Heat supply system leakage positioning method and system based on pipe network pressure characteristic analysis

Abstract

The invention discloses a heat supply system leakage positioning method and system based on pipe network pressure characteristic analysis, and relates to the technical field of heat supply pipe network operation detection, and the method comprises the steps: collecting pressure data of each pressure monitoring point, and outputting pressure time sequence data; constructing a reference pressure interval and reference pressure fluctuation intensity of each monitoring point and a reference pressure drop interval of a pipe section corresponding to the adjacent monitoring point during operation; quantitative comparison is conducted, and the monitoring points in the abnormal pressure state and the abnormal pipe sections are determined; according to the connection relation in the pipe network structure, connectivity and boundary analysis is conducted, and a leakage section corresponding to the abnormal pressure state is determined; and finally determining the specific position of the leakage in the section based on the pressure offset space distribution of each pressure monitoring point relative to the reference pressure state in the leakage section. According to the method, accurate section division and fine position positioning of heat supply pipe network leakage are realized through pressure characteristic reference modeling and space communication analysis.

Description

Technical Field

[0001] This invention relates to the field of heating network operation detection technology, specifically a method and system for locating leaks in heating systems based on pipeline pressure characteristic analysis. Background Technology

[0002] With the continuous expansion of urban centralized heating, the structure of heating pipe networks is becoming increasingly complex, and their operational safety and reliability have gradually become key issues in heating system management. Heating pipe networks operate under long-term high temperature, high pressure, and complex conditions, inevitably leading to problems such as pipe aging, loose joints, and local structural defects, thus increasing the risk of leakage. To ensure the stable operation of heating systems, related fields have gradually carried out research on monitoring and detecting the operational status of heating pipe networks, among which pressure parameter-based operational monitoring technology has been widely applied. Deploying pressure monitoring devices at key nodes of the pipe network to collect pressure data during operation and analyze and judge the network status has become a common technical means in heating system operation management. Simultaneously, with the improvement of data acquisition and processing capabilities, technical approaches based on operational data to analyze and locate the network status have gradually gained attention, providing a new technical foundation for leak detection and location in heating systems.

[0003] Although existing technologies can analyze the operational status of heating pipe networks to some extent through pressure monitoring, they still have significant shortcomings in terms of leak location accuracy and reliability. First, existing technologies mostly focus on monitoring single-point pressure values ​​or instantaneous pressure changes, lacking systematic modeling of long-term pressure characteristics. This makes it difficult to distinguish between normal pressure changes caused by load fluctuations and operating condition adjustments and abnormal pressure states caused by leaks, thus easily leading to misjudgments. Second, while some technologies incorporate pressure change trend analysis, they typically lack a unified benchmark pressure range and benchmark fluctuation characteristics. Their judgment thresholds rely on empirical settings, resulting in poor stability and repeatability, making it difficult to adapt to different pipe network structures and operating conditions.

[0004] Furthermore, existing leak location methods have significant limitations at the spatial analysis level. Most techniques can only determine the presence of anomalies near a specific monitoring point, failing to combine the pipeline network structure with a systematic connectivity and boundary analysis of abnormal pressure states. This results in the leak range often being roughly limited to a large area, making it difficult to further narrow down the location segment. More importantly, existing technologies generally lack in-depth analysis of the spatial distribution patterns of abnormal pressure within the pipeline network, failing to precisely determine the leak location based on the distribution characteristics of pressure offsets within the pipe segment, thus hindering accurate leak location. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing methods for monitoring and locating pressure in heating networks have several drawbacks: difficulty in establishing a stable and unified pressure operating benchmark, reliance on empirical thresholds for abnormal pressure determination with insufficient reliability, inability to effectively divide abnormal pressure states into sections based on the network structure, and difficulty in accurately locating leaks in heating networks based on pressure characteristic analysis.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for locating leaks in a heating system based on pipeline pressure characteristic analysis, comprising: numbering pressure monitoring points in the heating pipeline network and determining the correspondence between adjacent monitoring points and pipe sections; collecting pressure data from each pressure monitoring point and outputting pressure time series data; based on the pressure time series data, constructing a baseline pressure range, a baseline pressure fluctuation intensity, and a baseline pressure drop range for each monitoring point and the corresponding pipe section of adjacent monitoring points during operation; quantitatively comparing the real-time pressure data collected during operation with the baseline pressure range, the baseline pressure fluctuation intensity, and the baseline pressure drop range to determine the monitoring points and abnormal pipe sections with abnormal pressure states; performing connectivity and boundary analysis based on the connection relationships of the monitoring points and abnormal pipe sections with abnormal pressure states in the pipeline network structure to determine the leak section corresponding to the abnormal pressure state; and finally determining the specific location of the leak within the leak section based on the spatial distribution of the pressure offset of each pressure monitoring point relative to the baseline pressure state.

