Drainage inspection well pipe network detection system and method based on low-frequency high-energy ultrasound

Abstract

The invention discloses a drainage inspection well pipe network detection system and method based on low-frequency high-energy ultrasound. The system comprises a main controller, a main control module, a Mesh main router and a plurality of detection terminals. The detection terminal comprises a slave control module, a Mesh node route and an ultrasonic transducer; the main controller is electrically connected with the main control module and the Mesh main route respectively; the slave control module is electrically connected with the ultrasonic transducer and the Mesh node route respectively; the Mesh main route and each Mesh node route form a wireless Mesh network for networking communication; the master control module is in wireless networking communication with each slave control module to realize time synchronization; the ultrasonic sensing module is used for entering the drainage pipe from the inspection well in a suspended mode to send or receive ultrasonic signals. According to the drainage inspection well pipe network detection system and method, the communication state of the pipelines between the inspection wells can be rapidly and accurately recognized, and the system and method have important practical significance on timely finding and processing pipeline blockage and guaranteeing normal operation of an urban drainage system.

Description

Technical Field

[0001] The present invention relates to a drainage manhole pipe network detection technology, in particular to a drainage manhole pipe network detection system and method based on low-frequency high-energy ultrasound. Background Art

[0002] Currently, in the field of drainage network detection, there are relatively few devices and methods specifically used to discover the topological connectivity status of the network. Existing related technologies mainly include the following categories:

[0003] Manual inspection: Manual inspection is a time-honored traditional method. On-site operators must open manhole covers and enter the underground pipe network through the manhole to check for blockage. During the inspection, operators must wear professional protective equipment such as waterproof clothing and safety ropes. Once inside the manhole, they use tools such as flashlights, pipe detectors, and probes to inspect the siltation in each connected pipe to determine whether there are any issues such as sediment or garbage blocking the pipes, which may affect drainage. This inspection method can relatively intuitively detect pipe blockage.

[0004] Closed-circuit television (CCTV) inspection: CCTV inspection technology uses cameras to provide real-time imaging of the pipeline interior. The specific operation involves inserting a camera probe into the pipeline entrance and advancing it along the pipeline, while simultaneously transmitting real-time images to the control terminal display. The operator monitors the video footage to observe the pipeline's patency and the presence of cracks, deformation, or other structural issues. During the inspection process, video data is simultaneously recorded for subsequent analysis and long-term storage. CCTV inspection technology improves inspection accuracy through image visualization and is widely used in municipal pipeline network monitoring and maintenance.

[0005] For pipeline inspections in low-flow or still water conditions: A floating sensor equipped with an electric propeller provides traction in still water or low-flow conditions. The floating sensor consists of multiple floats, each equipped with a detection module that collects video images or siltation status data from the pipeline interior. In operation, a portable host controls the propeller to propel the sensor along the pipeline while simultaneously collecting and storing inspection data. This technology is suitable for most pipeline inspection scenarios in still water or low-flow environments.

[0006] Liquid-level blockage detection: Liquid-level meters are deployed to monitor manhole liquid level changes in real time. The average water volume change is calculated based on the water level drop period and rate of change, and blockage is determined based on a set threshold. This method identifies blockage areas by analyzing the time-dependent rate of change of the liquid level, water volume differences, and waveform characteristics. This method is cost-effective and suitable for large-scale detection. Summary of the Invention

[0007] The purpose of the invention is to provide a drainage manhole network detection system and method based on low-frequency high-energy ultrasound, which can realize rapid detection of drainage network connectivity, thereby timely discovering drainage pipe blockage and ensuring the normal operation of the urban drainage system.

[0008] Technical solution: The drainage manhole network detection system based on low-frequency high-energy ultrasound described in the present invention includes a main controller, a main control module, a Mesh master router, and multiple detection terminals; the detection terminals include a slave control module, a Mesh node router, and an ultrasonic transducer;

[0009] The main controller is electrically connected to the main control module and the Mesh main router respectively; the slave control module is electrically connected to the ultrasonic transducer and the Mesh node router respectively; the Mesh main router and each Mesh node router form a wireless Mesh network for networking communication; the main control module and each slave control module wirelessly communicate to achieve time synchronization; the ultrasonic sensor module is used to suspend from the manhole into the drainage pipe to send or receive ultrasonic signals.

[0010] Furthermore, the slave control module includes a time synchronization unit, a core control unit, an ultrasonic drive and receiving circuit, and an A / D conversion unit; the core control unit is electrically connected to the time synchronization unit, the ultrasonic drive and receiving circuit, and the A / D conversion unit respectively; the time synchronization unit communicates wirelessly with the main control module and receives the time synchronization signal sent by the main control module; the ultrasonic drive and receiving circuit is electrically connected to the A / D conversion unit and the ultrasonic transducer respectively.

[0011] The present invention also provides a detection method for a drainage manhole network detection system based on low-frequency high-energy ultrasound, comprising the following steps:

[0012] Step 1: The main controller sends a time synchronization instruction to the master control module, and the master control module sends a time synchronization signal to each slave control module, and each slave control module performs time synchronization according to the time synchronization signal;

[0013] Step 2: The main controller selects a detection terminal that has not been selected as an ultrasonic transmitter as the ultrasonic transmitter, selects the remaining detection terminals as ultrasonic receivers, and uses the Mesh main router to send an ultrasonic transmission instruction to the Mesh node router of the ultrasonic transmitter. At the same time, the Mesh main router also uses the Mesh main router to send an ultrasonic collection instruction to the Mesh node routers of each ultrasonic receiver.

[0014] Step 3: After receiving the ultrasonic transmission instruction from the Mesh node router, the slave control module at the ultrasonic transmitting end transmits four waves of ultrasonic signals through the ultrasonic transducer;

[0015] Step 4: The slave control module at the ultrasonic receiving end receives the ultrasonic signal through the ultrasonic transducer, collects the received ultrasonic signal, and sends the collected ultrasonic signal to the Mesh master router through the Mesh node router;

[0016] In step 5, the main controller stores the ultrasonic signals sent by each Mesh node router received by the Mesh main router as a data group, and then determines whether each detection terminal has been selected as an ultrasonic transmitter. If there is a detection terminal that has not been selected as an ultrasonic transmitter, the process returns to step 2; otherwise, the process proceeds to step 6.

