Sewage treatment pipe network monitoring system, method, equipment and medium thereof

Abstract

The invention discloses a sewage treatment pipe network monitoring system, method and equipment and a medium thereof, and relates to the technical field of pipe network monitoring, and the system comprises a liquid level monitoring module, a maintenance robot and a monitoring mechanism; the liquid level monitoring module is located in a bottom water tank of the target monitoring pipe gallery; the maintenance robot obtains a multi-angle maintenance video; the monitoring mechanism analyzes a first predicted water leakage position when the detection request is triggered and a second predicted water leakage position detected by the maintenance robot according to the pipeline code identification information and the pipe network design parameters; according to the pipe network design parameters, the first predicted water leakage position and the second predicted water leakage position, a fault recognition model used for guiding operation of the maintenance robot is generated; according to the liquid level change information, the detection precision change reference range and the multi-angle maintenance video, the pipeline water leakage position is comprehensively analyzed, and a final water leakage position analysis result is determined; the method and the device have the effect of improving the monitoring efficiency of the sewage treatment pipe network.

Description

Technical Field

[0001] The present application relates to the technical field of pipe network monitoring, and in particular to a sewage treatment pipe network monitoring system, method, equipment and medium thereof. Background Art

[0002] With the acceleration of urbanization, the city's drainage network is becoming increasingly complex and large. In electroplating and PCB (printed circuit board) industrial parks, due to the wide variety of influent pollutants and strict treatment process requirements, sewage and wastewater need to be collected and treated according to their quality. This results in the sewage network in the park being not only huge in scale but also complex in structure, which puts higher requirements on the maintenance of the network.

[0003] However, the traditional maintenance management model can no longer meet current needs. The existing pipe network is slow to respond when leaks, bursts, and other faults are discovered, and it is impossible to locate the problem in time and repair it quickly; the entire process from problem discovery to final repair takes too long. In addition, in the existing closed pipe corridor, due to the dense pipelines, although the maintenance robot can detect the approximate location of the leak, it cannot accurately locate the specific leaking pipe or distinguish whether it is a leak or a burst pipe. This requires staff to go to the site to check one by one, which greatly reduces maintenance efficiency and increases the operating costs of the park.

[0004] In summary, the current sewage treatment network monitoring has the problem of low monitoring efficiency, which urgently needs to be improved. Summary of the invention

[0005] In order to improve the monitoring efficiency of sewage treatment pipe network monitoring, the present application provides a sewage treatment pipe network monitoring system, method, equipment and medium thereof.

[0006] In the first aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A sewage treatment pipe network monitoring system, comprising a liquid level monitoring module, a maintenance robot and a monitoring mechanism; the liquid level monitoring module is located in a bottom water tank of a target monitoring pipe gallery; the maintenance robot is used to obtain multi-angle maintenance videos of the pipes of the target monitoring pipe gallery; The monitoring mechanism obtains pipeline coding identification information and pipeline network design parameters, and analyzes the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters; generating a fault identification model for guiding the operation of the maintenance robot according to the pipe network design parameters, the first predicted water leakage location, and the second predicted water leakage location; A detection accuracy change reference range representing a threshold value of detection accuracy change of the maintenance robot is obtained, and the liquid level change information in the water tank at the bottom of the closed pipe gallery is obtained through a liquid level monitoring module; based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video, a comprehensive analysis is performed on the pipeline leakage location to determine the final leakage location analysis result.

[0007] By adopting the above technical solution, the leakage location analysis result includes the specific leakage type and leakage location; the pipeline coding identification information is the unique coding identification of the pipeline of the target monitoring corridor. In order to improve the accuracy of fault location, the pipeline coding identification information and the pipe network design parameters are combined to generate a fault identification model to guide the operation of the maintenance robot, which not only improves the accuracy of the leakage location prediction, but also can dynamically adjust the detection strategy according to the actual situation, which is conducive to improving the maintenance efficiency of the maintenance robot; the liquid level monitoring module of the present application is arranged in the bottom water tank of the target monitoring corridor, which can monitor the liquid level change in real time, and distinguish different types of leakage such as dripping and bursting pipes according to the liquid flow characteristics. Combined with the reference range of the detection accuracy change, the system can more accurately determine the specific type of leakage, and then use the fault identification model to guide the operation of the maintenance robot, plus real-time liquid level change analysis, to determine the leakage location and type in the shortest time, improve the specific positioning accuracy of the leakage location, which is conducive to shortening the time from fault discovery to repair. At the same time, the present application can complete most of the fault detection and preliminary analysis work, reduce the dependence on human resources, and effectively reduce the operating cost while improving the monitoring efficiency of the sewage treatment pipe network monitoring.

