A drainage network joint adjustment monitoring system based on the Internet of Things

Abstract

The present invention belongs to the field of drainage network monitoring and management technology. Specifically, it is a drainage network joint scheduling and coordination monitoring system based on the Internet of Things, including a cloud platform and a monitoring module. The monitoring module is used to detect the liquid level, flow direction and water quality information in the pipeline. The cloud platform includes: a division module, which associates each monitoring module with the corresponding pipeline; an analysis module, which compares the received detection data with the standard value to determine whether pollution occurs; a tracing module, which traces the source of pollution based on the information of the monitoring module and the pipeline information; the present invention connects the monitoring equipment on the drainage network through the Internet of Things, and analyzes the monitoring data as a whole, to achieve intelligent and refined monitoring and control, improve the joint scheduling and coordination effect, and at the same time, through the overall analysis of the monitoring data, reduce the difficulty of tracing the source and facilitate tracking the location of the pollution source.

Description

Technical Field

[0001] The present invention belongs to the technical field of drainage pipe network monitoring and management, and specifically is a drainage pipe network joint adjustment monitoring system based on the Internet of Things. Background Art

[0002] In older residential and industrial areas, drainage and sewage pipes are in disarray, often forming a complex, interconnected network. When flow increases at single or multiple inlet points, the pipe network can experience cross-flow, overflow, and waterlogging during rainy days, making it difficult to monitor and control water quality and flow in a refined manner. When pollution occurs, tracing the source of the pollution is difficult and inefficient. Furthermore, drainage pipes are affected by geological subsidence and surrounding construction, resulting in poor sealing. Cross-flow between rainwater and sewage pipes, and groundwater infiltration, are common occurrences, leading to abnormal increases in sewage flow and reduced sewage concentration, placing an additional burden on downstream sewage treatment plants.

[0003] At the same time, most of the monitoring equipment in the existing drainage network is single-point monitoring. The data detected by numerous monitoring devices cannot be effectively linked, and the operation status of the drainage network cannot be judged as a whole, which leads to the inability to carry out joint adjustment of the drainage network or the joint adjustment effect is poor, affecting the stable operation of the drainage network.

[0004] Based on the above objective problems, the actual working effects of existing sewage discharge monitoring equipment and flow statistics equipment deviate greatly from the theoretical values, and single-point monitoring equipment cannot achieve intelligent management and control. Summary of the Invention

[0005] In order to make up for the shortcomings of the existing technology, the monitoring equipment on the drainage network is connected through the Internet of Things, and the monitoring data is analyzed as a whole to achieve intelligent and refined monitoring and control, and improve the joint row and joint adjustment effect. At the same time, through the overall analysis of the monitoring data, the difficulty of tracing the source is reduced, and the location of the pollution source is facilitated. The present invention proposes a drainage network joint row and joint adjustment monitoring system based on the Internet of Things.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows: the present invention provides a drainage network joint adjustment monitoring system based on the Internet of Things, including a cloud platform and a monitoring module. The monitoring module is installed on the pipes in the pipe network. The cloud platform runs in the cloud network. The cloud platform saves the data during the operation of the joint adjustment monitoring system. The monitoring module is used to detect the liquid level, flow direction and water quality information in the pipe. The monitoring module sends the detected data to the cloud platform through its own Internet of Things module via the Internet of Things. The monitoring module collects data in a pulsed manner according to a set period.

[0007] The cloud platform includes:

[0008] A division module, wherein the division module associates each monitoring module with a corresponding pipeline according to a housing and construction pipeline diagram, wherein each monitoring module and pipeline in the pipeline network has a unique number or name;

[0009] An analysis module compares the received test data with standard values to determine the difference between the two. The standard data is obtained according to relevant national standards. If the difference exceeds a normal range, the analysis module determines that contamination has occurred.

[0010] The tracing module, the analysis module sends the monitoring module information of the detected pollution to the tracing module, and the tracing module traces the pollution according to the monitoring module information and the divided pipeline information.

[0011] Preferably, the information sent by the analysis module to the traceability module includes water quality information, and the traceability module receives the monitoring module for ranking. The water quality data includes TDS value, and the ranking of the monitoring module is positively correlated with the TDS value.

