Heat supply pipe network line abnormity judgment method based on temperature and pressure detection

Abstract

The invention discloses a heat supply pipe network line anomaly judgment method based on temperature and pressure detection, and relates to the technical field of pipeline anomaly detection, and the method comprises the following steps: S1, scanning a two-dimensional code of a to-be-detected point, and reading equipment information of the to-be-detected point; s2, reading real-time temperature data and real-time pressure data measured by a built-in sensor of a point to be detected; s3, analyzing a reasonable temperature difference value between the pipeline branch where the to-be-detected point is located and the heat source by acquiring environmental data of the to-be-detected point; s4, analyzing a reasonable pressure difference value between the pipeline branch where the to-be-detected point is located and the heat source by acquiring terrain data of the to-be-detected point; and S5, according to the detected points in the distribution diagram of the heat supply network, verifying the abnormity judgment result of the current pipeline branch, and after the verification is passed, marking the abnormity information of the pipeline branch. Real-time temperature and pressure data are collected, and dynamic analysis is performed according to a set sampling period, so that the real-time performance and accuracy of a monitoring result are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline anomaly detection, and in particular to a method for determining anomaly of a heating pipeline network based on temperature and pressure detection. Background Art

[0002] In the heating (cooling) system, when the heat (cold) source outputs heat or cold into the pipe network system, the operator needs to understand the operating effect of the system to ensure that the appropriate amount of heat (cold) flows into the end of the system. Therefore, it is necessary to measure the water temperature to see if the water supply temperature at the end is consistent with or close to the temperature of the cold and heat sources. If the difference is large, it means that the circulation is not smooth or the pipeline is not good. In order to ensure the safety of the system, it is necessary to know whether the pressure is stable. Generally, the heating or cooling system is operated under pressure, and the circulating water circuit has a certain pressure. If the pressure is unstable, it means that there is a "bubbling floor leak" in the pipeline, which will affect the quality of heating (cooling) and cause energy waste. To find leaks in the pipe network, check one area at a time and measure the water pressure in the pipeline in that area.

[0003] It is also necessary to ensure the hydraulic balance of the system. The first method is to detect the pressure difference between the supply and return water at each end of the system. If the hydraulics are balanced, the pressure difference areas of the supply and return water are consistent. In the heating system, if the pressure difference at a certain point is too large, it means that the hydraulic flow of the line is excessive, and the amount of water transported by the end is too large, which will cause the room temperature to overheat. If the pressure difference at a certain point is too small, the amount of water transported by the end is too small, which will cause the room temperature to be too low. The second method is to look at the return water temperature of each end. If the hydraulics are balanced, the return water temperatures of each line tend to be consistent. In the heating system, if the return water temperature at a certain point is too high, it means that the flow rate of the branch at that end is higher than the average, which will cause the room temperature to overheat. If the return water temperature at a certain point is too low, it means that the flow rate of the branch at that end is lower than the average, which will cause the room temperature to be too low.

[0004] The existing technology for detecting or determining abnormalities in heating pipeline lines mainly relies on the deployment of temperature and pressure sensors. The temperature and pressure at each point in the pipeline network are monitored in real time to determine whether the pipeline network is operating normally. The flow, pressure, temperature and other physical parameters are then combined to evaluate the operating status of the pipeline network, and abnormalities are determined through data comparison or empirical thresholds.

[0005] However, the current density of detection equipment is often insufficient, making it difficult to fully cover every key point in the pipe network, resulting in some abnormal situations not being discovered in time. Secondly, traditional anomaly detection methods are mostly based on empirical judgments, lacking intelligent analysis methods, and difficult to make accurate judgments on complex environmental factors (such as terrain differences, pipe network structure diversity, etc.); and the depth and accuracy of data collection and analysis are insufficient, especially in hydraulic balance analysis and branch overflow and low flow problems. Traditional technology cannot provide real-time and accurate optimization solutions, resulting in low operating efficiency of the overall system and serious energy waste. Summary of the invention

[0006] Based on this, it is necessary to provide a method for determining abnormalities in a heating network line based on temperature and pressure detection in response to the above technical problems.

[0007] The present invention provides a method for determining abnormality of a heating network line based on temperature and pressure detection, the method comprising the following steps:

[0008] S1. Scan the QR code of the point to be tested, read the equipment information of the point to be tested, and obtain the distribution of pipeline branches of the point to be tested and other test points in the connecting area by querying the distribution map of the heating pipe network;

[0009] S2, read the real-time temperature data and real-time pressure data measured by the built-in sensor at the point to be detected, and obtain the supply and return water temperature and supply and return water pressure at the heat source in the current sampling period;

[0010] S3. By acquiring the environmental data of the point to be detected, analyzing the reasonable temperature difference between the pipeline branch where the point to be detected is located and the heat source, and determining the temperature anomaly information existing in the current pipeline branch based on the comparative analysis of the real-time temperature data and the supply and return water temperatures;

[0011] S4. By acquiring the terrain data of the point to be detected, analyzing the reasonable pressure difference between the pipeline branch where the point to be detected is located and the heat source, and determining the pressure abnormality information existing in the current pipeline branch based on the comparative analysis of the real-time pressure data and the supply and return water pressure;

[0012] S5. According to the detected points in the heating network distribution map, the abnormality determination result of the current pipeline branch is verified, and after the verification is passed, the abnormal information of the pipeline branch is marked.

[0013] Furthermore, by acquiring the environmental data of the point to be detected, analyzing the reasonable temperature difference between the pipeline branch where the point to be detected is located and the heat source, and comparing the real-time temperature data with the supply and return water temperatures, determining the temperature anomaly information existing in the current pipeline branch includes the following steps:

[0014] S31, by querying the equipment information and the distribution map of the heating pipe network, identifying the pipeline type of the pipeline branch where the point to be detected is located, and the pipeline type includes a water supply pipe, a return pipe, and a supply and return pipe;

[0015] S32, setting a sampling period, obtaining environmental data of the point to be detected within the current sampling period, and marking data labels according to the data type of the environmental data, the environmental data including ambient temperature, indoor temperature, heat source pipeline distance and pipeline thermal conductivity;

[0016] S33, define the weight value of each type of environmental data, calculate the Euclidean distance between the data, find the historical environmental data similar to the environmental data in the current sampling period in the historical sampling period, and calculate the reasonable temperature difference value through the historical temperature difference data corresponding to the historical environmental data;

[0017] S34. According to the pipeline type of the pipeline branch, the real-time temperature data of the point to be detected is compared with the supply and return water temperature at the corresponding heat source, and combined with a reasonable temperature difference, the temperature anomaly information existing in the current pipeline branch is determined.

[0018] Furthermore, the weight values ​​of various types of environmental data are defined, and the Euclidean distance between the data is calculated. Historical environmental data similar to the environmental data in the current sampling period are searched in the historical sampling period, and the reasonable temperature difference is calculated through the historical temperature difference data corresponding to the historical environmental data. The following steps are included:

[0019] S331. Standardize and normalize environmental data and historical environmental data;

[0020] S332, using the environmental data in the current sampling period, calculating the information entropy and information utility value of each type of environmental data, and then calculating the weight value of each type of environmental data;

[0021] S333, using the time reverse order method, according to the Euclidean distance, calculate the dynamic time normalization value corresponding to each type of historical environmental data in the historical sampling period one by one, and then multiply it by the weight value and add it up to obtain the comprehensive dynamic time normalization value of the historical sampling period;

[0022] S334. Sort the comprehensive dynamic time normalization values ​​in order from small to large, select the first N historical sampling periods as the approximate period of the current sampling period, extract the actual historical temperature difference data of the approximate period, and then take the average value of multiple historical temperature difference data as the reasonable temperature difference value.