[0017] Step 6: The main controller reads a data group and performs connectivity analysis on each ultrasonic signal in the data group to obtain the connectivity between the ultrasonic transmitter and each ultrasonic receiver corresponding to the current data group;

[0018] Step 7: Determine whether there are any data groups that have not been subjected to connectivity analysis. If there are any data groups that have not been subjected to connectivity analysis, return to step 6. Otherwise, construct a connectivity topology map of the pipe network based on all connectivity judgment results.

[0019] Furthermore, in step 6, when performing connectivity analysis on each ultrasonic signal in the data set, the specific steps are as follows:

[0020] Step 6.1, extract information from the data group to obtain the ultrasonic transmitter and each ultrasonic receiver corresponding to the data group, and further determine the detection terminal corresponding to the ultrasonic transmitter and each detection terminal corresponding to each ultrasonic receiver;

[0021] Step 6.2: extract an ultrasonic signal from the data set, each ultrasonic signal including four waves of ultrasonic waves, perform data preprocessing on the extracted ultrasonic signal, and remove the DC offset signal mixed in the ultrasonic signal;

[0022] Step 6.3: Set three judgment thresholds and use them to mark three amplitude observation points at the beginning of the ultrasonic signal. Then, use the three amplitude observation points to draw three threshold lines for observing the up and down fluctuations of the ultrasonic wave.

[0023] Step 6.4, using three threshold lines to intercept the four waves of ultrasound, and obtain three characteristic time intervals of each wave of ultrasound, namely Δt, Δt1 and Δt2;

[0024] Step 6.5: Perform connectivity analysis based on the characteristic time intervals Δt, Δt1, and Δt2 of the four waves of ultrasound to obtain the connectivity status between the ultrasound transmitting end and the ultrasound receiving end corresponding to the current ultrasound signal.

[0025] Furthermore, in step 6.3, the specific steps for setting the three determination thresholds are as follows:

[0026] Step 6.3.1, extract the maximum amplitude of the first ultrasonic wave of the four waves in the ultrasonic signal and set it as H;

[0027] In step 6.3.2, three judgment thresholds are set as 50% H, 30% H, and 20% H.

[0028] Furthermore, in step 6.4, the specific steps for obtaining the three characteristic time intervals of each wave of ultrasound are as follows:

[0029] Step 6.4.1: Select an ultrasonic wave and record the moment when the ultrasonic wave first crosses the three threshold lines and the moment when the ultrasonic wave last crosses the three threshold lines;

[0030] Step 6.4.2: Calculate the difference between the down-crossing time and the up-crossing time for each of the three threshold lines, and obtain the three characteristic time intervals corresponding to the current ultrasound wave, namely Δt, Δt1, and Δt2;

[0031] Step 6.4.3, determine whether the four waves of ultrasound have all obtained the corresponding characteristic time intervals. If there are still ultrasound waves that have not obtained the corresponding characteristic time intervals, return to step 6.4.1, otherwise store the three characteristic time intervals of each of the four waves of ultrasound.

[0032] Furthermore, in step 6.5, the specific steps of performing connectivity analysis based on the characteristic time intervals Δt, Δt1, and Δt2 of the four waves of ultrasound are as follows:

[0033] Step 6.5.1: Extract the characteristic time interval Δt of the first ultrasonic wave among the four ultrasonic waves and perform a validity analysis on the extracted characteristic time interval Δt. If the characteristic time interval Δt is valid, proceed to step 6.5.2; otherwise, determine that the connectivity state is disconnected and proceed to step 6.5.10.

[0034] Step 6.5.2: Extract the characteristic time intervals Δt of the remaining three ultrasonic waves from the four waves and perform validity analysis on the three extracted characteristic time intervals Δt. If all three characteristic time intervals Δt are valid, proceed to step 6.5.3; otherwise, determine that the connectivity is disconnected and proceed to step 6.5.10.

[0035] Step 6.5.3: Determine the similarity between the three characteristic time intervals Δt of the remaining three ultrasound waves and the characteristic time interval Δt of the first ultrasound wave. If two or more characteristic time intervals Δt meet the similarity requirement, the first-level analysis is considered passed and the process proceeds to Step 6.5.4. Otherwise, the connectivity is determined to be disconnected and the process proceeds to Step 6.5.10.

[0036] Step 6.5.4: Extract the characteristic time interval Δt1 of the first ultrasonic wave of the four waves and perform a validity analysis on the extracted characteristic time interval Δt1. If the characteristic time interval Δt1 is valid, proceed to step 6.5.5; otherwise, determine that it is secondary connectivity and proceed to step 6.5.10.

[0037] Step 6.5.5: Extract the characteristic time intervals Δt1 of the remaining three ultrasonic waves of the four waves and perform a validity analysis on the three extracted characteristic time intervals Δt1. If all three characteristic time intervals Δt1 are valid, proceed to step 6.5.6; otherwise, determine that the connectivity is secondary connectivity and proceed to step 6.5.10.

[0038] Step 6.5.6: Determine the similarity between the three characteristic time intervals Δt1 of the remaining three ultrasound waves and the characteristic time interval Δt1 of the first ultrasound wave. If two or more characteristic time intervals Δt1 meet the similarity requirement, the second-level analysis is passed and the process proceeds to step 6.5.7. Otherwise, the connectivity is determined to be level 2 connectivity and the process proceeds to step 6.5.10.

[0039] Step 6.5.7: Extract the characteristic time interval Δt2 of the first ultrasonic wave of the four waves and perform a validity analysis on the extracted characteristic time interval Δt2. If the characteristic time interval Δt2 is valid, proceed to step 6.5.8; otherwise, determine that the connectivity is primary connectivity and proceed to step 6.5.10.

