Urban drainage pipeline management method and system based on Internet of Things database service

Abstract

The present invention provides an urban drainage pipeline management method and system based on an Internet of Things database service, and relates to the technical field of big data service management. The present invention combines adaptive data processing with an event-driven mechanism to form an effective monitoring and response system, which can adjust the monitoring strategy and response threshold in real time, thereby improving the prediction capability and response efficiency of possible failures of urban drainage pipelines; through the combination of dynamic threshold setting and real-time data analysis, managers can respond quickly when dealing with emergencies, thereby achieving more efficient resource utilization and maintenance strategies, so that urban drainage pipeline management not only focuses on the current status, but also pays more attention to future trends, forming a more forward-looking management system.

Description

Technical Field

[0001] The present invention relates to the technical field of big data service management, and in particular to an urban drainage pipeline management method and system based on Internet of Things database service. Background Art

[0002] Traditional drainage pipeline management methods mostly rely on manual inspections and regular maintenance, which is not only inefficient, but also often fails to respond in a timely manner when faced with sudden climate changes and drainage needs. Therefore, in recent years, the rapid development of Internet of Things (IoT) technology has provided new possibilities for the intelligent management of urban drainage systems. By installing sensors, data acquisition equipment and wireless communication technology, real-time monitoring and data analysis of urban drainage systems can be achieved, thereby improving the efficiency and accuracy of drainage pipeline management. At the same time, the application of edge computing and cloud computing technologies makes real-time processing and storage of data more efficient, providing reliable data support for decision-making. This series of technological advances has enabled the management of urban drainage pipelines to gradually develop in the direction of intelligence and automation, forming a management system with data-driven as the core;

[0003] In the prior art, publication number CN105022769B discloses an urban underground pipeline data interaction system and method thereof, the system comprising a data layer, which is composed of a basic terrain database, a thematic pipeline network database, other relational databases, a spatial database engine and other relational database engines; the layer is connected to a data resource management center through a spatial data engine and a relational data engine; the business logic layer, which is located between the data layer and the application layer, is a data resource platform interface management center, responsible for providing various functional interfaces used by the application layer, including management and allocation of user roles and data, demarcation of data permissions, and review and release of data; the application layer is used to provide various applications based on the business logic layer, pass user requests to the data layer, and is also responsible for processing the data obtained by the data layer and sending it to the user.

[0004] The shortcomings are as follows: the existing monitoring system mainly relies on fixed monitoring frequency and static threshold settings, and cannot effectively respond to changes in environmental conditions and emergencies. For example, in the case of a sharp increase in rainfall, the thickness of sediment in the pipeline may increase rapidly, causing a risk of blockage, and the fixed monitoring frequency and threshold cannot capture this change in time, resulting in a lag in early warning and increasing the risk of pipeline failure; in addition, the existing technology often lacks flexibility in data processing and analysis, and cannot achieve adaptive data collection and dynamic adjustment mechanisms. Ultimately, this system that lacks dynamic response capabilities leads to a waste of resources and low maintenance efficiency, and it is difficult to meet the actual needs of modern urban drainage systems.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an urban drainage pipeline management method based on an Internet of Things database service to solve the problems raised in the above background technology.

[0007] The purpose of the present invention is achieved by: a method for managing urban drainage pipelines based on Internet of Things database services, which is specifically applied during rainy periods, and the specific steps include:

[0008] Step S1: Mark and sort a number of drainage pipes in the urban drainage pipeline to form a pipe set sequence, and use the Internet of Things technology to collect real-time environmental rainfall data of the spatial area to which the pipe set sequence belongs, as well as monitoring indicators of each drainage pipe in the pipe set sequence, and the monitoring indicators include but are not limited to sediment thickness, fluid flow rate and chemical composition indicators;

[0009] Step S2: receiving environmental rainfall data and monitoring indicators and transmitting them to the edge computing device through a wireless network for preliminary data processing, wherein the data processing includes cleaning, filtering and aggregating the raw data;

[0010] Step S3: Apply an adaptive data processing algorithm to the data after preliminary data processing, specifically by analyzing the environmental rainfall data and monitoring indicators at the previous monitoring time and the current monitoring time, and dynamically adjust the data collection frequency and analysis depth, wherein:

[0011] The blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored. The more serious the blockage degree is, the higher the corresponding score value is. According to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time;

[0012] If at the current monitoring moment, the change in sediment thickness of the monitoring indicator exceeds the preset upper fluctuation threshold, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe are increased; the increase amount is the preset basic change amount;

[0013] Step S4: uploading the valid data after preliminary data processing to the cloud server in an encrypted manner for in-depth analysis and storage, and forming the historical data of each drainage pipe in the previous monitoring period, the historical data including environmental rainfall data and monitoring indicators;

[0014] For deep analytics, this includes running an integrated event-driven data processing framework on cloud servers to dynamically adjust thresholds based on real-time and historical data;

[0015] Dynamically adjust the threshold value, including:

[0016] Step S4.1: Calculate the historical correlation evaluation coefficient between the environmental rainfall data and the sediment thickness in the historical data corresponding to the previous monitoring period for each drainage pipe, and the historical correlation evaluation coefficient is used to evaluate the change trend value of the sediment thickness under different numerical ranges of the environmental rainfall data;

[0017] Step S4.2: collect the real-time data of each drainage pipe in the current monitoring time period, including environmental rainfall data and monitoring indicators, and calculate the real-time correlation evaluation coefficient between the environmental rainfall data in the current real-time data and the sediment thickness in the monitoring indicators;

[0018] Step S4.3: Compare and analyze the real-time correlation evaluation coefficient with the historical correlation evaluation coefficient to generate a dynamic adjustment threshold for adjusting the fluctuation upper limit threshold and the basic change amount;

[0019] Step S5: adjusting the monitoring frequency and analysis depth of the monitoring indicators on the corresponding drainage pipe according to the adjusted basic change amount, and using the adjusted fluctuation upper limit threshold as the fluctuation upper limit threshold at the next monitoring moment;

[0020] The change trend value of the sediment thickness at the next monitoring moment is calculated within the current monitoring time period. If the change trend value of the sediment thickness exceeds the corresponding adjusted fluctuation upper limit threshold, a corresponding maintenance request is generated.

[0021] Furthermore, environmental rainfall data and monitoring indicators are obtained, including:

[0022] Several drainage pipes are defined as several secondary drainage pipes connected to the main drainage pipe;

[0023] The pipeline set sequence is represented as {1,2,…,i,…,N}, where i represents the i-th drainage pipeline and N represents the total number of drainage pipelines;

[0024] Multifunctional sensors are installed in each drainage pipe to monitor sediment thickness, fluid flow rate and chemical composition indicators in real time, and the chemical composition indicators include nutrient content, pH value and heavy metal content.