[0012] Preferably, the cloud platform further includes:

[0013] A rating module, which is used to assess the impact of pollution on the overall water quality of the pipe network;

[0014] The analysis module sends the detection data of contamination to the rating module, and the traceability module sends the pipeline information corresponding to the detection data to the rating module. The rating module multiplies the inverse of the liquid level value in the detection data with the inverse of the inner diameter value of the corresponding pipeline to obtain the influence coefficient. The influence coefficients are accumulated to obtain a comprehensive coefficient, and the comprehensive coefficient is negatively correlated with the degree of influence.

[0015] Preferably, the cloud platform further includes an execution module, which adjusts the liquid level in the pipeline by controlling the operation of a pump station in the drainage network, and the execution module sends control information to the pump station via the Internet of Things;

[0016] The monitoring module transmits the liquid level information in the pipeline to the execution module, and the division module sends the association information between the monitoring module and the pipeline to the execution module. The execution module controls the operation of the pump station in the pipeline network according to the preset liquid level height in the pipeline.

[0017] Preferably, the monitoring module also includes a rain sensor, which is used to monitor the weather conditions at the location of the pipeline. The rain sensor transmits the detected rainfall information to the execution module, and the execution module immediately controls the pump station to start working or increase the working power after receiving the rainfall information.

[0018] Preferably, the analysis module establishes a liquid level model after analyzing the liquid level change data detected by the monitoring module in the past. The analysis module substitutes the received current liquid level information in the pipeline into the liquid level model for analysis, and predicts subsequent liquid level changes in the pipeline. When the analysis module predicts that the liquid level in the pipeline will increase based on the liquid level model, the analysis module sends a liquid level lowering instruction to the execution module. When the analysis module predicts that the liquid level in the pipeline will decrease based on the liquid level model, the analysis module sends a liquid level raising instruction to the execution module. After receiving the liquid level lowering instruction or the liquid level raising instruction, the execution module controls the pump station to raise or lower the liquid level in the pipeline.

[0019] Preferably, the division module divides the drainage network into multiple regional units based on the housing and construction pipeline map, and any of the regional units includes at least one main pipeline. The analysis module analyzes the overall liquid level changes of the regional unit based on the liquid level model, and the analysis module averages the predicted liquid level data of all pipelines in the regional unit to obtain a predicted average. The analysis module averages the actual liquid level data of all pipelines in the regional unit to obtain an actual average. The analysis module compares the predicted average with the actual average to obtain the overall liquid level changes of the regional unit. If the liquid level in the regional unit is generally low, the analysis module determines that there is leakage in the drainage network in the regional unit. If the liquid level in the regional unit is generally high, the analysis module determines that there is groundwater infiltration in the drainage network in the regional unit.

[0020] Preferably, the analysis module determines that the flow rate in the pipeline is low for a long time based on the liquid level model, and the analysis module issues an alarm to remind staff to go for inspection.

[0021] The beneficial effects of the present invention are as follows:

[0022] 1. The present invention describes a drainage network joint monitoring system based on the Internet of Things. By setting up a monitoring module and a cloud platform, the data detected by the monitoring module are sent to the cloud platform through the Internet of Things, so that the detection data are uniformly processed and analyzed. Afterwards, through the cooperation of the division module, analysis module and tracing module on the cloud platform, the data transmitted by the monitoring module is analyzed to achieve refined monitoring of the drainage pipes, facilitate the tracing of pollution and find the location of the pollution source.

[0023] 2. The present invention describes a drainage network joint control and monitoring system based on the Internet of Things. By setting an analysis module, an execution module and a rain sensor, when rainfall information is detected, the execution module is used to control the pump station to work in advance, thereby reducing the liquid level height in the main pipeline in the drainage network, and utilizing the hysteresis of rainwater flowing into the main pipeline to avoid waterlogging. At the same time, the analysis module is used to analyze the changes in the liquid level in the pipeline, and the execution module is used to control the changes in the liquid level in the pipeline to ensure the stability of the liquid level in the pipeline, thereby avoiding the liquid level in the drainage pipeline being too high or too low, which has an adverse effect on the operational stability of the drainage network. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described below with reference to the accompanying drawings.

