Mountain area underground pipe network monitoring method based on BIM technology optimization

Abstract

The invention relates to the technical field of pipe network construction, and particularly discloses a mountain area underground pipe network monitoring method based on BIM (Building Information Modeling) technology optimization, which comprises the following steps: acquiring parameter data of underground pipe network nodes in real time through a sensor network; processing the parameter data by using an edge calculation method, and filtering noise to obtain processed parameter data; uploading the processed parameter data to a cloud platform, and performing deep analysis in combination with a big data analysis tool to predict the trend of the parameter data changing with time; integrating the trend of the parameter data along with the time change with a BIM model, displaying the state of the pipe network, identifying a potential problem area, and performing real-time anomaly detection to obtain an abnormal value; a threshold value alarm mechanism is set, and when monitoring data exceed a set range, the system automatically gives an alarm; the monitoring data comprises the parameter data processed in the step S2 and the abnormal value obtained in the step S3. The system can perform real-time monitoring and early warning on the underground pipe network in the mountainous area.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipeline network construction, and in particular relates to a method for monitoring underground pipeline networks in mountainous areas based on BIM technology optimization. Background Art

[0002] Traditional methods for the construction, maintenance, and maintenance of underground pipeline networks in mountainous areas often rely on manual inspections and empirical judgment. This approach is not only inefficient but also makes it difficult to fully and accurately understand the actual operating status of the pipeline network. The rapid development of information technology, particularly the Internet of Things, big data, cloud computing, and BIM (Building Information Modeling), has provided new solutions for the construction, maintenance, and maintenance of underground pipeline networks in mountainous areas. However, the application of existing technologies in this area still has many shortcomings.

[0003] Traditional methods for parameter data collection are often limited to surface inspections, which lack comprehensive and in-depth monitoring of key nodes and potential fault areas in underground pipeline networks. This results in an inaccurate understanding of the pipeline network status, making it difficult to detect and address potential problems in a timely manner.

[0004] In terms of data processing and analysis, existing technologies often lack efficient data processing methods, resulting in low real-time and low accuracy. Furthermore, the inadequate ability to deeply analyze big data makes it difficult to extract valuable information from massive amounts of data to guide construction and maintenance decisions.

[0005] In terms of alarm and response mechanisms, existing technologies often lack intelligent alarm mechanisms, resulting in delayed problem detection and resolution. Furthermore, there is a lack of systematic planning and management for the processing and response of alarm information, making it difficult for relevant personnel to take quick and accurate measures to resolve problems.

[0006] When it comes to pipeline network performance assessment and construction and maintenance plan development, existing technologies often rely on empirical judgment and lack scientific and systematic evaluation methods. This results in an inaccurate understanding of pipeline network performance and a lack of targeted and effective construction and maintenance plans.

[0007] In summary, the application of existing technologies in the construction, construction and maintenance of underground pipeline networks in mountainous areas still has many shortcomings and needs to be improved and optimized with the help of more advanced technical means and methods. Summary of the Invention

[0008] The purpose of the present invention is to provide a method for monitoring underground pipeline networks in mountainous areas based on BIM technology optimization, which can perform real-time monitoring and early warning of underground pipeline networks in mountainous areas.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] In a first aspect, the present application provides a method for monitoring underground pipeline networks in mountainous areas based on BIM technology optimization, comprising:

[0011] S1, obtain parameter data of underground pipe network nodes in real time through sensor network;

[0012] S2. Processing the parameter data using an edge computing method to filter out noise and obtain processed parameter data; uploading the processed parameter data to a cloud platform, and performing in-depth analysis in combination with big data analysis tools to predict the trend of the parameter data over time;

[0013] S3. Integrate the time-varying trend of the parameter data with the BIM model to achieve visual monitoring, display the status of the pipe network, identify potential problem areas, and perform real-time anomaly detection to obtain abnormal values;

[0014] S4. Setting a threshold alarm mechanism. When the monitoring data exceeds the set range, the system automatically issues an alarm. The monitoring data includes the processed parameter data in step S2 and the abnormal value obtained in step S3.

[0015] Preferably, the sensor network is arranged at key nodes and potential fault areas of the underground pipe network.

[0016] Preferably, the sensor network includes a temperature sensor, a pressure sensor and a flow sensor.