[0025] Furthermore, the blockage degree of each drainage pipe in the pipe set sequence at the previous monitoring time is scored, and according to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time, specifically including:

[0026] The sediment thickness, fluid flow rate and chemical composition index of the i-th drainage pipe after preliminary data processing at the previous monitoring time t-1 are marked as ;and The output values ​​of are adjusted to the same range by scaling and offsetting;

[0027] The chemical composition indicators of the i-th drainage pipe after preliminary data processing at the previous monitoring time t-1, including the nutrient content, pH value, and heavy metal content, are recorded as ; and based on experimental demonstration or expert group system analysis, set The comparison threshold is .

[0028] Furthermore, the determination of chemical composition indicators specifically includes:

[0029] The output values ​​of the chemical composition indicators are expressed as follows:

[0030] If in When all the ratios in are less than 1, then define ;

[0031] If in If any ratio in the equation is greater than 1, then define ;

[0032] If in Among the ratios in , if there are any two ratios greater than 1, then define ;

[0033] If in When all the ratios in are greater than 1, then we define .

[0034] Furthermore, at the previous monitoring time t-1, the ratio of the sediment thickness of the i-th drainage pipe to the inner diameter of the drainage pipe is calculated to determine the blockage degree score of the i-th drainage pipe. Specifically, the blockage degree score of the i-th drainage pipe is marked as ;

[0035] when When ;

[0036] when When ;

[0037] when When ;

[0038] when When ;

[0039] when When ;

[0040] in, It is also the inner diameter value of the i-th drainage pipe after preliminary data processing;

[0041] At the current monitoring time, the fluctuation upper limit thresholds of the monitoring indicators corresponding to the i-th drainage pipe are set as , the calculation formula is as follows:

[0042]

[0043] in is the correction factor used to ensure , and ensure The value range is (0,1).

[0044] Furthermore, if the change in the monitoring indicator exceeds the preset upper fluctuation threshold at the current monitoring moment, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe are increased; the increase amount is the preset basic change amount; specifically including:

[0045] At the current monitoring time t, the sediment thickness, fluid flow rate and chemical composition index in the i-th drainage pipe are recorded as ; and the current monitoring time t Compared with the previous monitoring moment The corresponding parameters are calculated by difference, and we get ;

[0046] The monitoring frequency and analysis depth of the monitoring indicators on the i-th drainage pipe at the previous monitoring time are set as follows:

[0047] Set the monitoring frequency to M1 times per day;

[0048] The analysis depth is: at the previous monitoring moment, when the blockage degree score of the i-th drainage pipe is calculated, the blockage degree scores of the m1 adjacent drainage pipes are calculated at the same time, and the blockage degree scores of the i-th drainage pipe and its m1 adjacent drainage pipes are averaged, and the average value is used as the final blockage degree score of the i-th drainage pipe at the previous monitoring moment;

[0049] 1.1) If When , it means that the change of fluid velocity and chemical composition index in the i-th drainage pipe is within the corresponding fluctuation upper limit threshold, but the change of sediment thickness exceeds ;

[0050] The preset basic change is ;in The monitoring frequency after the increase; is the number of adjacent drainage pipes after increase;

[0051] 1.2) If ,and When any of the following is true, set the basic change amount to ;

[0052] 1.3) If When , it means that the changes in chemical composition index, fluid velocity and sediment thickness have exceeded the corresponding fluctuation upper limit threshold; set the basic change amount to .

[0053] Furthermore, the acquisition of the historical correlation evaluation coefficient and the real-time correlation evaluation coefficient specifically includes:

[0054] Set the number of monitoring times in the previous monitoring period to U, and set j∈{1,2,…,U}. For the jth monitoring, record the sediment thickness of the i-th drainage pipe as ;

[0055] The current environmental rainfall range is determined by historical data, and the environmental rainfall range is divided into K environmental rainfall intervals, which are expressed as:

[0056]

[0057] in, represents the kth environmental rainfall interval, k is The position index is k∈{1,2,…,K}, K is the total number of divided intervals, and the i-th drainage pipe is in the environmental rainfall interval in the j-th monitoring. The ambient rainfall under ;

[0058] The environmental rainfall range is In the case of , the historical correlation evaluation coefficient of the i-th drainage pipe in the previous monitoring period is defined as , the calculation formula is as follows:

[0059]

[0060] in, is the historical correlation evaluation coefficient of the ith drainage pipe in the kth environmental rainfall interval; is the number of monitoring times in the kth environmental rainfall interval; and are the normal term and the correction term respectively, and The settings are used to ensure The valid value range of is (0, 1);

[0061] when The closer it is to 1, the higher the correlation between sediment thickness and environmental rainfall is within the range of environmental rainfall;

[0062] when The closer it is to 0, the lower the correlation between sediment thickness and environmental rainfall is within this environmental rainfall range.

[0063] Furthermore, based on the expert group system or experimental demonstration, we set The weak correlation interval and strong correlation interval of are (0,0.58) and [0.58,1] respectively;

[0064] In the strong correlation range, the trend value of sediment thickness is ;

[0065] In the weak correlation range, the trend value of sediment thickness is ;

[0066] in, is the trend prediction value of sediment thickness;

[0067] Collect real-time data for each drainage pipe during the current monitoring period, including ambient rainfall and sediment thickness of monitoring indicators;

[0068] The calculation method of historical correlation evaluation coefficient is the same as that of historical correlation evaluation coefficient. The real-time correlation evaluation coefficient of the environmental rainfall interval is .

[0069] Furthermore, the historical correlation evaluation coefficient is compared and analyzed with the real-time correlation evaluation coefficient to generate the following dynamic adjustment threshold:

[0070]

[0071] in, is the dynamic adjustment threshold of the i-th drainage pipe in the current monitoring time period;

[0072] 2.1) If When , it means that the real-time correlation evaluation coefficient of the current monitoring period is lower than the historical correlation evaluation coefficient;

[0073] At this time, only when and When the values ​​are all in the weak correlation interval (0, 0.58), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0074]

[0075]

[0076] Only when and When the values ​​are all in the strong correlation interval [0.58,1), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0077]

[0078]

[0079] in, Indicates rounding up to an integer; and They represent the adjusted monitoring frequency and number of adjacent drainage pipes respectively; They are the upper threshold values ​​of fluctuation of the adjusted sediment thickness, fluid velocity and chemical composition index, respectively;

[0080] 2.2) If When , it means that the real-time correlation evaluation coefficient of the current monitoring period is higher than the historical correlation evaluation coefficient;

[0081] At this time, only when and When the values ​​are all in the weak correlation interval (0, 0.58), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0082]

[0083]

[0084] Only when and When the values ​​are all in the strong correlation interval [0.58,1), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0085]

[0086]

[0087] in, Indicates rounding up to an integer.

