GIS-based pipe network asset management and updating method and system

Abstract

The invention relates to a GIS-based pipe network asset management and updating method and system, and relates to the field of water affair asset management.The method comprises the steps that 200, when a preset pipe network health degree is lower than a preset health early warning value, water meter readings of a pipe network are collected based on a pipe network number; step 201, when the water meter reading is abnormal, determining a water meter number in response to the water meter reading; 202, determining an adjacent number in response to the water meter number; 203, generating a detection area in response to the adjacent numbers; 204, generating a detection path in response to the detection area; and step 205, controlling a preset unmanned aerial vehicle to collect the pipe network image according to the detection path. The method has the advantages that the water asset maintenance efficiency is improved, and the damaged position of the pipe network can be rapidly determined.

Description

Technical Field

[0001] This invention relates to the field of water asset management, and in particular to a GIS-based method and system for pipeline network asset management and updating. Background Technology

[0002] Water assets refer to facilities including pipelines, sewage treatment plants, reservoirs, sluice gates, and pumping stations.

[0003] In existing technologies, water assets need to be inspected and maintained regularly. The condition of water assets is usually checked by drone patrols, and maintenance is carried out in a timely manner when problems occur. A comprehensive assessment is conducted by recording the entire life cycle of the pipeline facilities and the operation and maintenance monitoring information to generate a health score for the facilities, which assists in the decision-making of subsequent inspection and leak detection resources.

[0004] When a section of the pipeline is found to be in poor health, staff need to conduct on-site inspections to further locate the actual damage, which leads to low efficiency in pipeline maintenance. Summary of the Invention

[0005] To improve the efficiency of water asset maintenance and quickly identify damaged areas in the pipeline network, this invention provides a GIS-based pipeline asset management and updating method and system.

[0006] In a first aspect, the present invention provides a GIS-based method for pipeline network asset management and updating, employing the following technical solution: Methods for determining the health of pipeline networks include: Step 100: Acquire images of the pipeline network; Step 101: Identify the pipeline route from the pipeline network image; Step 102: Generate a data acquisition path in response to the pipeline network orientation; Step 103: Control the preset drone to continuously collect images of the pipeline network according to the collection path, and determine the health status of the pipeline network from the images; Step 104: When the health status of the pipeline network is lower than the preset health warning value, collect the risk location of the drone; Step 105: Generate a pipeline risk report in response to the risk location and pipeline image; Step 106: Send the pipeline risk report to the preset management terminal.

[0007] By adopting the above technical solution, drones are used to collect images of the pipeline network's appearance along its route. This allows for an assessment of the network's health based on its appearance, and an alarm is issued when the network's health is too low, thereby improving the efficiency of water asset inspections.

[0008] Optionally, the method for setting the health warning value includes: Step 107: Identify the pipeline number based on the acquisition path; Step 108: Retrieve the baseline early warning value in response to the pipeline number, and retrieve the maintenance record in response to the pipeline number; Step 109: Generate a correction warning value in response to the maintenance record; Step 110: Generate a health warning value in response to the baseline warning value and the corrected warning value.

[0009] By adopting the above technical solution, the maintenance records of the pipeline network can be retrieved, thereby assessing the health status of the pipeline network according to the maintenance intensity and area, and dynamically adjusting the health level of the alarm based on the health status, thereby improving the accuracy of the alarm.

[0010] Optionally, the method for setting the health warning value further includes: Step 111: When the pipeline route falls into the preset pressure transformation range, a height difference is generated in response to the pipeline route; Step 112: Generate a boost correction value in response to the height difference; Step 113: Generate a boost warning value in response to the boost correction value and the health warning value; Step 114: Update the health warning value in response to the pressure warning value.

[0011] By adopting the above technical solution, when the height of the pipeline network changes, the water pressure in the pipeline network will easily change with the height. The appearance of the pipeline network can be used to determine whether there is a height change, and the health status of the alarm will be dynamically adjusted when the height of the pipeline network changes, thereby improving the accuracy of the alarm.

[0012] Optionally, a GIS-based method for pipeline network asset management and updating includes: Step 200: When the preset network health status is lower than the preset health warning value, collect the water meter reading of the network based on the network number; Step 201: When the water meter reading is abnormal, determine the water meter number in response to the water meter reading; Step 202: Determine adjacent numbers in response to the water meter number; Step 203: Generate a detection area in response to the adjacent numbers; Step 204: Generate a detection path in response to the detection area; Step 205: Control the preset drone to collect images of the pipeline network according to the detection path.

[0013] By adopting the above technical solution, when the health of the pipeline network is low, the risk of pipeline network failure is high. Real-time monitoring of water meter readings in high-risk pipeline networks allows for timely detection of the area around the water meter with abnormal readings using drones, thereby providing targeted operation and maintenance services for high-risk pipeline networks.