[0008] In a preferred example of the present application, the step of obtaining pipeline coding identification information and pipeline network design parameters, and analyzing the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters, specifically includes: Obtain pipeline coding identification information and pipeline network design parameters, wherein the pipeline coding identification information includes pipeline type, pipeline diameter, pipeline installation angle, and relative position of the pipeline in the pipeline gallery, and the pipeline network design parameters include pipeline network layout diagram, historical fault data, and design withstand voltage value; Determine a first predicted water leakage position when a detection request is triggered based on the pipeline type, the pipeline diameter, the pipeline installation angle, and the relative position of the pipeline in the pipe gallery in combination with historical fault data; According to the pipeline network layout diagram, the designed pressure resistance value, the pipeline installation angle and the relative position of the pipeline in the pipeline corridor, combined with the multi-angle maintenance video provided by the maintenance robot, the second predicted water leakage position detected by the maintenance robot is determined.

[0009] By adopting the above technical scheme, the detailed pipeline structure and historical operation data of the target monitoring pipeline corridor are obtained according to the pipeline coding identification information and the pipeline network design parameters, so as to accurately predict the possible leakage location. By combining the historical fault data, high-risk leakage areas can be identified to quickly locate the first predicted leakage location when the detection request is triggered. By using the multi-angle maintenance video of the maintenance robot from different angles, the actual condition of the pipeline can be intuitively observed. Combined with the pipeline network layout diagram and the designed pressure resistance value, it is helpful to confirm the second predicted leakage location. Different from the problem of the difficulty in accurately locating the leakage location due to the lack of detailed pipeline network information in the prior art, the present application can improve the accuracy of leakage prediction and the speed of leakage response.

[0010] In a preferred example of the present application, the fault identification model for guiding the operation of the maintenance robot is generated according to the pipe network design parameters, the first predicted water leakage location and the second predicted water leakage location, specifically including: Calculate the possible leakage path length according to the pipe network layout diagram, the first predicted leakage location and the second predicted leakage location, and obtain the corresponding leakage risk assessment value according to the pipe type and the design pressure resistance value; Generate a preliminary fault identification model according to the water leakage path length and the water leakage risk assessment value; Obtain a detection accuracy change reference range representing a change threshold of the detection accuracy of the maintenance robot, and obtain liquid level change information in the water tank at the bottom of the closed pipe gallery through a liquid level monitoring module; Analyze the leakage speed and leakage amount of the leakage point according to the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video; calculate the proportional relationship between the leakage speed and the leakage amount to obtain a leakage ratio for predicting the severity of the leakage; Obtaining a leakage level for judging a specific leakage situation according to the leakage path length, the leakage risk assessment value and the leakage ratio; A final fault identification model is generated according to the water leakage level and the preliminary fault identification model.

[0011] By adopting the above technical scheme, firstly, the possible leakage path length is calculated through the pipeline layout diagram, the first predicted leakage location and the second predicted leakage location, and the corresponding leakage risk assessment value is obtained according to the pipeline type and the design pressure resistance value. Then, the reference range of detection accuracy change and the liquid level change information provided by the liquid level monitoring module are combined with the multi-angle maintenance video to comprehensively analyze the specific situation of the leakage point, such as the leakage speed and leakage amount. Then, the proportional relationship between the leakage speed and the leakage amount is calculated to form a leakage ratio for predicting the severity of the leakage. Combined with the leakage path length, the leakage risk assessment value and the leakage ratio, the leakage level can be determined to generate the final fault identification model. The problem of inaccurate fault identification caused by a single data source is solved by multi-source data fusion, thereby achieving more accurate fault location and classification.

[0012] In a preferred example of the present application, the leakage speed and leakage amount of the leakage point are analyzed according to the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video, including: The liquid level change information of the bottom water tank of the target monitoring corridor is processed in sections to obtain multiple continuous time series data, and the future liquid level change trend is predicted using the time series analysis algorithm; The convolutional neural network in deep learning is used to perform image recognition on the multi-angle maintenance video provided by the maintenance robot, extract possible leakage areas, and determine the image recognition results by comparing the leaking pipe areas determined by video frames at different angles; The liquid level change information is combined with the image recognition result, and the actual leakage speed and leakage amount are calculated using the principles of fluid mechanics.

[0013] By adopting the above technical solution, the potential leakage risk of the closed pipe gallery is predicted through the time series analysis algorithm. At the same time, the convolutional neural network (CNN) in deep learning is used to perform image recognition on the multi-angle maintenance video provided by the maintenance robot, and the possible leakage area is extracted. The specific leaking pipeline area is determined by comparing the video frames at different angles, which is conducive to the accurate identification of the pipeline leakage location. Then, the actual leakage speed and leakage amount are calculated using the principles of fluid mechanics to improve the accuracy and reliability of leakage detection.