[0040] Step 6.5.8: Extract the characteristic time intervals Δt2 of the remaining three ultrasonic waves from the four waves and perform validity analysis on the three extracted characteristic time intervals Δt2. If all three characteristic time intervals Δt2 are valid, proceed to step 6.5.9; otherwise, determine that the connectivity is primary connectivity and proceed to step 6.5.10.

[0041] Step 6.5.9: Determine the similarity between the three characteristic time intervals Δt2 of the remaining three ultrasound waves and the characteristic time interval Δt2 of the first ultrasound wave. If two or more characteristic time intervals Δt2 meet the similarity requirement, the connectivity is determined to be good connectivity, and the process proceeds to Step 6.5.10. Otherwise, the connectivity is determined to be first-level connectivity, and the process proceeds to Step 6.5.10.

[0042] Step 6.5.10: Output the corresponding connectivity status as the connectivity status between the ultrasonic transmitting end and the ultrasonic receiving end corresponding to the current ultrasonic signal.

[0043] Furthermore, when conducting validity analysis of characteristic time intervals, the specific steps are as follows:

[0044] Step a: Obtain the transmission duration T and transmission frequency W of each wave of ultrasound, and calculate the time intervals between two consecutive crossings of the threshold line by the ultrasound within the current characteristic time interval;

[0045] Step b: setting the analysis time interval to 0.5T to 2.5T based on the transmission duration T, and setting the time interval between two adjacent crossings of the threshold line to 0.8 / W to 1.2 / W based on the transmission frequency W;

[0046] Step c: compare the current feature time interval with the analysis time interval. If the feature time interval is smaller than the analysis time interval, proceed to step d. Otherwise, determine that the validity analysis result of the current feature time interval is an invalid feature time interval, and proceed to step f.

[0047] Step d: Determine whether all time intervals are within the crossing time interval range. If so, the validity analysis result of the current characteristic time interval is determined to be a valid characteristic time interval. Otherwise, the validity analysis result of the current characteristic time interval is determined to be an invalid characteristic time interval, and then proceed to step f.

[0048] Step f: output the validity analysis result of the current feature time interval.

[0049] Furthermore, when determining the approximation of the characteristic time interval, the specific steps are as follows:

[0050] First, obtain the two characteristic time intervals to be judged and calculate the difference Δt0 between the two characteristic time intervals;

[0051] Then, the difference Δt0 and the percentage value of the two characteristic time intervals are calculated respectively. If the percentage value is within the range of ±10%, it is determined that the two characteristic time intervals meet the similarity requirement; otherwise, it is determined that the two characteristic time intervals are not similar.

[0052] Furthermore, in step 7, the specific steps of constructing the connectivity topology map of the pipe network based on all the connectivity judgment results are as follows:

[0053] Step 7.1: Establish a topology coordinate system, obtain the coordinate information of each detection terminal, and mark the coordinates of each detection terminal in the topology coordinate system;

[0054] Step 7.2, connect topology lines between each detection terminal;

[0055] Step 7.3: Replace the colors of the topology lines according to the connectivity judgment results between the detection terminals, so that topology lines of different colors represent different connectivity judgment results.

[0056] Compared with the prior art, the present invention has the following advantages: time synchronization is achieved by wireless networking communication between the master control module and each slave control module, thereby ensuring that the ultrasonic wave can be accurately collected within a smaller collection interval after being sent, on the one hand ensuring the accuracy of data collection, and on the other hand ensuring a smaller amount of data transmission; a wireless Mesh network is formed by using the Mesh main route and each Mesh node route for networking communication, which can quickly send the collected data to the main controller for analysis and processing, thereby realizing rapid detection of connectivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Schematic diagram of the detection system structure of the present invention;

[0058] Figure 2 Schematic diagram of three threshold lines of the present invention;

[0059] Figure 3 It is a signal strength schematic diagram of the present invention;

[0060] Figure 4 This is a schematic diagram of the placement of the ultrasonic transducer of the present invention;

[0061] Figure 5 Schematic diagram of pipeline topology connectivity of the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention is described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the embodiments.

[0063] like Figure 1 As shown, the drainage manhole network detection system based on low-frequency high-energy ultrasound disclosed in the present invention includes: a main controller, a main control module, a Mesh main router and multiple detection terminals; the detection terminal includes a slave control module, a Mesh node router and an ultrasonic transducer;

[0064] The main controller is electrically connected to the main control module and the Mesh main router respectively; the slave control module is electrically connected to the ultrasonic transducer and the Mesh node router respectively; the Mesh main router and each Mesh node router form a wireless Mesh network for networking communication; the main control module and each slave control module wirelessly communicate to achieve time synchronization; the ultrasonic sensor module is used to suspend from the manhole into the drainage pipe to send or receive ultrasonic signals.

[0065] Wireless networking and communication between the master control module and each slave control module achieve time synchronization, ensuring that ultrasonic waves are accurately collected within a small acquisition interval after transmission. This not only ensures data collection accuracy, but also keeps data transmission volume low. A wireless mesh network, formed by a mesh master router and each mesh node router, quickly transmits collected data to the master controller for analysis and processing, enabling rapid connectivity testing. Compared to wired solutions, wireless mesh networks offer significant advantages: First, the elimination of physical cables greatly simplifies system deployment and improves system flexibility and scalability. Second, the self-organizing nature of mesh networks provides enhanced fault tolerance. Although wireless transmission is susceptible to external interference and requires a five-fold engineering margin (i.e., 9.6 Mbps bandwidth), WiFi Mesh communication technology can provide actual transmission rates of hundreds of Mbps, fully meeting the system's bandwidth requirements. More importantly, when direct communication between any two nodes is blocked by an obstacle, the mesh self-organizing network automatically finds alternative feasible transmission paths and forwards data through intermediate nodes, ensuring network connectivity. This adaptive network topology is particularly suitable for the application scenarios of distributed sensor networks and can significantly improve the robustness and practicality of the system.

