A real-time monitoring and early warning method for leakage status of long-line public facilities

Abstract

The present invention discloses a real-time monitoring and early warning method for leakage status of long-line public facilities, which relates to the field of long-line public facilities leakage status monitoring and early warning technology, and specifically includes the following steps: analyzing the environmental dynamic response information stored in the central monitoring system, and generating soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient respectively; constructing a leakage risk assessment model for the generated soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient, and generating a leakage risk coefficient; determining a pre-set leakage risk coefficient threshold, and comparing it with the generated leakage risk coefficient after determination, assessing whether there is leakage risk in the pipe gallery area close to the surface, and generating normal signals and abnormal signals respectively according to the assessment results. The present invention solves the problem of underground pipe gallery leakage monitoring, accurately distinguishes abnormal signals, and improves monitoring accuracy and safety.

Description

Technical Field

[0001] The invention relates to the technical field of long-line public facility leakage status monitoring and early warning, and in particular to a real-time monitoring and early warning method for the leakage status of a long-line public facility. Background Art

[0002] Long-line public facilities refer to infrastructure systems with large spatial ductility and a length far greater than the width, and pipe corridors are a typical representative of them. Pipe corridors are usually used to centrally accommodate and protect various pipelines in cities, such as power, telecommunications, water supply, drainage, gas and heating pipelines. Since pipe corridors are usually buried underground or erected below the surface, and their structures are large and cover a wide range, leakage problems are prone to occur but difficult to be discovered in time. Leakage of long-line public facilities refers to the liquid or gas in the pipe corridor penetrating into the internal or external environment of the facility due to structural damage, pipeline aging, changes in geological conditions, etc., which may cause environmental pollution, facility damage, energy waste and even safety accidents. It is particularly important to conduct real-time monitoring and early warning of the leakage status of long-line public facilities such as pipe corridors, because such monitoring can detect potential leakage problems in time and prevent small problems from turning into serious accidents, thereby ensuring the safe operation and reliability of urban infrastructure and reducing economic losses and environmental hazards caused by leakage. Through real-time monitoring and early warning, the service life of the pipe corridor can be effectively extended and the continuous stability of its functions can be ensured.

[0003] The existing real-time monitoring and early warning technology for leakage status of long-line public facilities is mainly achieved by arranging a variety of highly sensitive sensors inside and around the tunnel. These sensors usually include water level sensors, humidity sensors, pressure sensors, and temperature sensors, which can collect environmental parameters inside and outside the tunnel in real time. When a tunnel leaks, these sensors will detect signs such as rising water levels, humidity changes, pressure fluctuations, or abnormal temperatures, and transmit these data to the central monitoring system through wireless or wired networks. The central monitoring system calculates the leakage status determination coefficient and environmental change index by real-time analysis of the collected data, and compares them with the preset safety threshold. If the determination coefficient exceeds the threshold, the system will immediately trigger an early warning signal to notify maintenance personnel of the specific location and severity of the leakage, and generate emergency treatment suggestions to help quickly take repair measures. The entire process realizes all-round and real-time monitoring of the leakage status of the tunnel, which can intervene in the early stage of the problem to avoid further deterioration of the leakage, and ensure the safe operation of the tunnel and the normal operation of urban infrastructure.

[0004] The prior art has the following deficiencies:

[0005] In underground pipe corridors, especially in areas close to the surface, soil moisture content can change significantly due to seasonal changes or fluctuations in precipitation. In this case, sensors may detect abnormal humidity and pressure signals caused by changes in soil moisture content. However, since existing leakage monitoring technologies cannot effectively eliminate the interference of these soil moisture fluctuations on sensor data, it is difficult for the system to accurately distinguish the difference between these signals and actual leakage signals. This signal confusion may cause frequent false alarms in the system, increase the workload of maintenance personnel, and waste resources. At the same time, if the system fails to accurately detect leakage signals when the soil moisture content changes, it may be missed, causing the leakage problem to continue to deteriorate without being discovered in time, which may eventually cause serious damage to the pipe corridor structure, increase the difficulty and cost of repair, and affect the long-term safety and stable operation of the pipe corridor.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a real-time monitoring and early warning method for the leakage status of long-line public facilities to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a real-time monitoring and early warning method for leakage status of long-line public facilities, specifically comprising the following steps:

[0009] In the pipe gallery area close to the ground, a sensor network is deployed to obtain the environmental dynamic response information in the pipe gallery area close to the ground in real time, and store the environmental dynamic response information in the central monitoring system after acquisition;

[0010] Analyze the environmental dynamic response information stored in the central monitoring system to generate soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient;

[0011] A leakage risk assessment model is constructed based on the generated soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient to generate a leakage risk coefficient;

[0012] Determine a preset leakage risk coefficient threshold, and compare it with the generated leakage risk coefficient after determination, evaluate whether there is leakage risk in the pipeline corridor area close to the surface, and generate normal signals and abnormal signals respectively according to the evaluation results;

[0013] In the case of generating an abnormal signal, a number of leakage risk coefficients generated subsequently are obtained, and a comprehensive analysis is performed to generate a risk signal of a corresponding level, and corresponding solutions are taken according to the generated risk signal of the corresponding level.