[0014] In a preferred example, the present application also includes: The monitoring mechanism and the maintenance robot are connected using a preset communication protocol and a preset communication interaction middleware; The monitoring mechanism generates a corresponding detection instruction based on the collected detection request and distributes it to the corresponding maintenance robot, wherein the detection instruction includes first confirmation information having a unique communication identifier; The maintenance robot performs corresponding detection operations based on the received detection instructions, generates execution confirmation information based on the first confirmation information, and sends the execution confirmation information to the monitoring agency according to the unique communication identifier; The monitoring agency obtains corresponding detection results based on the execution confirmation information and performs fault location simulation display based on the detection results; The maintenance robot sends first reception status information for judging the reception status of the first confirmation information to the monitoring agency within a preset first confirmation time period according to the received first confirmation information; the monitoring agency judges whether it has received the first reception status information within a preset second confirmation time period; if the monitoring agency does not receive the first reception status information within the preset second confirmation time period, the monitoring agency resends the first confirmation information according to the preset first information resending condition.

[0015] By adopting the above technical solutions, the data interaction situation between the monitoring agency and the maintenance robot and the instruction reception and instruction execution status of the maintenance robot are verified; to record and track the maintenance status and maintenance effect of the maintenance robot; specifically, the monitoring agency and the maintenance robot are connected by a preset communication protocol and a preset communication interaction middleware, which is conducive to realizing efficient communication between the two; since in actual applications, the number of pipeline distributions in the closed pipe gallery is large and the distribution situation is complex, and at the same time, there are many robots for maintenance, in order to improve the monitoring effect of the sewage treatment pipe network monitoring in the intricate closed pipe gallery, the present application also ensures the accurate transmission of instructions through the first confirmation information with a unique communication identifier. The maintenance robot performs corresponding detection operations based on the received detection instructions, generates execution confirmation information based on the first confirmation information, and then sends the execution confirmation information to the monitoring agency according to the unique communication identifier, thereby realizing a two-way confirmation mechanism, enhancing the reliability and traceability of the pipe network monitoring system; the monitoring agency obtains corresponding detection results based on the execution confirmation information and performs fault location simulation display based on the detection results, so as to facilitate the monitoring personnel to understand the real-time abnormal situation of the pipe network; further, the maintenance robot sends first reception status information for judging the reception status of the first confirmation information to the monitoring agency within a preset first confirmation time period. If the monitoring agency does not receive this information within the preset time, it resends the first confirmation information according to the preset first information resending condition, ensuring the integrity of information transmission, so as to facilitate tracking and recording the instruction execution situation of the maintenance robot.

[0016] In a preferred example of the present application: The maintenance robot performs corresponding detection operations based on the received detection instructions, generates execution confirmation information based on the first confirmation information, and sends the execution confirmation information to the monitoring agency according to the unique communication identifier, including: The maintenance robot sends the execution confirmation information to the monitoring mechanism according to the unique communication identifier within a preset execution time period; The monitoring mechanism sends second receiving status information for determining a receiving status of the execution confirmation information to the maintenance robot within a preset third confirmation time period based on the received execution confirmation information; The maintenance robot determines whether the second receiving status information is received within the preset second confirmation time period; If the maintenance robot does not receive the second receiving status information within a preset second confirmation time period, the maintenance robot resends the execution confirmation information according to a preset second information resending condition.

[0017] By adopting the above technical solution, the execution confirmation information is sent to the monitoring agency based on the unique communication identifier, which is conducive to ensuring that the results of each detection operation can be accurately recorded and transmitted, reducing operational errors caused by data loss or errors; further, by sending the second receiving status information used to determine the reception status of the execution confirmation information to the maintenance robot within a preset third confirmation time period, a two-way confirmation mechanism is adopted to improve the reliability of information exchange and transmission between the monitoring agency and the maintenance robot, so as to facilitate real-time monitoring of the status of each operation step, thereby improving the efficiency of pipeline network monitoring; at the same time, if the maintenance machine does not receive the second receiving status information, it will automatically trigger the retransmission mechanism to ensure the integrity and accuracy of the detection instructions and detection information.

[0018] In the second aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A sewage treatment pipe network monitoring method is applied to a sewage treatment pipe network monitoring system as described above, the method comprising: The maintenance robot is used to obtain multi-angle maintenance videos of the pipelines in the target monitoring corridor; Acquire pipeline coding identification information and pipeline network design parameters based on the monitoring mechanism, and analyze the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters; generating a fault identification model for guiding the operation of the maintenance robot according to the pipe network design parameters, the first predicted water leakage location, and the second predicted water leakage location; A detection accuracy change reference range representing a threshold value of detection accuracy change of the maintenance robot is obtained, and the liquid level change information in the water tank at the bottom of the closed pipe gallery is obtained through a liquid level monitoring module; based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video, a comprehensive analysis is performed on the pipeline leakage location to determine the final leakage location analysis result.

[0019] In a preferred example of the present application: the method further comprises: upon receiving the maintenance instruction, the maintenance robot sends execution confirmation information to the monitoring mechanism; The monitoring mechanism records the time data of the corresponding first confirmation information, the first receiving state information, the second confirmation information and the second receiving state information and the corresponding sending output state information into a preset record list based on the collected detection request; If the number of retransmissions based on the execution confirmation information by the maintenance robot is equal to a preset second retransmission threshold, and the monitoring mechanism has not received the execution confirmation information within a preset second confirmation time period, the monitoring mechanism outputs abnormal prompt information corresponding to the detection request.