[0017] Preferably, the parameter data includes temperature parameters, pressure parameters and flow parameters.

[0018] Preferably, the expression for calculating the temperature parameter is:

[0019]

[0020] Where ΔT is the change in fluid temperature, in °C;

[0021] Q1: The amount of heat absorbed or released by the fluid, in J;

[0022] m: the total mass of the fluid in the pipeline, in kg;

[0023] c: Specific heat capacity of the fluid in the pipe, which represents the heat required for unit temperature change of unit mass of fluid, with the unit of J / (kg·℃).

[0024] Monitor temperature changes in pipelines to prevent damage due to temperature anomalies.

[0025] Preferably, the expression for calculating the pressure parameter is:

[0026]

[0027] Where ΔP: pressure loss of the fluid, in Pa, represents the pressure drop of the fluid in the pipeline due to friction;

[0028] f: friction factor, dimensionless, depends on the flow state and the pipe surface roughness;

[0029] L: length of the pipeline, in m;

[0030] D: inner diameter of the pipe, in m;

[0031] ρ: density of the fluid in the pipe, which represents the ratio of the mass of the fluid to its volume, in kg / m 3 ;

[0032] v: Flow velocity of the fluid in the pipe, in m / s.

[0033] Helps optimize pipeline design, reduce energy consumption and prevent pipeline rupture.

[0034] Preferably, the expression for calculating the flow parameter is:

[0035] Q2=A·v;

[0036] Q2: Flow rate in the pipe, which indicates the amount of water flowing through the pipe per unit time, in m 3 / s;

[0037] A: cross-sectional area of the pipe, in m 2 , calculated by the inner diameter of the pipe: A=π= 2 / 4;

[0038] v: Flow velocity of the fluid in the pipeline, in m / s, indicating the average velocity of the fluid in the pipeline.

[0039] Used to determine the pipeline's transportation capacity and meet design requirements.

[0040] Preferably, the depth analysis method is linear regression, and its expression is:

[0041] y=kx+b;

[0042] y: dependent variable, predicted value;

[0043] k: slope, which indicates the change in the dependent variable when the independent variable changes by one unit;

[0044] x: independent variable;

[0045] b: intercept, which represents the value of the dependent variable when the independent variable is zero.

[0046] Among them, the parameters k and b can be obtained by fitting the least square method based on historical parameter data; thus, the trend of pipeline flow, pressure, etc. changing over time can be predicted.

[0047] Preferably, the anomaly detection algorithm is Z-score, which is expressed as:

[0048]

[0049] Z: Z-score, which represents the multiple of the standard deviation of the data point X from the mean;

[0050] X: data point to be detected;

[0051] μ: the mean value of the data set;

[0052] σ: standard deviation of the data set.

[0053] Used to identify outliers. When the Z-score exceeds a certain threshold, it is marked as an anomaly.

[0054] Abnormal situations can be notified to relevant personnel in real time through mobile applications or web interfaces so that they can take appropriate measures and help respond to pipeline network problems in a timely manner.

[0055] Preferably, the calculation expression of the reliability analysis is:

[0056] R(t)=e -λt

[0057] R(t): The reliability of the pipe network at time t, which indicates the probability that the system will not fail within time t;

[0058] λ: failure rate, the unit can be 1 / hour, which means the average number of failures per unit time;

[0059] t: time, in hours, the time period to be considered.

[0060] Help predict the service life of the pipeline network and formulate reasonable construction and maintenance plans.

[0061] Preferably, the method further comprises:

[0062] S5. Conduct reliability analysis and generate reports based on the system's automatic alarms.

[0063] S6. Use the generated report to evaluate the performance of the pipeline network and improve the pipeline network construction and maintenance plans based on the results of the pipeline network performance evaluation.

[0064] Thus, risks in future construction and maintenance can be reduced based on the results of pipeline network performance assessment.

[0065] In a second aspect, the present application provides an electronic device, comprising: a memory and a processor;

[0066] The memory is used to store computer programs;

[0067] The processor is configured to call the computer program to execute the method described above.

[0068] In a third aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed on an electronic device, the electronic device implements the method described above.

[0069] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed on an electronic device, enables the electronic device to implement the method described above.

[0070] The specific implementation methods of the second to fourth aspects of this application can refer to the implementation method of the first aspect above, and will not be repeated here.