[0014] Preferably, the environmental dynamic response information stored in the central monitoring system is analyzed to generate a soil moisture variation coefficient, an environmental pressure response index and a temperature impact compensation coefficient, respectively, which specifically includes the following steps:

[0015] Pre-process the environmental dynamic response information in the pipeline corridor area close to the surface stored in the central monitoring system;

[0016] Extracting comprehensive environmental control information from the preprocessed environmental dynamic response information, the comprehensive environmental control information including soil moisture data, soil temperature data and environmental pressure data;

[0017] The extracted comprehensive environmental regulation information is analyzed to generate soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient.

[0018] Preferably, the acquisition logic of the soil moisture variation coefficient is as follows:

[0019] Extract the soil moisture content and soil temperature data from the comprehensive environmental control information, including the actual soil moisture content and actual soil temperature at different times in the pipeline corridor area close to the surface, and calibrate the actual soil moisture content and actual soil temperature at different times in the pipeline corridor area close to the surface as and , The number indicating the actual soil moisture content and actual soil temperature at different times over a period of time in the pipeline corridor area close to the ground surface. , is a positive integer;

[0020] Obtain the preset actual soil moisture content and preset actual soil temperature in the pipeline corridor area close to the surface within a period of time, and calibrate them as and ;

[0021] Calculate the soil moisture variation coefficient. The specific calculation formula is as follows: In the formula, is the soil moisture variation coefficient.

[0022] Preferably, the acquisition logic of the environmental pressure response index is as follows:

[0023] Extract the environmental pressure data and soil temperature data from the comprehensive environmental control information, including the actual environmental pressure and actual soil temperature at different time points in the pipeline corridor area close to the surface, and calibrate these data as and , The number indicating the actual environmental pressure and actual soil temperature in the pipeline corridor area close to the ground at different time points over a period of time. , is a positive integer;

[0024] Obtain the actual pressure of the preset environment in the pipeline corridor area close to the surface over a period of time and calibrate it as ;

[0025] Calculate the environmental pressure response index. The specific calculation formula is as follows: In the formula, is the environmental stress response index, It is the preset actual soil temperature in the pipeline corridor area close to the ground surface over a period of time.

[0026] Preferably, the logic for obtaining the temperature impact compensation coefficient is as follows:

[0027] Extract the environmental pressure data and soil temperature data from the comprehensive environmental control information, including the actual environmental pressure and actual soil temperature at different time points in the pipeline corridor area close to the surface, and calibrate these data as and , The number indicating the actual environmental pressure and actual soil temperature in the pipeline corridor area close to the ground at different time points over a period of time. , is a positive integer;

[0028] Obtain the historical benchmark temperature and historical benchmark pressure in the pipeline corridor area close to the surface over a period of time and calibrate them as and ;

[0029] Calculate the temperature influence compensation coefficient. The specific calculation formula is as follows: In the formula, is the temperature compensation coefficient.

[0030] Preferably, the generated soil moisture variation coefficient , Environmental Stress Response Index and temperature effect compensation coefficient Construct a leakage risk assessment model and generate a leakage risk coefficient through weighted summation .

[0031] Preferably, a preset leakage risk coefficient threshold is determined , and after determination, the leakage risk factor generated Compare and evaluate whether there is leakage risk in the pipe gallery area close to the surface, and generate normal signals and abnormal signals according to the evaluation results. The specific comparison and analysis are as follows:

[0032] like , there is no leakage risk in the pipeline corridor area close to the surface, and a normal signal is generated;

[0033] like , there is a leakage risk in the pipeline corridor area close to the surface, generating an abnormal signal.

[0034] Preferably, when an abnormal signal is generated, several leakage risk coefficients generated subsequently are obtained, and the several leakage risk coefficients generated subsequently are recalibrated as , Indicates the numbers of several leakage risk coefficients generated subsequently when an abnormal signal is generated. , is a positive integer;

[0035] The average value of several leakage risk coefficients is calibrated as ,but: ;

[0036] The standard deviation of several leakage risk coefficients is calibrated as ,but: .

[0037] Preferably, a preset average value and a preset standard deviation of several leakage risk coefficients subsequently generated when the abnormal signal is generated are obtained, and the preset average values ​​and the preset standard deviations of several leakage risk coefficients subsequently generated when the abnormal signal is generated are calibrated as and , the average value of several leakage risk coefficients generated subsequently when an abnormal signal is generated and standard deviation Respectively with the preset average and preset standard deviation Comparative analysis is performed to generate risk signals of corresponding levels, and corresponding solutions are taken according to the generated risk signals of corresponding levels. The specific analysis is as follows:

[0038] like , generating a high-risk signal and taking emergency measures immediately;

[0039] like and , generate medium risk signals and conduct regular monitoring and maintenance;

[0040] like and , generate low-risk signals and maintain regular inspections and monitoring.