[0014] Optionally, an anomaly detection method may also be included, wherein the anomaly detection method further includes: Step 206: When the water meter reading is abnormal, determine the endpoint position in response to the detection path and collect the detection position of the drone; Step 207: When the detection position coincides with the endpoint position, determine whether there is an obstacle on the pipeline network from the pipeline network image; Step 208: When there is an obstacle on the pipeline, determine the location of the obstacle in response to the pipeline image, and determine the location of the water meter in response to the water meter number; Step 209: When the water meter position coincides with the obstacle position, the water pressure in the pipeline is retrieved in response to the water meter reading, and the obstacle volume is generated in response to the water pressure in the pipeline; Step 210: Generate the network health status in response to the obstruction volume.

[0015] By adopting the above technical solution, when the pipeline is affected by external forces, it is easy to deform, which will lead to abnormal water meter readings at the deformed parts of the pipeline. When the pipeline is covered by obstacles, it is difficult to judge whether the pipeline is deformed from the collected appearance of the pipeline. At this time, the water pressure at the pipeline with obstacles is retrieved to estimate the degree of deformation of the pipeline, and the health status is generated according to the degree of deformation of the pipeline, thereby improving the accuracy of the health status.

[0016] Optionally, the anomaly detection method further includes: Step 211: When the water meter position coincides with the obstacle position, identify the obstacle weight from the pipeline network image; Step 212: When the weight of the obstacle is greater than the water pressure in the pipeline, identify the contact contour between the obstacle and the pipeline from the pipeline network image, and identify the surface angle of the obstacle from the pipeline network image; Step 213: When the surface angle falls within the preset receiving range, the receiving distance is determined in response to the contact profile and the surface angle; Step 214: Determine the receiving test point in response to the received distance; Step 215: Control the preset drone to fly to the receiving test point to detect the vibration of the receiving test point, and determine the knocking test point based on the receiving test point; Step 216: Determine the striking force in response to the weight of the obstacle; Step 217: Control the preset drone to launch a preset test projectile at the impact test point with the impact force, and control the preset drone to collect the vibration amplitude of the receiving test point. Step 218: Generate the pipeline health status in response to the vibration amplitude.

[0017] By adopting the above technical solution, when the pipeline is subjected to excessive external force, it is easy for the pipeline to be damaged. When the pipeline is covered by an obstacle, the propagation characteristics of the vibration on the pipeline to the obstacle can be detected to determine whether the pipeline is damaged, thereby improving the accuracy of the health status.

[0018] Optionally, it also includes an occlusion detection method, the occlusion detection method comprising: Step 300: When there are obstacles in the pipeline, identify the gap contour from the pipeline image; Step 301: In response to the gap profile and the preset device profile, determine whether the preset ejection device can enter the gap; Step 302: When the preset ejection device can enter the gap, the entry position is identified from the gap profile; Step 303: Generate a launch path in response to the entered position; Step 304: Control the preset drone to launch the preset catapult into the gap according to the launch stroke, and control the preset catapult to collect the pipeline network image.

[0019] By adopting the above technical solution, when the pipeline is covered by an obstacle, the gap between the obstacle and the pipeline is detected, and when the gap is large enough for the ejector device to enter, the ejector device is launched by a drone to detect the appearance of the covered part of the pipeline, thereby improving the efficiency of pipeline inspection.

[0020] Optionally, the occlusion detection method further includes: Step 305: When the preset ejection device can enter the gap, the coverage length is identified from the pipeline image; Step 306: When the coverage length exceeds a preset detection threshold, the coverage thickness is identified from the pipeline image; Step 307: Determine the magnetic strength in response to the covering thickness, and generate a magnetic attraction path based on the acquisition path; Step 308: After launching the preset catapult device, control the preset magnetic attraction device on the UAV to start according to the magnetic force intensity, and control the preset UAV to fly along the magnetic attraction path to drive the preset catapult device to move.

[0021] By adopting the above technical solution, when the length of the pipeline covered by the obstacle is long, the ejector needs to move within the gap between the obstacle and the pipeline to obtain the appearance of the covered part of the pipeline. The magnetic attraction device attracts the ejector through the obstacle, so that the ejector moves with the magnetic attraction device on the drone within the gap, thereby improving the accuracy of the health status.

[0022] Optionally, the occlusion detection method further includes: Step 309: When the occlusion length exceeds the preset detection threshold, determine the acquisition interval in response to the occlusion length; Step 310: Determine the standby duration in response to the launch stroke; Step 311: After the specified standby time, control the preset ejection device to acquire the pipeline network image according to the specified acquisition interval.

[0023] By adopting the above technical solution, when the length of the pipeline covered by obstacles is long, the ejector device needs to periodically collect multiple images in the gap between the obstacle and the pipeline to obtain the appearance of the covered part of the pipeline. Before the UAV launches the ejector device, the working cycle of the ejector device is set so as to collect images of the pipeline evenly, thereby improving the accuracy of the health status.