[0023] Furthermore, according to the pipeline type of the pipeline branch, the real-time temperature data of the point to be detected is compared with the supply and return water temperature at the corresponding heat source, and combined with the reasonable temperature difference, the temperature anomaly information existing in the current pipeline branch is determined, including the following steps:

[0024] S341. According to the pipeline type of the pipeline branch where the point to be detected is located, the corresponding reasonable temperature difference and temperature anomaly judgment strategy, as well as the corresponding supply and return water temperatures at the heat source, are called, and the supply and return water temperatures include the heat source supply water temperature, the heat source return water temperature and the heat source supply and return water temperature difference at the heat source;

[0025] S342, identifying temperature anomalies existing in pipeline branches of each pipeline type in the current sampling period according to the temperature anomaly determination strategy;

[0026] S343. Mark the points to be detected where temperature anomalies exist in the distribution map of the heating network, and record the temperature anomaly information of each point to be detected based on the anomaly determination result.

[0027] Further, according to the temperature anomaly determination strategy, identifying the temperature anomaly existing in the pipeline branches of each pipeline type in the current sampling period includes the following steps:

[0028] S3421. When the pipeline branch is a water supply pipe, the real-time temperature data of the current sampling period is compared with the heat source water supply temperature. If the real-time temperature data is higher than the heat source water supply temperature, it is determined to be an instrument failure. If the real-time temperature data is lower than the heat source water supply temperature and the difference is less than the reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time temperature data is lower than the heat source water supply temperature and the difference is greater than or equal to the reasonable temperature difference, it is determined that there is an abnormality of too slow flow rate in the pipeline branch.

[0029] S3422. When the pipeline branch is a return pipe, the real-time temperature data of the current sampling period is compared with the heat source return water temperature. If the real-time temperature data is higher than the heat source return water temperature and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has a flow overshoot abnormality or a supply and return water short circuit abnormality. If the real-time temperature data is higher than the heat source return water temperature and the difference is less than the reasonable temperature difference, it is determined that the pipeline branch has no abnormality. If the real-time temperature data is lower than the heat source return water temperature and the difference is less than the reasonable temperature difference, it is determined that the pipeline branch has no abnormality. If the real-time temperature data is lower than the heat source return water temperature and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has a low flow abnormality or an excessive heat consumption abnormality in the branch.

[0030] S3423. When the pipeline branch is a supply and return water pipe, calculate the real-time supply and return water temperature difference of the pipeline branch, and compare the real-time supply and return water temperature difference with the heat source supply and return water temperature difference. If the difference between the real-time supply and return water temperature difference and the heat source supply and return water temperature difference is less than the reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time supply and return water temperature difference is higher than the heat source supply and return water temperature difference and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has an abnormality of too low flow or too much heat consumption. If the real-time supply and return water temperature difference is lower than the heat source supply and return water temperature difference and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has an abnormality of too high flow or too little heat consumption.

[0031] Furthermore, by acquiring the terrain data of the point to be detected, analyzing the reasonable pressure difference between the pipeline branch where the point to be detected is located and the heat source, and comparing the real-time pressure data with the supply and return water pressures, determining the pressure anomaly information existing in the current pipeline branch includes the following steps:

[0032] S41, obtaining the pipeline type of the pipeline branch where the point to be detected is located, and obtaining the terrain data of the point to be detected by querying the distribution map of the heating pipe network, the terrain data including the altitude of the point to be detected, the relative height difference between the point to be detected and the heat source, the pipeline length and the pressure loss;

[0033] S42. Construct a neural network model by defining the input layer, output layer and model structure, use historical pressure data to train and verify the model, and analyze reasonable pressure differences;

[0034] S43. According to the pipeline type of the pipeline branch, the real-time pressure data of the point to be detected is compared with the supply and return water pressure at the corresponding heat source, and combined with a reasonable pressure difference, the pressure abnormality information existing in the current pipeline branch is determined.

[0035] Furthermore, by defining the input layer, output layer and model structure, a neural network model is constructed, and historical pressure data is used for model training and verification. The analysis of reasonable pressure difference includes the following steps:

[0036] S421, normalizing the terrain data by deviation standardization, extracting data features of the terrain data, and then obtaining input variables of the neural network model by data dimensionality reduction;

[0037] S422, defining the input variables after dimensionality reduction and the real-time pressure data as input nodes to construct an input layer, and defining the reasonable pressure difference as the model output to construct an output layer;

[0038] S423, designing a radial basis function layer as the core layer of the neural network model, constructing a training data set by inputting historical pressure data, training the neural network model using a gradient descent method, and then obtaining a trained neural network model after testing and verification;

[0039] S424. Analyze the reasonable pressure difference of the current sampling period using a neural network model.

[0040] Furthermore, according to the pipeline type of the pipeline branch, the real-time pressure data of the point to be detected is compared with the supply and return water pressure at the corresponding heat source, and combined with the reasonable pressure difference, the pressure abnormality information existing in the current pipeline branch is determined, including the following steps:

[0041] S431. According to the pipeline type of the pipeline branch where the point to be detected is located, the corresponding reasonable pressure difference and pressure anomaly judgment strategy, as well as the corresponding supply and return water pressure, are called. The supply and return water pressure includes the heat source supply water pressure at the heat source, the heat source return water pressure and the supply and return water pressure difference of the adjacent point;

[0042] S432, according to the pressure anomaly determination strategy, identifying the pressure anomaly existing in the pipeline branches of each pipeline type in the current sampling period;

[0043] S433. Mark the points to be tested where abnormal pressure exists in the distribution map of the heating pipe network, and record the abnormal pressure information of each point to be tested based on the abnormality determination result.

[0044] Further, according to the pressure anomaly determination strategy, identifying the pressure anomaly existing in the pipeline branches of each pipeline type in the current sampling period includes the following steps:

[0045] S4321. When the pipeline branch is a water supply pipe, the real-time pressure data of the current sampling period is compared with the heat source water supply pressure. If the real-time pressure data is higher than the heat source water supply pressure and the pressure difference is greater than the reasonable pressure difference, it is determined that the pipeline branch has an abnormal high pressure. If the real-time pressure data is lower than the heat source water supply pressure, it is determined that there is no abnormality in the pipeline branch.

[0046] S4322, when the pipeline branch is a return pipe, compare the real-time pressure data of the current sampling period with the heat source return pressure. If the real-time pressure data is higher than the heat source return pressure, it is determined that there is no abnormality in the pipeline branch. If the real-time pressure data is lower than the heat source return pressure and the pressure difference is greater than the reasonable pressure difference, it is determined that there is a water leakage abnormality in the pipeline branch;

[0047] S4323. When the pipeline branch is a water supply and return pipe, calculate the real-time water supply and return pressure difference of the pipeline branch, and compare the real-time water supply and return pressure difference with the water supply and return pressure difference of the adjacent point. If the difference between the real-time water supply and return pressure difference and the water supply and return pressure difference of the adjacent point is less than the reasonable pressure difference, it is determined that there is no abnormality in the pipeline branch. If the real-time water supply and return pressure difference is higher than the water supply and return pressure difference of the adjacent point and the difference is greater than the reasonable pressure difference, it is determined that there is a pressure abnormality or a blockage abnormality in the pipeline branch. If the real-time water supply and return pressure difference is lower than the water supply and return pressure difference of the adjacent point and the difference is greater than the reasonable pressure difference, it is determined that there is an overflow abnormality or a supply and return short circuit abnormality in the pipeline branch.