[0066] Furthermore, the slave control module includes a time synchronization unit, a core control unit, an ultrasonic drive and receiving circuit, and an A / D conversion unit; the core control unit is electrically connected to the time synchronization unit, the ultrasonic drive and receiving circuit, and the A / D conversion unit respectively; the time synchronization unit communicates wirelessly with the main control module and receives the time synchronization signal sent by the main control module; the ultrasonic drive and receiving circuit is electrically connected to the A / D conversion unit and the ultrasonic transducer respectively.

[0067] By cooperating with the ultrasonic driving and receiving circuit and the ultrasonic transducer, the function switching of the ultrasonic transducer in two states can be realized. In the ultrasonic transmitting state, the ultrasonic driving and receiving circuit drives the ultrasonic transducer to generate ultrasonic waves with a frequency of 15kHz. This frequency exhibits excellent penetration performance and moderate attenuation characteristics in practical applications. It can not only effectively penetrate various media in the pipeline, but its signal attenuation characteristics are also suitable for pipe section detection of hundreds of meters. The wavelength characteristics match the pipe size, providing reliable technical support for drainage network connectivity detection; in the ultrasonic receiving state, the ultrasonic driving and receiving circuit receives the ultrasonic signal through the ultrasonic transducer, and uses the A / D conversion unit for digital acquisition; using the wireless communication between the time synchronization unit and the master control module, it can receive the time synchronization signal, so that each slave control module works under the same time conditions.

[0068] like Figure 3 As shown, to ensure that the signal strength of the ultrasonic signal after transmitting L meters is still X times higher than the background noise, thereby ensuring the reliability of subsequent signal analysis, the present invention adopts an ultrasonic transducer with a high-voltage design. Through the high-voltage drive solution, the ultrasonic transducer's transmission sound pressure level can reach 170dB (reference 1μPa@1m), and the receiving sensitivity is -180dB (reference 1V / μPa@1kHz), thus ensuring the reliability of long-distance signal transmission. This design, combining high-energy transmission with high-sensitivity reception, enables the system to achieve stable signal detection in complex pipe network environments.

[0069] The ultrasonic transducer of the present invention is placed as follows Figure 4 As shown, the ultrasonic transducer is electrically connected to the ultrasonic driver and receiver circuits via a waterproof connector. These circuits generate an excitation signal, ensuring sufficient sound pressure levels during ultrasonic transmission. High-strength waterproof cables are used to ensure stable signal transmission even under prolonged submersion. A suspended design allows the ultrasonic transducer to be placed in the water within the manhole pipe. Due to the propagation characteristics of ultrasonic waves in water, the position of the ultrasonic transducer has a significant impact on signal transmission quality. For optimal detection results, the vertical position of the ultrasonic transducer must be appropriately adjusted based on the actual pipeline conditions to ensure efficient transmission of signal energy.

[0070] The A / D conversion unit of the present invention adopts a sampling rate of 100 kHz (about 6 times the frequency of the ultrasonic signal), so that the waveform characteristics and transition details of the ultrasonic signal can be accurately captured.

[0071] The present invention also provides a detection method for a drainage manhole network detection system based on low-frequency high-energy ultrasound, comprising the following steps:

[0072] Step 1: The main controller sends a time synchronization instruction to the master control module, and the master control module sends a time synchronization signal to each slave control module, and each slave control module performs time synchronization according to the time synchronization signal;

[0073] Step 2: The main controller selects a detection terminal that has not been selected as an ultrasonic transmitter as the ultrasonic transmitter, selects the remaining detection terminals as ultrasonic receivers, and uses the Mesh main router to send an ultrasonic transmission instruction to the Mesh node router of the ultrasonic transmitter. At the same time, the Mesh main router also uses the Mesh main router to send an ultrasonic collection instruction to the Mesh node routers of each ultrasonic receiver.

[0074] Step 3: After receiving the ultrasonic transmission instruction from the Mesh node router, the slave control module at the ultrasonic transmitting end transmits four waves of ultrasonic signals through the ultrasonic transducer;

[0075] Step 4: The slave control module at the ultrasonic receiving end receives the ultrasonic signal through the ultrasonic transducer, collects the received ultrasonic signal, and sends the collected ultrasonic signal to the Mesh master router through the Mesh node router;

[0076] In step 5, the main controller stores the ultrasonic signals sent by each Mesh node router received by the Mesh main router as a data group, and then determines whether each detection terminal has been selected as an ultrasonic transmitter. If there is a detection terminal that has not been selected as an ultrasonic transmitter, the process returns to step 2; otherwise, the process proceeds to step 6.

[0077] Step 6: The main controller reads a data group and performs connectivity analysis on each ultrasonic signal in the data group to obtain the connectivity between the ultrasonic transmitter and each ultrasonic receiver corresponding to the current data group;

[0078] Step 7: Determine whether there are any data groups that have not been subjected to connectivity analysis. If there are any data groups that have not been subjected to connectivity analysis, return to step 6. Otherwise, construct a connectivity topology map of the pipe network based on all connectivity judgment results.

[0079] Furthermore, in step 6, when performing connectivity analysis on each ultrasonic signal in the data set, the specific steps are as follows:

[0080] Step 6.1, extract information from the data group to obtain the ultrasonic transmitter and each ultrasonic receiver corresponding to the data group, and further determine the detection terminal corresponding to the ultrasonic transmitter and each detection terminal corresponding to each ultrasonic receiver;

[0081] Step 6.2: Extract an ultrasonic signal from the data set. Each ultrasonic signal consists of four waves of ultrasound. Perform data preprocessing on the extracted ultrasonic signal to remove the DC offset signal mixed in the ultrasonic signal to highlight the dynamic change characteristics of the ultrasonic signal and facilitate subsequent threshold analysis.

[0082] Step 6.3, set three judgment thresholds, and use the three judgment thresholds to mark three amplitude observation points at the beginning of the ultrasonic signal, and then use the three amplitude observation points to make three threshold lines for observing the up and down fluctuations of the ultrasonic wave, such as Figure 2 As shown;

[0083] Step 6.4, using three threshold lines to intercept the four waves of ultrasound, and obtain three characteristic time intervals of each wave of ultrasound, namely Δt, Δt1 and Δt2;

[0084] Step 6.5: Perform connectivity analysis based on the characteristic time intervals Δt, Δt1, and Δt2 of the four waves of ultrasound to obtain the connectivity status between the ultrasound transmitting end and the ultrasound receiving end corresponding to the current ultrasound signal.