[0024] Secondly, this application provides a GIS-based pipeline asset management and updating system, which adopts the following technical solution: A GIS-based pipeline asset management and updating system includes: The data acquisition module is used to acquire images of the pipeline network, risk locations, water meter readings, detection locations, and vibration amplitudes. A memory is used to store any of the above-mentioned GIS-based pipeline asset management and update methods; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

[0025] By adopting the above technical solution, drones are used to collect images of the pipeline network's appearance along its route. This allows for an assessment of the network's health based on its appearance, and an alarm is issued when the network's health is too low, thereby improving the efficiency of water asset inspections.

[0026] In summary, this application includes at least one of the following beneficial technical effects: Drones are used to collect images of the pipeline network along its route, thereby assessing the network's health based on its appearance and issuing alarms when the network's health is too low, thus improving the efficiency of water asset inspections. By retrieving the maintenance records of the pipeline network, the health status of the pipeline network can be assessed according to the maintenance intensity and area, and the health level of the alarm can be dynamically adjusted according to the health status, thereby improving the accuracy of the alarm. When the health of the pipeline network is low, the risk of pipeline failure is greater. Real-time monitoring of water meter readings in high-risk pipeline networks allows drones to promptly detect abnormal water meter readings and provide targeted operation and maintenance services for high-risk pipeline networks. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the method for determining the health of the pipeline network; Figure 2 This is the process for setting health warning values. Figure 1 ; Figure 3 This is the process for setting health warning values. Figure 2 ; Figure 4 This is a flowchart of a GIS-based pipeline asset management and update method; Figure 5 This is the process of anomaly detection methods. Figure 1 ; Figure 6 This is the process of anomaly detection methods. Figure 2 ; Figure 7 This is the process of the masking detection method. Figure 1 ; Figure 8 This is the process of the masking detection method. Figure 2 ; Figure 9 This is the process of the masking detection method. Figure 3 . Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] Reference Figure 1 Methods for determining the health of the pipeline network include: Step 100: Acquire images of the pipeline network.

[0030] Drones refer to devices used to collect images of the appearance of pipeline networks. The choice of drone is made by staff based on the actual situation and will not be elaborated upon here. Pipeline network images refer to a collection of images of the appearance of the pipeline network. These images can be collected at fixed intervals using cameras fixed to drones. The method of collecting these images is also chosen by staff based on the actual situation and will not be elaborated upon here.

[0031] Step 101: Identify the pipeline route from the pipeline network image.

[0032] The pipeline route refers to the direction in which the pipeline extends. The pipeline route can be identified from the pipeline image using image recognition technology. The method for identifying the pipeline route is common knowledge to those in the field and will not be elaborated here.

[0033] Step 102: Generate a data acquisition path in response to the pipeline network orientation.

[0034] The data collection path refers to the route taken by a drone to collect the complete appearance of the pipeline network. That is, the route taken by the drone to fly along the extension direction of the pipeline network. The drone can automatically generate the data collection path according to the pipeline network direction. The method of generating the data collection path is common knowledge to those in the field and will not be elaborated here.

[0035] Step 103: Control the preset drone to continuously collect images of the pipeline network according to the collection path, and determine the health status of the pipeline network from the images.

[0036] Pipeline health refers to a numerical value used to represent the condition of a pipeline network. It can be achieved by using image recognition technology to identify rust and deformation in pipeline images, and then combining these two indicators to generate the pipeline health score. The more severe the rust and deformation, the lower the pipeline health score. For example, the pipeline health score can be calculated by identifying the rust area and the deformation area, and then calculating the negative sum of the product of the rust area and a preset rust coefficient and the product of the deformation area and a preset deformation coefficient. The rust coefficient and deformation coefficient are related to the material of the pipeline and can be set in advance by the staff. The method for determining the pipeline health score is selected by the staff based on the actual situation and will not be elaborated here.

[0037] Step 104: When the health status of the pipeline network is lower than the preset health warning value, collect the risk location of the drone.

[0038] The health warning value refers to the pipeline health level used to determine high-risk areas on the pipeline network. The health warning value is selected by staff based on the actual situation and will not be elaborated upon here. A pipeline health level below the health warning value indicates that the pipeline segment corresponding to the pipeline image has severe rusting and / or deformation, making the pipeline more prone to failure. The risk location refers to the drone's position information when a pipeline segment with low health is detected; that is, the location of the pipeline segment corresponding to the pipeline image. The risk location can be collected through the drone's positioning system. The method for collecting the risk location is selected by staff based on the actual situation and will not be elaborated upon here.

[0039] Step 105: Generate a pipeline risk report in response to the risk location and pipeline image.

[0040] A pipeline risk report is a set of information used to display the location and image information of pipeline segments with low pipeline health to staff. The method for generating pipeline risk reports is common knowledge in the field and will not be elaborated here.

[0041] Step 106: Send the pipeline risk report to the preset management terminal.

[0042] The management terminal is a device used to display the overall situation of the pipeline network to the staff. The staff selects the management terminal according to the actual situation, which will not be elaborated here.

[0043] Drones are used to collect images of the pipeline network along its route, thereby assessing the network's health based on its appearance. An alarm is then issued when the network's health is too low, thus improving the efficiency of water asset inspections.