[0041] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0042] 1. The technical effect of the present invention is mainly reflected in the ability to accurately deal with the difficulty of leakage monitoring in underground pipe corridors due to fluctuations in soil moisture content. By extracting comprehensive environmental control information and generating key leakage risk assessment parameters, such as soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient, the system can accurately identify and distinguish the difference between abnormal signals caused by changes in soil moisture content and actual leakage signals. This technical effect greatly improves the accuracy of leakage monitoring, reduces the occurrence of false alarms and missed alarms, and thus ensures the long-term safety and stable operation of the pipe corridor.

[0043] 2. The advantages of the present invention also lie in its high adaptability and dynamic response capability. When the system detects an abnormal signal, it can obtain the leakage risk coefficient generated subsequently through automated data analysis and real-time calculation, and perform accurate risk assessment by comparing the preset value. The present invention can not only cope with complex environmental changes, but also dynamically adjust the risk assessment and early warning mechanism according to real-time data, so that the system can maintain efficient and accurate monitoring capabilities under various environmental conditions.

[0044] 3. The present invention is also highly practical and flexible. At different risk levels, the system can automatically generate high, medium and low risk signals and take corresponding countermeasures, from emergency treatment to routine inspections, to ensure the efficiency of monitoring and maintenance. This hierarchical response mechanism not only improves the automation of the system, but also effectively reduces the workload and resource waste of maintenance personnel, further improving the safety of the corridor structure and the economic efficiency of operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 The present invention is a flow chart of a real-time monitoring and early warning method for leakage status of long-line public facilities. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0048] The present invention provides Figure 1A real-time monitoring and early warning method for leakage status of a long-line public facility is shown, which specifically includes the following steps:

[0049] In the pipe gallery area close to the ground, a sensor network is deployed to obtain the environmental dynamic response information in the pipe gallery area close to the ground in real time, and store the environmental dynamic response information in the central monitoring system after acquisition;

[0050] The deployment of sensor networks can be done in a variety of ways to ensure full coverage of the tunnel area close to the surface. First, humidity sensors, pressure sensors, and temperature sensors can be buried below the surface at key locations along the tunnel path by ground or shallow burial, ensuring that the sensors are close to the tunnel structure to accurately sense environmental changes. Secondly, wireless sensor nodes can be used, which are interconnected through low-power wireless communication technologies (such as LoRa or Zigbee) to form a self-organizing network to ensure the stability and coverage of data transmission. A sensor network specifically refers to a system consisting of multiple sensor nodes, which communicate with each other through wired or wireless means, and transmit data to a central monitoring system through gateway nodes, forming a network structure for real-time monitoring and data collection.

[0051] In order to obtain the environmental dynamic response information in the tunnel area close to the surface in real time, it can be achieved through the multi-point layout of the sensor network and timed data collection. The sensor can automatically obtain information such as soil moisture content, environmental humidity, pressure changes and temperature fluctuations at fixed time intervals through timed collection. In addition, the event-driven mode can be used, that is, when the sensor detects a sharp change in environmental parameters (such as a sudden increase in humidity caused by a sudden rainstorm), the data collection process is immediately triggered to capture the dynamic changes under abnormal environmental conditions. The environmental dynamic response information specifically includes the soil moisture content, environmental humidity, pressure distribution and temperature gradient at the current moment. These data reflect the real-time impact of environmental conditions on the tunnel structure.

[0052] After the environmental dynamic response information is acquired, it can be stored in the central monitoring system in a variety of ways. First, the data can be transmitted directly to the server of the central monitoring system through a wired network, such as optical fiber or Ethernet, to ensure the stability and high bandwidth of data transmission. Secondly, for wireless sensor networks, gateway nodes can be used to aggregate wireless data and upload the data to the cloud monitoring platform through cellular networks (such as 4G / 5G) or Wi-Fi connections. After the data reaches the central monitoring system, it can be stored in a distributed database system, such as using an SQL database for structured data storage, or using a NoSQL database to manage large-scale time series data. The central monitoring system can also configure a data backup mechanism and a real-time monitoring interface to ensure the security and availability of the data, and provide an analysis interface for subsequent data processing and risk assessment.

[0053] Analyze the environmental dynamic response information stored in the central monitoring system to generate soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient;

[0054] In this embodiment, the environmental dynamic response information stored in the central monitoring system is analyzed to generate a soil moisture variation coefficient, an environmental pressure response index, and a temperature impact compensation coefficient, respectively, which specifically includes the following steps:

[0055] Pre-process the environmental dynamic response information in the pipeline corridor area close to the surface stored in the central monitoring system;

[0056] Preprocessing the environmental dynamic response information is an important step to ensure data accuracy, integrity and consistency. First, preprocessing can filter out outliers caused by sensor noise, data transmission errors or environmental interference to ensure the reliability of subsequent analysis data. Specific preprocessing includes the following items: First, data cleaning, by identifying and removing outliers, missing values ​​or duplicate data to ensure data integrity and consistency; second, data smoothing, by moving average, weighted average or other smoothing algorithms, to reduce short-term fluctuations in the data and highlight long-term trends; third, data normalization, by normalizing different sensor data, the data is converted to a unified scale to ensure comparability between different types of data. These preprocessing steps can be implemented through data processing software, which automatically detects abnormal data, performs cleaning, smoothing and normalization operations, and ensures that the generated soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient are based on high-quality data, thereby improving the accuracy of leakage risk assessment.