[0071] The present invention can solve the problems of pipeline network in parameter data collection, data processing and analysis, alarm and response mechanism, pipeline network performance evaluation and construction and maintenance plan formulation. Compared with the existing technology, the present invention has the following advantages:

[0072] (1) By placing sensors at key nodes and potential fault areas in the underground pipeline network, the present invention can obtain parameter data of the pipeline network nodes in real time. Combined with edge computing technology, the data is quickly processed and uploaded to the cloud platform for in-depth analysis, effectively improving the real-time monitoring capability of the pipeline network operation. Once the monitoring data exceeds the preset threshold, the system immediately issues an alarm and notifies relevant personnel in real time through a mobile phone application or web interface, enabling rapid response and resolution of potential problems, preventing the occurrence or escalation of faults.

[0073] (2) The present invention integrates the parameter data of underground pipeline network nodes with the BIM model, realizing the visual monitoring of pipeline network status. This intuitive and three-dimensional display method helps relevant personnel to quickly identify potential problem areas, conduct real-time anomaly detection, and provide strong data support for decision-making. Through the BIM model, the performance of the pipeline network can be evaluated more accurately, the subsequent construction and maintenance plans can be optimized, and the construction and maintenance efficiency can be improved.

[0074] (3) The present invention uses parameter data for analysis to improve construction plans and reduce risks in future construction. At the same time, through real-time monitoring and early warning, it can promptly detect and handle pipeline network failures, avoiding shutdowns and increased maintenance costs caused by failures. In addition, the optimized construction method based on BIM technology can also improve construction efficiency, shorten construction period, and further reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a flow chart of a method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization according to an embodiment of the present application;

[0076] Figure 2 This is a block diagram of the sensor network composition of an embodiment of the present application. DETAILED DESCRIPTION

[0077] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0078] Example 1:

[0079] See also Figures 1 to 2 As shown, the embodiment of the present application provides a method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization, including:

[0080] S1, obtain parameter data of underground pipe network nodes in real time through sensor network;

[0081] S2. Processing the parameter data using an edge computing method to filter out noise and obtain processed parameter data; uploading the processed parameter data to a cloud platform, and performing in-depth analysis in combination with big data analysis tools to predict the trend of the parameter data over time;

[0082] S3. Integrate the time-varying trend of the parameter data with the BIM model to achieve visual monitoring, display the status of the pipe network, identify potential problem areas, and perform real-time anomaly detection to obtain abnormal values;

[0083] S4. Setting a threshold alarm mechanism. When the monitoring data exceeds the set range, the system automatically issues an alarm. The monitoring data includes the processed parameter data in step S2 and the abnormal value obtained in step S3.

[0084] As can be seen above, the real-time acquisition of parameter data from underground pipeline nodes through a sensor network, combined with edge computing technology to rapidly process this data and upload it to a cloud platform for in-depth analysis, effectively enhances the real-time monitoring capabilities of pipeline network operations. If monitoring data exceeds a preset threshold, the system immediately issues an alarm and notifies relevant personnel in real time via a mobile app or web interface, enabling rapid response and resolution of potential issues before they occur or escalate.

[0085] Integrating real-time data with BIM models enables visual monitoring of pipeline network status. This intuitive, three-dimensional display helps personnel quickly identify potential problem areas, conduct real-time anomaly detection, and provide strong data support for decision-making.

[0086] At the same time, through real-time monitoring and early warning, pipeline faults can be discovered and handled in a timely manner, avoiding shutdowns and increased maintenance costs caused by faults.

[0087] In some embodiments, in step S1, the sensor network is deployed at key nodes and potential fault areas of the underground pipeline network.

[0088] Specifically, the sensor network includes temperature sensors, pressure sensors, and flow sensors.

[0089] Specifically, the parameter data includes temperature parameters, pressure parameters and flow parameters.

[0090] Specifically, the expression for temperature parameter calculation is:

[0091]

[0092] Where ΔT is the change in fluid temperature, in °C;

[0093] Q1: The amount of heat absorbed or released by the fluid, in J;

[0094] m: the total mass of the fluid in the pipeline, in kg;

[0095] c: Specific heat capacity of the fluid in the pipe, which represents the amount of heat required for a unit temperature change of a unit mass of fluid, in J / (kg·℃);

[0096] Monitor temperature changes in pipelines to prevent damage due to temperature anomalies.