[0044] Reference Figure 2 The methods for setting health warning values ​​include: Step 107: Identify the pipeline number based on the acquisition path.

[0045] Pipeline number refers to the number used to distinguish pipelines. Each pipeline number corresponds one-to-one with a pipeline segment. The data collection path can be extracted to determine the flight direction of the UAV, and the flight starting point can be combined to determine the flight position of the UAV. The pipeline number can be obtained by looking up the numbering relationship table. The method of identifying the pipeline number is selected by the staff according to the actual situation, and will not be elaborated here.

[0046] The flight start point refers to the location of the first pipeline network image, which can be determined from the pipeline network image using image recognition technology or pre-set by staff. The numbering relationship table is a data table recording different flight positions and their corresponding pipeline network numbers. Whenever a risk location is collected, the flight position is calibrated according to the collected location.

[0047] Step 108: Retrieve the baseline early warning value in response to the pipeline number, and retrieve the maintenance record in response to the pipeline number.

[0048] The baseline warning value refers to the health warning value of the pipeline network at the time of its manufacture. The baseline warning value can be obtained from the baseline relationship table, which is a data table that records different pipeline network numbers and their corresponding baseline warning values.

[0049] Maintenance records refer to information such as the area and method of maintenance carried out on the pipeline segment corresponding to the pipeline number. The maintenance methods include welding, repair welding and pipe clamping, etc. Maintenance records can be obtained from the maintenance record table, which is a data table that records different pipeline numbers and their corresponding maintenance records.

[0050] Step 109: Generate a correction warning value in response to the maintenance record.

[0051] The corrected warning value is a value used to correct the baseline warning value. It can be calculated as the sum of the product of the repair area and the corresponding repair coefficient. The repair coefficient is related to the repair method and can be preset by the staff. The method for generating the corrected warning value is selected by the staff according to the actual situation, which will not be elaborated here.

[0052] Step 110: Generate a health warning value in response to the baseline warning value and the corrected warning value.

[0053] When a pipeline network is repaired, its stability tends to decrease, making it more prone to failure. By using maintenance records to estimate the impact of maintenance on pipeline stability, the baseline warning value can be improved, and pipeline sections prone to failure can be identified in a timely manner, thereby improving the accuracy of alarms.

[0054] Reference Figure 3 The methods for setting health warning values ​​also include: Step 111: When the pipeline route falls into the preset pressure change range, a height difference is generated in response to the pipeline route.

[0055] A pressure variation zone refers to the range of a pipeline route where water pressure is prone to change. The pressure variation zone is selected by staff based on actual conditions and will not be elaborated upon here. A pipeline route falling within a pressure variation zone indicates that water pressure within the network is prone to change. The height difference refers to the change in height of the pipeline. The height difference can be obtained from a height difference correspondence table, which records data on different pipeline routes and their corresponding height differences.

[0056] Step 112: Generate a boost correction value in response to the height difference.

[0057] When the water pressure in a pipeline section is high, it can easily lead to a malfunction in that section. In other words, the higher the water pressure in a pipeline section, the higher the corresponding health warning value. The pressure increase correction value is the value used to adjust the health warning value according to the pressure changes in the pipeline section. You can first look up the pressure difference corresponding to the height difference in the pressure relationship table, and then look up the pressure increase correction value corresponding to the pressure difference in the pressure increase relationship table. The pressure relationship table is a data table that records different height differences and their corresponding pressure differences, while the pressure increase relationship table is a data table that records different pressure differences and their corresponding pressure increase correction values.

[0058] Step 113: Generate a boost warning value in response to the boost correction value and the health warning value.

[0059] The boost warning value is the health warning value obtained after correcting the health warning value according to the boost correction value. The method for generating the health warning value is selected by the staff according to the actual situation, and will not be elaborated here.

[0060] Step 114: Update the health warning value in response to the pressure warning value.

[0061] When the height of the pipeline network changes, the water pressure within the network can easily fluctuate with the height. By observing the appearance of the pipeline network, it is possible to determine whether there is a change in the height of the network, and to dynamically adjust the health status of the alarm when the height of the network changes, thereby improving the accuracy of the alarm.

[0062] Reference Figure 4 A GIS-based method for pipeline network asset management and updating includes: Step 200: When the preset network health status is lower than the preset health warning value, collect the water meter reading of the network based on the network number.

[0063] Water meter readings refer to the readings of water meters such as flow rate and water pressure within the pipe network segment corresponding to the pipe network number. The method for collecting water meter readings is selected by the staff based on the actual situation, and will not be elaborated here.

[0064] Step 201: When the water meter reading is abnormal, determine the water meter number in response to the water meter reading.

[0065] Abnormal water meter readings refer to water meter readings exceeding the reference range. The reference range refers to the normal flow rate and water pressure range within the pipeline network that is preset by the staff. Abnormal water meter readings indicate that there may be a fault in the pipeline section where the water meter is located. The water meter number refers to the number of the water meter with abnormal readings. Each water meter number corresponds one-to-one with the water meter. The method for determining the water meter number is common knowledge among those in the field and will not be elaborated here.