[0048] Further, according to the detected points in the heating network distribution map, the abnormality determination result of the current pipeline branch is verified, and after the verification is passed, the abnormal information of the pipeline branch is marked, including the following steps:

[0049] S51, obtaining the abnormality determination result of the pipeline branch where the point to be detected is located, and obtaining the abnormality determination results of other detected points located in the connected area of ​​the pipeline branch according to the distribution map of the heating network;

[0050] S52, verifying the rationality of the abnormality determination result of the point to be detected according to the pipeline type and abnormality determination result of the detected point, and the upstream and downstream relationship between the detected point and the point to be detected;

[0051] S53, drawing a change trend curve based on the historical detection data of the points to be detected and the detected points, and verifying the consistency of the change trends of the real-time temperature data and the real-time pressure data;

[0052] S54. Integrate the abnormality determination results after verification, and mark the abnormal information of the point to be detected in the current sampling period in the heating pipe network distribution map.

[0053] The beneficial effects of the present invention are:

[0054] 1. By scanning the QR code of the point to be tested, combined with the assistance of the heating network distribution map, the detection process is made more convenient and efficient, the tediousness of manual input is reduced, and the accuracy of data collection is improved; using the collected real-time temperature and pressure data, dynamic analysis can be performed based on the heat source data within the actual sampling period to ensure the real-time and accuracy of the monitoring results; at the same time, through comprehensive analysis of environmental data and terrain information, the reasonable difference in temperature and pressure can be accurately determined, which not only improves the ability to detect abnormalities in pipeline branches, but also effectively responds to the impact of different geographical conditions on temperature and pressure, thereby effectively improving the sensitivity and reliability of abnormal detection of pipeline lines.

[0055] 2. Through multi-dimensional data analysis, more intelligent abnormality judgment is achieved. At the same time, through real-time monitoring and automatic verification optimization of the heating pipeline network, the accuracy and speed of abnormality detection are significantly improved, and missed detection and misjudgment phenomena are reduced; the present invention makes full use of the Internet of Things sensing technology and intelligent algorithms to achieve automated and precise troubleshooting and pipeline management, thereby optimizing the energy efficiency of the heating system and reducing energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0057] Figure 1 The present invention is a flowchart of a method for determining abnormality of a heating network line based on temperature and pressure detection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] See also Figure 1 , provides a method for determining abnormality of a heating network line based on temperature and pressure detection, the method comprising the following steps:

[0060] S1. Scan the QR code of the point to be tested, read the equipment information of the point to be tested, and obtain the distribution of pipeline branches at the point to be tested and other test points in the connecting area by querying the distribution map of the heating pipe network.

[0061] Specifically, a number of distributed detection points are configured in the heating pipeline. The detection points are equipped with built-in sensors (including temperature, pressure sensors, etc.) for detecting the temperature data and pressure data of the pipeline branches. Each detection point is configured with a QR code associated with it, and the equipment information of the detection point is recorded and displayed through the QR code. The equipment information includes: 1. Equipment number, which uniquely identifies the equipment at the detection point. 2. Equipment type (pipeline type), distinguishing between supply pipes, return pipes or supply and return mixing pipes. 3. Geographical location, geographic coordinate information of the detection point. 4. Installation time and maintenance records, equipment installation history and regular maintenance records.

[0062] By combining the detection point with QR code scanning technology, it provides great convenience for equipment management and troubleshooting. Each detection point, such as the heating equipment in a building, is equipped with a unique QR code, and scanning these QR codes can quickly read the relevant equipment information. Maintenance personnel need to use mobile phones or professional code scanning equipment to obtain the equipment information and temperature and pressure data of the detection point. At the same time, the data is stored in the background database to ensure the real-time and accuracy of the data. In addition, through the application integrated into the mobile terminal, the database of the heating system is accessed, the distribution map of the heating network is retrieved, and the network connection information of the detection point and its adjacent detection points is analyzed, including the layout of the supply and return pipes, the pipeline branches and node information in the area. The location information and branch distribution need to use GPS or GIS system to locate the specific location of the detection point, so as to match it with the geographical distribution of the pipe network to ensure the accuracy of the environment and geographical location of the detection point.

[0063] The heating network distribution map provides an intuitive display of each detection point and its pipelines in the system. It not only shows the location of each detection point, but also indicates the pipeline connection relationship between them. Through the digital heating network distribution map, maintenance personnel can quickly identify the upstream and downstream equipment of the fault point, which is particularly important when a fault occurs. For example, when the temperature or pressure of a certain point is abnormal, the operation and maintenance personnel can quickly locate the connection status of the point through the distribution map and quickly check whether there is a pipeline leak or uneven flow.

[0064] According to the geographical coordinates of the detection point and the distribution map of the pipe network, the pipeline branch where the detection point is located and its adjacent connected area are automatically identified. The distribution of other detection points in the connected area includes the physical location and relative distance of each detection point, the water supply or return branch where the detection point is located, and the heat source location and connection nodes between the branches.

[0065] S2. Read the real-time temperature data and real-time pressure data measured by the built-in sensor at the point to be detected, and obtain the supply and return water temperature and supply and return water pressure at the heat source in the current sampling period.

[0066] Specifically, the temperature and pressure sensors at the detection point will monitor the supply and return water temperature and pressure data in the pipeline in real time, and transmit the data to the operator's detection equipment through wireless transmission technology (such as LoRa, NB-IoT, etc.) or wired connection. While reading the real-time data, the supply and return water temperature and pressure values ​​at the current heat source will also be collected as the benchmark data of the heating system. These benchmark data will be automatically updated every sampling period (such as 1 minute, 5 minutes) to ensure that the analysis is based on the latest heating conditions.

[0067] In addition, sensor data may contain errors, and preliminary error correction and data filtering should be performed when collecting data to ensure the accuracy of the data used.

[0068] S3. By acquiring the environmental data of the point to be detected, analyzing the reasonable temperature difference between the pipeline branch where the point to be detected is located and the heat source, and based on the comparative analysis of the real-time temperature data and the supply and return water temperatures, determining the temperature anomaly information existing in the current pipeline branch.

[0069] In the description of the present invention, by acquiring the environmental data of the point to be detected, analyzing the reasonable temperature difference between the pipeline branch where the point to be detected is located and the heat source, and comparing the real-time temperature data with the supply and return water temperatures, determining the temperature anomaly information existing in the current pipeline branch includes the following steps:

[0070] S31. By querying the equipment information and the distribution map of the heating network, the pipeline type of the pipeline branch where the point to be tested is located is identified, and the pipeline type includes a water supply pipe, a return pipe, and a supply and return pipe.

[0071] S32, setting a sampling period, obtaining environmental data of the point to be detected within the current sampling period, and marking data labels according to the data type of the environmental data, the environmental data including ambient temperature, indoor temperature, heat source pipeline distance and pipeline thermal conductivity.

[0072] Specifically, the ambient temperature, that is, the atmospheric temperature around the point to be tested, affects the heat conduction and heat dissipation effect of the pipeline. The indoor temperature, especially for the terminal test point, is an important parameter for judging heat loss. The heat source pipeline distance, that is, the physical distance between the test point and the heat source, affects the heat loss during transmission. The pipeline thermal conductivity, this data is based on the pipeline material properties and determines the heat conduction rate through the pipeline wall.