[0008] As a preferred embodiment of the heating system leakage location method based on pipeline pressure characteristic analysis described in this invention, the step of numbering the pressure monitoring points in the heating pipeline network and determining the correspondence between adjacent monitoring points and pipe sections includes, when numbering the pressure monitoring points in the heating pipeline network and determining the correspondence between adjacent monitoring points and pipe sections, taking the actual pipeline connection structure of the heating pipeline network as the basis, taking the pressure monitoring points set at the pipeline nodes as the basic monitoring objects, and determining two pressure monitoring points directly connected in the pipeline structure as a group of adjacent monitoring points, and taking the pipeline section as the pipe section number, keeping the pressure monitoring point number and pipe section number unchanged throughout the entire operation.

[0009] As a preferred embodiment of the heating system leakage location method based on pipeline pressure characteristic analysis described in this invention, the step of collecting pressure data from each pressure monitoring point and outputting pressure time series data includes: when collecting pressure data from each pressure monitoring point, each pressure monitoring point uses the same sampling period to synchronously collect the pressure inside the pipe, and records the pressure data collected at each sampling moment in correspondence with the corresponding monitoring point number and sampling moment, and outputs pressure time series data arranged in chronological order.

[0010] As a preferred embodiment of the heating system leakage location method based on pipeline pressure characteristic analysis described in this invention, the following is included: when constructing the benchmark pressure range, benchmark pressure fluctuation intensity, and benchmark pressure drop range of the corresponding pipe section of each monitoring point based on pressure time series data during operation, the operation period during which the heating system is confirmed to be leak-free is selected as the benchmark operation period, and statistical analysis is performed only based on the pressure time series data collected within this benchmark operation period.

[0011] As a preferred embodiment of the heating system leakage location method based on pipeline pressure characteristic analysis described in this invention, the step of quantitatively comparing the real-time pressure data collected during operation with the benchmark pressure range, benchmark pressure fluctuation intensity, and benchmark pressure drop range to determine the monitoring points and abnormal pipe sections with abnormal pressure states includes judging the continuously sampled real-time pressure data within a fixed-length time window when making quantitative comparisons with the real-time pressure data collected during operation. Only when the real-time pressure data continuously meets the corresponding abnormal judgment conditions throughout the entire time window is the corresponding pressure monitoring point and pipe section determined to have an abnormal pressure state.

[0012] As a preferred embodiment of the heating system leakage location method based on pipeline pressure characteristic analysis described in this invention, the step of performing connectivity and boundary analysis based on the connection relationship between monitoring points with abnormal pressure states and abnormal pipe segments in the pipeline structure to determine the leakage section corresponding to the abnormal pressure state includes, when performing connectivity and boundary analysis based on the connection relationship between monitoring points with abnormal pressure states and abnormal pipe segments in the pipeline structure, determining whether the abnormal pipe segments are spatially connected based on whether they share the same pressure monitoring points, dividing the spatially connected abnormal pipe segments into the same abnormal pipe segment set, and determining the boundary range of the abnormal pipe segment set in the pipeline structure based on the boundary position between the abnormal pipe segments and normal pipe segments.

[0013] As a preferred embodiment of the heating system leakage location method based on pipeline pressure characteristic analysis described in this invention, the step of finally determining the specific location of the leak within the leakage section based on the spatial distribution of pressure offsets of each pressure monitoring point relative to the reference pressure state includes, when finally determining the specific location of the leak within the determined leakage section, based on the pressure offset of each pressure monitoring point within the leakage section relative to its reference pressure state during operation, spatially sorting the pressure monitoring points according to the actual connection sequence of the pipeline network, and determining the specific monitoring point location and pipe section location corresponding to the leak within the leakage section based on the maximum location and adjacent maximum interval of the pressure offset in the spatial distribution.

[0014] Another objective of this invention is to provide a heating system leakage location system based on pipeline pressure characteristic analysis. This system can quantitatively compare real-time pressure data collected during operation with a reference pressure range, a reference pressure fluctuation intensity, and a reference pressure drop range to identify monitoring points and abnormal pipe sections with abnormal pressure conditions. This solves the problem that current heating pipeline pressure monitoring and leakage location methods cannot effectively divide abnormal pressure conditions into sections based on the pipeline structure.

[0015] As a preferred embodiment of the heating system leak location system based on pipeline pressure characteristic analysis described in this invention, the system includes: a pressure data construction module, a pressure characteristic determination module, and a leak location determination module. The pressure data construction module is used to uniformly number the pressure monitoring points in the heating pipeline network, determine the correspondence between adjacent monitoring points and pipe sections, and collect pressure data from each monitoring point under a unified sampling period to form a pressure time series data organized in chronological order. The pressure characteristic determination module is used to construct the benchmark pressure range, benchmark pressure fluctuation intensity, and benchmark pressure drop range of each monitoring point based on the pressure time series data, and quantitatively compare the real-time pressure data collected during operation with the benchmark characteristics to determine the monitoring points and abnormal pipe sections with abnormal pressure states. The leak location determination module is used to determine the leak section corresponding to the abnormal pressure state based on the connection relationship between the abnormal pressure monitoring points and abnormal pipe sections in the pipeline network structure, and determine the specific location of the leak in the pipeline network by combining the spatial distribution of pressure offset within the leak section.