[0088] An urban drainage pipeline management system based on an Internet of Things database service, the system is used to execute the urban drainage pipeline management method based on an Internet of Things database service, comprising:

[0089] Data collection module: used to mark and sort several drainage pipes in the urban drainage pipeline to form a pipe set sequence, and use the Internet of Things technology to collect real-time environmental rainfall data in the spatial area to which the pipe set sequence belongs, as well as monitoring indicators of each drainage pipe in the pipe set sequence, and the monitoring indicators include but are not limited to sediment thickness, fluid flow rate and chemical composition indicators;

[0090] Data processing module: used to receive environmental rainfall data and monitoring indicators and transmit them to the edge computing device through a wireless network for preliminary data processing, wherein the data processing includes cleaning, filtering and aggregating the raw data;

[0091] Dynamic adjustment module: used to apply adaptive data processing algorithms to data after preliminary data processing, specifically by analyzing environmental rainfall data and monitoring indicators at the previous monitoring moment and the current monitoring moment, dynamically adjusting data collection frequency and analysis depth, where:

[0092] The blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored. The more serious the blockage degree is, the higher the corresponding score value is. According to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time;

[0093] If at the current monitoring moment, the change in sediment thickness of the monitoring indicator exceeds the preset upper fluctuation threshold, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe are increased; the increase amount is the preset basic change amount;

[0094] Dynamic adjustment threshold determination module: used to upload the valid data after preliminary data processing to the cloud server in an encrypted manner for in-depth analysis and storage, and to form the historical data of each drainage pipe in the previous monitoring period, which includes environmental rainfall data and monitoring indicators;

[0095] For deep analytics, this includes running an integrated event-driven data processing framework on cloud servers to dynamically adjust thresholds based on real-time and historical data;

[0096] Dynamically adjust the threshold value, including:

[0097] Calculate the historical correlation evaluation coefficient between the environmental rainfall data and the sediment thickness in the historical data corresponding to the previous monitoring period for each drainage pipe. The historical correlation evaluation coefficient is used to evaluate the change trend value of the sediment thickness under different numerical ranges of the environmental rainfall data.

[0098] Collect the real-time data of each drainage pipe in the current monitoring period, including environmental rainfall data and monitoring indicators, and calculate the real-time correlation evaluation coefficient between the environmental rainfall data in the current real-time data and the sediment thickness in the monitoring indicators;

[0099] Compare and analyze the real-time correlation evaluation coefficient with the historical correlation evaluation coefficient to generate a dynamic adjustment threshold for adjusting the fluctuation upper limit threshold and the basic change amount;

[0100] Result execution module: used to adjust the monitoring frequency and analysis depth of the monitoring indicators on the corresponding drainage pipe according to the adjusted basic change amount, and use the adjusted fluctuation upper limit threshold as the fluctuation upper limit threshold at the next monitoring moment;

[0101] The change trend value of the sediment thickness at the next monitoring moment is calculated within the current monitoring time period. If the change trend value of the sediment thickness exceeds the corresponding adjusted fluctuation upper limit threshold, a corresponding maintenance request is generated.

[0102] Compared with the prior art, the present invention has the following beneficial effects:

[0103] 1. By combining adaptive data processing with event-driven mechanisms, an effective monitoring and response system is formed, which can adjust monitoring strategies and response thresholds in real time, thereby improving the ability to predict possible failures in urban drainage pipes and the efficiency of response;

[0104] 2. By combining the setting of dynamic thresholds with real-time data analysis, managers can respond quickly to emergencies, achieve more efficient resource utilization and maintenance strategies, and enable urban drainage pipeline management to focus not only on the current status but also on future trends, forming a more forward-looking management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0106] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0107] Figure 2 It is a system module block diagram of the present invention. DETAILED DESCRIPTION

[0108] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0109] Embodiment 1: like Figure 1 The urban drainage pipeline management method based on the Internet of Things database service is specifically applied during the rainy period. The specific steps include:

[0110] Step S1: Mark and sort a number of drainage pipes in the urban drainage pipeline to form a pipe set sequence, and use the Internet of Things technology to collect real-time environmental rainfall data of the spatial area to which the pipe set sequence belongs, as well as monitoring indicators of each drainage pipe in the pipe set sequence, and the monitoring indicators include but are not limited to sediment thickness, fluid flow rate and chemical composition indicators;

[0111] Step S2: receiving environmental rainfall data and monitoring indicators and transmitting them to the edge computing device through a wireless network for preliminary data processing, wherein the data processing includes cleaning, filtering and aggregating the raw data;

[0112] Step S3: In the local data processing device, an adaptive data processing algorithm is applied to the data after preliminary data processing, specifically by analyzing the environmental rainfall data and monitoring indicators at the previous monitoring moment and the current monitoring moment, and dynamically adjusting the data collection frequency and analysis depth, wherein:

[0113] The blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored. The more serious the blockage degree is, the higher the corresponding score value is. According to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time;

[0114] If at the current monitoring moment, the change in sediment thickness of the monitoring indicator exceeds the preset upper fluctuation threshold, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe are increased; the increase amount is the preset basic change amount;

[0115] Step S4: uploading the valid data after preliminary data processing to the cloud server in an encrypted manner for in-depth analysis and storage, and forming the historical data of each drainage pipe in the previous monitoring period, the historical data including environmental rainfall data and monitoring indicators;

[0116] For deep analytics, this includes running an integrated event-driven data processing framework on cloud servers to dynamically adjust thresholds based on real-time and historical data;

[0117] Dynamically adjust the threshold value, including:

[0118] Step S4.1: Calculate the historical correlation evaluation coefficient between the environmental rainfall data and the sediment thickness in the historical data corresponding to the previous monitoring time period for each drainage pipe, and the historical correlation evaluation coefficient is used to evaluate the change trend value of the sediment thickness under different numerical intervals of the environmental rainfall data; the previous monitoring time period is the last rainfall period;

[0119] Step S4.2: collect the real-time data of each drainage pipe in the current rainfall monitoring time period, including environmental rainfall data and monitoring indicators, and calculate the real-time correlation evaluation coefficient between the environmental rainfall data in the current real-time data and the sediment thickness in the monitoring indicators; the current monitoring time period is the current rainfall period;

[0120] Step S4.3: Compare and analyze the real-time correlation evaluation coefficient with the historical correlation evaluation coefficient to generate a dynamic adjustment threshold for adjusting the fluctuation upper limit threshold and the basic change amount;

[0121] Step S5: adjusting the monitoring frequency and analysis depth of the monitoring indicators on the corresponding drainage pipe according to the adjusted basic change amount, and using the adjusted fluctuation upper limit threshold as the fluctuation upper limit threshold at the next monitoring moment.