[0057] Extracting comprehensive environmental control information from the preprocessed environmental dynamic response information, the comprehensive environmental control information including soil moisture data, soil temperature data and environmental pressure data;

[0058] Extracting the environmental comprehensive regulation information from the preprocessed environmental dynamic response information can be achieved through the automated data extraction and classification functions in the data processing software. First, the software retrieves the preprocessed environmental dynamic response information from the central monitoring system, and then automatically classifies and filters the data according to predefined tags or classification rules. Specifically, the software identifies and extracts relevant soil moisture data, soil temperature data, and environmental pressure data, which are usually collected in real time by sensors and stored in the system. The software aligns different data types according to timestamps to ensure the time synchronization of various types of data. Then, the software further filters out invalid or abnormal data to ensure that the extracted data is of high quality and accuracy. The extracted data is aggregated into a comprehensive data set, labeled as environmental comprehensive regulation information, and stored in the database for subsequent analysis and calculation. In this way, the software can efficiently and accurately extract and organize environmental comprehensive regulation information, thereby supporting further leakage risk assessment and parameter calculation.

[0059] The extracted comprehensive environmental regulation information is analyzed to generate soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient.

[0060] In this embodiment, the logic for obtaining the soil moisture variation coefficient is as follows:

[0061] Extract the soil moisture content and soil temperature data from the comprehensive environmental control information, including the actual soil moisture content and actual soil temperature at different times in the pipeline corridor area close to the surface, and calibrate the actual soil moisture content and actual soil temperature at different times in the pipeline corridor area close to the surface as and , The number indicating the actual soil moisture content and actual soil temperature at different times over a period of time in the pipeline corridor area close to the ground surface. , is a positive integer;

[0062] Extracting soil moisture and soil temperature data from the comprehensive environmental control information can be achieved through the automated data acquisition and time series analysis functions of the data processing software. First, the software will obtain the real-time data stream collected from the sensor network, identify and extract data points related to soil moisture and soil temperature. In order to ensure the temporal consistency of the data, the software will automatically align the data according to the timestamp to ensure that the soil moisture and soil temperature data are synchronized at the same time point. Next, the software will filter and clean the extracted data to filter out abnormal values ​​and missing values ​​caused by sensor errors or data transmission problems. Then, the software will extract these processed data at preset time intervals (such as every hour, every minute) and generate a time series data set containing the actual soil moisture and soil temperature at different times over a period of time. Finally, these extracted data will be marked as part of the comprehensive environmental control information and stored in the database of the central monitoring system for further analysis and calculation. In this way, the software can efficiently and accurately extract key environmental data, providing a reliable basis for subsequent leakage risk assessment.

[0063] Obtain the preset actual soil moisture content and preset actual soil temperature in the pipeline corridor area close to the surface within a period of time, and calibrate them as and ;

[0064] Obtaining the preset actual soil moisture content and preset actual soil temperature in the tunnel area close to the surface over a period of time can be achieved by combining historical environmental data and environmental simulation models. First, the software retrieves historical soil moisture and temperature data under similar seasons, weather conditions and geographical locations from the database of the central monitoring system as a preliminary benchmark. Next, the software uses an environmental simulation model, which is based on factors such as geographic information system (GIS), meteorological data, and soil physical properties to predict the changing trends of soil moisture and temperature in a specific time period. The software generates the preset actual soil moisture content and preset actual soil temperature in the tunnel area by comparing and integrating historical data with model prediction results. These preset values ​​reflect the typical environmental state without external interference and serve as benchmark data for soil moisture change assessment. The software can also dynamically adjust these preset values ​​to reflect changes in real-time environmental conditions, thereby ensuring the timeliness and accuracy of the preset data, and ultimately providing a reliable benchmark for accurate leakage risk assessment.

[0065] Calculate the soil moisture variation coefficient. The specific calculation formula is as follows: In the formula, is the soil moisture variation coefficient.

[0066] The size of the soil moisture variation coefficient directly affects the assessment of whether there is a leakage risk in the pipeline corridor area close to the surface. Specifically, when the soil moisture variation coefficient is large, it means that there is a significant difference between the current actual soil moisture content and the preset soil moisture content, and this difference exceeds the normal environmental fluctuation range, which may indicate that there is a leakage risk in the area. On the contrary, if the soil moisture variation coefficient is small, it means that the actual soil moisture content is close to the preset value, and the humidity change is within the normal range, then the leakage risk is low. This coefficient, as a key indicator for assessing leakage risk, helps to accurately identify possible leakage problems by quantifying abnormal fluctuations in humidity, so that necessary early warning and treatment measures can be taken in a timely manner.