[0016] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a method for locating leaks in a heating system based on pipeline pressure characteristic analysis.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for locating leaks in a heating system based on pipeline pressure characteristic analysis.

[0018] The beneficial effects of this invention are as follows: The heating system leakage location method based on pipeline pressure characteristic analysis provided by this invention establishes a stable mapping foundation between pressure data and pipeline topology by uniformly numbering the pressure monitoring points of the heating pipeline network and clarifying the correspondence between adjacent monitoring points and pipe sections. Based on this, pressure time series data is used to form the benchmark pressure range, benchmark pressure fluctuation intensity, and benchmark pressure drop range of each monitoring point, thereby quantitatively solidifying the pressure characteristics of the heating pipeline network under normal operating conditions. Furthermore, by comparing the real-time pressure data during operation with the aforementioned benchmark characteristics, the method enables the detection of abnormal pressure levels, The collaborative identification of abnormal pressure fluctuations and abnormal pressure drop relationships in pipe sections avoids misjudgments caused by relying solely on single-point threshold judgments. Building upon this, by combining the connectivity between abnormal monitoring points and abnormal pipe sections within the pipeline network structure, connectivity and boundary analysis are performed on the abnormal results, converging scattered abnormal information into clearly located and spatially continuous leak sections within the topological structure, thus significantly narrowing the investigation scope. Finally, within the identified leak sections, the leak location is further converged based on the spatial distribution of pressure offsets of each pressure monitoring point relative to the baseline pressure state, yielding a unique monitoring point or pipe section-level location result. Through this multi-level, step-by-step convergence analysis process, this invention achieves a complete closed loop from pressure data acquisition, baseline modeling, anomaly identification, section determination to specific location positioning. This provides the leak location process with clear data support, clear spatial logic, and a reproducible judgment path, effectively improving the accuracy and stability of leak location in heating pipeline networks, reducing reliance on human experience, and demonstrating good engineering applicability. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The first embodiment of the present invention provides an overall flowchart of a method for locating leaks in a heating system based on pipeline pressure characteristic analysis. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for locating leaks in a heating system based on pipeline pressure characteristic analysis is provided, comprising: S1: Number the pressure monitoring points in the heating network and determine the correspondence between adjacent monitoring points and pipe sections. Collect pressure data from each pressure monitoring point and output pressure time series data.

[0023] Furthermore, before identifying and locating leaks in the heating system, the objects used for pressure monitoring and analysis in the heating network are first determined, and the pressure data at the corresponding locations are collected and organized in a unified manner.

[0024] The heating network is divided into several interconnected pipe segments according to the actual pipe connection relationship. Each segment is defined by two adjacent pipe nodes. Using the pipe nodes as the basic monitoring objects, pressure monitoring points are set up at each node, and each pressure monitoring point is assigned a unique number. Any two pressure monitoring points directly connected on the pipeline constitute a unique pair of adjacent monitoring points, corresponding to a unique pipe segment.

[0025] Record any two adjacent pressure monitoring points as monitoring points. and monitoring points The pipe section between the two is defined as the pipe segment. The pipe segment number remains unchanged throughout the operation of the heating system and serves as the basic spatial unit for subsequent pressure characteristic modeling and anomaly detection.

[0026] It should be noted that after determining the correspondence between pressure monitoring points and pipe sections, pressure data from each monitoring point are collected synchronously. Each monitoring point uses the same sampling period to continuously sample the pressure within the pipe, and this sampling period remains constant throughout the entire data acquisition and analysis process. Each monitoring point corresponds to a unique pressure sample value at a given sampling time, ensuring temporal consistency of pressure data collected from different monitoring points at the same sampling time.

[0027] For any monitoring point During continuous sampling time The pressure value collected at the location is recorded as ,in This is the sampling sequence number. All pressure sampling values ​​are recorded in order of monitoring point number and sampling time, forming traceable pressure time series data.

[0028] While collecting pressure data at each monitoring point, the pressure difference between adjacent monitoring points is organized based on the established relationships between them. For the pipe section... , will monitor points With monitoring points The pressure values ​​collected at the same sampling time are compared.

[0029] All collected pressure data are uniformly organized according to monitoring point number, pipe section number and sampling time before entering the subsequent analysis, to ensure that any pressure data sample can be uniquely matched with a specific monitoring point and pipe section location.

[0030] During the data acquisition process, the sampling method, sampling cycle, monitoring point numbering rules, and pipe segment correspondence remain unchanged throughout the entire operation.

[0031] By identifying the pressure monitoring targets and collecting and processing pressure data, pressure time series data covering key nodes and adjacent pipe sections of the heating network are generated.