[0122] And calculate the change trend value of the sediment thickness at the next monitoring moment within the current monitoring time period. If the change trend value of the sediment thickness exceeds the corresponding adjusted fluctuation upper limit threshold, generate a corresponding maintenance request, which includes the location of the abnormal drainage pipe and the recommended maintenance operation; and the next monitoring moment is the rainfall moment after the end of the current monitoring time period; and based on the historical data of rainfall intensity and duration, and the introduction of weather forecast data, determine the middle time point of the later predicted rainfall period, and use the middle time point as the next monitoring moment; the previous monitoring moment and the next monitoring moment are determined in the same way, which will not be repeated;

[0123] The current monitoring time is the time during the current rainfall period;

[0124] The location of the abnormal drainage pipe is determined by the mark i of each drainage pipe; it contains the geographical coordinates (latitude and longitude) of the pipe and the detailed address of the street or area where it is located, so that maintenance personnel can quickly find the problem;

[0125] Recommended maintenance operations are determined based on sediment trends and recommended maintenance operations.

[0126] Further explanation: Environmental rainfall data and monitoring indicators are obtained, including:

[0127] Several drainage pipes are defined as several secondary drainage pipes connected to the main drainage pipe;

[0128] The pipeline set sequence is represented as {1,2,…,i,…,N}, where i represents the i-th drainage pipeline and N represents the total number of drainage pipelines; and the larger the value of i is, the lower the terrain of the corresponding drainage pipeline is;

[0129] At the entrance and exit of each drainage pipe, an IoT sensor with a unique identification code is installed as an IoT node; the identification code is an RFID tag or a QR code, which is used to identify each drainage pipe;

[0130] Adopt MQTT protocol for data transmission to ensure low latency and efficient data upload;

[0131] Use geographic information system (GIS) technology to locate the pipeline in space and enter the location and identification code of the pipeline into the database;

[0132] Install rainfall sensors in the catchment area of ​​the space to which the pipeline set sequence belongs to collect rainfall data in real time;

[0133] Upload the collected rainfall data to the IoT database via wireless network to ensure the real-time and accuracy of the data;

[0134] Each rainfall sensor has a data tagging function that can associate the collected data with the relevant pipeline set sequence to facilitate subsequent data analysis;

[0135] Multifunctional sensors are installed in each drainage pipe to monitor sediment thickness, fluid flow rate and chemical composition indicators in real time, and the chemical composition indicators include nutrient content, pH value and heavy metal content.

[0136] 1.1) The sensor used to monitor the sediment thickness is installed in the representative area of ​​sediment accumulation, which can reflect the maximum sediment thickness of the current drainage pipe; the sediment thickness is calculated as follows:

[0137] Calculate the round trip time of the signal and calculate the distance using the speed of sound or light, depending on the type of sensor used;

[0138] The thickness of the sediment is obtained by calculating the difference between the measured distance and the known internal height of the pipe;

[0139] 1.2) Calculation of fluid flow rate, including:

[0140] Install the selected flow meter in the drain pipe, ensuring that it is positioned to effectively capture fluid flow;

[0141] Data collection: The flow meter starts to collect the flow data of the fluid regularly, usually once per second;

[0142] Depending on the type of sensor, flow rate data is expressed as flow rate (liters per second) or velocity (meters per second).

[0143] The actual flow rate value is calculated by converting the output signal (voltage, frequency, etc.) of the flow meter.

[0144] For obtaining nutrient content, pH value, and heavy metal content, including:

[0145] 2.1) Determination of nutrient content:

[0146] Sampling location: Select the inlet and outlet of the drainage pipe for sampling to ensure that the sample is representative;

[0147] Sampling tools: Use sampling bottles or samplers to collect liquid samples in drains or extract solid samples in sediments;

[0148] Sampling frequency: Regular sampling is once a week, with additional sampling after specific rainfall events to capture changes in nutrient content under different conditions;

[0149] The analysis method is:

[0150] Photometry, using colorimetry to determine the concentration of nitrogen and phosphorus in water samples; the concentration is determined by comparison with a standard curve;

[0151] On-site rapid testing: Using portable water quality analysis instruments, the nutrient content in water samples can be quickly determined;

[0152] 2.2) Determination of pH value:

[0153] Use a pH meter to measure the pH of the water sample immediately after sample collection in 2.1);

[0154] Record the time, location and pH value of each measurement to ensure data traceability;

[0155] 2.3) Determination of heavy metal content:

[0156] Sampling tools: Use special sampling bottles to collect liquid samples to avoid metal contamination;

[0157] Sampling method: Select the flow area in the drainage pipe to sample the heavy metal content;

[0158] The analysis method is:

[0159] Using inductively coupled plasma mass spectrometry (ICP-MS);

[0160] On-site rapid testing: Use heavy metal test strips or portable analytical instruments to quickly detect the heavy metal content in water samples.

[0161] For real-time data collection settings: configure the data transmission frequency to be once every 5 minutes to reduce network load and keep data updated in a timely manner;

[0162] The edge computing device is set to listen to the data sent by the sensor nodes and adopts a subscription mode to receive real-time data streams from different drainage pipes;

[0163] Set the data format to JSON to ensure that the data is structured and easy to parse.

[0164] Further explanation, for preliminary data processing:

[0165] In the received data stream, based on the expert group system, normal range values ​​of environmental rainfall data and monitoring indicators are set respectively, and abnormal value detection is performed using the set normal range values;

[0166] Annotate and record the detected abnormal data;

[0167] Data filtering is: using time series analysis methods to filter out redundant data and retain time points with significant changes in rainfall to reduce the complexity of subsequent analysis;

[0168] According to the relationship between sediment thickness and fluid velocity in the monitoring indicators, the data with the greatest impact on subsequent decision-making are further selected;

[0169] Data aggregation includes: summarizing the monitoring indicators of each pipeline within a 5-minute time window, such as calculating the average flow rate and maximum sediment thickness in each time period;

[0170] Integrate data within the same time period to generate a comprehensive report containing the status of each pipeline for subsequent analysis;

[0171] The cleaned and processed data is stored in the database of the edge computing device, using the NoSQL database MongoDB to efficiently process unstructured data;

[0172] Set up regular backup strategies and synchronize processed data to the cloud database every hour to ensure data security and accessibility;

[0173] Every hour, the processed data packets in the edge computing device are transmitted to the central database through an encrypted API interface to ensure the security of the data during transmission;

[0174] During data transmission, a transmission log is generated to record the time, data volume and status of each data transmission for subsequent review.