[0073] S33. Define the weight value of each type of environmental data, calculate the Euclidean distance between the data, find historical environmental data similar to the environmental data in the current sampling period in the historical sampling period, and calculate the reasonable temperature difference through the historical temperature difference data corresponding to the historical environmental data.

[0074] In the description of the present invention, the weight values ​​of various types of environmental data are defined, the Euclidean distance between the data is calculated, historical environmental data similar to the environmental data in the current sampling period are searched in the historical sampling period, and the reasonable temperature difference is calculated by the historical temperature difference data corresponding to the historical environmental data, including the following steps:

[0075] S331. Standardize and normalize environmental data and historical environmental data.

[0076] The specific, standardized calculation formula is:

[0077]

[0078] Where, X ij represents the environmental data after the jth data is standardized in the current sampling period; X i represents the jth environmental data; max(X j ) represents the maximum value of the jth item in the environmental data; min(X j ) represents the minimum value of the jth item in the environmental data.

[0079] The normalized calculation formula is:

[0080]

[0081] Where Y ij represents the proportion of the jth data in the current sampling period; n represents the number of sampling periods, i = 1, 2, 3, ..., n;

[0082] S332. Using the environmental data in the current sampling period, calculate the information entropy and information utility value of each type of environmental data, and then calculate the weight value of each type of environmental data.

[0083] Specifically, the calculation formula of information entropy is:

[0084]

[0085] In the formula, q j Represents the information entropy of the j-th data.

[0086] The calculation formula of information utility value is:

[0087] p j =1-q j

[0088] In the formula, p j Represents the information utility value of the j-th data.

[0089] The calculation formula for the weight value of each type of environmental data is:

[0090]

[0091] In the formula, w j Represents the weight value of the j-th data.

[0092] S333. Using the time reverse method, according to the Euclidean distance, calculate the dynamic time normalization value corresponding to each type of historical environmental data in the historical sampling period one by one, then multiply by the weight value and add them up to obtain the comprehensive dynamic time normalization value of the historical sampling period.

[0093] Specifically, dynamic time warping is an algorithm that calculates the similarity between two time series, allowing the series to be nonlinearly scaled on the time axis, and is used to calculate the minimum alignment path between the series. It is suitable for comparing time series with different sampling frequencies, and can identify similar patterns that are delayed or advanced on the time axis. In this step, dynamic time warping is used to analyze the similarity between the environmental data in the current sampling period and the historical environmental data, and to determine the difference between the two in the nonlinear time period, so as to improve the accuracy of calculating reasonable temperature differences.

[0094] The overall calculation process of the above comprehensive dynamic time normalization can be summarized as follows:

[0095] 1. Calculate the dynamic time warping value based on the environmental data of the current sampling period and the time series of various types of environmental data in the historical sampling period. Standardized and normalized environmental data in the current sampling period (such as ambient temperature, heat source pipeline distance, etc.).

[0096] First, align the two environmental data sequences on the time axis. In order to calculate the difference between each two data points, define a cumulative distance matrix, in which each element of the matrix represents the distance between two time points (i.e., the difference between two values). Use the dynamic programming algorithm to find the optimal alignment path for the two data sequences so that the cumulative distance is minimized. This path not only considers the difference between the current data points, but also the alignment results of the previous points. The DTW distance between the environmental data in the current sampling period and the historical environmental data sequence is calculated, that is, the dynamic time warping value, which is used to reflect the similarity of the two time series.

[0097] 2. Combined with the environmental data weight value calculated in the previous step, the DTW distance is weighted to better reflect the impact of different environmental data types on the calculation of reasonable temperature difference.

[0098] It is necessary to calculate the DTW distance for each environmental data (such as ambient temperature, indoor temperature, etc.) and multiply it by the corresponding weight value. The weighted dynamic time warping value is called the weighted DTW distance, which combines the impact of various environmental data on the reasonable temperature difference.

[0099] 3. The weighted DTW distances in each historical sampling period are integrated to calculate the overall similarity of each historical sampling period.

[0100] It is necessary to summarize the weighted DTW distances of all types of environmental data for each historical sampling period to obtain a comprehensive dynamic time normalization value. The smaller this value is, the closer the environmental conditions of the historical period are to the current sampling period. The final output is the comprehensive dynamic time normalization value of each historical sampling period, which is used for sorting and selecting the closest historical period in the subsequent step (S334).

[0101] S334. Sort the comprehensive dynamic time normalization values ​​in order from small to large, select the first N historical sampling periods as the approximate period of the current sampling period, extract the actual historical temperature difference data of the approximate period, and then take the average value of multiple historical temperature difference data as the reasonable temperature difference value.

[0102] S34. According to the pipeline type of the pipeline branch, the real-time temperature data of the point to be detected is compared with the supply and return water temperature at the corresponding heat source, and combined with a reasonable temperature difference, the temperature anomaly information existing in the current pipeline branch is determined.

[0103] In the description of the present invention, according to the pipeline type of the pipeline branch, the real-time temperature data of the point to be detected is compared with the supply and return water temperature at the corresponding heat source, and combined with the reasonable temperature difference, the temperature abnormality information existing in the current pipeline branch is determined, including the following steps:

[0104] S341. According to the pipeline type of the pipeline branch where the point to be detected is located, call the corresponding reasonable temperature difference and temperature anomaly judgment strategy, as well as the corresponding supply and return water temperatures at the heat source, and the supply and return water temperatures include the heat source supply water temperature, the heat source return water temperature and the heat source supply and return water temperature difference at the heat source.

[0105] S342. According to the temperature anomaly determination strategy, identify the temperature anomalies existing in the pipeline branches of each pipeline type in the current sampling period.

[0106] In the description of the present invention, according to the temperature anomaly determination strategy, identifying the temperature anomaly existing in the pipeline branches of each pipeline type in the current sampling period includes the following steps:

[0107] S3421. When the pipeline branch is a water supply pipe, the real-time temperature data of the current sampling period is compared with the heat source water supply temperature. If the real-time temperature data is higher than the heat source water supply temperature, it is determined to be an instrument failure. If the real-time temperature data is lower than the heat source water supply temperature and the difference is less than the reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time temperature data is lower than the heat source water supply temperature and the difference is greater than or equal to the reasonable temperature difference, it is determined that the pipeline branch has a slow flow rate abnormality.

[0108] S3422. When the pipeline branch is a return pipe, compare the real-time temperature data of the current sampling period with the heat source return water temperature. If the real-time temperature data is higher than the heat source return water temperature and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has a flow overshoot abnormality or a supply and return water short circuit abnormality. If the real-time temperature data is higher than the heat source return water temperature and the difference is less than the reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time temperature data is lower than the heat source return water temperature and the difference is less than the reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time temperature data is lower than the heat source return water temperature and the difference is greater than the reasonable temperature difference, it is determined that there is an abnormality in the pipeline branch that the flow is too low or the branch heat consumption is too large.

[0109] S3423. When the pipeline branch is a supply and return water pipe, calculate the real-time supply and return water temperature difference of the pipeline branch, and compare the real-time supply and return water temperature difference with the heat source supply and return water temperature difference. If the difference between the real-time supply and return water temperature difference and the heat source supply and return water temperature difference is less than the reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time supply and return water temperature difference is higher than the heat source supply and return water temperature difference and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has an abnormality of too low flow or too much heat consumption. If the real-time supply and return water temperature difference is lower than the heat source supply and return water temperature difference and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has an abnormality of too high flow or too little heat consumption.