[0066] Step 202: Determine adjacent numbers in response to the water meter number.

[0067] Adjacent numbers refer to the numbers of water meters adjacent to the water meter number. Adjacent numbers can be obtained from the number data table, which records different water meter numbers and their corresponding water meter locations.

[0068] Step 203: Generate a detection area in response to the adjacent number.

[0069] The detection area refers to the area enclosed by adjacent water meters with different numbers, that is, the area enclosed by the positions of the water meters corresponding to adjacent numbers. The method for generating the detection area is common knowledge to those in the field and will not be elaborated here.

[0070] Step 204: Generate a detection path in response to the detection area.

[0071] The detection path refers to the route taken by the UAV to collect images of the detection area, that is, the route along which the UAV flies along the pipeline network within the detection area. The method for generating the detection path is common knowledge to those in the field and will not be elaborated here.

[0072] Step 205: Control the preset drone to collect images of the pipeline network according to the detection path.

[0073] When the health of the pipeline network is low, the risk of pipeline failure is greater. Real-time monitoring of water meter readings in high-risk pipeline networks allows drones to promptly detect abnormal water meter readings and provide targeted operation and maintenance services for high-risk pipeline networks.

[0074] Reference Figure 5 Anomaly detection methods include: Step 206: When the water meter reading is abnormal, the endpoint position is determined in response to the detection path, and the detection position of the UAV is collected.

[0075] The endpoint location refers to the location of the end point of the detection path, which can be extracted from the detection path. The detection location refers to the location information of the drone, which can be collected through the positioning system on the drone. The method for collecting the detection location is selected by the staff according to the actual situation and will not be elaborated here.

[0076] Step 207: When the detection position is consistent with the endpoint position, determine whether there is an obstacle on the pipeline from the pipeline network image.

[0077] The fact that the detection location matches the endpoint location indicates that the UAV has completed the acquisition of pipeline network images in the detection area. Obstacles refer to objects that block the pipeline network. The presence of obstacles on the pipeline network can be initially determined through image recognition. The method for identifying obstacles is common knowledge to those in the field and will not be elaborated here.

[0078] Step 208: When there is an obstacle on the pipeline, determine the location of the obstacle in response to the pipeline image, and determine the location of the water meter in response to the water meter number.

[0079] The presence of obstacles on the pipeline network indicates that part of the pipeline network is obscured, making it difficult to observe the appearance of the pipeline network from the pipeline network image. The location of the obstacle is the position of the obstacle on the pipeline network. The location of the obstacle can be determined by image recognition technology. The method for determining the location of the obstacle is common knowledge to those in the field and will not be elaborated here.

[0080] The water meter location refers to the location information of water meters with abnormal readings. The location of the water meter corresponding to the water meter number can be found in the number data table.

[0081] Step 209: When the water meter position coincides with the obstacle position, the water pressure in the pipeline is retrieved in response to the water meter reading, and the obstacle volume is generated in response to the water pressure in the pipeline.

[0082] The fact that the water meter location matches the obstruction location indicates that the abnormal pipe reading may be caused by the obstruction. Pipeline water pressure refers to the water pressure information at the water meter location, which can be retrieved from the water meter reading. When the pipe network is deformed or impurities are deposited, it can easily lead to changes in water pressure at the pipe network. Obstruction volume refers to the volume of deformation or impurities deposited at the water meter location of the pipe network, that is, the amount of reduction in the cross-section of the pipe network. Obstruction volume can be obtained from the obstruction data table, which is a data table that records different pipe water pressures and their corresponding obstruction volumes.

[0083] Step 210: Generate the network health status in response to the obstruction volume.

[0084] When the pipeline network is affected by external forces, it is prone to deformation, which can lead to abnormal water meter readings at the deformation points. When the pipeline network is covered by obstacles, it is difficult to determine whether the pipeline network is deformed from the collected appearance data. In this case, the water pressure at the pipeline network with obstacles is retrieved to estimate the degree of deformation, and a health status is generated according to the degree of deformation, thereby improving the accuracy of the health status.

[0085] Reference Figure 6 Anomaly detection methods also include: Step 211: When the water meter position coincides with the obstacle position, the obstacle weight is identified from the pipeline network image.

[0086] Obstacle weight refers to the weight value of an obstacle. It can be determined by image recognition technology to identify the material of the obstacle and the external volume of the obstacle protruding from the pipe surface. Then, the density of the obstacle is obtained by matching the material of the obstacle, and the sum of the external volume and the obstruction volume is calculated as the obstacle volume. Finally, the product of the density and the obstacle volume is calculated as the obstacle weight. The method of obstacle weight identification is common knowledge in the field and will not be elaborated here.