[0097] Specifically, the expression for calculating the pressure parameter is:

[0098]

[0099] Where ΔP: pressure loss of the fluid, in Pa, represents the pressure drop of the fluid in the pipeline due to friction;

[0100] f: friction factor, dimensionless, depends on the flow state and the pipe surface roughness;

[0101] L: length of the pipeline, in m;

[0102] D: inner diameter of the pipe, in m;

[0103] ρ: density of the fluid in the pipe, which represents the ratio of the mass of the fluid to its volume, in kg / m3 ;

[0104] v: Flow velocity of the fluid in the pipe, in m / s.

[0105] Helps optimize pipeline design, reduce energy consumption and prevent pipeline rupture.

[0106] Specifically, the expression for calculating the flow parameter is:

[0107] Q2=A·v;

[0108] Q2: Flow rate in the pipe, which indicates the amount of water flowing through the pipe per unit time, in m 3 / s;

[0109] A: cross-sectional area of the pipe, in m 2 , calculated from the inner diameter of the pipe;

[0110] v: Flow velocity of the fluid in the pipeline, in m / s, indicating the average velocity of the fluid in the pipeline.

[0111] Used to determine the pipeline's transportation capacity and meet design requirements.

[0112] Specifically, the in-depth analysis method is linear regression, and its expression is:

[0113] y=kx+b;

[0114] y: dependent variable, predicted value;

[0115] k: slope, which indicates the change in the dependent variable when the independent variable changes by one unit;

[0116] x: independent variable;

[0117] b: intercept, which represents the value of the dependent variable when the independent variable is zero.

[0118] Among them, the parameters k and b can be obtained by fitting the least square method based on historical parameter data; thus, the trend of pipeline flow, pressure, etc. changing over time can be predicted.

[0119] Specifically, the anomaly detection algorithm is Z-score, which is expressed as:

[0120]

[0121] Z: Z-score, which represents the multiple of the standard deviation of the data point X from the mean;

[0122] X: data point to be detected;

[0123] μ: the mean value of the data set;

[0124] σ: standard deviation of the data set.

[0125] Used to identify outliers. When the Z-score exceeds a specific threshold, it is marked as an anomaly, helping to respond to pipe network problems in a timely manner.

[0126] Specifically, the calculation expression of reliability analysis is:

[0127] R(t)=e -λt ;

[0128] R(t): The reliability of the pipe network at time t, which indicates the probability that the system will not fail within time t;

[0129] λ: failure rate, the unit can be 1 / hour, which means the average number of failures per unit time;

[0130] t: time, in hours, the time period to be considered.

[0131] Help predict the service life of the pipeline network and formulate reasonable construction and maintenance plans.

[0132] In some embodiments, the method further comprises:

[0133] S5. Conduct reliability analysis and generate reports based on the system's automatic alarms.

[0134] S6. Use the generated report to evaluate the performance of the pipeline network and improve the pipeline network construction and maintenance plans based on the results of the pipeline network performance evaluation.

[0135] As can be seen above, by placing sensors at key nodes and potential fault areas in the underground pipeline network, parameter data of the pipeline network nodes can be obtained in real time. By real-time monitoring of flow, pressure, and temperature, abnormal conditions can be promptly detected, safety hazards such as pipeline ruptures or leaks can be prevented, and construction and operation safety can be ensured. Through accurate data collection and analysis, water resource usage can be effectively evaluated, waste can be reduced, and water resource management efficiency can be improved. Real-time monitoring data provides a basis for targeted construction and maintenance, reducing unnecessary inspections and repairs, thereby reducing overall construction and maintenance costs. Through visual monitoring data, construction teams can quickly respond to problems, optimize construction scheduling, improve construction plans, improve work efficiency, and reduce risks in future construction.

[0136] By setting threshold alarms and generating reports, the system automatically performs reliability analysis and generates construction and maintenance plans based on the results. This intelligent approach to construction and maintenance management not only improves efficiency but also reduces the cost and risk of manual intervention. Furthermore, the generated reports provide valuable insights for pipeline network performance evaluation and future planning.