[0032] S2: Based on pressure time series data, construct the baseline pressure range, baseline pressure fluctuation intensity, and baseline pressure drop range of the corresponding pipe section of each monitoring point during operation.

[0033] Furthermore, a baseline operating period is selected when the heating system operates stably, with minimal load fluctuations, and where no leaks have been confirmed manually or through historical records. During this baseline period, pressure data is simultaneously collected from each pressure monitoring point located in the heating network.

[0034] Two pressure monitoring points are deployed adjacent to each other. and For example, the corresponding pipe segment is denoted as pipe segment. During the base period, the first Each pressure monitoring point is at a constant time The pressure value collected at the location is recorded as , No. The pressure values ​​collected by each pressure monitoring point at the same time are recorded as follows: ,in , This represents the total number of sampling points within the baseline period.

[0035] To describe the pressure level of each pressure monitoring point under normal operating conditions, statistical analysis was performed on the pressure data collected during the baseline period to construct a baseline pressure level characteristic.

[0036] With monitoring points For example, its baseline average pressure is defined as:

[0037] in, Indicates monitoring points within the baseline period The average pressure level.

[0038] Meanwhile, to characterize the natural fluctuation range of pressure under normal operating conditions, the pressure standard deviation is calculated:

[0039] in, Indicates monitoring point The degree of pressure dispersion within the baseline period.

[0040] Based on the above statistical results, monitoring points were constructed. The baseline pressure range:

[0041] in, Indicates monitoring point The reference pressure range.

[0042] This baseline pressure range characterizes the normal pressure range of the monitoring point under leak-free operating conditions, providing a benchmark for subsequent judgment on whether abnormal pressure deviations have occurred. Monitoring Point The benchmark pressure range can be constructed in the same way.

[0043] It should be noted that, in addition to pressure levels, leaks in heating networks are often accompanied by changes in pressure fluctuation characteristics. To characterize the intensity of pressure fluctuations under normal operating conditions, the pressure changes within the baseline period are analyzed.

[0044] Define monitoring points The pressure change between adjacent sampling times is:

[0045] in, Indicates monitoring point At any moment The instantaneous pressure value collected at the location, During the baseline period, the pressure changes were statistically analyzed to obtain the average change value:

[0046] in, Indicates monitoring point The average pressure change during the baseline period.

[0047] The standard deviation of the pressure change is further calculated and used as a baseline pressure fluctuation intensity.

[0048] in, Indicates monitoring point The intensity of the benchmark pressure fluctuation.

[0049] To reflect the stable characteristics of the hydraulic state of the pipeline section under normal operating conditions, the pressure difference between adjacent monitoring points is modeled.

[0050] Define pipe section At any moment The instantaneous pressure drop at point is:

[0051] in, Indicates pipe section At any moment The instantaneous voltage drop value, During the baseline operating period, statistical analysis was performed on the pressure drop of the pipeline section to obtain the average baseline pressure drop:

[0052] in, Indicates pipe section The average pressure drop during the baseline operating period, And the degree of dispersion of the reference voltage drop:

[0053] in, Indicates pipe section The degree of dispersion of pressure drop during the baseline operating period.

[0054] Pipeline section constructed accordingly The baseline voltage drop range:

[0055] in, Indicates pipe section The baseline voltage drop range.

[0056] This baseline pressure drop range is used to characterize the stable pressure drop relationship between adjacent monitoring points under leak-free conditions.

[0057] S3: Quantitatively compare the real-time pressure data collected during operation with the benchmark pressure range, benchmark pressure fluctuation intensity, and benchmark pressure drop range to identify monitoring points and abnormal pipe sections with abnormal pressure conditions.

[0058] Furthermore, during the normal operation of the heating system, real-time pressure data from each pressure monitoring point is continuously collected according to a sampling cycle consistent with the baseline period. Monitoring points are set up... At any moment The real-time pressure value collected at the location And ensure that the monitoring point number, pipe section number, and benchmark pressure characteristic model correspond one-to-one.

[0059] To avoid the impact of transient disturbances, communication jitter, or single sampling anomalies on the judgment results, a fixed-length time window is used for judgment processing of real-time pressure data. The time window includes continuous... Each sampling time, and It is a pre-set constant that remains unchanged throughout the entire operation.

[0060] All subsequent abnormal pressure status determinations are based on data within the same time window.

[0061] For the established monitoring points The baseline pressure range:

[0062] Within each time window, the monitoring points The real-time pressure values ​​are compared one by one with the reference pressure range.

[0063] The real-time pressure values ​​at all sampling times within the time window satisfy one of the following conditions:

[0064] or:

[0065] in, This indicates the sampling time within the time window, thus determining the monitoring point. The pressure level was abnormal during this time window. Indicates pressure monitoring point At sampling time The collected real-time pressure values.

[0066] By requiring the pressure value to continuously exceed the limit throughout the entire time window, the interference of short-term fluctuations on the anomaly determination is eliminated, making the determination of pressure level anomalies more stable and certain.