[0175] Further explanation: the blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored, and according to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time, specifically including:

[0176] The sediment thickness, fluid flow rate and chemical composition index of the i-th drainage pipe after preliminary data processing at the previous monitoring time t-1 are marked as ;and The output values ​​of are adjusted to the same range by scaling and offsetting;

[0177] The chemical composition indicators of the i-th drainage pipe after preliminary data processing at the previous monitoring time t-1, including the nutrient content, pH value, and heavy metal content, are recorded as ; and based on experimental demonstration or expert group system analysis, set The comparison threshold is In this embodiment, the comparison threshold is determined when the ratio of the sediment thickness of the corresponding drainage pipe to the inner diameter of the drainage pipe is 0.35, and the corresponding nutrient content, pH value, and heavy metal content are used as the comparison threshold. ;

[0178] The output values ​​of the chemical composition indicators are expressed as follows:

[0179] If in When all the ratios in are less than 1, then define ;

[0180] If in If any ratio in the equation is greater than 1, then define ;

[0181] If in Among the ratios in , if there are any two ratios greater than 1, then define ;

[0182] If in When all the ratios in are greater than 1, then we define ;

[0183] At the previous monitoring time t-1, the ratio of the sediment thickness of the i-th drainage pipe to the inner diameter of the drainage pipe is calculated to determine the blockage degree score of the i-th drainage pipe. Specifically, the blockage degree score of the i-th drainage pipe is marked as ;

[0184] when When ;

[0185] when When ;

[0186] when When ;

[0187] when When ;

[0188] when When ;

[0189] in, It is also the inner diameter value of the i-th drainage pipe after preliminary data processing;

[0190] At the current monitoring time, the fluctuation upper limit thresholds of the monitoring indicators corresponding to the i-th drainage pipe are set as , the calculation formula is as follows:

[0191]

[0192] in is the correction factor used to ensure , and ensure The value range is (0,1).

[0193] In this embodiment, The value range is (0, 1). The specific value is determined by an expert group based on experimental data.

[0194] Further explanation: if the change of the monitoring indicator exceeds the preset upper limit of fluctuation at the current monitoring time, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe will be increased; the increase amount is the preset basic change amount; specifically including:

[0195] At the current monitoring time t, the sediment thickness, fluid flow rate and chemical composition index in the i-th drainage pipe are recorded as ; and the current monitoring time t Compared with the previous monitoring moment The corresponding parameters are calculated by difference, and we get ;

[0196] The monitoring frequency and analysis depth of the monitoring indicators on the i-th drainage pipe at the previous monitoring time are set as follows:

[0197] The monitoring frequency is M1 times per day;

[0198] The analysis depth is: at the previous monitoring moment, when the blockage degree score of the i-th drainage pipe is calculated, the blockage degree scores of the m1 adjacent drainage pipes are calculated at the same time, and the blockage degree scores of the i-th drainage pipe and its m1 adjacent drainage pipes are averaged, and the average value is used as the final blockage degree score of the i-th drainage pipe at the previous monitoring moment;

[0199] 1.1) If When , it means that the change of fluid velocity and chemical composition index in the i-th drainage pipe is within the corresponding fluctuation upper limit threshold, but the change of sediment thickness exceeds ;

[0200] The preset basic change is ;in The monitoring frequency after the increase; is the number of adjacent drainage pipes after increase;

[0201] 1.2) If ,and When any of the following is true, set the basic change amount to ;

[0202] 1.3) If When , it means that the changes in chemical composition index, fluid velocity and sediment thickness have exceeded the corresponding fluctuation upper limit threshold; set the basic change amount to .

[0203] Further explanation: the acquisition of historical correlation evaluation coefficient and real-time correlation evaluation coefficient includes:

[0204] The processed valid data is encrypted and protected by the AES encryption algorithm to ensure the security and privacy of data transmission;

[0205] The data is uploaded to the cloud server to form the historical data of the previous monitoring period, retaining the environmental rainfall data and monitoring indicators;

[0206] Set the number of monitoring times in the previous monitoring period to U, and set j∈{1,2,…,U}. For the jth monitoring, record the sediment thickness of the i-th drainage pipe as ;

[0207] The current environmental rainfall range is determined by historical data, and the environmental rainfall range is divided into K environmental rainfall intervals, which are expressed as:

[0208]

[0209] in, represents the kth environmental rainfall interval, k is The position index is k∈{1,2,…,K}, K is the total number of divided intervals, and the i-th drainage pipe is in the environmental rainfall interval in the j-th monitoring. The ambient rainfall under ;

[0210] The environmental rainfall range is In the case of , the historical correlation evaluation coefficient of the i-th drainage pipe in the previous monitoring period is defined as , the calculation formula is as follows:

[0211]

[0212] in, is the historical correlation evaluation coefficient of the ith drainage pipe in the kth environmental rainfall interval; is the number of monitoring times in the kth environmental rainfall interval; and are the normal term and the correction term respectively, and The settings are used to ensure The valid value range of is (0, 1); since this method is used in the rainy period, there is no situation where the environmental rainfall is 0; in this embodiment, based on the expert group system and experimental demonstration, the limit The value range is (0,1); the specific value is determined by the expert group through experimental data;

[0213] when The closer it is to 1, the higher the correlation between sediment thickness and environmental rainfall is within the range of environmental rainfall;

[0214] when The closer it is to 0, the lower the correlation between sediment thickness and environmental rainfall is within the range of environmental rainfall;

[0215] Based on the expert group system or experimental demonstration, set The weak correlation interval and strong correlation interval of are (0,0.58) and [0.58,1] respectively;

[0216] The experimental contents for weak correlation intervals and strong correlation intervals are as follows:

[0217] The experimental goal was to evaluate the correlation between sediment thickness and rainfall in drainage pipes under different environmental rainfall conditions. Three drainage pipes (pipes A, B, and C) were selected for monitoring over a one-month period, during which daily rainfall and sediment thickness were recorded.

[0218] The experimental steps are as follows:

[0219] Data preparation: Collect rainfall and sediment thickness data within 30 days. Obtain rainfall data through weather monitoring stations, and combine with field monitoring to record the sediment thickness of each drainage pipe. The data collection cycle is set to once a day, with a total of 30 monitorings.

[0220] Data encryption: The collected data is encrypted using the AES encryption algorithm to ensure the security and privacy of the data during transmission. The encrypted data is uploaded to the cloud server to form the historical data of the previous monitoring period;

[0221] Based on the analysis of historical data, the range of environmental rainfall is determined and divided into five intervals;

[0222] In each monitoring, the sediment thickness and its corresponding rainfall on each drainage pipe on the monitoring day were recorded, and the interval it fell into was determined based on the recorded rainfall;

[0223] Calculation of historical correlation evaluation coefficient: Use the formula to calculate the historical correlation evaluation coefficient of each drainage pipe in each environmental rainfall interval;

[0224] The correlation between sediment thickness and rainfall in different intervals was analyzed by calculating the historical correlation evaluation coefficient;

[0225] The experimental data are shown in Table 1 below:

[0226]

[0227] The above data show the correlation between sediment thickness and rainfall in different drainage pipes in different rainfall intervals. According to the calculated historical correlation evaluation coefficient, it is found that there is a strong correlation between sediment thickness and rainfall in pipe A in the interval [5,10), and a weak correlation in the interval [0,5).