[0020] In the third aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned sewage treatment network monitoring method when executing the computer program.

[0021] In a fourth aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned sewage treatment pipe network monitoring method are implemented.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. The fault identification model not only takes into account the overall layout and design parameters of the pipe network, but also combines the actual test results, making the maintenance strategy more scientific and reasonable, improving the maintenance efficiency and success rate. At the same time, it integrates multiple data sources, uses fluid mechanics principles and image recognition technology to accurately calculate the leakage speed and leakage amount, and further optimizes the accuracy of fault location; 2. It effectively solves the problems of operational errors and task delays caused by unstable communications, inaccurate information transmission and lack of an effective retransmission mechanism in the existing technology, thereby achieving the technical effect of improving the stability and reliability of system communications, enhancing the accuracy of operations and the timeliness of fault response, and improving the overall operation and maintenance efficiency and technical management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a framework diagram of a sewage treatment pipe network monitoring system in one embodiment of the present application; Figure 2 This is a flow chart of a sewage treatment network monitoring method in one embodiment of the present application; Figure 3It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses a sewage treatment pipe network monitoring system, which includes a liquid level monitoring module, a maintenance robot and a monitoring mechanism; the liquid level monitoring module is located in the bottom water tank of the target monitoring pipe gallery, the bottom water tank is located at the bottom of the pipe gallery, and is a space for collecting and temporarily storing sewage. The liquid level monitoring module usually adopts an ultrasonic sensor or a pressure sensor; the maintenance robot is an automated device that can move in a narrow space and carry detection equipment such as a camera. The maintenance robot is used to obtain multi-angle maintenance videos of the pipes of the target monitoring pipe gallery and pipe images at different angles. The movement path of the maintenance robot can cover the entire pipe area of ​​the target monitoring pipe gallery.

[0026] The monitoring agency obtains pipeline coding identification information and pipeline network design parameters, and analyzes the first predicted leakage position when the detection request is triggered and the second predicted leakage position detected by the maintenance robot based on the pipeline coding identification information and the pipeline network design parameters; generates a fault recognition model for guiding the operation of the maintenance robot based on the pipeline network design parameters, the first predicted leakage position and the second predicted leakage position; obtains a detection accuracy change reference range representing a threshold value of the change in the detection accuracy of the maintenance robot, and obtains liquid level change information in the water tank at the bottom of the closed pipe gallery through a liquid level monitoring module; conducts a comprehensive analysis of the pipeline leakage position based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video, and determines the final leakage position analysis result.

[0027] In this embodiment, the pipeline coding identification information and the pipeline network design parameters are obtained, and according to the pipeline coding identification information and the pipeline network design parameters, the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot are analyzed, which specifically includes the following steps: S1: Obtain pipeline coding identification information and pipeline network design parameters. The pipeline coding identification information includes pipeline type, pipeline diameter, pipeline installation angle and relative position of pipeline in the pipeline corridor. The pipeline network design parameters include pipeline network layout diagram, historical fault data and design pressure resistance value.

[0028] In this embodiment, the pipeline coding identification information is used to uniquely identify the detailed information of each section of the pipeline.

[0029] Specifically, each section of pipeline is numbered, and its type (such as PVC, cast iron, etc.), diameter, installation angle and relative position in the pipeline corridor are recorded; the pipeline network layout diagram is collected and organized to ensure that the location and connection relationship of all pipelines are accurately recorded; historical fault data is summarized to analyze which areas or types of pipelines are more likely to have problems; the design pressure resistance value of each section of pipeline is confirmed to evaluate its ability to withstand pressure.

[0030] S2: According to the pipeline type, pipeline diameter, pipeline installation angle and relative position of the pipeline in the pipeline corridor, combined with historical fault data, determine the first predicted leakage position when the detection request is triggered.

[0031] In this embodiment, the first predicted water leakage location is a location where the water leakage is most likely to occur based on the existing data and analysis results. The application analyzes the historical fault data through a machine learning algorithm (such as a decision tree, a random forest) to find out high-risk factors (such as specific types of pipes, problems under certain installation angles, etc.); when a detection request is triggered, according to the above-mentioned predicted water leakage location analysis results, the area with the highest score is preferentially checked as the first predicted water leakage location.

[0032] S3: According to the pipeline network layout diagram, the designed pressure resistance value, the pipeline installation angle and the relative position of the pipeline in the pipeline corridor, combined with the multi-angle maintenance video provided by the maintenance robot, the second predicted water leakage position detected by the maintenance robot is determined.

[0033] In this embodiment, the second predicted water leakage location is a potential water leakage point location determined based on the movement path of the robot and the video captured by the robot; Specifically, image processing technology (such as convolutional neural network (CNN)) is used to analyze multi-angle maintenance videos to identify possible leakage areas. Combined with the pipeline layout diagram, design pressure resistance value and specific installation conditions of the pipeline, the actual risks of these areas are evaluated, and the specific leakage point is determined as the second predicted leakage location.