[0085] Furthermore, in step 6.3, the specific steps for setting the three determination thresholds are as follows:

[0086] Step 6.3.1, extract the maximum amplitude of the first ultrasonic wave of the four waves in the ultrasonic signal and set it as H;

[0087] Step 6.3.2, set three decision thresholds to 50% H, 30% H and 20% H respectively. The selection of these three decision thresholds is the optimal solution determined based on a large number of experimental verifications. Figure 2 The 75% maximum value often exhibits irregular fluctuations, likely due to multipath effects or the superposition of environmental noise, making it unsuitable as a benchmark for feature extraction. Therefore, 50% of the maximum value is selected as the first judgment threshold, avoiding the unstable region near the peak while maintaining sufficient signal strength. The 30% and 20% thresholds are located within the stable attenuation range of the signal, effectively tracking the propagation characteristics of ultrasonic waves in pipes.

[0088] Furthermore, in step 6.4, the specific steps for obtaining the three characteristic time intervals of each wave of ultrasound are as follows:

[0089] Step 6.4.1: Select an ultrasonic wave and record the moment when the ultrasonic wave first crosses the three threshold lines and the moment when the ultrasonic wave last crosses the three threshold lines;

[0090] Step 6.4.2: Calculate the difference between the down-crossing time and the up-crossing time for each of the three threshold lines, and obtain the three characteristic time intervals corresponding to the current ultrasound wave, namely Δt, Δt1, and Δt2;

[0091] Step 6.4.3, determine whether the four waves of ultrasound have all obtained the corresponding characteristic time intervals. If there are still ultrasound waves that have not obtained the corresponding characteristic time intervals, return to step 6.4.1, otherwise store the three characteristic time intervals of each of the four waves of ultrasound.

[0092] Furthermore, in step 6.5, the specific steps of performing connectivity analysis based on the characteristic time intervals Δt, Δt1, and Δt2 of the four waves of ultrasound are as follows:

[0093] Step 6.5.1: Extract the characteristic time interval Δt of the first ultrasonic wave among the four ultrasonic waves and perform a validity analysis on the extracted characteristic time interval Δt. If the characteristic time interval Δt is valid, proceed to step 6.5.2; otherwise, determine that the connectivity state is disconnected and proceed to step 6.5.10.

[0094] Step 6.5.2: Extract the characteristic time intervals Δt of the remaining three ultrasonic waves from the four waves and perform validity analysis on the three extracted characteristic time intervals Δt. If all three characteristic time intervals Δt are valid, proceed to step 6.5.3; otherwise, determine that the connectivity is disconnected and proceed to step 6.5.10.

[0095] Step 6.5.3: Determine the similarity between the three characteristic time intervals Δt of the remaining three ultrasound waves and the characteristic time interval Δt of the first ultrasound wave. If two or more characteristic time intervals Δt meet the similarity requirement, the first-level analysis is considered passed and the process proceeds to Step 6.5.4. Otherwise, the connectivity is determined to be disconnected and the process proceeds to Step 6.5.10.

[0096] Step 6.5.4: Extract the characteristic time interval Δt1 of the first ultrasonic wave of the four waves and perform a validity analysis on the extracted characteristic time interval Δt1. If the characteristic time interval Δt1 is valid, proceed to step 6.5.5; otherwise, determine that it is secondary connectivity and proceed to step 6.5.10.

[0097] Step 6.5.5: Extract the characteristic time intervals Δt1 of the remaining three ultrasonic waves of the four waves and perform a validity analysis on the three extracted characteristic time intervals Δt1. If all three characteristic time intervals Δt1 are valid, proceed to step 6.5.6; otherwise, determine that the connectivity is secondary connectivity and proceed to step 6.5.10.

[0098] Step 6.5.6: Determine the similarity between the three characteristic time intervals Δt1 of the remaining three ultrasound waves and the characteristic time interval Δt1 of the first ultrasound wave. If two or more characteristic time intervals Δt1 meet the similarity requirement, the second-level analysis is passed and the process proceeds to step 6.5.7. Otherwise, the connectivity is determined to be level 2 connectivity and the process proceeds to step 6.5.10.

[0099] Step 6.5.7: Extract the characteristic time interval Δt2 of the first ultrasonic wave of the four waves and perform a validity analysis on the extracted characteristic time interval Δt2. If the characteristic time interval Δt2 is valid, proceed to step 6.5.8; otherwise, determine that the connectivity is primary connectivity and proceed to step 6.5.10.

[0100] Step 6.5.8: Extract the characteristic time intervals Δt2 of the remaining three ultrasonic waves from the four waves and perform validity analysis on the three extracted characteristic time intervals Δt2. If all three characteristic time intervals Δt2 are valid, proceed to step 6.5.9; otherwise, determine that the connectivity is primary connectivity and proceed to step 6.5.10.

[0101] Step 6.5.9: Determine the similarity between the three characteristic time intervals Δt2 of the remaining three ultrasound waves and the characteristic time interval Δt2 of the first ultrasound wave. If two or more characteristic time intervals Δt2 meet the similarity requirement, the connectivity is determined to be good connectivity, and the process proceeds to Step 6.5.10. Otherwise, the connectivity is determined to be first-level connectivity, and the process proceeds to Step 6.5.10.

[0102] Step 6.5.10, output the corresponding connectivity status as the connectivity status between the ultrasonic transmitting end and the ultrasonic receiving end corresponding to the current ultrasonic signal, including good connectivity, first-level connectivity, second-level connectivity and no connectivity.

[0103] By establishing a characteristic template based on the first ultrasonic wave and then verifying the similarity of other waveforms, the present invention can quickly and effectively assess the connectivity status of a pipeline. Compared to methods that only judge a single waveform, the strategy of adopting multiple tests significantly improves the system's anti-interference ability and judgment reliability. In addition, the threshold line analysis strategy not only provides a richer basis for determining the connectivity status, but also reflects subtle differences in the degree of pipeline connectivity through signal characteristics at different thresholds, making the connectivity status assessment more comprehensive and accurate. This multiple verification mechanism enables the system to maintain stable and reliable detection performance even in complex underground pipeline network environments.