[0110] S343. Mark the points to be detected where temperature anomalies exist in the distribution map of the heating network, and record the temperature anomaly information of each point to be detected based on the anomaly determination result.

[0111] S4. By acquiring the terrain data of the point to be detected, analyzing the reasonable pressure difference between the pipeline branch where the point to be detected is located and the heat source, and based on the comparative analysis of the real-time pressure data and the supply and return water pressure, determining the pressure anomaly information existing in the current pipeline branch.

[0112] In the description of the present invention, by acquiring the terrain data of the point to be detected, analyzing the reasonable pressure difference between the pipeline branch where the point to be detected is located and the heat source, and comparing the real-time pressure data with the supply and return water pressure, determining the pressure anomaly information existing in the current pipeline branch includes the following steps:

[0113] S41. Obtain the pipeline type of the pipeline branch where the point to be detected is located, and obtain the terrain data of the point to be detected by querying the heating network distribution map. The terrain data includes the altitude of the point to be detected, the relative height difference between the point to be detected and the heat source, the pipeline length and the pressure loss.

[0114] Specifically, the altitude of the point to be tested, the relative height difference between the point to be tested and the heat source, the length of the pipeline, and the pressure loss are extracted from the terrain data. Altitude is one of the main factors affecting pressure, and higher altitudes usually lead to lower static pressure. The relative height difference, which is the height difference between the point to be tested and the heat source, determines the hydrostatic pressure difference, which can be estimated based on the Bernoulli equation. Pipeline length and pressure loss, according to the fluid mechanics formula, longer pipelines and pipes of different materials and diameters will cause friction pressure loss.

[0115] S42. By defining the input layer, output layer and model structure, a neural network model is constructed, and historical pressure data is used for model training and verification to analyze reasonable pressure differences.

[0116] In the description of the present invention, by defining the input layer, output layer and model structure, constructing a neural network model, using historical pressure data for model training and verification, and analyzing the reasonable pressure difference, the following steps are included:

[0117] S421. Normalize the terrain data by deviation standardization, extract the data features of the terrain data, and then obtain the input variables of the neural network model by data dimension reduction.

[0118] S422. Define the input variables after dimensionality reduction and the real-time pressure data as input nodes to construct an input layer, and define the reasonable pressure difference as model output to construct an output layer.

[0119] S423. Design a radial basis function layer as the core layer of the neural network model, construct a training data set by inputting historical pressure data, use the gradient descent method to train the neural network model, and then obtain the trained neural network model after testing and verification.

[0120] Specifically, the radial basis function layer is the core layer of the neural network, which is used to calculate the nonlinear transformation of the input variables through radial basis functions (such as Gaussian functions). The radial basis function layer has a strong approximation ability and is suitable for processing nonlinear relationships. Through the pressure data within the historical sampling period, the model parameters are adjusted using the gradient descent method so that the model can gradually approach the target value of the training data. During the training process, the error is calculated and the model parameters are continuously optimized. After the training is completed, the model is verified using the test set to ensure that the model can accurately predict a reasonable pressure difference.

[0121] S424. Analyze the reasonable pressure difference of the current sampling period using a neural network model.

[0122] S43. According to the pipeline type of the pipeline branch, the real-time pressure data of the point to be detected is compared with the supply and return water pressure at the corresponding heat source, and combined with a reasonable pressure difference, the pressure abnormality information existing in the current pipeline branch is determined.

[0123] In the description of the present invention, according to the pipeline type of the pipeline branch, the real-time pressure data of the point to be detected is compared with the supply and return water pressure at the corresponding heat source, and combined with the reasonable pressure difference, the pressure abnormality information existing in the current pipeline branch is determined, including the following steps:

[0124] S431. According to the pipeline type of the pipeline branch where the point to be detected is located, call the corresponding reasonable pressure difference and pressure anomaly judgment strategy, as well as the corresponding supply and return water pressure, and the supply and return water pressure includes the heat source supply water pressure at the heat source, the heat source return water pressure and the supply and return water pressure difference of the adjacent point.

[0125] S432. According to the pressure anomaly determination strategy, identify the pressure anomaly conditions existing in the pipeline branches of each pipeline type in the current sampling period.

[0126] In the description of the present invention, according to the pressure anomaly determination strategy, identifying the pressure anomaly existing in the pipeline branches of each pipeline type in the current sampling period includes the following steps:

[0127] S4321. When the pipeline branch is a water supply pipe, compare the real-time pressure data of the current sampling period with the heat source water supply pressure. If the real-time pressure data is higher than the heat source water supply pressure and the pressure difference is greater than the reasonable pressure difference, it is determined that the pipeline branch has an abnormal high pressure. If the real-time pressure data is lower than the heat source water supply pressure, it is determined that there is no abnormality in the pipeline branch.

[0128] S4322. When the pipeline branch is a return pipe, compare the real-time pressure data of the current sampling period with the heat source return pressure. If the real-time pressure data is higher than the heat source return pressure, it is determined that there is no abnormality in the pipeline branch. If the real-time pressure data is lower than the heat source return pressure and the pressure difference is greater than the reasonable pressure difference, it is determined that there is a leakage abnormality in the pipeline branch.

[0129] S4323. When the pipeline branch is a water supply and return pipe, calculate the real-time water supply and return pressure difference of the pipeline branch, and compare the real-time water supply and return pressure difference with the water supply and return pressure difference of the adjacent point. If the difference between the real-time water supply and return pressure difference and the water supply and return pressure difference of the adjacent point is less than the reasonable pressure difference, it is determined that there is no abnormality in the pipeline branch. If the real-time water supply and return pressure difference is higher than the water supply and return pressure difference of the adjacent point and the difference is greater than the reasonable pressure difference, it is determined that there is a pressure abnormality or a blockage abnormality in the pipeline branch. If the real-time water supply and return pressure difference is lower than the water supply and return pressure difference of the adjacent point and the difference is greater than the reasonable pressure difference, it is determined that there is an overflow abnormality or a supply and return short circuit abnormality in the pipeline branch.

[0130] S433. Mark the points to be tested where abnormal pressure exists in the distribution map of the heating pipe network, and record the abnormal pressure information of each point to be tested based on the abnormality determination result.

[0131] S5. According to the detected points in the heating network distribution map, the abnormality determination result of the current pipeline branch is verified, and after the verification is passed, the abnormal information of the pipeline branch is marked.

[0132] In the description of the present invention, according to the detected points in the heating network distribution map, the abnormality determination result of the current pipeline branch is verified, and after the verification is passed, the abnormal information of the pipeline branch is marked, which includes the following steps:

[0133] S51, obtaining the abnormality determination result of the pipeline branch where the point to be detected is located, and obtaining the abnormality determination results of other detected points located in the connection area of ​​the pipeline branch according to the heating network distribution map.

[0134] S52: verify the rationality of the abnormality determination result of the point to be detected based on the pipeline type and abnormality determination result of the detected point, as well as the upstream and downstream relationship between the detected point and the point to be detected.

[0135] Specifically, according to the type of pipeline (water supply or return water) between the point to be tested and the tested point, verify whether the changes in the temperature and pressure data between the upstream and downstream are in line with expectations. For example, the temperature in the water supply pipeline should gradually decrease with the increase in the distance from the heat source, while the return pipe is the opposite, and the pressure gradually decreases along the length of the pipeline. Then, by analyzing the upstream and downstream relationship between the point to be tested and the tested point, check whether the relationship between the temperature and pressure data of the two conforms to the laws of thermal fluid mechanics. If there is an abnormality at the upstream point, it may cause deviations in the temperature and pressure data of the downstream point. This step reversely infers whether the abnormal information of the point to be tested is reasonable through the abnormal data of the upstream and downstream.