[0067] In this embodiment, the logic for obtaining the environmental pressure response index is as follows:

[0068] Extract the environmental pressure data and soil temperature data from the comprehensive environmental control information, including the actual environmental pressure and actual soil temperature at different time points in the pipeline corridor area close to the surface, and calibrate these data as and , The number indicating the actual environmental pressure and actual soil temperature in the pipeline corridor area close to the ground at different time points over a period of time. , is a positive integer;

[0069] Extracting environmental pressure data and soil temperature data from comprehensive environmental control information can be achieved through the data screening and time series analysis functions in the automated data processing software. First, the software retrieves the pre-processed environmental dynamic response information from the central monitoring system, which has been marked by timestamps. Then, the software automatically identifies data points related to environmental pressure and soil temperature through preset screening rules, and extracts these data in chronological order. Specifically, the software will screen the multidimensional data stored in the system and extract the actual environmental pressure and actual soil temperature data at different times in the pipeline corridor area close to the surface. In order to ensure the timeliness and accuracy of these data, the software will automatically time-align the extracted data, filter out outliers caused by sensor errors or data noise, and aggregate the data at different time points. Finally, the software organizes the extracted environmental pressure and soil temperature data into a time series data set and stores it in the specified database for subsequent analysis and calculation. In this way, the software can efficiently and accurately extract key environmental data, providing a reliable basis for leakage risk assessment.

[0070] Obtain the actual pressure of the preset environment in the pipeline corridor area close to the surface over a period of time and calibrate it as ;

[0071] Obtaining the preset environmental actual pressure in the tunnel area close to the surface over a period of time can be achieved by combining historical environmental data and environmental prediction models. First, the software retrieves historical environmental pressure data under similar seasons, weather conditions and geographical locations from the database of the central monitoring system, which provides the basis for setting the preset environmental actual pressure. Then, the software uses a prediction model based on geographic information system (GIS), meteorological data and environmental pressure sensor data to simulate the trend of environmental pressure changes over a specific time period. By comparing and weighted averaging the historical data with the results of the prediction model, the software can generate the preset environmental actual pressure values ​​in the tunnel area over a period of time. These preset values ​​not only reflect the pressure conditions under typical environmental conditions, but can also be dynamically adjusted to adapt to real-time environmental changes. Finally, the software stores the generated preset environmental pressure data in the database as the benchmark data in the subsequent leakage risk assessment to ensure the accuracy and reliability of the assessment. In this way, the software can efficiently obtain and generate the preset environmental actual pressure, providing an accurate benchmark for leakage monitoring and risk assessment.

[0072] Calculate the environmental pressure response index. The specific calculation formula is as follows: In the formula, is the environmental stress response index, It is the preset actual soil temperature in the pipeline corridor area close to the ground surface over a period of time.

[0073] The size of the environmental pressure response index is directly related to the assessment of whether there is a leakage risk in the pipeline corridor area close to the surface. When the environmental pressure response index is large, it indicates that there is a significant difference between the current actual environmental pressure and the historical benchmark pressure or preset pressure. This abnormal pressure change may be caused by the change in soil pressure caused by leakage, which implies that there is a high leakage risk in the area. On the contrary, if the environmental pressure response index is small, it means that the difference between the current pressure and the historical or preset benchmark is small, and the pressure change is within the normal range, then the leakage risk is low. This index helps to accurately identify and evaluate possible leakage problems by quantifying abnormal fluctuations in pressure, and provides a basis for timely early warning and repair measures.

[0074] In this embodiment, the logic for obtaining the temperature impact compensation coefficient is as follows:

[0075] Extract the environmental pressure data and soil temperature data from the comprehensive environmental control information, including the actual environmental pressure and actual soil temperature at different time points in the pipeline corridor area close to the surface, and calibrate these data as and , The number indicating the actual environmental pressure and actual soil temperature in the pipeline corridor area close to the ground at different time points over a period of time. , is a positive integer;

[0076] Obtain the historical benchmark temperature and historical benchmark pressure in the pipeline corridor area close to the surface over a period of time and calibrate them as and ;

[0077] Obtaining the historical benchmark temperature and historical benchmark pressure in the tunnel area close to the surface over a period of time can be achieved by retrieving and analyzing the long-term stored environmental historical data from the database of the central monitoring system. First, the software will automatically filter out the temperature and pressure data for the corresponding period according to the required time period and geographical area. These historical data usually come from the sensor network deployed in the area for a long time, and are collected and stored by the system for a long time. The software will perform time alignment and aggregation processing on these historical data to ensure the integrity and consistency of the data. Then, the software determines the historical benchmark temperature and historical benchmark pressure in the time period by calculating the average or median of the historical data. In order to improve the accuracy of the benchmark value, the software will also consider excluding the influence of extreme weather events or abnormal data to ensure that the extracted benchmark value can reflect the temperature and pressure conditions of the area under typical environmental conditions. Finally, the extracted historical benchmark temperature and pressure will be stored and marked in the database as a reference benchmark for subsequent assessment of environmental changes and leakage risks. This method of processing through automated software can ensure the accuracy and reliability of historical benchmark data and provide a solid foundation for leakage risk assessment.

[0078] Calculate the temperature influence compensation coefficient. The specific calculation formula is as follows: In the formula, is the temperature compensation coefficient.