[0104] Furthermore, when conducting validity analysis of characteristic time intervals, the specific steps are as follows:

[0105] Step a: Obtain the transmission duration T and transmission frequency W of each wave of ultrasound, and calculate the time intervals between two consecutive crossings of the threshold line by the ultrasound within the current characteristic time interval;

[0106] Step b: setting the analysis time interval to 0.5T to 2.5T based on the transmission duration T, and setting the time interval between two adjacent crossings of the threshold line to 0.8 / W to 1.2 / W based on the transmission frequency W;

[0107] Step c: compare the current feature time interval with the analysis time interval. If the feature time interval is smaller than the analysis time interval, proceed to step d. Otherwise, determine that the validity analysis result of the current feature time interval is an invalid feature time interval, and proceed to step f.

[0108] Step d: Determine whether all time intervals are within the crossing time interval range. If so, the validity analysis result of the current characteristic time interval is determined to be a valid characteristic time interval. Otherwise, the validity analysis result of the current characteristic time interval is determined to be an invalid characteristic time interval, and then proceed to step f.

[0109] Step f: output the validity analysis result of the current feature time interval.

[0110] By setting the analysis time interval, the characteristic time interval is limited, preventing large interval variations due to noise. By setting the crossing time interval range, the time interval between two consecutive threshold crossings is defined, ensuring that valid ultrasonic signals repeatedly oscillate above and below the corresponding threshold line. This oscillation reflects the propagation characteristics of ultrasonic waves in pipes. However, due to the randomness and irregularity of noise waveforms, the intersections between the waveform and the threshold line are often discrete and short, failing to form a stable time interval and therefore falling outside the crossing time interval range. This significant difference enables the system to effectively distinguish valid signals from noise waveforms. The crossing time interval range is chosen as the judgment basis for two main reasons: First, it reflects the complete oscillation cycle of the signal, better reflecting the overall signal characteristics than a single time point or amplitude. Second, because it is defined based on the relative position of the threshold line, this judgment method is insensitive to the absolute amplitude of the signal, improving detection robustness. Even if the signal is attenuated or interfered with to some extent, as long as the basic oscillation characteristics are maintained, it can be accurately identified.

[0111] Furthermore, when determining the approximation of the characteristic time interval, the specific steps are as follows:

[0112] First, obtain the two characteristic time intervals to be judged and calculate the difference Δt0 between the two characteristic time intervals;

[0113] Then, the difference Δt0 and the percentage value of the two characteristic time intervals are calculated respectively. If the percentage value is within the range of ±10%, it is determined that the two characteristic time intervals meet the similarity requirement; otherwise, it is determined that the two characteristic time intervals are not similar.

[0114] Furthermore, in step 7, the specific steps of constructing the connectivity topology map of the pipe network based on all the connectivity judgment results are as follows:

[0115] Step 7.1: Establish a topology coordinate system, obtain the coordinate information of each detection terminal, and mark the coordinates of each detection terminal in the topology coordinate system;

[0116] Step 7.2, connect topology lines between each detection terminal;

[0117] Step 7.3: Replace the color of each topology line according to the connectivity judgment result between each detection terminal, so that topology lines of different colors represent different connectivity judgment results, such as Figure 5 The generated topology map is output in an intuitive visual form, which allows users to quickly understand the overall status of the pipeline network and provides clear data support and reference for pipeline network maintenance.

[0118] The main innovative features and positive effects of the present invention are as follows:

[0119] (1) Distributed detection and automatic generation of connectivity topology: The present invention significantly improves the efficiency of pipe network connectivity detection and realizes the automatic topology generation function through the combination of distributed detection terminals and wireless Mesh networks. In actual applications, the detection terminals are distributedly arranged at multiple manholes, and the detection data is transmitted in real time through the wireless Mesh network. Comprehensive detection and analysis of several manholes can be completed within a few minutes, realizing parallel detection of multiple nodes and avoiding the inefficiency of traditional manual detection and CCTV detection in a segmented manner. The system uses a polling mechanism to activate each detection terminal one by one and completes the sending and receiving of signals in sequence. After all the detection data are processed, the system can automatically generate a complete drainage network connectivity topology, accurately displaying the connectivity status of the pipes between manholes, and marking good connectivity, general connectivity and non-connectivity. This process does not rely on manual operation at all, which not only avoids the safety hazards of personnel entering the manhole, but also significantly improves the detection efficiency and coverage, providing an intuitive decision-making basis for subsequent pipe network maintenance.

[0120] (2) Environmental adaptability of simplex low-frequency ultrasonic sensors: The present invention fully utilizes and optimizes the layout and application scheme of simplex low-frequency sensors for complex underground pipe network environments, making them have excellent environmental adaptability. During the detection process, a 15kHz low-frequency ultrasonic signal is used as the operating frequency. This frequency has good medium penetration performance and can maintain effective signal propagation under sewage, suspended matter and full pipe conditions, while avoiding the limitations of high-frequency signals caused by high attenuation under similar conditions. By adopting an ultrasonic transducer with high-pressure resistant design and combining it with optimized ultrasonic driving and receiving circuits, the system realizes high-energy ultrasonic signal transmission, ensuring that the signal can effectively penetrate the complex pipe environment. The ultrasonic transducer is arranged in the manhole in a fixed hoisting manner, which does not rely on the water flow conditions in the pipe, thereby adapting to various complex scenarios such as still water, low flow rate and full pipe. Through this layout method, the problem of floating sensors in complex pipe paths due to difficulty in controlling the motion trajectory is effectively avoided. In addition, the combination of the penetration and stability of the low-frequency ultrasonic signal ensures an effective detection distance of at least 75 meters, providing reliable protection for pipe network connectivity detection.