[0087] Step 212: When the weight of the obstacle is greater than the water pressure in the pipeline, the contact contour between the obstacle and the pipeline is identified from the pipeline network image, and the surface angle of the obstacle is identified from the pipeline network image.

[0088] The fact that the obstacle weight is greater than the pipe water pressure means that when the obstacle penetrates the outer wall of the pipe network, it can still withstand the pipe water pressure and hold the pipe network in place. The contact profile is the boundary line between the obstacle and the outer wall of the pipe network. The surface angle refers to the angle value of the obstacle surface. Both the surface angle and the contact profile can be determined by image recognition technology. The recognition methods for the surface angle and the contact profile are common knowledge to those in the field and will not be elaborated here.

[0089] Step 213: When the surface angle falls within the preset receiving range, the receiving distance is determined in response to the contact profile and the surface angle.

[0090] The receiving range refers to the range of surface angles within which the drone can stably contact the obstacle surface. Generally, a range of -90 degrees to +90 degrees is used as the receiving range. The receiving range is selected by the staff according to the actual situation, and will not be elaborated here. The receiving distance refers to the shortest distance from the position where the surface angle on the obstacle falls into the receiving range to the contact profile. The method for determining the receiving distance is common knowledge to those in the field, and will not be elaborated here.

[0091] Step 214: Determine the receiving test point in response to the received distance.

[0092] The receiving test point refers to the position on the obstacle with the shortest receiving distance. The method for determining the receiving test point is common knowledge to those in the field and will not be elaborated here.

[0093] Step 215: Control the preset drone to fly to the receiving test point to detect the vibration of the receiving test point, and determine the knocking test point based on the receiving test point.

[0094] The impact test point refers to the location on the outer wall of the pipeline used to generate vibration by striking. Generally, the position closest to the test point in the contact profile is selected as the impact test point. The method for determining the impact test point is common knowledge to those in the field and will not be elaborated here.

[0095] Step 216: Determine the striking force in response to the weight of the obstacle.

[0096] The striking force refers to the force applied when striking a test point. The striking force can be obtained from the striking relationship table, which is a data table that records different obstacle weights and their corresponding striking forces.

[0097] Step 217: Control the preset drone to launch a preset test projectile at the impact test point with the impact force, and control the preset drone to collect the vibration amplitude of the receiving test point.

[0098] The test projectile refers to a small metal ball used to strike the outer wall of the pipeline. The vibration amplitude can be collected by the vibration sensor on the drone. The drone is controlled to fly to the receiving test point so that the vibration probe on the drone contacts the receiving test point, and then the test projectile is launched to collect the vibration amplitude. The method of collecting the vibration amplitude is common knowledge to those in the field and will not be described in detail here.

[0099] Step 218: Generate the pipeline health status in response to the vibration amplitude.

[0100] When a pipeline is damaged, the contact area between the pipeline and obstacles is reduced, which alters the characteristics of vibration propagation from the pipeline to the obstacle. The larger the damaged area, the lower the propagation rate of vibration from the pipeline to the obstacle. The degree of pipeline damage can be estimated based on the vibration amplitude. Thus, when obstacles block the pipeline and make it difficult to determine the pipeline health, the degree of damage can be used to generate the pipeline health status, thereby improving the accuracy of the pipeline health status.

[0101] Reference Figure 7 The methods for detecting occlusion include: Step 300: When there are obstacles in the pipeline, the gap contour is identified from the pipeline image.

[0102] The gap profile refers to the cross-sectional profile of the gap between the pipeline and the obstacle. The gap profile can be determined by image recognition technology. The method of gap profile recognition is common knowledge to those in the field and will not be elaborated here.

[0103] Step 301: In response to the gap profile and the preset device profile, determine whether the preset ejection device can enter the gap.

[0104] A catapult is a device used to acquire images of the appearance of a pipeline network obscured by obstacles. The catapult has a spherical outer shell and is equipped with a miniature camera. A permanent magnet is fixed to the side of the catapult away from the miniature camera. The choice of catapult is made by the operator based on the actual situation and will not be elaborated upon here. The device outline is the cross-sectional outline of the catapult, which can be predetermined by the operator. The method for determining whether the catapult can enter the gap is common knowledge to those skilled in the art and will not be elaborated upon here.

[0105] Step 302: When the preset ejection device can enter the gap, the entry position is identified from the gap profile.

[0106] The ability of the ejector to enter the gap means that the appearance of the pipeline network obscured by the obstacle can be detected by the ejector. The entry position is the position where the ejector enters the gap. Generally, the center of the largest inscribed circle in the gap profile is selected as the entry position. The method for determining the entry position is common knowledge to those skilled in the art and will not be elaborated here.

[0107] Step 303: Generate a launch path in response to the entry position.

[0108] The launch path refers to the route along which the UAV launches the catapult from the entry position into the gap. The method for generating the launch path is common knowledge to those in the field and will not be elaborated here.

[0109] Step 304: Control the preset drone to launch the preset catapult into the gap according to the launch stroke, and control the preset catapult to collect the pipeline network image.