[0228] In the strong correlation range, the trend value of sediment thickness is ;

[0229] In the weak correlation range, the trend value of sediment thickness is ;

[0230] in, is the trend prediction value of sediment thickness;

[0231] Collect real-time data for each drainage pipe during the current monitoring period, including ambient rainfall and sediment thickness of monitoring indicators;

[0232] The calculation method of historical correlation evaluation coefficient is the same as that of historical correlation evaluation coefficient. The real-time correlation evaluation coefficient of the environmental rainfall interval is The calculation formula is as follows:

[0233]

[0234] in, is the number of drainage pipes in the current Real-time correlation evaluation coefficient of environmental rainfall interval; Indicated in The number of monitoring times within the environmental rainfall interval, ∈{1,2,…,K}; Indicates the number of Monitoring.

[0235] To further illustrate, the historical correlation evaluation coefficient is compared and analyzed with the real-time correlation evaluation coefficient to generate the following dynamic adjustment thresholds:

[0236]

[0237] in, is the dynamic adjustment threshold of the i-th drainage pipe in the current monitoring time period;

[0238] 2.1) If When , it means that the real-time correlation evaluation coefficient of the current monitoring period is lower than the historical correlation evaluation coefficient;

[0239] At this time, only when and When the values ​​are all in the weak correlation interval (0, 0.58), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0240]

[0241]

[0242] Only when and When the values ​​are all in the strong correlation interval [0.58,1), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0243]

[0244]

[0245] in, Indicates rounding up to an integer; and They represent the adjusted monitoring frequency and number of adjacent drainage pipes respectively; They are the upper threshold values ​​of fluctuation of the adjusted sediment thickness, fluid velocity and chemical composition index, respectively;

[0246] 2.2) If When , it means that the real-time correlation evaluation coefficient of the current monitoring period is higher than the historical correlation evaluation coefficient;

[0247] At this time, only when and When the values ​​are all in the weak correlation interval (0, 0.58), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0248]

[0249]

[0250] Only when and When the values ​​are all in the strong correlation interval [0.58,1), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows:

[0251]

[0252]

[0253] in, Indicates rounding up to an integer.

[0254] for The other ratios are not adjusted and are not described in detail.

[0255] Further explanation: Based on the same calculation method as step S4.1, the change trend value of the sediment thickness at the next monitoring moment is determined as ; If the change trend value of the sediment thickness exceeds the corresponding adjusted fluctuation upper limit threshold, the following strategy is executed:

[0256] Slight sedimentation: If the trend of sediment thickness is predicted If the ratio to the inner diameter of the drainage pipe is less than 0.25, regular cleaning is recommended to prevent the problem from getting worse; a comprehensive inspection and cleaning should be carried out every quarter or every six months;

[0257] Moderate sedimentation: If the trend of sediment thickness is predicted When the ratio of the inner diameter of the drainage pipe is in the interval [0.25, 0.35], it is recommended to increase the period of regular cleaning and perform cleaning and flushing;

[0258] Severe sedimentation: If the trend forecast of sediment thickness If the ratio to the inner diameter of the drainage pipe is above 0.35, emergency cleaning and comprehensive inspection are required;

[0259] Maintenance operations include:

[0260] Cleaning and unblocking:

[0261] Mechanical cleaning: Use mechanical equipment such as high-pressure water guns and vacuum trucks for physical cleaning to ensure that the sediment is effectively removed;

[0262] Chemical Cleaning: Use environmentally friendly cleaners to treat hard-to-remove deposits and ensure clear pipes.

[0263] Embodiment 2:

[0264] See also Figure 2 , a city drainage pipeline management system based on Internet of Things database service, the system is used to execute the city drainage pipeline management method based on Internet of Things database service, including:

[0265] Data collection module: used to mark and sort several drainage pipes in the urban drainage pipeline to form a pipe set sequence, and use the Internet of Things technology to collect real-time environmental rainfall data in the spatial area to which the pipe set sequence belongs, as well as monitoring indicators of each drainage pipe in the pipe set sequence, and the monitoring indicators include but are not limited to sediment thickness, fluid flow rate and chemical composition indicators;

[0266] Data processing module: used to receive environmental rainfall data and monitoring indicators and transmit them to the edge computing device through a wireless network for preliminary data processing, wherein the data processing includes cleaning, filtering and aggregating the raw data;

[0267] Dynamic adjustment module: used to apply adaptive data processing algorithms to data after preliminary data processing, specifically by analyzing environmental rainfall data and monitoring indicators at the previous monitoring moment and the current monitoring moment, dynamically adjusting data collection frequency and analysis depth, where:

[0268] The blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored. The more serious the blockage degree is, the higher the corresponding score value is. According to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time;

[0269] If at the current monitoring moment, the change in sediment thickness of the monitoring indicator exceeds the preset upper fluctuation threshold, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe are increased; the increase amount is the preset basic change amount;

[0270] Dynamic adjustment threshold determination module: used to upload the valid data after preliminary data processing to the cloud server in an encrypted manner for in-depth analysis and storage, and to form the historical data of each drainage pipe in the previous monitoring period, which includes environmental rainfall data and monitoring indicators;

[0271] For deep analytics, this includes running an integrated event-driven data processing framework on cloud servers to dynamically adjust thresholds based on real-time and historical data;

[0272] Dynamically adjust the threshold value, including:

[0273] Calculate the historical correlation evaluation coefficient between the environmental rainfall data and the sediment thickness in the historical data corresponding to the previous monitoring period for each drainage pipe. The historical correlation evaluation coefficient is used to evaluate the change trend value of the sediment thickness under different numerical ranges of the environmental rainfall data.

[0274] Collect the real-time data of each drainage pipe in the current monitoring period, including environmental rainfall data and monitoring indicators, and calculate the real-time correlation evaluation coefficient between the environmental rainfall data in the current real-time data and the sediment thickness in the monitoring indicators;

[0275] Compare and analyze the real-time correlation evaluation coefficient with the historical correlation evaluation coefficient to generate a dynamic adjustment threshold for adjusting the fluctuation upper limit threshold and the basic change amount;

[0276] Result execution module: used to adjust the monitoring frequency and analysis depth of the monitoring indicators on the corresponding drainage pipe according to the adjusted basic change amount, and use the adjusted fluctuation upper limit threshold as the fluctuation upper limit threshold at the next monitoring moment;

[0277] The change trend value of the sediment thickness at the next monitoring moment is calculated within the current monitoring time period. If the change trend value of the sediment thickness exceeds the corresponding adjusted fluctuation upper limit threshold, a corresponding maintenance request is generated.