[0067] It should be noted that within the same time window, for monitoring points The system uses real-time pressure data to calculate the pressure change between adjacent sampling times and reflects the stability of pressure changes over time.

[0068] The intensity of real-time pressure change fluctuations calculated within this time window is compared with the constructed baseline pressure fluctuation intensity. Perform numerical comparisons. The following criteria are used to determine if the real-time fluctuation intensity meets the following criteria:

[0069] in, Indicates pressure monitoring point The intensity of real-time pressure fluctuations calculated within the current time window during real-time operation.

[0070] Then determine the monitoring points The pressure fluctuates abnormally within this time window.

[0071] The above determination achieves the identification of pressure stability changes by quantitatively comparing the dispersion of pressure changes, without relying on subjective threshold adjustments or human experience judgment.

[0072] For adjacent pressure monitoring points and The corresponding pipe segments are denoted as pipe segments. Within each time window, the real-time pressure drop of the pipe segment at each sampling moment is calculated based on the real-time collected pressure data, and compared with the established baseline pressure drop range. Compare them.

[0073] When the real-time voltage drop values ​​corresponding to all sampling times within this time window do not fall within the baseline voltage drop range At that time, determine the pipe section It was in an abnormally weak state during that time window.

[0074] This determination reflects the changes in the hydraulic conditions within the pipe section relative to the baseline state by the continuous deviation of the pressure relationship between adjacent monitoring points.

[0075] Within the same time window, the abnormal pressure state region is determined according to the following joint judgment rules: When a monitoring point is determined to have an abnormal pressure level or abnormal pressure fluctuation, and at least one adjacent pipe segment associated with the monitoring point is determined to be an abnormal pipe segment, the monitoring point and its associated pipe segment are jointly identified as an abnormal pressure state area.

[0076] The joint judgment rule is used to ensure that the judgment of abnormal pressure state reflects both changes in local pressure behavior and changes in the hydraulic relationship of the pipe section, avoiding misjudgment caused by a single indicator.

[0077] S4: Based on the monitoring points with abnormal pressure conditions and the connection relationship of abnormal pipe sections in the pipeline network structure, conduct connectivity and boundary analysis to determine the leakage section corresponding to the abnormal pressure condition.

[0078] Furthermore, based on the established heating network structure, the output abnormal pressure monitoring point numbers are mapped to the network topology. For each abnormal pressure monitoring point, the upstream and downstream pipe segment numbers directly connected to it in the network structure are extracted.

[0079] Simultaneously, for each pipe segment identified as abnormal, the corresponding monitoring point numbers at its two endpoints are extracted, and a correspondence is established between the abnormal pipe segments and the abnormal monitoring points. This correspondence is used to clarify the specific location of the abnormal pressure state in the pipeline network and its interconnections.

[0080] In the heating network structure, connectivity analysis is performed on abnormal pipe sections according to the rule of whether the pipe sections share the same monitoring point.

[0081] When two abnormal pipe segments have at least one common monitoring point number in the pipe network structure, the two abnormal pipe segments are determined to be spatially connected; when two abnormal pipe segments do not have a common monitoring point number, the two abnormal pipe segments are determined to be spatially disconnected.

[0082] Based on the above connectivity determination rules, all abnormal pipe segments are divided into several abnormal pipe segment sets, where any two pipe segments in each abnormal pipe segment set are connected continuously through one or more monitoring points.

[0083] For any set of abnormal pipe segments, each abnormal pipe segment within the set is sequentially arranged along the actual connection direction of the heating network. The arrangement order is based on the connection relationship of monitoring points in the network, without introducing manual sorting rules.

[0084] After the arrangement is completed, determine the spatial boundaries of the abnormal pipe segment set: If one end of a certain abnormal pipe segment is not connected to other abnormal pipe segments in the set, then the abnormal pipe segment is determined as the boundary pipe segment of the set of abnormal pipe segments. If both ends of a certain abnormal pipe segment are connected to other abnormal pipe segments within the set, then the abnormal pipe segment is identified as an internal pipe segment of the abnormal pipe segment set.

[0085] Based on the above rules, the starting and ending boundary pipe segments of each abnormal pipe segment set in the pipeline network structure are determined.

[0086] For each set of abnormal pipe segments, all continuous pipe segments between its starting boundary segment and its ending boundary segment are identified as a leaking segment.

[0087] When the set of abnormal pipe segments contains only a single abnormal pipe segment, the abnormal pipe segment directly constitutes a leaking section; when the set of abnormal pipe segments contains multiple consecutive abnormal pipe segments, the multiple abnormal pipe segments and all the consecutive pipe segments in between together constitute a leaking section.

[0088] Each leaking section is represented by a set of pipe section numbers, and within the same time window, any abnormal pipe section can only belong to one leaking section, and cross-assignment or duplicate assignment is not allowed.

[0089] Within multiple consecutive time windows, the process of identifying abnormal pressure states and determining leakage sections is repeated, and the sets of leakage section numbers obtained in different time windows are compared.