[0136] S53, drawing a change trend curve based on the historical detection data of the points to be detected and the detected points, and verifying the consistency of the change trends of the real-time temperature data and the real-time pressure data.

[0137] Specifically, historical data such as temperature and pressure of the point to be detected and its upstream and downstream detected points are obtained from historical sampling cycles, and data changes of each detection point in the past multiple cycles are recorded.

[0138] Based on the historical temperature and pressure data, draw the trend curve of each detection point in different past cycles to compare the change trend of the real-time data in the current sampling cycle. By comparing the temperature and pressure change trends of the points to be detected with those of the detected points, verify whether there is an obvious abnormal deviation. For example, if the temperature changes in the historical data are stable, but there are drastic fluctuations in the current sampling cycle, the anomaly has a high degree of credibility.

[0139] S54. Integrate the abnormality determination results after verification, and mark the abnormal information of the point to be detected in the current sampling period in the heating pipe network distribution map.

[0140] It should be noted that in the operation and management of the heating network, the verification of abnormal judgment results is an important link to ensure the stable operation of the system. The abnormal judgment objects include various types such as network failures, detection point failures, and pipeline branch failures. Any failure may affect the efficiency and safety of the entire heating system. Therefore, the present invention is mainly used to timely and accurately identify and mark these abnormal information, provide important decision-making basis for operation and maintenance personnel, and ensure the smooth operation of the heating system.

[0141] First of all, for the object of abnormal determination, pipeline failure may manifest as pipeline leakage, corrosion or blockage, etc. These failures will not only cause heat loss, but may also cause safety hazards, such as insufficient heating or system overload. The detection point failure mainly refers to the failure of the sensor or the error of data collection, which causes the lack or misleading of real-time monitoring information, further affecting the decision-making basis of the system. Branch failures are usually related to hydraulic imbalance, which may cause insufficient heating or overheating in certain areas, affecting the comfort of users. To this end, the present invention determines the source, location and abnormal type of the fault in the heating pipeline by analyzing the temperature and pressure data of the detection point.

[0142] In the process of verifying the abnormality judgment results, the abnormal information of the detected points is extracted from the distribution map of the heating network, and compared and analyzed based on this information. The status and historical data of these detected points can provide a reference for the abnormal judgment of the current pipeline branch. For example, if the temperature and pressure data of a branch obviously deviate from the historical standards, the system will automatically judge and mark it as abnormal. By comparing the status of upstream and downstream equipment, operation and maintenance personnel can quickly identify the possible source of the fault and conduct targeted troubleshooting.

[0143] In addition, the verification process also requires the use of historical data analysis tools to draw a trend curve between the current pipeline branch and the detected points. If the real-time data of the current sampling period is consistent with the historical data trend, the credibility of the abnormal judgment result can be enhanced; otherwise, further investigation may be required. Through these steps, the system can not only monitor the operating status of each pipeline in real time, but also quickly locate the problem and take measures when an abnormality occurs.

[0144] Example

[0145] Regarding the temperature anomaly determination strategy and pressure anomaly determination strategy introduced in the present invention, and the matching reasonable temperature difference and reasonable pressure difference, examples in the actual anomaly determination process are as follows:

[0146] 1. Temperature is too high

[0147] 1.1. If the test point is water supply: the water supply temperature cannot exceed the heat source temperature. If it exceeds the heat source temperature by >1°C, it is determined as follows:

[0148] a. Instrument failure.

[0149] 1.2 If the detection point is return water: compare the return water temperature with the heat source return water temperature. If the return water temperature of the detection point is greater than the heat source, and the return water temperature is +2 degrees or above, it is determined that:

[0150] a. The flow rate at this detection point is greater than the design flow rate. This detection point is in a flow overshoot state, and the room temperature in this area is significantly higher than that in other areas;

[0151] b. If the return water temperature at the detection point is greater than the return water temperature of the heat source by 5 degrees or more, there may be a short circuit in the supply and return water downstream of the detection point, causing part of the supply water to return from the return water pipe, resulting in a high return water temperature.

[0152] 2. The temperature is too low

[0153] 2.1. If the test point is the water supply temperature: If the water supply temperature at the test point is 5+℃ lower than the water supply temperature of the heat source, determine:

[0154] a. The temperature of other nearby measuring points is not so low, which means that the flow velocity of the measuring point branch is too slow, resulting in excessive heat loss and low temperature;

[0155] b. The temperatures at other nearby measuring points are all low, indicating that the tassel of the main pipe is too low and the pipe insulation is poor, resulting in low temperature.

[0156] 2.2. If the test point is return water, if the return water temperature is more than 2 degrees lower than the hot water return water temperature, it is determined as follows:

[0157] a. The flow rate of the pipeline branch is too low, resulting in low return water temperature;

[0158] b. The pipeline branch consumes a lot of heat and has a large heat loss, which may be due to the poor insulation enclosure structure in this area;

[0159] c. The pipeline branch consumes a lot of heat and has a large heat loss. This may be because the heating area of ​​the area exceeds the design expectations, resulting in heat dissipation greater than the design value, causing the return water temperature to be low.

[0160] 3. The temperature difference between supply and return water is too large

[0161] 3.1. When the supply and return water temperature difference of the detection point is greater than the supply and return water temperature difference of the heat source by more than 2 degrees, it is judged that:

[0162] a. The flow rate of the pipeline branch is too low;

[0163] b. The pipeline branch consumes a lot of heat and has a large heat loss, which may be due to the poor insulation enclosure structure in this area;

[0164] c. The pipeline branch consumes a lot of heat and has a large heat loss. This may be because the heating area of ​​the area exceeds the design expectations, resulting in heat dissipation greater than the design value.

[0165] 4. The temperature difference between supply and return water is too small

[0166] 4.1 When the supply and return water temperature difference at the detection point is less than the supply and return water temperature difference of the heat source by more than 2 degrees, it is judged that:

[0167] a. The flow rate of the pipeline branch is too large;

[0168] b. The pipeline branch uses less heat and has less heat loss, which may be due to the better and newer insulation enclosure structure in this area;

[0169] c. The pipeline branch uses less heat and has less heat loss. This may be because the heating area of ​​the area is lower than the design expectation, resulting in less heat dissipation than the design value;

[0170] d. There is a short circuit between the supply and return water in the downstream pipeline.

[0171] 5. Excessive pressure

[0172] 5.1. If the water supply pressure at the detection point is higher than the heat source water supply pressure by more than 0.01MPa, it is determined that:

[0173] a. The detection point is lower than the heat source;

[0174] b. A pump is turned on in the main pipe upstream of the detection point, pushing up the pressure.

[0175] 5.2. If the detection point is in the return water, and the pressure is higher than the return water pressure of the heat source, it is generally a normal phenomenon. If the return water pressure is higher than the supply water pressure, it is determined that:

[0176] a. Check whether there is a terrain height difference between the water supply and return points, and whether the return point is lower than the water supply point;

[0177] b. The upstream return water main (near the end of the pipe network) may have a pump to pump water to the heat source, pushing up the return water pressure.