[0079] The size of the temperature effect compensation coefficient is closely related to the assessment of whether there is a leakage risk in the pipeline corridor area close to the surface. When the temperature effect compensation coefficient is large, it means that there is a large deviation between the current soil temperature and environmental pressure and the historical benchmark temperature and benchmark pressure. This deviation may affect the sensor's accurate detection of environmental changes, causing abnormal changes in pressure or humidity data to be more likely to be mistaken for leakage. By compensating and adjusting these temperature and pressure changes, the temperature effect compensation coefficient helps improve the accuracy of leakage risk assessment. If the temperature effect compensation coefficient is small, it indicates that the temperature and pressure conditions are close to the historical benchmark and the environmental changes are within the normal range, then the leakage risk is low. Therefore, the temperature effect compensation coefficient ensures the accuracy of leakage risk assessment and reduces the possibility of false alarms by correcting the impact of temperature and pressure on the data.

[0080] A leakage risk assessment model is constructed based on the generated soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient to generate a leakage risk coefficient;

[0081] In this embodiment, the generated soil moisture variation coefficient , Environmental Stress Response Index and temperature effect compensation coefficient Construct a leakage risk assessment model and generate a leakage risk coefficient through weighted summation .

[0082] Construct leakage risk assessment model and generate leakage risk coefficient The coefficient of variation of soil moisture is generated , Environmental Stress Response Index and temperature effect compensation coefficient First, the software will assign weight coefficients to each coefficient based on the geographical conditions of the corridor area, historical leakage data, environmental characteristics and other factors (such as ), these weights reflect the relative importance of each factor in the overall leakage risk. Generally, the soil moisture variation coefficient may have a greater weight in the leakage risk, because abnormal changes in soil moisture often directly indicate the occurrence of leakage; the environmental pressure response index reflects the impact of changes in environmental pressure on leakage, and its weight is second; and the temperature impact compensation coefficient is mainly used to correct the impact of temperature on the other two coefficients, and its weight is relatively small. The model construction process first uses the weighted summation formula These weight coefficients are applied to the corresponding indicators, and then the leakage risk coefficient is calculated comprehensively. This LRC value accurately assesses the magnitude of leakage risk by reflecting the comprehensive impact of various environmental factors, thereby providing data-based risk warnings for the monitoring system. This weighted summation method ensures the applicability and flexibility of the model under different environmental conditions, making the assessment of leakage risk more accurate and reliable.

[0083] Determine a preset leakage risk coefficient threshold, and compare it with the generated leakage risk coefficient after determination, evaluate whether there is leakage risk in the pipeline corridor area close to the surface, and generate normal signals and abnormal signals respectively according to the evaluation results;

[0084] In this embodiment, a preset leakage risk coefficient threshold is determined. , and after determination, the leakage risk factor generated Compare and evaluate whether there is leakage risk in the pipe gallery area close to the surface, and generate normal signals and abnormal signals according to the evaluation results. The specific comparison and analysis are as follows:

[0085] like , there is no leakage risk in the pipe gallery area close to the surface, and a normal signal is generated. This means that the current environmental conditions are relatively normal in the pipe gallery area close to the surface, and no significant abnormalities that may cause leakage are detected. At this time, the system generates a normal signal, indicating that the area is in a safe state and no additional preventive or repair measures are required. In terms of impact, this situation usually means that the differences between key parameters such as environmental pressure, temperature, and soil moisture and historical benchmarks or preset values ​​are within an acceptable range. Therefore, the structural integrity and operational stability of the pipe gallery are not threatened, and the system can continue routine monitoring to ensure the safe operation of the pipe gallery;

[0086] like , there is a leakage risk in the tunnel area close to the surface, and an abnormal signal is generated, which means that the current environmental conditions show obvious abnormalities and there may be a high risk of leakage. The system generates abnormal signals, indicating that the area may be experiencing significant changes in environmental pressure, temperature or soil moisture, which may cause damage or leakage to the tunnel structure. In terms of impact, this situation requires immediate attention from maintenance personnel, and emergency measures may need to be taken for on-site inspection and repair. Failure to respond in time may lead to the deterioration of the leakage problem, increase the difficulty and cost of repair, and even pose a threat to the long-term safety and stable operation of the tunnel.

[0087] In the case of generating an abnormal signal, a number of leakage risk coefficients generated subsequently are obtained, and a comprehensive analysis is performed to generate a risk signal of a corresponding level, and corresponding solutions are taken according to the generated risk signal of the corresponding level.

[0088] In this embodiment, when an abnormal signal is generated, several leakage risk coefficients generated subsequently are obtained, and the several leakage risk coefficients generated subsequently are recalibrated as , Indicates the numbers of several leakage risk coefficients generated subsequently when an abnormal signal is generated. , is a positive integer;

[0089] In the case of generating abnormal signals, obtaining several leakage risk coefficients generated subsequently can be achieved through automated data collection and real-time analysis software. First, when the system detects an abnormal signal, the software will automatically enter intensive monitoring mode, increase the frequency of data collection, and obtain real-time environmental data related to leakage from the sensor network, including key parameters such as soil moisture, environmental pressure and temperature. Subsequently, the software uses the newly acquired data to calculate a series of new leakage risk coefficients based on the preset leakage risk assessment model. These leakage risk coefficients represent the system's continuous assessment results of environmental changes after the abnormal signal appears. The software will save these coefficients in time series and mark them as several leakage risk coefficients generated subsequently for further statistical analysis and risk comparison. This process ensures that the system can capture and analyze environmental changes in a timely manner when abnormal situations occur, dynamically assess leakage risks, and provide accurate data support for taking corresponding countermeasures.