[0090] When the same set of pipe segment numbers is repeatedly identified as a leaking section within a number of consecutive time windows not less than a preset number, the set of pipe segment numbers is confirmed as the final leaking section.

[0091] When the leak sections identified in different time windows partially overlap but are not completely consistent, the common part of the set of leak section numbers in each time window shall be taken as the final leak section.

[0092] S5: Based on the spatial distribution of pressure offsets of each pressure monitoring point relative to the baseline pressure state within the leak section, the specific location of the leak within the section is finally determined.

[0093] Furthermore, for any leaking section in the output, extract all pipe segment numbers contained in that section, and determine the set of pressure monitoring point numbers corresponding to both ends and the interior of that section.

[0094] The set of pressure monitoring point numbers includes: monitoring points located at one end of the pipe segment at the beginning of the leak zone; monitoring points located at the other end of the pipe segment at the end of the leak zone; and monitoring points located at the connection points of various pipe segments within the leak zone.

[0095] The set of monitoring point numbers remains fixed throughout the analysis process and is used to describe the spatial distribution of pressure anomalies within the leak section.

[0096] For each pressure monitoring point within the leak section, the real-time pressure data of that monitoring point within the current time window is compared according to the established baseline pressure range.

[0097] For any monitoring point, calculate its average real-time pressure value within the current time window, and compare the average real-time pressure value with the average baseline pressure value corresponding to the monitoring point to obtain the pressure offset of the monitoring point.

[0098] The pressure offset is used to characterize the degree of deviation of the current pressure state of the monitoring point from the baseline operating state, and all monitoring points use the same calculation rules.

[0099] Within the leak section, all pressure monitoring points are spatially ordered according to the actual connection sequence of the pipeline network. The order is solely based on the pipeline connection relationship, without any manually specified direction.

[0100] After sorting, the pressure offsets corresponding to each pressure monitoring point are arranged in spatial order to form a spatial distribution sequence of pressure offsets within the leakage section.

[0101] This spatial distribution sequence is used to reflect the variation of pressure offset along the pipeline direction within the leak section.

[0102] In the spatial distribution sequence of pressure offset within the leak section, determine the monitoring point number with the largest pressure offset value.

[0103] When there is a single monitoring point whose pressure offset is greater than that of all other monitoring points in the leak section, the pipeline location corresponding to that monitoring point is determined as the most likely location of the leak.

[0104] When multiple adjacent monitoring points have the same maximum pressure offset, the pipe segment corresponding to the adjacent monitoring points is determined as the pipe section where the leak is located.

[0105] The process of calculating the pressure offset within the leak section and determining the location of the maximum pressure offset is repeated within multiple consecutive time windows.

[0106] When the same monitoring point number or the same pipe segment number is repeatedly identified as the location of maximum pressure deviation within a number of consecutive time windows not less than a preset number, the monitoring point or the pipe segment is identified as the final leak location.

[0107] When the maximum pressure offset locations determined within different time windows change adjacently, the pipe segment corresponding to the intersection of the locations determined within each time window is taken as the final leak location.

[0108] Example 2, one embodiment of the present invention, provides a method for locating leaks in a heating system based on pipeline pressure characteristic analysis. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0109] First, a typical urban centralized heating primary pipeline network was selected as the test object. This network has a mixed structure of ring and branch lines, covering multiple heat exchange stations, with pipe diameters ranging from DN300 to DN700, and the operating pressure has been maintained within a stable range for a long time. To ensure the feasibility and authenticity of the test, pressure monitoring points were deployed only using existing pressure acquisition interfaces without changing the original pipeline network structure and operation mode.

[0110] First, based on the pipeline topology, six pressure monitoring points were selected at the main pipeline and key branch nodes, numbered P1 to P6. The physical connections between the monitoring points were confirmed through pipeline design drawings and on-site verification, and the one-to-one correspondence between adjacent monitoring points and pipe sections was determined accordingly. Pressure data was collected at all monitoring points using a uniform sampling period, which remained constant throughout the entire test to ensure the comparability of data from different time periods.

[0111] Subsequently, during the period when the heating system was operating stably and no leaks were confirmed by operation records, pressure data from each monitoring point was continuously collected to form a complete pressure time series data. This data was used only to characterize the pressure features of the pipeline network under normal operating conditions and was not included in anomaly analysis. Based on this pressure time series, the pressure distribution range and pressure change stability characteristics of each monitoring point under normal operating conditions were statistically analyzed, and the pressure drop relationship between adjacent monitoring points was statistically described, thus forming a set of benchmark pressure characteristics corresponding one-to-one with specific monitoring points and pipe sections.

[0112] After establishing the baseline pressure characteristics, the operational monitoring phase begins. During this phase, real-time pressure data from each monitoring point is continuously collected and processed under the same sampling rules. By comparing the real-time pressure data with the aforementioned baseline pressure characteristics item by item, monitoring points and corresponding pipe sections that continuously deviate from the baseline state within a certain time window are identified.