[0110] When the pipeline network is covered by an obstacle, the gap between the obstacle and the pipeline network is detected. When the gap is large enough for the ejector device to enter, the ejector device is launched by a drone to detect the appearance of the covered part of the pipeline network, thereby improving the efficiency of pipeline network inspection.

[0111] Reference Figure 8 The occlusion detection method also includes: Step 305: When the preset ejection device can enter the gap, the coverage length is identified from the pipeline image.

[0112] Coverage length refers to the length of the pipeline network that is blocked by an obstacle. Coverage length can be determined by image recognition technology. The method for recognizing coverage length is common knowledge in the field and will not be elaborated here.

[0113] Step 306: When the coverage length exceeds the preset detection threshold, the coverage thickness is identified from the pipeline image.

[0114] The detection threshold refers to the maximum length of the pipeline network that the ejector device can determine with a single image acquisition. The detection threshold is selected by the operator based on the actual situation and will not be elaborated upon here. If the coverage length exceeds the detection threshold, the ejector device needs to acquire multiple images. Coverage thickness refers to the distance from the outer wall of the pipeline network to the outer surface position. The outer surface position refers to the closest point on the obstacle to any point along the acquisition path. Coverage thickness can be determined using image recognition technology; the method for determining coverage thickness is common knowledge in the field and will not be elaborated upon here.

[0115] Step 307: Determine the magnetic strength in response to the covering thickness, and generate a magnetic attraction path based on the acquisition path.

[0116] The magnetic attraction device refers to the electromagnet device fixed to the drone. The choice of magnetic attraction device is made by the staff based on the actual situation and will not be elaborated here. The magnetic strength is the magnetic strength value required for the drone to attract the permanent magnet inside the catapult through the magnetic attraction device at its external position. The magnetic strength can be obtained from the magnetic force relationship table, which is a data table that records different cover thicknesses and their corresponding magnetic strengths.

[0117] A magnetic path refers to the route a drone takes when moving on its surface. Magnetic paths can be automatically generated from the surface position. The method for generating magnetic paths is common knowledge to those in the field and will not be elaborated here.

[0118] Step 308: After launching the preset catapult device, control the preset magnetic attraction device on the UAV to start according to the magnetic force intensity, and control the preset UAV to fly along the magnetic attraction path to drive the preset catapult device to move.

[0119] When the length of the pipeline covered by obstacles is long, the ejector needs to move within the gap between the obstacle and the pipeline to obtain the appearance of the covered part of the pipeline. The ejector is attracted through the obstacle by the magnetic attraction device, so that the ejector moves with the magnetic attraction device on the drone within the gap, thereby improving the accuracy of the health status.

[0120] Reference Figure 9 The occlusion detection method also includes: Step 309: When the occlusion length exceeds the preset detection threshold, determine the acquisition interval in response to the occlusion length.

[0121] The acquisition interval refers to the time interval between each image acquisition by the ejection device. It can be calculated by first calculating the quotient of the occlusion length and the detection threshold, and then rounding the quotient up to obtain the minimum number of acquisitions. Then, the quotient of the occlusion length and the flight speed of the UAV is calculated as the flight time. Finally, the quotient of the flight time and the minimum number of acquisitions is calculated as the acquisition interval.

[0122] Step 310: Determine the standby time in response to the launch stroke.

[0123] Standby time refers to the time spent by the catapult during the launch stroke, that is, the time interval from launch to entry into the gap. The method for determining the standby time is common knowledge to those in the field and will not be elaborated here.

[0124] Step 311: After the specified standby time, control the preset ejection device to acquire the pipeline network image according to the specified acquisition interval.

[0125] When the length of the pipeline network covered by obstacles is long, the ejector device needs to periodically collect multiple images within the gap between the obstacle and the pipeline network to obtain the appearance of the covered area. Before the UAV launches the ejector device, the working cycle of the ejector device should be set to collect images of the pipeline network evenly, thereby improving the accuracy of the health status.

[0126] Based on the same inventive concept, embodiments of the present invention provide a GIS-based pipeline asset management and updating system, comprising: The data acquisition module is used to acquire images of the pipeline network, risk locations, water meter readings, detection locations, and vibration amplitudes. A memory is used to store any of the above-mentioned GIS-based pipeline asset management and update methods; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0128] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A GIS-based method for pipeline network asset management and updating, characterized in that, include: Step 200: When the preset network health status is lower than the preset health warning value, collect the water meter reading of the network based on the network number; Step 201: When the water meter reading is abnormal, determine the water meter number in response to the water meter reading; Step 202: Determine adjacent numbers in response to the water meter number; Step 203: Generate a detection area in response to the adjacent numbers; Step 204: Generate a detection path in response to the detection area; Step 205: Control the preset drone to collect images of the pipeline network according to the detection path.