[0025] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0026] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0027] like Figure 1 As shown, the present invention discloses a drainage network joint adjustment and monitoring system based on the Internet of Things, including a cloud platform and a monitoring module. The monitoring module is installed on a pipe in the network, and the cloud platform runs in the cloud network. The cloud platform saves the data during the operation of the joint adjustment and monitoring system. The monitoring module is used to detect the liquid level, flow direction and water quality information in the pipe. The monitoring module sends the detected data to the cloud platform through its own Internet of Things module via the Internet of Things. The monitoring module collects data in a pulsed manner according to a set period.

[0028] The cloud platform includes:

[0029] A division module, wherein the division module associates each monitoring module with a corresponding pipeline according to a housing and construction pipeline diagram, wherein each monitoring module and pipeline in the pipeline network has a unique number or name;

[0030] An analysis module compares the received test data with standard values to determine the difference between the two. The standard data is obtained according to relevant national standards. If the difference exceeds a normal range, the analysis module determines that contamination has occurred.

[0031] The tracing module, the analysis module sends the monitoring module information of the detected pollution to the tracing module, and the tracing module traces the pollution according to the monitoring module information and the divided pipeline information;

[0032] When monitoring the drainage network, the pipe connections in the network are used as nodes, and the monitoring module is installed in the pipes between the two nodes, so that the pipes in the network can be detected by the monitoring module. After that, the monitoring module sends the liquid level, flow direction and water quality information of the detected liquid in the pipe to the cloud platform through the Internet of Things, for analysis and comparison by relevant modules in the cloud platform, to determine the pollution situation in the pipe network and trace the source of the pollution. At the same time, in the actual operation process, when the analysis module compares the water quality data detected by the monitoring module with the relevant national standards, if the water quality data exceeds the relevant national standards, the analysis module can determine that there is pollution in the pipe where the monitoring module that uploaded the water quality data is located. After that, the analysis module sends the flow direction and number information of the monitoring module sent by the monitoring module to the tracing module. After receiving the flow direction and number information sent by the analysis module, the tracing module retrieves the association information between the pipeline and the monitoring module from the division module, thereby determining the location of the polluted pipeline in the pipeline network through the number information of the monitoring module. After that, based on the flow direction information, the "upstream" and "downstream" of the pipeline are defined, and the source of pollution is found by tracing back upwards, thereby tracing the source of the pollution. At the same time, after completing the tracing of the source of the pollution, the tracing module issues a prompt message, waiting for the staff to process it, and saves the relevant information in the database of the cloud platform for subsequent reference.

[0033] As an embodiment of the present invention, the information sent by the analysis module to the traceability module includes water quality information, and the traceability module receives the water quality data from the monitoring module for ranking. The water quality data includes TDS value, and the ranking of the monitoring module is positively correlated with the TDS value.

[0034] The actual drainage network is often complicated and interconnected. There are multiple branch drainage pipes that merge into one main drainage pipe, that is, "multi-pipe integration". When pollution occurs in the drainage network, it is relatively rare for only one drainage pipe to be polluted. It is more in line with the actual situation that pollution occurs simultaneously in multiple drainage pipes in the network in a region. At this time, the traceability module cannot trace the pollution well by only analyzing the flow direction information of the liquid in the pipeline by the monitoring module, so as to find the source of pollution. At the same time, the traceability module uses the TDS value in the received water quality information to trace the pollution. The monitoring modules are sorted so that the monitoring modules with large TDS values in the detected water quality information are ranked higher. Since the branch drainage pipes merge and connect with each other, the pollution in each branch drainage pipe also merges with each other, resulting in the pollution in the merged drainage pipe being greater than the pollution in each branch drainage pipe, that is, the TDS value of the liquid in the merged drainage pipe is larger. Therefore, the higher the ranking of the monitoring modules, the farther the pipeline corresponding to the monitoring modules is from the pollution source. Based on this, the tracing module combines the flow information and ranking data to trace the pollution source in the pipe network and find the location or area of the pollution source.

[0035] As an embodiment of the present invention, the cloud platform further includes:

[0036] A rating module, which is used to assess the impact of pollution on the overall water quality of the pipe network;

[0037] The analysis module sends the detection data of contamination to the rating module, and the traceability module sends the pipeline information corresponding to the detection data to the rating module. The rating module multiplies the reciprocal of the liquid level value in the detection data with the reciprocal of the inner diameter value of the corresponding pipeline to obtain an influence coefficient. The influence coefficients are accumulated to obtain a comprehensive coefficient, which is negatively correlated with the degree of influence.