[0034] In one embodiment, the leakage speed and leakage amount of the leaking point are analyzed according to the liquid level change information, the reference range of the detection accuracy change and the multi-angle maintenance video, which specifically includes the following steps: S101: The liquid level change information of the bottom water tank of the target monitoring corridor is processed in segments to obtain multiple continuous time series data, and the future liquid level change trend is predicted using a time series analysis algorithm.

[0035] In this embodiment, the liquid level change information is the data of the liquid level change in the water tank at the bottom of the closed pipe gallery within a specific time period; time series data refers to a series of observations arranged in chronological order, which is used to analyze and predict future trends; the time series analysis algorithm adopts the long short-term memory network (LSTM).

[0036] Specifically, a high-precision liquid level sensor is installed in the bottom water tank, and the sampling frequency of the liquid level sensor is set (such as once every 5 minutes) to obtain continuous liquid level data.

[0037] S102: Use the convolutional neural network in deep learning to perform image recognition on the multi-angle maintenance video provided by the maintenance robot, extract possible leakage areas, and determine the leaking pipe areas by comparing video frames at different angles to determine the image recognition results.

[0038] In this embodiment, a convolutional neural network (CNN) is a deep learning algorithm suitable for image recognition tasks and capable of automatically extracting image features; a multi-angle maintenance video refers to video clips of different angles inside the pipeline taken by a maintenance robot to provide comprehensive visual information; an image is processed by CNN and the result refers to the conclusion about the location of the leaking area and its severity output.

[0039] S103: combining the liquid level change information with the image recognition result, and using the principles of fluid mechanics to calculate the actual water leakage speed and leakage amount.

[0040] In this embodiment, the water leakage rate refers to the amount of water flowing out from the water leakage point per unit time; the leakage amount refers to the total water leakage amount in a specific time period.

[0041] Furthermore, in actual applications, multiple baffles can be set in the water tank at the bottom of the closed pipe gallery, with each baffle being spaced apart from the bottom wall of the water tank. The liquid level changes in the water tank can be used to determine whether the pipeline is dripping or bursting. If the liquid passes slowly along different baffles, it is dripping. If the water on the baffle rises too quickly, it may be a burst pipe.

[0042] In one embodiment, a sewage treatment pipe network monitoring system also includes: the monitoring mechanism and the maintenance robot are connected using a preset communication protocol and a preset communication interaction middleware, the communication protocol being such as MQTT, AMQP, etc.; the communication interaction middleware being such as Apache Kafka, RabbitMQ, etc.

[0043] Based on the collected detection requests, the monitoring agency generates corresponding detection instructions and distributes them to the corresponding maintenance robots. The detection instructions include first confirmation information with a unique communication identifier. The detection request is a request issued by an operator or an automation system to start a specific detection task. The first confirmation information is an information segment containing a unique communication identifier, which is used to subsequently confirm whether the message is received correctly.

[0044] The maintenance robot performs the corresponding detection operation based on the received detection instruction, generates execution confirmation information based on the first confirmation information, and sends the execution confirmation information to the monitoring agency based on the unique communication identifier; the execution confirmation information is feedback information generated by the maintenance robot after completing the detection task, and contains the unique communication identifier in the first confirmation information; after receiving the detection instruction, the maintenance robot performs the corresponding detection operation (such as shooting multi-angle video, measuring liquid level, etc.) according to the instruction requirements; the monitoring agency obtains the corresponding detection result based on the execution confirmation information, and performs fault location simulation display based on the detection result, that is, according to the detection result, the fault location is marked on GIS or other visualization platforms, and the severity of the fault is indicated by color coding and other methods.

[0045] The maintenance robot sends the first receiving status information used to determine the receiving status of the first confirmation information to the monitoring mechanism within the preset first confirmation time period based on the received first confirmation information; the monitoring mechanism determines whether the first receiving status information is received within the preset second confirmation time period; if the monitoring mechanism does not receive the first receiving status information within the preset second confirmation time period, the monitoring mechanism resends the first confirmation information based on the preset first information retransmission condition. The first information retransmission condition satisfies that the number of retransmissions of the first confirmation information does not exceed the preset first retransmission number threshold, and that the information reception waiting time of the monitoring mechanism does not exceed the preset first timeout time; if the monitoring mechanism receives the first receiving status information within the preset second confirmation time period, the receiving status of the first confirmation information is successfully received.

[0046] Specifically, the first confirmation time period is a set time window, during which the maintenance robot must send the first reception status information, for example, within 5 seconds; the second confirmation time period refers to a set time window, during which the monitoring agency must receive the first reception status information, for example, within 5 seconds; the first information retransmission condition specifies the conditions under which the first confirmation information can be resent, including the maximum number of retransmissions and the waiting time limit, for example, a maximum of 3 retransmissions, each with an interval of 10 seconds.