2. The method for pipeline network asset management and updating based on GIS according to claim 1, characterized in that, It also includes an anomaly detection method, which further includes: Step 206: When the water meter reading is abnormal, determine the endpoint position in response to the detection path and collect the detection position of the drone; Step 207: When the detection position coincides with the endpoint position, determine whether there is an obstacle on the pipeline network from the pipeline network image; Step 208: When there is an obstacle on the pipeline, determine the location of the obstacle in response to the pipeline image, and determine the location of the water meter in response to the water meter number; Step 209: When the water meter position coincides with the obstacle position, the water pressure in the pipeline is retrieved in response to the water meter reading, and the obstacle volume is generated in response to the water pressure in the pipeline; Step 210: Generate the network health status in response to the obstruction volume.

3. The method for GIS-based pipeline asset management and updating according to claim 2, characterized in that, The anomaly detection method further includes: Step 211: When the water meter position coincides with the obstacle position, identify the obstacle weight from the pipeline network image; Step 212: When the weight of the obstacle is greater than the water pressure in the pipeline, identify the contact contour between the obstacle and the pipeline from the pipeline network image, and identify the surface angle of the obstacle from the pipeline network image; Step 213: When the surface angle falls within the preset receiving range, the receiving distance is determined in response to the contact profile and the surface angle; Step 214: Determine the receiving test point in response to the received distance; Step 215: Control the preset drone to fly to the receiving test point to detect the vibration of the receiving test point, and determine the knocking test point based on the receiving test point; Step 216: Determine the striking force in response to the weight of the obstacle; Step 217: Control the preset drone to launch a preset test projectile at the impact test point with the impact force, and control the preset drone to collect the vibration amplitude of the receiving test point. Step 218: Generate the pipeline health status in response to the vibration amplitude.

4. The GIS-based pipeline asset management and updating method according to claim 3, characterized in that, It also includes a occlusion detection method, which includes: Step 300: When there are obstacles in the pipeline, identify the gap contour from the pipeline image; Step 301: In response to the gap profile and the preset device profile, determine whether the preset ejection device can enter the gap; Step 302: When the preset ejection device can enter the gap, the entry position is identified from the gap profile; Step 303: Generate a launch path in response to the entered position; Step 304: Control the preset drone to launch the preset catapult into the gap according to the launch stroke, and control the preset catapult to collect the pipeline network image.

5. A GIS-based pipeline asset management and updating method according to claim 4, characterized in that, The occlusion detection method further includes: Step 305: When the preset ejection device can enter the gap, the coverage length is identified from the pipeline image; Step 306: When the coverage length exceeds a preset detection threshold, the coverage thickness is identified from the pipeline image; Step 307: Determine the magnetic strength in response to the covering thickness, and generate a magnetic attraction path based on the acquisition path; Step 308: After launching the preset catapult device, control the preset magnetic attraction device on the UAV to start according to the magnetic force intensity, and control the preset UAV to fly along the magnetic attraction path to drive the preset catapult device to move.

6. The method for GIS-based pipeline asset management and updating according to claim 5, characterized in that, The occlusion detection method further includes: Step 309: When the occlusion length exceeds the preset detection threshold, determine the acquisition interval in response to the occlusion length; Step 310: Determine the standby duration in response to the launch stroke; Step 311: After the specified standby time, control the preset ejection device to acquire the pipeline network image according to the specified acquisition interval.

7. The method for pipeline network asset management and updating based on GIS according to claim 1, characterized in that, The methods for determining the health of the pipeline network include: Step 100: Acquire images of the pipeline network; Step 101: Identify the pipeline route from the pipeline network image; Step 102: Generate a data acquisition path in response to the pipeline network orientation; Step 103: Control the preset drone to continuously collect the pipeline network images according to the collection path, and determine the pipeline network health from the pipeline network images; Step 104: When the health status of the pipeline network is lower than the preset health warning value, collect the risk location of the drone; Step 105: Generate a pipeline risk report in response to the risk location and pipeline image; Step 106: Send the pipeline risk report to the preset management terminal.

8. The method for pipeline network asset management and updating based on GIS according to claim 1, characterized in that, The method for setting the health warning value includes: Step 107: Identify the pipeline number based on the acquisition path; Step 108: Retrieve the baseline early warning value in response to the pipeline number, and retrieve the maintenance record in response to the pipeline number; Step 109: Generate a correction warning value in response to the maintenance record; Step 110: Generate a health warning value in response to the baseline warning value and the corrected warning value.

9. A GIS-based pipeline asset management and updating method according to claim 8, characterized in that, The method for setting the health warning value also includes: Step 111: When the pipeline route falls into the preset pressure transformation range, a height difference is generated in response to the pipeline route; Step 112: Generate a boost correction value in response to the height difference; Step 113: Generate a boost warning value in response to the boost correction value and the health warning value; Step 114: Update the health warning value in response to the pressure warning value.

10. A GIS-based pipeline asset management and updating system, characterized in that, include: The data acquisition module is used to acquire images of the pipeline network, risk locations, water meter readings, detection locations, and vibration amplitudes. A memory for storing a GIS-based pipeline asset management and updating method as described in any one of claims 1 to 9; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

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