[0113] In this embodiment, a small-flow leakage condition was artificially introduced during the experiment (simulating a micro-leakage state through a maintenance valve). However, this information was considered an unknown condition during the analysis phase and was only used for post-event verification of the location results. Real-time analysis results showed that some monitoring points exhibited inconsistencies with the baseline state in terms of pressure levels and fluctuation characteristics, and the pressure drop relationship between adjacent pipe sections changed synchronously. Based on these abnormal results, the connectivity of the monitoring points and pipe sections in the pipeline network structure was further analyzed to determine the abnormal pipe sections and the corresponding leakage sections based on this.

[0114] After identifying the leak section, pressure offset information from all monitoring points within that section is extracted, and the pressure offsets are spatially arranged according to the pipeline connection sequence. By comparing the distribution characteristics of the pressure offsets at each monitoring point, the specific pipeline segment corresponding to the leak location is finally determined. After the test, the location results are compared with the leak location confirmed by on-site maintenance to evaluate the effectiveness of the method of this invention.

[0115]

[0116] As shown in Table 1, during the baseline operation phase, the average pressure values ​​at each pressure monitoring point exhibit a stable distribution characteristic of gradually decreasing along the pipeline direction, and the overall pressure fluctuation range is relatively small, indicating that the pipeline network operates relatively smoothly under leak-free conditions. Simultaneously, the baseline pressure drop between adjacent monitoring points shows a continuous and increasing reasonable trend, reflecting that the hydraulic conditions of the pipeline network are consistent with the design conditions.

[0117] During the operational monitoring phase, the average operating pressure at monitoring points P2, P3, and P4 was significantly lower than their corresponding baseline average operating pressure, and their pressure fluctuations were significantly higher than the baseline level. In contrast, the pressure changes at monitoring points P1, P5, and P6 remained largely within the baseline characteristic range. This phenomenon indicates that relying solely on single-point pressure monitoring is insufficient to distinguish between global operating condition fluctuations and local anomalies. This invention, by introducing a baseline pressure range and baseline fluctuation characteristics, provides a clear benchmark for determining pressure anomalies.

[0118] Further analysis of the pressure drop characteristics of adjacent pipe sections reveals that the pressure drop relationship between the corresponding pipe sections P2–P3 and P3–P4 shifted synchronously during operation, while the adjacent pipe sections P1–P2 and P4–P5 did not exhibit the same trend.

[0119] Based on this, by combining the pipeline network topology with connectivity analysis, scattered abnormal monitoring points and abnormal pipe segment results can be integrated into a spatially continuous leakage section, namely the pipe segment interval corresponding to P2 to P4. Compared with existing technologies that can only provide results for anomalies near a certain point or anomalies over a large area, this method significantly narrows the scope of investigation.

[0120] Finally, further comparison of the spatial distribution of pressure offset within the leak section reveals that the pressure offset is greatest in the P3–P4 pipe section and remains consistent within a continuous time window, thus achieving further convergence in the location of the leak. On-site inspection confirmed that the actual leak point is located near this pipe section, highly consistent with the positioning results of this invention.

[0121] In summary, this embodiment demonstrates that the present invention, by constructing benchmark features based on pressure time series and combining anomaly identification, connectivity analysis, and spatial distribution determination within segments, achieves a complete closed loop from anomaly detection to specific location positioning. Compared to existing technical solutions that rely on empirical thresholds or single-point judgments, the present invention exhibits significant advantages in positioning accuracy, result stability, and engineering feasibility, fully demonstrating its innovation and practical value.

[0122] Example 3, an embodiment of the present invention, provides a leak location system for a heating system based on pipeline pressure characteristic analysis, including a pressure data construction module, a pressure characteristic determination module, and a leak location determination module.

[0123] The pressure data construction module is used to uniformly number the pressure monitoring points in the heating network, determine the correspondence between adjacent monitoring points and pipe sections, and collect pressure data from each pressure monitoring point under a unified sampling period to form pressure time series data organized in chronological order. The pressure characteristic determination module is used to construct the benchmark pressure range, benchmark pressure fluctuation intensity, and benchmark pressure drop range of each monitoring point based on the pressure time series data, and quantitatively compare the real-time pressure data collected during operation with the benchmark characteristics to determine the monitoring points and abnormal pipe sections with abnormal pressure states. The leak location determination module is used to determine the leak section corresponding to the abnormal pressure state based on the connection relationship between the abnormal pressure monitoring points and abnormal pipe sections in the pipeline structure, and determine the specific location of the leak in the pipeline network by combining the spatial distribution of pressure offset within the leak section.