[0047] In this embodiment, the maintenance robot performs a corresponding detection operation based on the received detection instruction, generates execution confirmation information based on the first confirmation information, and sends the execution confirmation information to the monitoring organization according to the unique communication identifier, including: S111: The maintenance robot sends execution confirmation information to the monitoring agency according to the unique communication identifier within the preset execution time period.

[0048] In this embodiment, the communication protocol defines the rules, format and method of data transmission to ensure smooth information exchange between the monitoring mechanism and the maintenance robot.

[0049] S112: Based on the received execution confirmation information, the monitoring mechanism sends second reception status information for determining the reception status of the execution confirmation information to the maintenance robot within a preset third confirmation time period.

[0050] In this embodiment, the execution time period is a set time window, during which the maintenance robot must complete the detection operation and send an execution confirmation message; in actual application, a reasonable execution time period (for example, 5 minutes) can be set according to the complexity of the specific detection task.

[0051] S113: The maintenance robot determines whether the second receiving status information is received within a preset second confirmation time period.

[0052] In this embodiment, during the second confirmation time period, the maintenance robot continuously monitors the second reception status information from the monitoring mechanism; if the second reception status information is received within the specified time, the communication is considered successful; otherwise, the retransmission mechanism is triggered.

[0053] S114: If the maintenance robot does not receive the second receiving status information within the preset second confirmation time period, the maintenance robot resends the execution confirmation information according to the preset second information resending condition.

[0054] In this embodiment, the second information retransmission condition specifies the conditions under which the execution confirmation information can be retransmitted, including the maximum number of retransmissions and the waiting time limit.

[0055] Specifically, the second information retransmission condition satisfies that the number of retransmissions of the execution confirmation information does not exceed a preset second retransmission threshold, and that the information reception waiting time of the maintenance robot does not exceed a preset second timeout period; if the maintenance robot does not receive the second reception status information within the preset second confirmation time period, the retransmission process is automatically triggered; when retransmitting, the maintenance robot uses the same unique communication identifier to resend the execution confirmation information, and records the number of retransmissions. When the preset maximum number of retransmissions is reached, the retransmission is stopped, and an error log is recorded for subsequent analysis. At the same time, an alarm mechanism is set to remind maintenance personnel to perform manual intervention.

[0056] In one embodiment, if Figure 2 As shown, a sewage treatment pipe network monitoring method is provided, and the sewage treatment pipe network monitoring method is applied to a sewage treatment pipe network monitoring system.

[0057] A sewage treatment pipe network monitoring method specifically comprises the following steps: S10: Obtain multi-angle maintenance videos of the pipelines in the target monitoring corridor through the maintenance robot.

[0058] S20: Obtain pipeline coding identification information and pipeline network design parameters based on the monitoring organization, and analyze the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters.

[0059] Specifically, the first predicted leakage location is based on historical fault data and the current status of the pipeline network, using a machine learning algorithm to predict the location where the leakage is most likely to occur; for example, a specific area is marked as a high-risk area due to frequent pressure fluctuations; the second predicted leakage location refers to the multi-angle video taken by the maintenance robot. For example, a suspected leakage point was initially found, which is located in the above-mentioned high-risk area.

[0060] S30: generating a fault identification model for guiding the operation of the maintenance robot according to the pipe network design parameters, the first predicted water leakage location and the second predicted water leakage location.

[0061] In this embodiment, the fault identification model is a pre-trained regression analysis model, which can calculate the water leakage risk assessment value in combination with the pipeline type and the design pressure resistance value. Assume that the calculated risk score is 8 (out of 10 points), indicating that there is a high risk of water leakage.

[0062] S40: Obtain a detection accuracy change reference range representing a threshold value of detection accuracy change of the maintenance robot, and obtain the liquid level change information in the water tank at the bottom of the closed pipe gallery through the liquid level monitoring module; conduct a comprehensive analysis of the pipeline leakage location based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video to determine the final leakage location analysis result.

[0063] In this embodiment, the reference range of detection accuracy variation refers to the maximum range of allowable fluctuations in the detection accuracy of the maintenance robot. Recalibration is required if it exceeds this range; the leakage position analysis result refers to the final leakage point location and its severity obtained after comprehensive consideration of multiple factors.

[0064] In one embodiment, a sewage treatment network monitoring method further includes: when the maintenance robot receives the maintenance instruction, it sends execution confirmation information to the monitoring agency.

[0065] S50: Based on the collected detection request, the monitoring mechanism records the time data of the corresponding first confirmation information, the first receiving status information, the second confirmation information and the second receiving status information and the corresponding sending output status information into a preset record list.

[0066] Specifically, when the monitoring agency sends the first confirmation message, the sending time, the content of the first confirmation message and the sending status (success / failure) are recorded; when the maintenance robot sends the first receiving status message, the receiving time, the content of the first receiving status message and the receiving status are recorded; similarly, when the monitoring agency sends the second confirmation message, the sending time, the content of the second confirmation message and the sending status are recorded; when the maintenance robot sends the second receiving status message, the receiving time, the content of the second receiving status message and the receiving status are recorded.