[0090] The average value of several leakage risk coefficients is calibrated as ,but: ;

[0091] The standard deviation of several leakage risk coefficients is calibrated as ,but: .

[0092] In this embodiment, the preset average value and the preset standard deviation of several leakage risk coefficients generated subsequently when the abnormal signal is generated are obtained, and the preset average value and the preset standard deviation of several leakage risk coefficients generated subsequently when the abnormal signal is generated are calibrated as and , the average value of several leakage risk coefficients generated subsequently when an abnormal signal is generated and standard deviation Respectively with the preset average and preset standard deviation Comparative analysis is performed to generate risk signals of corresponding levels, and corresponding solutions are taken according to the generated risk signals of corresponding levels. The specific analysis is as follows:

[0093] like , generating a high-risk signal and taking emergency measures immediately;

[0094] In this case, the average value of the subsequent generated leakage risk factors Significantly higher than the expected average , indicating that the current leakage risk continues to rise and has exceeded the safety threshold. This means that environmental conditions are deteriorating and the risk of leakage is extremely high, which may pose a major threat to the structural integrity and safety of the tunnel. At this time, a high-risk signal is generated, indicating that emergency measures need to be taken immediately. Specific measures include issuing emergency alerts through the software system and automatically dispatching maintenance teams for on-site inspections. Immediate operations such as leakage detection, soil remediation, or pressure release may be required to prevent catastrophic structural damage and environmental pollution. The software system can also monitor the progress of repairs in real time and dynamically adjust the response strategy based on the newly generated risk factors.

[0095] like and , generate medium risk signals and conduct regular monitoring and maintenance;

[0096] In this case, it means that although the overall risk is within controllable range, the environmental conditions fluctuate greatly and the uncertainty is high. This means that the risk of leakage has not increased significantly, but there are potential unstable factors that may cause the situation to deteriorate. At this time, a medium-risk signal is generated, indicating the need for enhanced monitoring and maintenance. Specific measures include increasing the frequency of data collection through the software system, continuously monitoring key environmental parameters, and regularly generating leakage risk assessment reports. The software can also arrange for maintenance personnel to conduct routine inspections, focusing on high-fluctuation areas to ensure that problems are dealt with at an early stage to prevent risks from escalating.

[0097] like and , generate low-risk signals and maintain regular inspections and monitoring.

[0098] In this case, the current environmental conditions are stable, the risk of leakage is low, and the volatility is small. This means that the operation of the tunnel area is good and there is no significant risk of leakage. At this time, a low risk signal is generated, indicating that regular inspection and monitoring work can be maintained. Specific measures include scheduling regular environmental parameter monitoring and data recording through the software system, ensuring continuous updating and analysis of data, and ensuring the normal operation of equipment through regular maintenance procedures. The system will also continue to track changes in environmental parameters so that it can respond in a timely manner when the situation changes.

[0099] Obtaining the preset mean value and preset standard deviation of several leakage risk coefficients generated subsequently when an abnormal signal is generated can be achieved by combining historical data analysis and prediction models. First, the software will retrieve the historical leakage risk coefficient data under similar abnormal conditions from the database of the central monitoring system. These data are usually extracted from abnormal events recorded under similar environments and conditions in the past. Then, the software will use these historical data to calculate the corresponding mean value and standard deviation as the preset value. In addition, in order to ensure the timeliness and adaptability of these preset values, the software can also dynamically adjust and optimize the preset mean value and preset standard deviation according to the trend of current environmental changes and historical abnormal conditions through the leakage risk prediction model. Finally, the software stores these preset values ​​in the system for subsequent comparison and analysis, thereby helping the system to more accurately assess the leakage risk level when abnormal signals appear. In this way, the acquisition of preset values ​​is based on historical experience and adapts to changes in the current environment, ensuring the accuracy and reliability of risk assessment.