[0038] After pollution occurs in the pipe network, the analysis module and the traceability module will send the liquid level data and the inner diameter data of the contaminated pipe to the rating module respectively. After that, the rating module multiplies the liquid level data with the inverse of the inner diameter value to obtain the pipe coefficient. At the same time, the rating module accumulates the pipe coefficients corresponding to all the received detection information to obtain a comprehensive coefficient. In this case, the higher the liquid level of the sewage in the pipe and the larger the inner diameter of the pipe, the larger the total volume of sewage passing through the pipe per unit time. After the sewage is merged, the greater the proportion of sewage in the overall pipe network, the greater the impact on the overall pipe network water quality. Therefore, the comprehensive coefficient is negatively correlated with the degree of impact. Therefore, based on the same principle, assuming that there is no pollution in the drainage pipe and the liquid level is equal to the inner diameter of the pipe, the rating module can obtain the assumed coefficient of the entire pipe network. After that, the evaluation module calculates the impact ratio of the comprehensive coefficient and the assumed coefficient. At the same time, since the assumed coefficient can be calculated as a fixed value based on the data in the housing and construction pipeline map, and the comprehensive coefficient can be calculated as a range value based on the housing and construction pipeline map, the staff can divide the impact ratio into different levels in advance. The larger the impact ratio, the higher the level, and the greater the impact on water quality. Therefore, the rating module can reach different levels according to the impact ratio and issue corresponding levels of reminders, thereby carrying out early warning and supervision of pollution in the pipe network.

[0039] As an embodiment of the present invention, the cloud platform further includes an execution module, which adjusts the liquid level in the pipeline by controlling the operation of a pump station in the drainage network, and the execution module sends control information to the pump station via the Internet of Things;

[0040] The monitoring module transmits the liquid level information in the pipeline to the execution module, and the division module sends the association information between the monitoring module and the pipeline to the execution module. The execution module controls the operation of the pump station in the pipeline network according to the preset liquid level height in the pipeline;

[0041] In the existing drainage network, sewage, rainwater and other liquids are collected from the source through branch drainage pipes. Afterwards, the branch drainage pipes are connected and merged into the main pipe, which is then connected to the pump station. The pressure pump of the pump station is used to transport the liquid in the main pipe to the sewage treatment plant for treatment. In this process, since the drainage network is affected by ground subsidence, ground vehicle rolling, and squeezing by sharp stones, the pipe connections in the network and the pipes themselves will be affected, resulting in communication between the inside of the pipe and the outside world. Therefore, when the pump station transports the liquid in the network to the sewage treatment plant and the liquid level in the pipe is low, the pressure in the pipe will be low, and groundwater is likely to seep into the pipe from the pipe connection or damaged part. As a result, the actual amount of water transported by the pump station is much greater than the amount of water in the main pipe when the branch drainage pipes merge into the main pipe, which increases the working pressure of the pump station and the treatment pressure of the sewage treatment plant, and increases the operating cost of the drainage network. At the same time, the staff pre-sets the liquid level in the pipe in the execution module. Afterwards, the execution module executes the following information based on the received liquid level in the pipe: When the received liquid level information is less than the set liquid level height, the execution module controls the pump station to reduce the operating frequency or working power, increase the liquid level in the pipeline, and make it reach the set liquid level height; when the received liquid level information is greater than or equal to the set liquid level height, the execution module controls the pump station to increase the operating frequency or working power, reduce the liquid level in the pipeline, and make it reach the set liquid level height, so that the liquid level in the trunk pipeline is kept at a relatively high height to maintain the pressure balance inside and outside the pipeline, avoid or reduce the infiltration of groundwater into the pipeline, thereby avoiding overload of the pump station and sewage treatment plant, and increasing the operating cost of the drainage network. At the same time, through the regulation of the liquid level in the pipeline by the execution module, the liquid level in the trunk pipeline is kept relatively stable, avoiding the liquid level in the trunk pipeline being too high and the water storage capacity in the pipeline being reduced, resulting in a rapid increase in groundwater during rainfall. After the water in the branch drainage pipes is merged into the trunk pipeline, the trunk pipeline is congested, which generates a large drainage pressure on the downstream pump station. When the drainage capacity of the downstream pump station is insufficient, it will also cause waterlogging or well overflow, affecting the stable operation of the drainage network.