[0137] In complex terrain conditions such as mountainous areas, the BIM model can more accurately evaluate pipeline network performance, optimize subsequent construction and maintenance plans, improve construction and maintenance efficiency, shorten construction periods, and further reduce costs; it can better adapt to terrain changes and ensure the stability and safety of the pipeline network. At the same time, through real-time monitoring and construction and maintenance management, it can reduce the impact of the pipeline network on the environment, improve resource utilization efficiency, and achieve sustainable development.

[0138] The embodiments of this application are specifically applied to the construction of pipeline networks in complex environments, such as the complex environment of underground pipeline construction and maintenance in mountainous areas. For example, for a 5-kilometer underground pipeline project planned for construction in a certain mountainous area, traditional manual inspection and empirical judgment methods are no longer able to meet the needs of modern construction management. The project aims to improve the management and transportation efficiency of local water resources. However, the complex and changing terrain of the mountainous area poses many challenges to pipeline construction, such as soil stability assessment, environmental impact control, and safety hazard investigation.

[0139] To address these challenges, the present application utilizes advanced IoT technologies for real-time monitoring and management. By deploying sensors at key nodes and potential fault areas in the underground pipeline network, various parameter data at the network nodes, such as soil pressure, pipeline displacement, and water quality, can be acquired in real time. This data is crucial for assessing the stability and safety of the pipeline network.

[0140] Furthermore, to efficiently process and analyze this massive amount of data, the present application introduces edge computing methods. Edge computing can quickly process data near its source, filtering out noise and redundant information, thereby obtaining more accurate and real-time data. The processed data will be uploaded to the cloud platform and deeply mined and analyzed with big data analysis tools to identify potential failure points and optimization opportunities.

[0141] In addition, the embodiments of the present application also integrate the processed parameter data with the BIM (Building Information Model) model. As a digital three-dimensional representation method, the BIM model can intuitively display the structure and layout of the underground pipe network. By combining real-time data with the BIM model, visual monitoring can be achieved, allowing construction personnel to intuitively see the status and potential problems of the pipe network. This can not only improve construction efficiency, but also reduce decision-making errors caused by information asymmetry.

[0142] Specifically:

[0143] 1. Sensor selection and installation

[0144] Flow sensor: installed at each major node (such as valves, branch points) to monitor water flow.

[0145] Pressure sensor: installed in the middle and corners of the pipeline to detect changes in water pressure in the pipeline.

[0146] Temperature sensor: installed at the start and end of the pipeline to monitor water temperature.

[0147] Sensor data description:

[0148] Node A (starting point):

[0149] Flow rate: 0.05m 3 / s

[0150] Pressure: 200000Pa

[0151] Temperature: 15℃

[0152] Node B (transfer point):

[0153] Flow rate: 0.07m 3 / s

[0154] Pressure: 180000Pa

[0155] Temperature: 14°C

[0156] 2. Data transmission and storage

[0157] The LoRa wireless network is used to transmit data collected by sensors to the cloud platform in real time, ensuring secure storage and efficient access to data.

[0158] The cloud platform uses a structured database (such as an SQL database) to facilitate data management and query.

[0159] 3. Data Analysis and Processing

[0160] Data visualization: Use BIM models to display the pipeline network layout, integrate real-time monitoring data, and display the status of each node through a graphical interface.

[0161] Anomaly detection: Use the Z-score algorithm to calculate the mean and standard deviation of each monitoring parameter. When the Z-score of the monitoring data exceeds the set threshold (such as 3), the alarm mechanism is triggered.

[0162] 4. Early warning mechanism

[0163] Set pressure threshold: When the pressure monitoring value of node A exceeds 220,000 Pa, the system immediately sends an alarm to the project management team.

[0164] Notify relevant personnel via mobile app or email to ensure timely response.

[0165] 5. Implementation effect evaluation

[0166] Improved safety: After the project is implemented, the monitoring system can detect potential safety hazards in real time, ensuring the safety of construction and subsequent operations.

[0167] Environmental impact assessment: Monitoring data helps assess the impact of construction on the surrounding environment and ensures compliance with environmental standards.

[0168] 6. Post-construction and maintenance management

[0169] Regular monitoring: Set up a regular data review plan, analyze the operation status of the pipeline network every month, and evaluate the changing trends of each sensor data.

[0170] Construction and maintenance: Develop targeted construction and maintenance strategies based on current monitoring status to ensure the long-term stable operation of the pipeline network.