[0100] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0101] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0102] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0103] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0107] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A real-time monitoring and early warning method for leakage status of long-line public facilities, characterized in that: The specific steps include: In the pipe gallery area close to the ground, a sensor network is deployed to obtain the environmental dynamic response information in the pipe gallery area close to the ground in real time, and store the environmental dynamic response information in the central monitoring system after acquisition; Analyze the environmental dynamic response information stored in the central monitoring system to generate soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient; The specific steps include: Pre-process the environmental dynamic response information in the pipeline corridor area close to the surface stored in the central monitoring system; Extracting comprehensive environmental control information from the preprocessed environmental dynamic response information, the comprehensive environmental control information including soil moisture data, soil temperature data and environmental pressure data; The extracted comprehensive environmental control information is analyzed to generate soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient respectively; The acquisition logic of the environmental pressure response index is as follows: Extract the environmental pressure data and soil temperature data from the comprehensive environmental control information, including the actual environmental pressure and actual soil temperature at different time points in the pipeline corridor area close to the surface, and calibrate these data as and , The number indicating the actual environmental pressure and actual soil temperature in the pipeline corridor area close to the ground at different time points over a period of time. , is a positive integer; Obtain the actual pressure of the preset environment in the pipeline corridor area close to the surface over a period of time and calibrate it as ; Calculate the environmental pressure response index. The specific calculation formula is as follows: In the formula, is the environmental stress response index, The preset actual soil temperature in the pipeline corridor area close to the ground surface over a period of time; The logic for obtaining the temperature impact compensation coefficient is as follows: Extract the environmental pressure data and soil temperature data from the comprehensive environmental control information, including the actual environmental pressure and actual soil temperature at different time points in the pipeline corridor area close to the surface, and calibrate these data as and , The number indicating the actual environmental pressure and actual soil temperature in the pipeline corridor area close to the ground at different time points over a period of time. , is a positive integer; Obtain the historical benchmark temperature and historical benchmark pressure in the pipeline corridor area close to the surface over a period of time and calibrate them as and ; Calculate the temperature influence compensation coefficient. The specific calculation formula is as follows: In the formula, is the temperature effect compensation coefficient; A leakage risk assessment model is constructed based on the generated soil moisture variation coefficient, environmental pressure response index and temperature impact compensation coefficient to generate a leakage risk coefficient; Determine a preset leakage risk coefficient threshold, and compare it with the generated leakage risk coefficient after determination, evaluate whether there is leakage risk in the pipeline corridor area close to the surface, and generate normal signals and abnormal signals respectively according to the evaluation results; In the case of generating an abnormal signal, a number of leakage risk coefficients generated subsequently are obtained, and a comprehensive analysis is performed to generate a risk signal of a corresponding level, and corresponding solutions are taken according to the generated risk signal of the corresponding level.

2. A real-time monitoring and early warning method for leakage status of long-line public facilities according to claim 1, characterized in that: The acquisition logic of the soil moisture variation coefficient is as follows: Extract the soil moisture content and soil temperature data from the comprehensive environmental control information, including the actual soil moisture content and actual soil temperature at different times in the pipeline corridor area close to the surface, and calibrate the actual soil moisture content and actual soil temperature at different times in the pipeline corridor area close to the surface as and , The number indicating the actual soil moisture content and actual soil temperature at different times over a period of time in the pipeline corridor area close to the ground surface. , is a positive integer; Obtain the preset actual soil moisture content and preset actual soil temperature in the pipeline corridor area close to the surface within a period of time, and calibrate them as and ; Calculate the soil moisture variation coefficient. The specific calculation formula is as follows: In the formula, is the soil moisture variation coefficient.

3. A real-time monitoring and early warning method for leakage status of long-line public facilities according to claim 2, characterized in that: The coefficient of variation of soil moisture generated , Environmental Stress Response Index and temperature effect compensation coefficient Construct a leakage risk assessment model and generate a leakage risk coefficient through weighted summation .

4. A real-time monitoring and early warning method for leakage status of long-line public facilities according to claim 3, characterized in that: Determine the pre-set leakage risk factor threshold , and after determination, the leakage risk factor generated Compare and evaluate whether there is leakage risk in the pipe gallery area close to the surface, and generate normal signals and abnormal signals according to the evaluation results. The specific comparison and analysis are as follows: like , there is no leakage risk in the pipeline corridor area close to the surface, and a normal signal is generated; like , there is a leakage risk in the pipeline corridor area close to the surface, generating an abnormal signal.

5. A real-time monitoring and early warning method for leakage status of long-line public facilities according to claim 4, characterized in that: When an abnormal signal is generated, several leakage risk coefficients generated subsequently are obtained, and the several leakage risk coefficients generated subsequently are recalibrated as , Indicates the numbers of several leakage risk coefficients generated subsequently when an abnormal signal is generated. , is a positive integer; The average value of several leakage risk coefficients is calibrated as ,but: ; The standard deviation of several leakage risk coefficients is calibrated as ,but: .

6. A real-time monitoring and early warning method for leakage status of long-line public facilities according to claim 5, characterized in that: Obtain the preset average value and the preset standard deviation of several leakage risk coefficients subsequently generated when the abnormal signal is generated, and calibrate the preset average value and the preset standard deviation of several leakage risk coefficients subsequently generated when the abnormal signal is generated as and , the average value of several leakage risk coefficients generated subsequently when an abnormal signal is generated and standard deviation Respectively with the preset average and preset standard deviation Comparative analysis is performed to generate risk signals of corresponding levels, and corresponding solutions are taken according to the generated risk signals of corresponding levels. The specific analysis is as follows: like , generating a high-risk signal and taking emergency measures immediately; like and , generate medium risk signals and conduct regular monitoring and maintenance; like and , generate low-risk signals and maintain regular inspections and monitoring.

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