[0042] As an embodiment of the present invention, the monitoring module further includes a rain sensor, which is used to monitor the weather conditions at the location of the pipeline. The rain sensor transmits the detected rainfall information to the execution module, and the execution module immediately controls the pump station to start working or increase the working power upon receiving the rainfall information.

[0043] When it rains, a large amount of rainwater will fall to the ground in a short period of time. Afterwards, the rainwater that falls to the ground enters the branch drainage pipes from various wellheads, and then flows into the main pipeline through the branch drainage pipes. In the process of rainwater flowing into the main pipeline, it takes a certain amount of time, which causes the rainwater to flow into the main pipeline with a lag during rainfall, that is, it takes a certain amount of time from the start of rainfall to the start of rising liquid level in the main pipeline. Therefore, after the rain sensor detects the rainfall information and the execution module receives the rainfall information, the execution module starts to control the operation of the pump station. Compared with the rainwater flowing into the main pipeline, the pump station starts to lower the liquid level in the main pipeline in advance, so that there is more free space in the main pipeline, which improves the water storage capacity of the pipeline and can carry the rainwater flowing into the main pipeline, thereby avoiding insufficient free space in the main pipeline, rainwater cannot flow into the main pipeline, and is blocked in the branch drainage pipe, resulting in drainage efficiency lower than the peak rainfall, leading to waterlogging.

[0044] As an embodiment of the present invention, the analysis module establishes a liquid level model after analyzing the liquid level change data detected in the past by the monitoring module. The analysis module substitutes the received and current liquid level information in the pipeline into the liquid level model for analysis, and predicts subsequent liquid level changes in the pipeline. When the analysis module predicts that the liquid level in the pipeline will increase based on the liquid level model, the analysis module sends a liquid level lowering instruction to the execution module. When the analysis module predicts that the liquid level in the pipeline will decrease based on the liquid level model, the analysis module sends a liquid level raising instruction to the execution module. After receiving the liquid level lowering instruction or the liquid level raising instruction, the execution module controls the pump station to raise or lower the liquid level in the pipeline.

[0045] In the daily operation of the drainage network, the amount of liquid transported in the pipeline is usually relatively stable and will not change greatly. Therefore, the analysis module analyzes the liquid level data detected by the monitoring module in the daily operation, establishes a liquid level model, and then predicts and prejudges the liquid level changes in the pipeline through the liquid level model, so as to achieve "advance" regulation of the liquid level in the pipeline during the daily operation of the drainage network, so as to avoid the lag effect of liquid flow and convergence in the drainage network, which causes frequent fluctuations in the liquid level in the pipeline, affecting the stability of the liquid level in the drainage network and the stability of the drainage network operation. At the same time, the analysis module predicts and prejudges the liquid level in the pipeline based on the liquid level model, and controls the operation of the pump station "in advance" through the execution module to avoid the impact of periodic sewage discharge on the drainage network and reduce the possibility of unstable operation of the drainage network.

[0046] As an embodiment of the present invention, the division module divides the drainage network into multiple regional units based on a housing and construction pipeline map, and any of the regional units includes at least one trunk pipeline. The analysis module analyzes the overall liquid level change of the regional unit based on the liquid level model. The analysis module averages the predicted liquid level data of all pipelines in the regional unit to obtain a predicted average. The analysis module averages the actual liquid level data of all pipelines in the regional unit to obtain an actual average. The analysis module compares the predicted average with the actual average to obtain the overall liquid level change of the regional unit. If the overall liquid level in the regional unit is low, the analysis module determines that there is a leak in the drainage network in the regional unit. If the overall liquid level in the regional unit is high, the analysis module determines that there is groundwater infiltration in the drainage network in the regional unit.

[0047] By dividing the drainage network into multiple regional units through unit division, the accuracy of judging the status of the drainage network is improved. At the same time, when the predicted average is greater than the actual average, the flow in the drainage network within the regional unit decreases. In this case, it can be judged that the drainage pipe in the regional unit is damaged or the pipe connection is damaged, causing the liquid in the drainage pipe to leak to the outside, which is easy to pollute the environment. When the predicted average is less than the actual average, the flow in the drainage pipe in the regional unit increases, and groundwater seeps into the drainage pipe in the regional unit, which will put pressure on the downstream pumping station and sewage treatment plant, and increase the operation cost of the drainage network. At the same time, the staff analyzes and judges the status of the drainage network in the regional unit through the analysis module, and inspects and maintains the drainage network in the regional unit to ensure the stable operation of the drainage network and avoid sewage leakage in the drainage network, which pollutes the environment.

