeline with different laying methods. The acquisition module is configured to control the inspection equipment to move along the inspection path and acquire images of the target heating network based on the image acquisition method determined by the environmental information of the corresponding target heating network, thereby obtaining an inspection image sequence. The identification module is configured to identify the inspection image sequence and determine the abnormal areas included in the target heating network; The extraction module is configured to extract features from each abnormal region to obtain quantitative data characterizing the leakage attributes corresponding to the abnormal region.

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