[0278] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0279] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0280] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0281] The above embodiments are only used to help understand the method and core idea of ​​the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for managing urban drainage pipelines based on Internet of Things database services, specifically for use during rainy periods, characterized in that: The specific steps include: Step S1: Mark and sort a number of drainage pipes in the urban drainage pipeline to form a pipe set sequence, and use the Internet of Things technology to collect real-time environmental rainfall data of the spatial area to which the pipe set sequence belongs, as well as monitoring indicators of each drainage pipe in the pipe set sequence, and the monitoring indicators include but are not limited to sediment thickness, fluid flow rate and chemical composition indicators; Step S2: receiving environmental rainfall data and monitoring indicators and transmitting them to the edge computing device through a wireless network for preliminary data processing, wherein the data processing includes cleaning, filtering and aggregating the raw data; Step S3: Apply an adaptive data processing algorithm to the data after preliminary data processing, specifically by analyzing the environmental rainfall data and monitoring indicators at the previous monitoring time and the current monitoring time, and dynamically adjust the data collection frequency and analysis depth, wherein: The blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored. The more serious the blockage degree is, the higher the corresponding score value is. According to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time; If at the current monitoring moment, the change in sediment thickness of the monitoring indicator exceeds the preset upper fluctuation threshold, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe are increased; the increase amount is the preset basic change amount; Step S4: uploading the valid data after preliminary data processing to the cloud server in an encrypted manner for in-depth analysis and storage, and forming the historical data of each drainage pipe in the previous monitoring period, the historical data including environmental rainfall data and monitoring indicators; For deep analytics, this includes running an integrated event-driven data processing framework on cloud servers to dynamically adjust thresholds based on real-time and historical data; Dynamically adjust the threshold value, including: Step S4.1: Calculate the historical correlation evaluation coefficient between the environmental rainfall data and the sediment thickness in the historical data corresponding to the previous monitoring period for each drainage pipe, and the historical correlation evaluation coefficient is used to evaluate the change trend value of the sediment thickness under different numerical ranges of the environmental rainfall data; Step S4.2: collect the real-time data of each drainage pipe in the current monitoring time period, including environmental rainfall data and monitoring indicators, and calculate the real-time correlation evaluation coefficient between the environmental rainfall data in the current real-time data and the sediment thickness in the monitoring indicators; Step S4.3: Compare and analyze the real-time correlation evaluation coefficient with the historical correlation evaluation coefficient to generate a dynamic adjustment threshold for adjusting the fluctuation upper limit threshold and the basic change amount; Step S5: adjusting the monitoring frequency and analysis depth of the monitoring indicators on the corresponding drainage pipe according to the adjusted basic change amount, and using the adjusted fluctuation upper limit threshold as the fluctuation upper limit threshold at the next monitoring moment; The change trend value of the sediment thickness at the next monitoring moment is calculated within the current monitoring time period. If the change trend value of the sediment thickness exceeds the corresponding adjusted fluctuation upper limit threshold, a corresponding maintenance request is generated.

2. The urban drainage pipeline management method based on the Internet of Things database service according to claim 1 is characterized in that: Environmental rainfall data and monitoring indicators are obtained, including: Several drainage pipes are defined as several secondary drainage pipes connected to the main drainage pipe; The pipeline set sequence is represented as {1,2,…,i,…,N}, where i represents the i-th drainage pipeline and N represents the total number of drainage pipelines; Multifunctional sensors are installed in each drainage pipe to monitor sediment thickness, fluid flow rate and chemical composition indicators in real time, and the chemical composition indicators include nutrient content, pH value and heavy metal content.

3. The urban drainage pipeline management method based on the Internet of Things database service according to claim 2 is characterized in that: The blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored, and according to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time, including: The sediment thickness, fluid flow rate and chemical composition index of the i-th drainage pipe after preliminary data processing at the previous monitoring time t-1 are marked as ;and The output values ​​of are adjusted to the same range by scaling and offsetting; The chemical composition indicators of the i-th drainage pipe after preliminary data processing at the previous monitoring time t-1, including the nutrient content, pH value, and heavy metal content, are recorded as ; and based on experimental demonstration or expert group system analysis, set The comparison threshold is .

4. The urban drainage pipeline management method based on the Internet of Things database service according to claim 3 is characterized by: The determination of chemical composition indicators includes: The output values ​​of chemical composition indicators are expressed as follows: If in When all the ratios in are less than 1, then define ; If in If any ratio in the equation is greater than 1, then define ; If in Among the ratios in , if there are any two ratios greater than 1, then define ; If in When all the ratios in are greater than 1, then we define .

5. The urban drainage pipeline management method based on the Internet of Things database service according to claim 4 is characterized in that: At the previous monitoring time t-1, the ratio of the sediment thickness of the i-th drainage pipe to the inner diameter of the drainage pipe is calculated to determine the blockage degree score of the i-th drainage pipe. Specifically, the blockage degree score of the i-th drainage pipe is marked as ; when When ; when When ; when When ; when When ; when When ; in, It is also the inner diameter value of the i-th drainage pipe after preliminary data processing; At the current monitoring time, the fluctuation upper limit thresholds of the monitoring indicators corresponding to the i-th drainage pipe are set as , the calculation formula is as follows: ； in is the correction factor used to ensure , and ensure The value range is (0,1).

6. The urban drainage pipeline management method based on the Internet of Things database service according to claim 5 is characterized in that: If the change in the monitoring indicator exceeds the preset fluctuation upper limit threshold at the current monitoring moment, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe will be increased; the increase amount is the preset basic change amount; specifically including: At the current monitoring time t, the sediment thickness, fluid flow rate and chemical composition index in the i-th drainage pipe are recorded as ; and the current monitoring time t Compared with the previous monitoring moment The corresponding parameters are calculated by difference, and we get ; The monitoring frequency and analysis depth of the monitoring indicators on the i-th drainage pipe at the previous monitoring time are set as follows: Set the monitoring frequency to M1 times per day; The analysis depth is: at the previous monitoring moment, when the blockage degree score of the i-th drainage pipe is calculated, the blockage degree scores of the m1 adjacent drainage pipes are calculated at the same time, and the blockage degree scores of the i-th drainage pipe and its m1 adjacent drainage pipes are averaged, and the average value is used as the final blockage degree score of the i-th drainage pipe at the previous monitoring moment; 1-1) If When , it means that the change of fluid velocity and chemical composition index in the i-th drainage pipe is within the corresponding fluctuation upper limit threshold, but the change of sediment thickness exceeds ; The preset basic change is ;in The monitoring frequency after the increase; is the number of adjacent drainage pipes after increase; 1-2) If ,and When any of the following is true, set the basic change amount to ; 1-3) If When , it means that the changes in chemical composition index, fluid velocity and sediment thickness have exceeded the corresponding fluctuation upper limit threshold; set the basic change amount to .