[0171] From the above, it can be seen that the present invention ensures safe operation, improves the efficiency of water resource utilization, reduces waste, provides data support for later construction and maintenance, and extends the service life of the pipeline network through real-time monitoring of the pipeline network status. It also demonstrates the application of Internet of Things technology in the construction of underground pipeline networks in mountainous areas, and emphasizes the effectiveness of real-time monitoring, data analysis and response mechanisms in improving safety, optimizing resource utilization, and reducing construction and maintenance costs. This project not only improves the management level of the pipeline network, but also provides valuable experience and reference for similar projects in the future.

[0172] The underground pipeline network monitoring method in mountainous areas optimized based on BIM technology has many advantages, such as improved real-time monitoring and early warning capabilities, visual monitoring and decision support, reduced construction risks and cost savings, intelligent construction and maintenance management, and environmental adaptability and sustainability.

[0173] Example 2:

[0174] This embodiment provides an electronic device, including: a memory and a processor;

[0175] The memory is used to store computer programs;

[0176] The processor is configured to call the computer program to execute the method described in the first embodiment.

[0177] Example 3:

[0178] This embodiment provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed on an electronic device, the electronic device implements the method according to the first embodiment.

[0179] Example 4:

[0180] This embodiment provides a computer program product, including a computer program. When the computer program is run on an electronic device, the electronic device implements the method described in the first embodiment.

[0181] The specific implementation methods of a system, electronic device, computer-readable storage medium, and computer program product provided in the embodiments of the present application can refer to the specific embodiments of the above-mentioned method and will not be repeated here.

[0182] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented using a general-purpose computing device. They can be concentrated on a single computing device or distributed across a network composed of multiple computing devices. Alternatively, they can be implemented using program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0183] In the description of this specification, the reference terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0184] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0185] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization, characterized in that: include: S1, obtain parameter data of underground pipe network nodes in real time through sensor network; S2. Process the parameter data using an edge computing method to filter noise and obtain processed parameter data; Uploading the processed parameter data to a cloud platform and performing in-depth analysis using big data analysis tools to predict the trend of the parameter data over time; S3. Integrate the time-varying trend of the parameter data with the BIM model to display the status of the pipe network, identify potential problem areas, and perform real-time anomaly detection to obtain abnormal values; S4. Setting a threshold alarm mechanism. When the monitoring data exceeds the set range, the system automatically issues an alarm. The monitoring data includes the processed parameter data in step S2 and the abnormal value obtained in step S3.

2. The method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization according to claim 1, characterized in that: The sensor network is arranged at key nodes and potential fault areas of the underground pipeline network.

3. The method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization according to claim 2, characterized in that: The sensor network includes a temperature sensor, a pressure sensor and a flow sensor; the parameter data includes a temperature parameter, a pressure parameter and a flow parameter.

4. The method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization according to claim 1, characterized in that: The depth analysis method is linear regression, and its expression is: y=kx+b y: dependent variable, predicted value of parameter data; k: slope, which indicates the change in the dependent variable when the independent variable changes by one unit; x: independent variable, time; b: intercept, which represents the value of the dependent variable when the independent variable is zero.

5. The method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization according to claim 1, characterized in that: The anomaly detection algorithm is Z-score, which is expressed as: Z: Z-score, which represents the multiple of the standard deviation of the data point X from the mean; X: data point to be detected; μ: the mean value of the data set; σ: standard deviation of the data set.

6. The method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization according to claim 1, characterized in that: The calculation expression of the reliability analysis is: R(t)=e -λt ; R(t): The reliability of the pipe network at time t, which indicates the probability that the system will not fail within time t; λ: Failure rate, which represents the average number of failures per unit time, that is, the average number of times the system automatically issues an alarm per unit time; t: time, the time period considered.

7. A method for monitoring underground pipe networks in mountainous areas based on BIM technology optimization according to any one of claims 1 to 3, characterized in that: The method further comprises: S5. Conduct reliability analysis and generate reports based on the system's automatic alarms. S6. Use the generated report to evaluate the performance of the pipeline network and improve the pipeline network construction and maintenance plans based on the results of the pipeline network performance evaluation.

8. An electronic device, characterized in that: include: memory and processor; The memory is used to store computer programs; The processor is configured to call the computer program to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed on an electronic device, the electronic device implements the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed on an electronic device, the electronic device implements the method according to any one of claims 1 to 7.

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