[0178] 6. Pressure is too low

[0179] 6.1. If the water supply pressure at the detection point is lower than the heat source water supply pressure, it is generally a normal phenomenon. If the water supply pressure is lower than the return water pressure, it is determined that:

[0180] a. Check whether there is a terrain height difference between the water supply and return points, and whether the return point is lower than the water supply point;

[0181] b. The upstream return water main (near the end of the pipe network) may have a pump to pump water to the heat source, pushing up the return water pressure.

[0182] 6.2. If the measuring point is in the return water, and the pressure is lower than the heat source return water pressure by more than 0.01MPa, it is determined that:

[0183] a. The detection point is higher than the heat source;

[0184] b. The pipeline branch may have a water leak.

[0185] 7. The pressure difference between supply and return water is too large

[0186] 7.1. If the pressure difference at the detection point is greater than the pressure difference at the surrounding upstream and downstream detection points, it is determined that:

[0187] a. The valve opening of the pipeline branch is too small or completely closed, resulting in small flow and large pressure difference;

[0188] b. The circulation resistance of the pipeline branch is greater than the design value, and there may be blockage;

[0189] c. A pump may be turned on between the return water and the supply water, increasing the pressure difference;

[0190] d. There may be a pump in the downstream return water main (close to the heat source and between the next measuring point) pumping water toward the heat source.

[0191] 8. The differential pressure of supply and return water is too low

[0192] 8.1 If the pressure difference at the detection point is less than the pressure difference at the surrounding downstream measurement points, it is determined that:

[0193] a. The terrain of the water supply measuring point is higher than that of the return water measuring point;

[0194] b. The circulation resistance of the pipeline branch is less than the design value, and there may be overflow;

[0195] c. Short circuit of water supply and return;

[0196] d. The supply and return water are reversed.

[0197] In summary, with the help of the above technical solution of the present invention, by scanning the two-dimensional code of the point to be detected, combined with the assistance of the distribution map of the heating pipe network, the detection process is made more convenient and efficient, the cumbersomeness of manual input is reduced, and the accuracy of data collection is improved; using the real-time temperature and pressure data collected, dynamic analysis can be performed according to the heat source data in the actual sampling period to ensure the real-time and accuracy of the monitoring results; at the same time, by comprehensively analyzing the environmental data and terrain information, the reasonable difference of temperature and pressure can be accurately determined, which not only improves the abnormal detection ability of the pipeline branch, but also effectively responds to the influence of different geographical conditions on temperature and pressure, thereby effectively improving the sensitivity and reliability of abnormal detection of the pipeline line. Through multi-dimensional data analysis, more intelligent abnormal judgment is achieved. At the same time, through real-time monitoring and automatic verification optimization of the heating pipe network, the accuracy and speed of abnormal detection are significantly improved, and the phenomenon of missed detection and misjudgment is reduced; the present invention makes full use of the Internet of Things sensing technology and intelligent algorithms to realize automated and precise troubleshooting and pipeline management, thereby optimizing the energy efficiency of the heating system and reducing energy waste.

[0198] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

Claims

1. A method for determining abnormality of a heating network line based on temperature and pressure detection, characterized in that: The method comprises the following steps: S1. Scan the QR code of the point to be tested, read the equipment information of the point to be tested, and obtain the distribution of pipeline branches of the point to be tested and other test points in the connecting area by querying the distribution map of the heating pipe network; S2, read the real-time temperature data and real-time pressure data measured by the built-in sensor at the point to be detected, and obtain the supply and return water temperature and supply and return water pressure at the heat source in the current sampling period; S3. By acquiring the environmental data of the point to be detected, analyzing the reasonable temperature difference between the pipeline branch where the point to be detected is located and the heat source, and determining the temperature anomaly information existing in the current pipeline branch based on the comparative analysis of the real-time temperature data and the supply and return water temperatures; S4. By acquiring the terrain data of the point to be detected, analyzing the reasonable pressure difference between the pipeline branch where the point to be detected is located and the heat source, and determining the pressure abnormality information existing in the current pipeline branch based on the comparative analysis of the real-time pressure data and the supply and return water pressure; S5. According to the detected points in the heating network distribution map, the abnormality determination result of the current pipeline branch is verified, and after the verification is passed, the abnormal information of the pipeline branch is marked.

2. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 1, characterized in that: The method of acquiring environmental data of the point to be detected, analyzing the reasonable temperature difference between the pipeline branch where the point to be detected is located and the heat source, and determining the temperature anomaly information existing in the current pipeline branch based on the comparative analysis of the real-time temperature data and the supply and return water temperatures includes the following steps: S31, by querying the equipment information and the distribution map of the heating pipe network, identifying the pipeline type of the pipeline branch where the point to be detected is located, and the pipeline type includes a water supply pipe, a return pipe, and a supply and return pipe; S32, setting a sampling period, obtaining the environmental data of the point to be detected within the current sampling period, and marking data tags according to the data type of the environmental data, wherein the environmental data includes ambient temperature, indoor temperature, heat source pipeline distance, and pipeline thermal conductivity; S33, define the weight value of each type of environmental data, calculate the Euclidean distance between the data, find the historical environmental data similar to the environmental data in the current sampling period in the historical sampling period, and calculate the reasonable temperature difference value through the historical temperature difference data corresponding to the historical environmental data; S34. According to the pipeline type of the pipeline branch, the real-time temperature data of the point to be detected is compared with the supply and return water temperature at the corresponding heat source, and combined with the reasonable temperature difference, the temperature anomaly information existing in the current pipeline branch is determined.

3. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 2, characterized in that: The method of defining weight values ​​of various types of environmental data, calculating the Euclidean distance between data, searching for historical environmental data similar to the environmental data in the current sampling period in the historical sampling period, and calculating a reasonable temperature difference value through the historical temperature difference data corresponding to the historical environmental data includes the following steps: S331, standardizing and normalizing the environmental data and historical environmental data; S332, using the environmental data in the current sampling period, calculating the information entropy and information utility value of each type of environmental data, and then calculating the weight value of each type of environmental data; S333, using the time reverse order method, according to the Euclidean distance, calculate the dynamic time normalization value corresponding to each type of historical environmental data in the historical sampling period one by one, and then multiply it by the weight value and add it up to obtain the comprehensive dynamic time normalization value of the historical sampling period; S334. Sort the comprehensive dynamic time normalization values ​​in order from small to large, select the first N historical sampling periods as the approximate period of the current sampling period, extract the actual historical temperature difference data of the approximate period, and then take the average value of multiple historical temperature difference data as the reasonable temperature difference value.

4. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 2, characterized in that: According to the pipeline type of the pipeline branch, the real-time temperature data of the point to be detected is compared with the supply and return water temperature at the corresponding heat source, and combined with the reasonable temperature difference, the temperature abnormality information existing in the current pipeline branch is determined, including the following steps: S341. According to the pipeline type of the pipeline branch where the point to be detected is located, the corresponding reasonable temperature difference and temperature anomaly judgment strategy, as well as the corresponding supply and return water temperatures at the heat source, are called, and the supply and return water temperatures include the heat source supply water temperature, the heat source return water temperature and the heat source supply and return water temperature difference at the heat source; S342, identifying temperature anomalies existing in pipeline branches of each pipeline type in the current sampling period according to the temperature anomaly determination strategy; S343. Mark the points to be detected where temperature anomalies exist in the distribution map of the heating network, and record the temperature anomaly information of each point to be detected based on the anomaly determination result.

5. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 4, characterized in that: The method of identifying the temperature anomaly of each pipeline type in the current sampling period according to the temperature anomaly determination strategy includes the following steps: S3421. When the pipeline branch is a water supply pipe, the real-time temperature data of the current sampling period is compared with the heat source water supply temperature. If the real-time temperature data is higher than the heat source water supply temperature, it is determined to be an instrument failure. If the real-time temperature data is lower than the heat source water supply temperature and the difference is less than the reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time temperature data is lower than the heat source water supply temperature and the difference is greater than or equal to the reasonable temperature difference, it is determined that there is an abnormality of too slow flow rate in the pipeline branch. S3422, when the pipeline branch is a return water pipe, compare the real-time temperature data of the current sampling period with the heat source return water temperature. If the real-time temperature data is higher than the heat source return water temperature and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has a flow overshoot abnormality or a supply and return water short circuit abnormality. If the real-time temperature data is higher than the heat source return water temperature and the difference is less than the reasonable temperature difference, it is determined that the pipeline branch has no abnormality. If the real-time temperature data is lower than the heat source return water temperature and the difference is less than the reasonable temperature difference, it is determined that the pipeline branch has no abnormality. If the real-time temperature data is lower than the heat source return water temperature and the difference is greater than the reasonable temperature difference, it is determined that the pipeline branch has a low flow abnormality or a branch heat consumption abnormality; S3423. When the pipeline branch is a supply and return water pipe, calculate the real-time supply and return water temperature difference of the pipeline branch, and compare the real-time supply and return water temperature difference with the heat source supply and return water temperature difference. If the difference between the real-time supply and return water temperature difference and the heat source supply and return water temperature difference is less than a reasonable temperature difference, it is determined that there is no abnormality in the pipeline branch. If the real-time supply and return water temperature difference is higher than the heat source supply and return water temperature difference and the difference is greater than a reasonable temperature difference, it is determined that the pipeline branch has an abnormality of too low flow or too much heat consumption. If the real-time supply and return water temperature difference is lower than the heat source supply and return water temperature difference and the difference is greater than a reasonable temperature difference, it is determined that the pipeline branch has an abnormality of too high flow or too little heat consumption.

6. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 2, characterized in that: The method of obtaining the terrain data of the point to be detected, analyzing the reasonable pressure difference between the pipeline branch where the point to be detected is located and the heat source, and determining the pressure abnormality information existing in the current pipeline branch based on the comparison analysis of the real-time pressure data and the supply and return water pressure includes the following steps: S41, obtaining the pipeline type of the pipeline branch where the point to be detected is located, and obtaining the terrain data of the point to be detected by querying the distribution map of the heating pipe network, wherein the terrain data includes the altitude of the point to be detected, the relative height difference between the point to be detected and the heat source, the pipeline length and the pressure loss; S42. Construct a neural network model by defining the input layer, output layer and model structure, use historical pressure data to train and verify the model, and analyze reasonable pressure differences; S43. According to the pipeline type of the pipeline branch, the real-time pressure data of the point to be detected is compared with the supply and return water pressure at the corresponding heat source, and combined with the reasonable pressure difference, the pressure anomaly information existing in the current pipeline branch is determined.

7. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 6, characterized in that: The steps of defining the input layer, the output layer and the model structure, constructing a neural network model, using historical pressure data to train and verify the model, and analyzing the reasonable pressure difference include the following steps: S421, normalizing the terrain data by deviation standardization, extracting data features of the terrain data, and then obtaining input variables of the neural network model by data dimensionality reduction; S422, defining the input variables after dimensionality reduction and the real-time pressure data as input nodes to construct an input layer, and defining the reasonable pressure difference as the model output to construct an output layer; S423, designing a radial basis function layer as the core layer of the neural network model, constructing a training data set by inputting historical pressure data, training the neural network model using a gradient descent method, and obtaining a trained neural network model after testing and verification; S424. Analyze the reasonable pressure difference of the current sampling period using the neural network model.

8. The method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 6, characterized in that: According to the pipeline type of the pipeline branch, the real-time pressure data of the point to be detected is compared with the supply and return water pressure at the corresponding heat source, and combined with the reasonable pressure difference, the pressure abnormality information existing in the current pipeline branch is determined, including the following steps: S431. According to the pipeline type of the pipeline branch where the point to be detected is located, the corresponding reasonable pressure difference and pressure anomaly judgment strategy, as well as the corresponding supply and return water pressure are called, and the supply and return water pressure includes the heat source supply water pressure at the heat source, the heat source return water pressure and the supply and return water pressure difference of the adjacent point; S432, according to the pressure anomaly determination strategy, identifying the pressure anomaly existing in the pipeline branches of each pipeline type in the current sampling period; S433. Mark the points to be tested where abnormal pressure exists in the distribution map of the heating pipe network, and record the abnormal pressure information of each point to be tested based on the abnormality determination result.

9. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 8, characterized in that: The method of identifying the pressure anomaly conditions of the pipeline branches of each pipeline type in the current sampling period according to the pressure anomaly determination strategy includes the following steps: S4321. When the pipeline branch is a water supply pipe, the real-time pressure data of the current sampling period is compared with the heat source water supply pressure. If the real-time pressure data is higher than the heat source water supply pressure and the pressure difference is greater than the reasonable pressure difference, it is determined that the pipeline branch has an abnormal high pressure. If the real-time pressure data is lower than the heat source water supply pressure, it is determined that the pipeline branch has no abnormality. S4322, when the pipeline branch is a return pipe, compare the real-time pressure data of the current sampling period with the heat source return water pressure. If the real-time pressure data is higher than the heat source return water pressure, it is determined that there is no abnormality in the pipeline branch. If the real-time pressure data is lower than the heat source return water pressure and the pressure difference is greater than the reasonable pressure difference, it is determined that there is a water leakage abnormality in the pipeline branch. S4323. When the pipeline branch is a water supply and return pipe, calculate the real-time water supply and return pressure difference of the pipeline branch, and compare the real-time water supply and return pressure difference with the water supply and return pressure difference of the adjacent point. If the difference between the real-time water supply and return pressure difference and the water supply and return pressure difference of the adjacent point is less than a reasonable pressure difference, it is determined that there is no abnormality in the pipeline branch. If the real-time water supply and return pressure difference is higher than the water supply and return pressure difference of the adjacent point and the difference is greater than a reasonable pressure difference, it is determined that there is a pressure abnormality or a blockage abnormality in the pipeline branch. If the real-time water supply and return pressure difference is lower than the water supply and return pressure difference of the adjacent point and the difference is greater than a reasonable pressure difference, it is determined that there is an overflow abnormality or a supply and return water short circuit abnormality in the pipeline branch.

10. A method for determining abnormality of a heating network line based on temperature and pressure detection according to claim 6, characterized in that: The method of verifying the abnormality determination result of the current pipeline branch according to the detected points in the heating network distribution map, and marking the abnormal information of the pipeline branch after the verification is passed, includes the following steps: S51, obtaining the abnormality determination result of the pipeline branch where the point to be detected is located, and obtaining the abnormality determination results of other detected points located in the connected area of ​​the pipeline branch according to the heating network distribution map; S52, verifying the rationality of the abnormality determination result of the point to be detected according to the pipeline type and abnormality determination result of the detected point, and the upstream and downstream relationship between the detected point and the point to be detected; S53, drawing a change trend curve based on the historical detection data of the points to be detected and the detected points, and verifying the consistency of the change trends of the real-time temperature data and the real-time pressure data; S54. Integrate the abnormality determination results after verification, and mark the abnormal information of the point to be detected in the current sampling period in the heating pipe network distribution map.

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