7. The urban drainage pipeline management method based on the Internet of Things database service according to claim 6 is characterized by: The acquisition of historical correlation evaluation coefficients and real-time correlation evaluation coefficients specifically includes: Set the number of monitoring times in the previous monitoring period to U, and set j∈{1,2,…,U}. For the jth monitoring, record the sediment thickness of the i-th drainage pipe as ; The current environmental rainfall range is determined by historical data, and the environmental rainfall range is divided into K environmental rainfall intervals, which are expressed as: ； in, represents the kth environmental rainfall interval, k is The position index is k∈{1,2,…,K}, K is the total number of divided intervals, and the i-th drainage pipe is in the environmental rainfall interval in the j-th monitoring. The ambient rainfall under ; The environmental rainfall range is In the case of , the historical correlation evaluation coefficient of the i-th drainage pipe in the previous monitoring period is defined as , the calculation formula is as follows: ； in, is the historical correlation evaluation coefficient of the ith drainage pipe in the kth environmental rainfall interval; is the number of monitoring times in the kth environmental rainfall interval; and are the normal term and the correction term respectively, and The settings are used to ensure The valid value range of is (0, 1); when The closer it is to 1, the higher the correlation between sediment thickness and environmental rainfall is within the range of environmental rainfall; when The closer it is to 0, the lower the correlation between sediment thickness and environmental rainfall is within this environmental rainfall range.

8. The urban drainage pipeline management method based on the Internet of Things database service according to claim 7 is characterized in that: Based on the expert group system or experimental demonstration, set The weak correlation interval and strong correlation interval of are (0,0.58) and [0.58,1] respectively; In the strong correlation range, the trend value of sediment thickness is ; In the weak correlation range, the trend value of sediment thickness is ; in, is the trend prediction value of sediment thickness; Collect real-time data for each drainage pipe during the current monitoring period, including ambient rainfall and sediment thickness of monitoring indicators; The calculation method of historical correlation evaluation coefficient is the same as that of historical correlation evaluation coefficient. The real-time correlation evaluation coefficient of the environmental rainfall interval is .

9. The urban drainage pipeline management method based on the Internet of Things database service according to claim 8 is characterized in that: Compare and analyze the historical correlation evaluation coefficient with the real-time correlation evaluation coefficient to generate the following dynamic adjustment thresholds: ； in, is the dynamic adjustment threshold of the i-th drainage pipe in the current monitoring time period; 2-1) If When , it means that the real-time correlation evaluation coefficient of the current monitoring period is lower than the historical correlation evaluation coefficient; At this time, only when and When the values ​​are all in the weak correlation interval (0, 0.58), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows: ； ； Only when and When the values ​​are all in the strong correlation interval [0.58,1), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows: ； ； in, Indicates rounding up to an integer; and They represent the adjusted monitoring frequency and number of adjacent drainage pipes respectively; They are the upper threshold values ​​of fluctuation of the adjusted sediment thickness, fluid velocity and chemical composition index, respectively; 2-2) If When , it means that the real-time correlation evaluation coefficient of the current monitoring period is higher than the historical correlation evaluation coefficient; At this time, only when and When the values ​​are all in the weak correlation interval (0, 0.58), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows: ； ； Only when and When the values ​​are all in the strong correlation interval [0.58,1), the adjustment strategy for the volatility upper limit threshold and the basic change amount is as follows: ； ； in, Indicates rounding up to an integer.

10. An urban drainage pipeline management system based on Internet of Things database service, characterized by: The system is used to execute the urban drainage pipeline management method based on the Internet of Things database service according to any one of claims 1 to 9, comprising: Data collection module: used to mark and sort several drainage pipes in the urban drainage pipeline to form a pipe set sequence, and use the Internet of Things technology to collect real-time environmental rainfall data in the spatial area to which the pipe set sequence belongs, as well as monitoring indicators of each drainage pipe in the pipe set sequence, and the monitoring indicators include but are not limited to sediment thickness, fluid flow rate and chemical composition indicators; Data processing module: used to receive environmental rainfall data and monitoring indicators and transmit them to the edge computing device through a wireless network for preliminary data processing, wherein the data processing includes cleaning, filtering and aggregating the raw data; Dynamic adjustment module: used to apply adaptive data processing algorithms to data after preliminary data processing, specifically by analyzing environmental rainfall data and monitoring indicators at the previous monitoring moment and the current monitoring moment, dynamically adjusting data collection frequency and analysis depth, where: The blockage degree of each drainage pipe in the pipeline set sequence at the previous monitoring time is scored. The more serious the blockage degree is, the higher the corresponding score value is. According to the blockage degree score result of each drainage pipe, the fluctuation upper limit threshold of the corresponding monitoring indicator of each drainage pipe is set at the current monitoring time; If at the current monitoring moment, the change in sediment thickness of the monitoring indicator exceeds the preset upper fluctuation threshold, the monitoring frequency and analysis depth of the monitoring indicator on the corresponding drainage pipe are increased; the increase amount is the preset basic change amount; Dynamic adjustment threshold determination module: used to upload the valid data after preliminary data processing to the cloud server in an encrypted manner for in-depth analysis and storage, and to form the historical data of each drainage pipe in the previous monitoring period, which includes environmental rainfall data and monitoring indicators; For deep analytics, this includes running an integrated event-driven data processing framework on cloud servers to dynamically adjust thresholds based on real-time and historical data; Dynamically adjust the threshold value, including: Calculate the historical correlation evaluation coefficient between the environmental rainfall data and the sediment thickness in the historical data corresponding to the previous monitoring period for each drainage pipe. The historical correlation evaluation coefficient is used to evaluate the change trend value of the sediment thickness under different numerical ranges of the environmental rainfall data. Collect the real-time data of each drainage pipe in the current monitoring period, including environmental rainfall data and monitoring indicators, and calculate the real-time correlation evaluation coefficient between the environmental rainfall data in the current real-time data and the sediment thickness in the monitoring indicators; Compare and analyze the real-time correlation evaluation coefficient with the historical correlation evaluation coefficient to generate a dynamic adjustment threshold for adjusting the fluctuation upper limit threshold and the basic change amount; Result execution module: used to adjust the monitoring frequency and analysis depth of the monitoring indicators on the corresponding drainage pipe according to the adjusted basic change amount, and use the adjusted fluctuation upper limit threshold as the fluctuation upper limit threshold at the next monitoring moment; The change trend value of the sediment thickness at the next monitoring moment is calculated within the current monitoring time period. If the change trend value of the sediment thickness exceeds the corresponding adjusted fluctuation upper limit threshold, a corresponding maintenance request is generated.

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