[0067] S60: If the number of retransmissions based on the execution confirmation information by the maintenance robot is equal to a preset second retransmission threshold, and the monitoring mechanism does not receive the execution confirmation information within the preset second confirmation time period, the monitoring mechanism outputs abnormal prompt information corresponding to the detection request.

[0068] In this embodiment, the second retransmission number threshold is the set maximum retransmission number, and communication is considered to have failed if this number is exceeded; the second confirmation time period refers to the time window in which the monitoring agency waits to receive execution confirmation information; the abnormal prompt information is a notification issued by the monitoring agency when communication fails, reminding the operator to pay attention and take measures.

[0069] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0070] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multi-angle maintenance videos, liquid level change information, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a sewage treatment pipe network monitoring method is implemented.

[0071] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S10: Obtain multi-angle maintenance videos of the pipelines in the target monitoring corridor through the maintenance robot.

[0072] S20: Obtain pipeline coding identification information and pipeline network design parameters based on the monitoring organization, and detect the first predicted water leakage position when the request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters.

[0073] S30: generating a fault identification model for guiding the operation of the maintenance robot according to the pipe network design parameters, the first predicted water leakage location and the second predicted water leakage location.

[0074] S40: Obtain a detection accuracy change reference range representing a threshold value of detection accuracy change of the maintenance robot, and obtain the liquid level change information in the water tank at the bottom of the closed pipe gallery through the liquid level monitoring module; conduct a comprehensive analysis of the pipeline leakage location based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video to determine the final leakage location analysis result.

[0075] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: S10: Obtain multi-angle maintenance videos of the pipelines in the target monitoring corridor through the maintenance robot.

[0076] S20: Obtain pipeline coding identification information and pipeline network design parameters based on the monitoring organization, and detect the first predicted water leakage position when the request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters.

[0077] S30: generating a fault identification model for guiding the operation of the maintenance robot according to the pipe network design parameters, the first predicted water leakage location and the second predicted water leakage location.

[0078] S40: Obtain a detection accuracy change reference range representing a threshold value of detection accuracy change of the maintenance robot, and obtain the liquid level change information in the water tank at the bottom of the closed pipe gallery through the liquid level monitoring module; conduct a comprehensive analysis of the pipeline leakage location based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video to determine the final leakage location analysis result.

[0079] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0080] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0081] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A sewage treatment network monitoring system, characterized in that: It includes a liquid level monitoring module, a maintenance robot and a monitoring mechanism; the liquid level monitoring module is located in the bottom water tank of the target monitoring corridor; the maintenance robot is used to obtain multi-angle maintenance videos of the pipelines of the target monitoring corridor; The monitoring mechanism obtains pipeline coding identification information and pipeline network design parameters, and analyzes the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters; generating a fault identification model for guiding the operation of the maintenance robot according to the pipe network design parameters, the first predicted water leakage location, and the second predicted water leakage location; A detection accuracy change reference range representing a threshold value of detection accuracy change of the maintenance robot is obtained, and the liquid level change information in the water tank at the bottom of the closed pipe gallery is obtained through a liquid level monitoring module; based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video, a comprehensive analysis is performed on the pipeline leakage location to determine the final leakage location analysis result.

2. A sewage treatment pipe network monitoring system according to claim 1, characterized in that: The step of acquiring the pipeline coding identification information and the pipeline network design parameters and analyzing the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters specifically includes: Obtain pipeline coding identification information and pipeline network design parameters, wherein the pipeline coding identification information includes pipeline type, pipeline diameter, pipeline installation angle, and relative position of the pipeline in the pipeline gallery, and the pipeline network design parameters include pipeline network layout diagram, historical fault data, and design withstand voltage value; Determine a first predicted water leakage position when a detection request is triggered based on the pipeline type, the pipeline diameter, the pipeline installation angle, and the relative position of the pipeline in the pipe gallery in combination with historical fault data; According to the pipeline network layout diagram, the designed pressure resistance value, the pipeline installation angle and the relative position of the pipeline in the pipeline corridor, combined with the multi-angle maintenance video provided by the maintenance robot, the second predicted water leakage position detected by the maintenance robot is determined.

3. A sewage treatment pipe network monitoring system according to claim 2, characterized in that: The generating, according to the pipe network design parameters, the first predicted water leakage position and the second predicted water leakage position, a fault identification model for guiding the operation of the maintenance robot specifically includes: Calculate the possible leakage path length according to the pipe network layout diagram, the first predicted leakage location and the second predicted leakage location, and obtain the corresponding leakage risk assessment value according to the pipe type and the design pressure resistance value; generating a preliminary fault identification model according to the water leakage path length and the water leakage risk assessment value; Obtain a detection accuracy change reference range representing a change threshold of the detection accuracy of the maintenance robot, and obtain liquid level change information in the water tank at the bottom of the closed pipe gallery through a liquid level monitoring module; Analyze the leakage speed and leakage amount of the leakage point according to the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video; calculate the proportional relationship between the leakage speed and the leakage amount to obtain a leakage ratio for predicting the severity of the leakage; Obtaining a leakage level for judging a specific leakage situation according to the leakage path length, the leakage risk assessment value and the leakage ratio; A final fault identification model is generated according to the water leakage level and the preliminary fault identification model.