[0121] (3) High-precision signal capture and multi-threshold feature analysis: The present invention achieves high precision and robustness of connectivity detection through a multi-threshold line analysis method combined with a high-precision time synchronization mechanism; the system adopts μs-level synchronization error control to ensure strict coordination between sensor nodes during signal transmission and reception, significantly reducing the signal loss problem caused by synchronization error; in terms of signal analysis, the present invention proposes a multi-level feature extraction method based on three thresholds of 50%, 30%, and 20%. This method establishes a complete set of connectivity judgment standards by analyzing the oscillation characteristics of the signal at different thresholds and their time intervals. Even in harsh environments with strong noise interference, the system can still maintain stable judgment by comparing the signal characteristics of multiple threshold levels. Combined with the use of high-performance ultrasonic transducers, the system can achieve reliable detection in complex underground pipe network environments.

[0122] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to it in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A drainage manhole network detection system based on low-frequency high-energy ultrasound, characterized by: It includes a main controller, a main control module, a Mesh master router, and multiple detection terminals; the detection terminal includes a slave control module, a Mesh node router, and an ultrasonic transducer; The main controller is electrically connected to the main control module and the Mesh main router respectively; the slave control module is electrically connected to the ultrasonic transducer and the Mesh node router respectively; the Mesh main router and each Mesh node router form a wireless Mesh network for networking communication; the main control module and each slave control module wirelessly communicate to achieve time synchronization; the ultrasonic sensor module is used to suspend from the manhole into the drainage pipe to send or receive ultrasonic signals.

2. The drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 1 is characterized in that: The slave control module includes a time synchronization unit, a core control unit, an ultrasonic drive and receiving circuit, and an A / D conversion unit; the core control unit is electrically connected to the time synchronization unit, the ultrasonic drive and receiving circuit, and the A / D conversion unit respectively; the time synchronization unit wirelessly communicates with the master control module and receives the time synchronization signal sent by the master control module; the ultrasonic drive and receiving circuit is electrically connected to the A / D conversion unit and the ultrasonic transducer respectively.

3. A detection method for a drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 1, characterized in that: The steps include: Step 1: The main controller sends a time synchronization instruction to the master control module, and the master control module sends a time synchronization signal to each slave control module, and each slave control module performs time synchronization according to the time synchronization signal; Step 2: The main controller selects a detection terminal that has not been selected as an ultrasonic transmitter as the ultrasonic transmitter, selects the remaining detection terminals as ultrasonic receivers, and uses the Mesh main router to send an ultrasonic transmission instruction to the Mesh node router of the ultrasonic transmitter. At the same time, the Mesh main router also uses the Mesh main router to send an ultrasonic collection instruction to the Mesh node routers of each ultrasonic receiver. Step 3: After receiving the ultrasonic transmission instruction from the Mesh node router, the slave control module at the ultrasonic transmitting end transmits four waves of ultrasonic signals through the ultrasonic transducer; Step 4: The slave control module at the ultrasonic receiving end receives the ultrasonic signal through the ultrasonic transducer, collects the received ultrasonic signal, and sends the collected ultrasonic signal to the Mesh master router through the Mesh node router; In step 5, the main controller stores the ultrasonic signals sent by each Mesh node router received by the Mesh main router as a data group, and then determines whether each detection terminal has been selected as an ultrasonic transmitter. If there is a detection terminal that has not been selected as an ultrasonic transmitter, the process returns to step 2; otherwise, the process proceeds to step 6. Step 6: The main controller reads a data group and performs connectivity analysis on each ultrasonic signal in the data group to obtain the connectivity between the ultrasonic transmitter and each ultrasonic receiver corresponding to the current data group; Step 7: Determine whether there are any data groups that have not been subjected to connectivity analysis. If there are any data groups that have not been subjected to connectivity analysis, return to step 6. Otherwise, construct a connectivity topology map of the pipe network based on all connectivity judgment results.

4. The detection method of the drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 3 is characterized in that: In step 6, when performing connectivity analysis on each ultrasonic signal in the data set, the specific steps are as follows: Step 6.1, extract information from the data group to obtain the ultrasonic transmitter and each ultrasonic receiver corresponding to the data group, and further determine the detection terminal corresponding to the ultrasonic transmitter and each detection terminal corresponding to each ultrasonic receiver; Step 6.2: extract an ultrasonic signal from the data set, each ultrasonic signal including four waves of ultrasonic waves, perform data preprocessing on the extracted ultrasonic signal, and remove the DC offset signal mixed in the ultrasonic signal; Step 6.3: Set three judgment thresholds and use them to mark three amplitude observation points at the beginning of the ultrasonic signal. Then, use the three amplitude observation points to draw three threshold lines for observing the up and down fluctuations of the ultrasonic wave. Step 6.4, using three threshold lines to intercept the four waves of ultrasound, and obtain three characteristic time intervals of each wave of ultrasound, namely Δt, Δt1 and Δt2; Step 6.5: Perform connectivity analysis based on the characteristic time intervals Δt, Δt1, and Δt2 of the four waves of ultrasound to obtain the connectivity status between the ultrasound transmitting end and the ultrasound receiving end corresponding to the current ultrasound signal.

5. The detection method of the drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 4 is characterized in that: In step 6.3, the specific steps for setting the three judgment thresholds are as follows: Step 6.3.1, extract the maximum amplitude of the first ultrasonic wave of the four ultrasonic waves in the ultrasonic signal and set it as H; In step 6.3.2, three judgment thresholds are set as 50% H, 30% H, and 20% H.

6. The detection method of the drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 4 is characterized in that: In step 6.4, the specific steps for obtaining the three characteristic time intervals of each wave of ultrasound are: Step 6.4.1: Select an ultrasonic wave and record the moment when the ultrasonic wave first crosses the three threshold lines and the moment when the ultrasonic wave last crosses the three threshold lines; Step 6.4.2: Calculate the difference between the down-crossing time and the up-crossing time for each of the three threshold lines, and obtain the three characteristic time intervals corresponding to the current ultrasound wave, namely Δt, Δt1, and Δt2; Step 6.4.3, determine whether the four waves of ultrasound have all obtained the corresponding characteristic time intervals. If there are still ultrasound waves that have not obtained the corresponding characteristic time intervals, return to step 6.4.1, otherwise store the three characteristic time intervals of each of the four waves of ultrasound.