[0048] As an embodiment of the present invention, the analysis module determines that the flow rate in the pipeline is low for a long time based on the liquid level model, and the analysis module issues an alarm to remind staff to go for inspection;

[0049] During the normal operation of the drainage network, the liquid level in the pipe fluctuates to a certain extent, but the liquid level in the pipe will not drop for a long time. Therefore, when the analysis module determines that the flow in the pipe is low for a long time based on the liquid level model, it can be determined that the flow in the pipe at that location has decreased significantly compared to the past. It is necessary to investigate the source corresponding to the pipe and find the destination of the reduced flow, so as to avoid the situation where the flow in the pipe is significantly reduced due to the illegal discharge or leakage of sewage by lawless elements, and to avoid the pollution of the environment caused by the illegal discharge or leakage of sewage.

[0050] The specific workflow is as follows:

[0051] When monitoring the drainage network, the pipe connections in the network are used as nodes, and the monitoring module is installed in the pipes between the two nodes, so that the pipes in the network can be detected by the monitoring module. After that, the monitoring module sends the liquid level, flow direction and water quality information of the liquid in the pipe to the cloud platform through the Internet of Things, so that the relevant modules in the cloud platform can analyze and compare, judge the pollution situation in the network and trace the source of the pollution. At the same time, in the actual operation process, when the analysis module compares the water quality data detected by the monitoring module with the relevant national standards, if the water quality data exceeds the relevant national standards, the monitoring module will be used to detect the water quality data. When the national standard is met, the analysis module can determine that pollution occurs in the pipeline where the monitoring module that uploaded the water quality data is located. After that, the analysis module sends the flow direction and number information of the monitoring module to the tracing module. After receiving the flow direction and number information sent by the analysis module, the tracing module retrieves the association information between the pipeline and the monitoring module from the partitioning module, thereby determining the location of the polluted pipeline in the pipeline network through the number information of the monitoring module. After that, based on the flow direction information, the "upstream" and "downstream" of the pipeline are defined, and the source of pollution is found by tracing back upwards;

[0052] The tracing module sorts the monitoring modules based on the TDS values in the received water quality information, so that the monitoring modules with the largest TDS values in the detected water quality information are ranked higher. The tracing module combines the flow direction information with the ranking data to trace the pollution source in the pipe network and find the location or area of the pollution source;

[0053] The analysis module and the traceability module send the liquid level data and the inner diameter data of the contaminated pipe to the rating module. The rating module multiplies the liquid level data by the inverse of the inner diameter value to obtain the pipe coefficient. At the same time, the rating module accumulates the pipe coefficients corresponding to all the received detection information to obtain a comprehensive coefficient. The comprehensive coefficient is negatively correlated with the degree of impact. Assuming that there is no pollution in the drainage pipe and the liquid level is equal to the inner diameter of the pipe, the rating module can obtain the assumed coefficient of the entire pipe network. Afterwards, the evaluation module calculates the impact ratio of the comprehensive coefficient and the assumed coefficient. At the same time, the staff can pre-classify the impact ratio into different levels. The larger the impact ratio, the higher the level, and the greater the impact on water quality.

[0054] The staff pre-sets the liquid level height in the pipeline in the execution module. After that, the execution module controls the pump station to reduce the operating frequency or power when the received liquid level information is lower than the set liquid level height, and increase the liquid level in the pipeline to the set liquid level height; when the received liquid level information is greater than or equal to the set liquid level height, the execution module controls the pump station to increase the operating frequency or power, and reduce the liquid level in the pipeline to the set liquid level height;

[0055] After the rain sensor detects rainfall information and the execution module receives the rainfall information, the execution module starts to control the pump station to work. Compared with the rainwater entering the main pipeline, the pump station starts to lower the liquid level in the main pipeline in advance, so that there is more free space in the main pipeline and the water storage capacity of the pipeline is improved;

[0056] The analysis module analyzes the liquid level data detected by the monitoring module during daily operation, establishes a liquid level model, and then uses the liquid level model to predict and prejudge the liquid level changes in the pipeline, thereby achieving "preemptive" control of the liquid level in the pipeline during the daily operation of the drainage network;

[0057] When the predicted average is greater than the actual average, the flow rate in the drainage pipe network within the regional unit decreases, indicating that the drainage pipe or pipe connection within the regional unit is damaged, and the liquid in the drainage pipe is leaking to the outside. When the predicted average is less than the actual average, the flow rate in the drainage pipe within the regional unit increases, indicating that groundwater is seeping into the drainage pipe within the regional unit.