4. A sewage treatment pipe network monitoring system according to claim 3, characterized in that: The analyzing the leakage speed and leakage amount of the leakage point according to the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video includes: The liquid level change information of the bottom water tank of the target monitoring corridor is processed in sections to obtain multiple continuous time series data, and the future liquid level change trend is predicted using the time series analysis algorithm; The convolutional neural network in deep learning is used to perform image recognition on the multi-angle maintenance video provided by the maintenance robot, extract possible leakage areas, and determine the image recognition results by comparing the leaking pipe areas determined by video frames at different angles; The liquid level change information is combined with the image recognition result, and the actual leakage speed and leakage amount are calculated using the principles of fluid mechanics.

5. A sewage treatment pipe network monitoring system according to claim 1, characterized in that: Also includes: The monitoring mechanism and the maintenance robot are connected using a preset communication protocol and a preset communication interaction middleware; The monitoring mechanism generates a corresponding detection instruction based on the collected detection request and distributes it to the corresponding maintenance robot, wherein the detection instruction includes first confirmation information having a unique communication identifier; The maintenance robot performs a corresponding detection operation based on the received detection instruction, generates execution confirmation information based on the first confirmation information, and sends the execution confirmation information to the monitoring mechanism according to the unique communication identifier; The monitoring mechanism obtains corresponding detection results based on the execution confirmation information, and performs fault location simulation display based on the detection results; The maintenance robot sends first reception status information for determining the reception status of the first confirmation information to the monitoring mechanism within a preset first confirmation time period based on the received first confirmation information; the monitoring mechanism determines whether the first reception status information is received within a preset second confirmation time period; If the monitoring mechanism does not receive the first reception status information within a preset second confirmation time period, the monitoring mechanism resends the first confirmation information according to a preset first information retransmission condition.

6. A sewage treatment pipe network monitoring system according to claim 5, characterized in that: The maintenance robot performs a corresponding detection operation based on the received detection instruction, generates execution confirmation information based on the first confirmation information, and sends the execution confirmation information to the monitoring mechanism according to the unique communication identifier, including: The maintenance robot sends the execution confirmation information to the monitoring mechanism according to the unique communication identifier within a preset execution time period; The monitoring mechanism sends second receiving status information for determining a receiving status of the execution confirmation information to the maintenance robot within a preset third confirmation time period based on the received execution confirmation information; The maintenance robot determines whether the second receiving status information is received within the preset second confirmation time period; If the maintenance robot does not receive the second receiving status information within a preset second confirmation time period, the maintenance robot resends the execution confirmation information according to a preset second information resending condition.

7. A method for monitoring a sewage treatment network, characterized in that: Applied to a sewage treatment pipe network monitoring system as claimed in any one of claims 1 to 6, the method comprising: The maintenance robot is used to obtain multi-angle maintenance videos of the pipelines in the target monitoring corridor; Acquire pipeline coding identification information and pipeline network design parameters based on the monitoring mechanism, and analyze the first predicted water leakage position when the detection request is triggered and the second predicted water leakage position detected by the maintenance robot according to the pipeline coding identification information and the pipeline network design parameters; generating a fault identification model for guiding the operation of the maintenance robot according to the pipe network design parameters, the first predicted water leakage location, and the second predicted water leakage location; A detection accuracy change reference range representing a threshold value of detection accuracy change of the maintenance robot is obtained, and the liquid level change information in the water tank at the bottom of the closed pipe gallery is obtained through a liquid level monitoring module; based on the liquid level change information, the detection accuracy change reference range and the multi-angle maintenance video, a comprehensive analysis is performed on the pipeline leakage location to determine the final leakage location analysis result.

8. A method for monitoring a sewage treatment network according to claim 7, characterized in that: The method further comprises: upon receiving the maintenance instruction, the maintenance robot sends execution confirmation information to the monitoring mechanism; The monitoring mechanism records the time data of the corresponding first confirmation information, the first receiving state information, the second confirmation information and the second receiving state information and the corresponding sending output state information into a preset record list based on the collected detection request; If the number of retransmissions based on the execution confirmation information by the maintenance robot is equal to a preset second retransmission threshold, and the monitoring mechanism has not received the execution confirmation information within a preset second confirmation time period, the monitoring mechanism outputs abnormal prompt information corresponding to the detection request.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a sewage treatment network monitoring method as described in any one of claims 7 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of a sewage treatment network monitoring method as described in any one of claims 7 to 8 are implemented.

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