7. The detection method of the drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 4 is characterized in that: In step 6.5, the specific steps of performing connectivity analysis based on the characteristic time intervals Δt, Δt1, and Δt2 of the four waves of ultrasound are as follows: Step 6.5.1: Extract the characteristic time interval Δt of the first ultrasonic wave among the four ultrasonic waves and perform a validity analysis on the extracted characteristic time interval Δt. If the characteristic time interval Δt is valid, proceed to step 6.5.2; otherwise, determine that the connectivity state is disconnected and proceed to step 6.5.

10. Step 6.5.2: Extract the characteristic time intervals Δt of the remaining three ultrasonic waves from the four waves and perform validity analysis on the three extracted characteristic time intervals Δt. If all three characteristic time intervals Δt are valid, proceed to step 6.5.3; otherwise, determine that the connectivity is disconnected and proceed to step 6.5.

10. Step 6.5.3: Determine the similarity between the three characteristic time intervals Δt of the remaining three ultrasound waves and the characteristic time interval Δt of the first ultrasound wave. If two or more characteristic time intervals Δt meet the similarity requirement, the first-level analysis is considered passed and the process proceeds to Step 6.5.

4. Otherwise, the connectivity is determined to be disconnected and the process proceeds to Step 6.5.

10. Step 6.5.4: Extract the characteristic time interval Δt1 of the first ultrasonic wave of the four waves and perform a validity analysis on the extracted characteristic time interval Δt1. If the characteristic time interval Δt1 is valid, proceed to step 6.5.5; otherwise, determine that it is secondary connectivity and proceed to step 6.5.

10. Step 6.5.5: Extract the characteristic time intervals Δt1 of the remaining three ultrasonic waves of the four waves and perform a validity analysis on the three extracted characteristic time intervals Δt1. If all three characteristic time intervals Δt1 are valid, proceed to step 6.5.6; otherwise, determine that the connectivity is secondary connectivity and proceed to step 6.5.

10. Step 6.5.6: Determine the similarity between the three characteristic time intervals Δt1 of the remaining three ultrasound waves and the characteristic time interval Δt1 of the first ultrasound wave. If two or more characteristic time intervals Δt1 meet the similarity requirement, the second-level analysis is passed and the process proceeds to step 6.5.

7. Otherwise, the connectivity is determined to be level 2 connectivity and the process proceeds to step 6.5.

10. Step 6.5.7: Extract the characteristic time interval Δt2 of the first ultrasonic wave of the four waves and perform a validity analysis on the extracted characteristic time interval Δt2. If the characteristic time interval Δt2 is valid, proceed to step 6.5.8; otherwise, determine that the connectivity is primary connectivity and proceed to step 6.5.

10. Step 6.5.8: Extract the characteristic time intervals Δt2 of the remaining three ultrasonic waves from the four waves and perform validity analysis on the three extracted characteristic time intervals Δt2. If all three characteristic time intervals Δt2 are valid, proceed to step 6.5.9; otherwise, determine that the connectivity is primary connectivity and proceed to step 6.5.

10. Step 6.5.9: Determine the similarity between the three characteristic time intervals Δt2 of the remaining three ultrasound waves and the characteristic time interval Δt2 of the first ultrasound wave. If two or more characteristic time intervals Δt2 meet the similarity requirement, the connectivity is determined to be good connectivity, and the process proceeds to Step 6.5.

10. Otherwise, the connectivity is determined to be first-level connectivity, and the process proceeds to Step 6.5.

10. Step 6.5.10: Output the corresponding connectivity status as the connectivity status between the ultrasonic transmitting end and the ultrasonic receiving end corresponding to the current ultrasonic signal.

8. The detection method of the drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 7 is characterized in that: When conducting validity analysis of characteristic time intervals, the specific steps are as follows: Step a: Obtain the transmission duration T and transmission frequency W of each wave of ultrasound, and calculate the time intervals between two consecutive crossings of the threshold line by the ultrasound within the current characteristic time interval; Step b: setting the analysis time interval to 0.5T to 2.5T based on the transmission duration T, and setting the time interval between two adjacent crossings of the threshold line to 0.8 / W to 1.2 / W based on the transmission frequency W; Step c: compare the current feature time interval with the analysis time interval. If the feature time interval is smaller than the analysis time interval, proceed to step d. Otherwise, determine that the validity analysis result of the current feature time interval is an invalid feature time interval, and proceed to step f. Step d: Determine whether all time intervals are within the crossing time interval range. If so, the validity analysis result of the current characteristic time interval is determined to be a valid characteristic time interval. Otherwise, the validity analysis result of the current characteristic time interval is determined to be an invalid characteristic time interval, and then proceed to step f. Step f: output the validity analysis result of the current feature time interval.

9. The detection method of the drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 7, characterized in that: When judging the approximation of the characteristic time interval, the specific steps are as follows: First, obtain the two characteristic time intervals to be judged and calculate the difference Δt0 between the two characteristic time intervals; Then, the difference Δt0 and the percentage value of the two characteristic time intervals are calculated respectively. If the percentage value is within the range of ±10%, it is determined that the two characteristic time intervals meet the similarity requirement; otherwise, it is determined that the two characteristic time intervals are not similar.

10. The detection method of the drainage manhole network detection system based on low-frequency high-energy ultrasound according to claim 7, characterized in that: In step 7, the specific steps of constructing the connectivity topology diagram of the pipe network based on all the connectivity judgment results are as follows: Step 7.1: Establish a topology coordinate system, obtain the coordinate information of each detection terminal, and mark the coordinates of each detection terminal in the topology coordinate system; Step 7.2, connect topology lines between each detection terminal; Step 7.3: Replace the colors of the topology lines according to the connectivity judgment results between the detection terminals, so that topology lines of different colors represent different connectivity judgment results.

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