[0124] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0126] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0127] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for locating leaks in a heating system based on pipeline pressure characteristic analysis, characterized in that, include: The pressure monitoring points in the heating network are numbered and the correspondence between adjacent monitoring points and pipe sections is determined. Pressure data of each pressure monitoring point is collected and pressure time series data is output. Based on pressure time series data, during operation, the baseline pressure range, baseline pressure fluctuation intensity, and baseline pressure drop range of the corresponding pipe section of each monitoring point are constructed. The real-time pressure data collected during operation is quantitatively compared with the benchmark pressure range, benchmark pressure fluctuation intensity, and benchmark pressure drop range to identify monitoring points and abnormal pipe sections with abnormal pressure conditions. Based on the monitoring points with abnormal pressure conditions and the connection relationship of abnormal pipe sections in the pipeline network structure, connectivity and boundary analysis are performed to determine the leakage section corresponding to the abnormal pressure conditions. Based on the spatial distribution of pressure offsets of each pressure monitoring point relative to the baseline pressure state within the leak section, the specific location of the leak within the section is finally determined.

2. The method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in claim 1, characterized in that: The process of numbering pressure monitoring points in the heating network and determining the correspondence between adjacent monitoring points and pipe sections includes using the actual pipeline connection structure of the heating network as the basis. Pressure monitoring points set at pipeline nodes are taken as basic monitoring objects, and two pressure monitoring points directly connected in the pipeline structure are determined as a group of adjacent monitoring points. Pipeline intervals are used as pipe section numbers, and the pressure monitoring point numbers and pipe section numbers remain unchanged throughout the entire operation.

3. The method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in claim 2, characterized in that: The process of collecting pressure data from each pressure monitoring point and outputting pressure time series data includes the following: when collecting pressure data from each pressure monitoring point, each pressure monitoring point uses the same sampling period to synchronously collect the pressure inside the pipe, and records the pressure data collected at each sampling moment in correspondence with the corresponding monitoring point number and sampling moment, and outputs pressure time series data arranged in chronological order.

4. The method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in claim 3, characterized in that: The process of constructing the baseline pressure range, baseline pressure fluctuation intensity, and baseline pressure drop range of the corresponding pipe sections of adjacent monitoring points based on pressure time series data during operation includes selecting the operation period during which the heating system is confirmed to be leak-free as the baseline operation period, and performing statistical analysis only on the pressure time series data collected within this baseline operation period.

5. The method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in claim 4, characterized in that: The process of quantitatively comparing real-time pressure data collected during operation with a reference pressure range, reference pressure fluctuation intensity, and reference pressure drop range to determine monitoring points and abnormal pipe sections with abnormal pressure states includes judging continuously sampled real-time pressure data within a fixed-length time window. Only when the real-time pressure data continuously meets the corresponding abnormal judgment conditions throughout the entire time window is the corresponding pressure monitoring point and pipe section determined to have an abnormal pressure state.

6. The method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in claim 5, characterized in that: The process of performing connectivity and boundary analysis based on the connection relationships of monitoring points with abnormal pressure states and abnormal pipe segments in the pipeline network structure to determine the leakage section corresponding to the abnormal pressure state includes determining whether the abnormal pipe segments are spatially connected based on whether they share the same pressure monitoring points, dividing spatially connected abnormal pipe segments into the same abnormal pipe segment set, and determining the boundary range of the abnormal pipe segment set in the pipeline network structure based on the boundary position between the abnormal pipe segments and normal pipe segments.

7. The method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in claim 6, characterized in that: The final determination of the specific location of the leak within the leak section is based on the spatial distribution of pressure offsets of each pressure monitoring point relative to the reference pressure state. This includes determining the specific location of the leak within the determined leak section by spatially sorting the pressure monitoring points according to the actual connection sequence of the pipeline network based on the pressure offsets of each pressure monitoring point relative to its reference pressure state during operation, and determining the specific monitoring point location and pipeline section location corresponding to the leak within the leak section based on the maximum location and adjacent maximum interval of the pressure offset in the spatial distribution.

8. A system employing the method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in any one of claims 1 to 7, characterized in that: Includes a pressure data construction module, a pressure characteristic determination module, and a leak location determination module; The pressure data construction module is used to uniformly number the pressure monitoring points in the heating pipeline network, determine the correspondence between adjacent monitoring points and pipe sections, and collect pressure data from each pressure monitoring point under a unified sampling period to form pressure time series data organized in chronological order. The pressure characteristic determination module is used to construct the benchmark pressure range, benchmark pressure fluctuation intensity and benchmark pressure drop range of each monitoring point based on the pressure time series data, and to quantitatively compare the real-time pressure data collected during operation with the benchmark characteristics to determine the monitoring points and abnormal pipe sections with abnormal pressure states. The leak location determination module is used to determine the leak section corresponding to the abnormal pressure state based on the connection relationship between the abnormal pressure monitoring points and the abnormal pipe sections in the pipeline network structure, and to determine the specific location of the leak in the pipeline network by combining the spatial distribution of pressure offset within the leak section.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for locating leaks in a heating system based on pipeline pressure characteristic analysis as described in any one of claims 1 to 7.

Images