[0058] When the analysis module determines that the flow rate in the pipeline is low for a long time based on the liquid level model, the source corresponding to the pipeline is checked to find the destination of the reduced flow rate.

[0059] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A drainage network joint adjustment and monitoring system based on the Internet of Things, comprising a cloud platform and a monitoring module, wherein the monitoring module is installed on a pipe in the network and the cloud platform runs in a cloud network, characterized in that: The monitoring module is used to detect the liquid level, flow direction and water quality information in the pipeline. The monitoring module sends the detected data to the cloud platform through its own Internet of Things module; The cloud platform includes: A division module, wherein the division module associates each monitoring module with a corresponding pipeline according to a housing and construction pipeline diagram; An analysis module compares the received test data with a standard value to determine a difference between the two. If the difference exceeds a normal range, the analysis module determines that contamination has occurred. The tracing module, the analysis module sends the monitoring module information of the detected pollution to the tracing module, and the tracing module traces the pollution according to the monitoring module information and the divided pipeline information; The traceability module receives the monitoring module for ranking. The water quality data includes TDS value. The ranking of the monitoring module is positively correlated with the TDS value. The cloud platform also includes: A rating module, which is used to assess the impact of pollution on the overall water quality of the pipe network; The analysis module sends the detection data of contamination to the rating module, and the traceability module sends the pipeline information corresponding to the detection data to the rating module. The rating module multiplies the inverse of the liquid level value in the detection data with the inverse of the inner diameter value of the corresponding pipeline to obtain the influence coefficient. The influence coefficients are accumulated to obtain a comprehensive coefficient, and the comprehensive coefficient is negatively correlated with the degree of influence.

2. The Internet of Things-based drainage network joint adjustment monitoring system according to claim 1 is characterized by: The cloud platform further includes an execution module, which adjusts the liquid level in the pipeline by controlling the operation of a pump station in the drainage network; The monitoring module transmits the liquid level information in the pipeline to the execution module, and the dividing module sends the association information between the monitoring module and the pipeline to the execution module.

3. The Internet of Things-based drainage network joint adjustment and monitoring system according to claim 2 is characterized by: The monitoring module also includes a rain sensor, which is used to monitor the weather conditions at the location of the pipeline. The rain sensor transmits the detected rainfall information to the execution module, and the execution module controls the pump station to start working or increase the working power after receiving the rainfall information.

4. The Internet of Things-based drainage network joint adjustment and monitoring system according to claim 2 is characterized by: The analysis module establishes a liquid level model by analyzing the liquid level change data detected by the monitoring module in the past. The analysis module substitutes the received current liquid level information in the pipeline into the liquid level model for analysis and predicts subsequent liquid level changes in the pipeline. When the analysis module predicts that the liquid level in the pipeline will increase based on the liquid level model, the analysis module sends a liquid level lowering instruction to the execution module. When the analysis module predicts that the liquid level in the pipeline will decrease based on the liquid level model, the analysis module sends a liquid level raising instruction to the execution module. After receiving the liquid level lowering instruction or the liquid level raising instruction, the execution module controls the pump station to raise or lower the liquid level in the pipeline.

5. The Internet of Things-based drainage network joint adjustment and monitoring system according to claim 4 is characterized by: The division module divides the drainage network into multiple regional units based on the housing and construction pipeline map, and the analysis module analyzes the overall liquid level changes of the regional units based on the liquid level model. If the overall liquid level in the regional unit is low, the analysis module determines that there is a leakage in the drainage network in the regional unit. If the overall liquid level in the regional unit is high, the analysis module determines that there is groundwater infiltration in the drainage network in the regional unit.

6. The Internet of Things-based drainage network joint adjustment and monitoring system according to claim 4 is characterized by: The analysis module determines, based on the liquid level model, that the flow rate in the pipeline is low for a long time, and the analysis module issues an alarm to remind staff to go and check.

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