Intelligent gas installation management method and system based on Internet of Things

Abstract

The invention provides an intelligent gas installation management method and system based on the Internet of Things. The method comprises the following steps: firstly, collecting biological characteristic data of a user, combining building three-dimensional point cloud data and environmental parameters to generate a standardized data packet, and verifying the identity of the user; thirdly, carrying out space alignment on the three-dimensional point cloud data of the building and the three-dimensional model of the building through a point cloud registration technology, constructing a three-dimensional pipe network model, and generating a pipe network laying path based on real-time meteorological data and municipal underground pipeline information; and finally, a three-dimensional visual construction scheme is generated through a construction scheme generation module, the construction process is monitored in real time, deviation is detected in time, and a credible evidence storage record is generated. According to the invention, through integration of biological characteristic authentication, point cloud registration, three-dimensional pipe network modeling and real-time construction monitoring, the efficiency and safety of gas installation management are improved, the accuracy and transparency of the construction process are ensured, and an innovative intelligent gas installation management solution is provided.

Description

Technical Field

[0001] The present invention relates to the field of intelligent gas installation management, and specifically to a method and system for intelligent gas installation management based on the Internet of Things. Background Art

[0002] With the progress of society and the rapid development of intelligent technologies, the gas industry is gradually developing towards intelligence and digitization. Especially in gas installation management, there are some problems with traditional gas installation management methods, such as untimely information processing, complex operation processes, and great difficulty in user identity verification. These problems often lead to a slow gas installation process and certain safety hazards.

[0003] With the popularization of Internet of Things technology and the progress of sensor technology, intelligent systems based on the Internet of Things have become an effective solution to solve the problems of traditional gas installation management. Internet of Things devices can collect environmental parameters, user identity information, and construction site data in real time. Combining modern big data and cloud computing technologies, it can achieve efficient user identity verification, pipeline network planning, and construction management, improving the safety and efficiency of gas installation.

[0004] Existing related patent technologies mostly focus on the monitoring and management of gas pipeline networks, but there are still deficiencies in user identity verification, the application of building three-dimensional point cloud data, and real-time monitoring of construction sites. Through traditional identity authentication and pipeline network model establishment methods, existing technologies can achieve certain functions, but there is still a certain lag in aspects such as real-time update of on-site data, detection and adjustment of construction deviations during the construction process, and lack of flexible adaptability. Summary of the Invention

[0005] In order to solve the technical problems mentioned in the current background art, the present invention proposes a method and system for intelligent gas installation management based on the Internet of Things.

[0006] Therefore, the technical solution adopted by the present invention is as follows: An intelligent gas installation management system based on the Internet of Things, characterized in that the system includes: M1. A multi-source perception and intelligent verification module, which collects biometric data of users through mobile terminal devices, simultaneously scans and obtains building three-dimensional point cloud data, and collects environmental parameters in real time through a sensor array; through cross-verification, compares the biometric data with the government affairs database to verify the user identity, aligns the building three-dimensional point cloud data with the building three-dimensional model through point cloud registration technology for spatial alignment verification, and generates a standardized data packet in combination with the environmental parameters; M2, the theoretical planning module, based on the standardized data packet, constructs a three-dimensional pipe network model through a graph database and generates a basic theoretical path for equipment installation. When the deviation between the building three-dimensional space coordinates uploaded by the construction equipment and the basic theoretical path exceeds the first threshold, the three-dimensional pipe network model is updated, and the updated three-dimensional pipe network model and the basic theoretical path are output; M3, the construction plan generation module, based on the updated three-dimensional pipe network model and the basic theoretical path, combines real-time meteorological data and municipal underground pipeline data to generate a pipe network laying path and outputs a three-dimensional visual construction plan; M4, the holographic monitoring and safety traceability module, constructs according to the three-dimensional visual construction plan, collects real-time construction data at the construction site, performs compliance verification on the construction path based on the real-time construction data. When it is detected that the deviation between the real-time construction data and the pipe network laying path exceeds the second threshold, a credible evidence record is generated, and the credible evidence record is fed back to the construction plan generation module for adjustment of the pipe network laying path.

[0007] Further, the biometric data includes fingerprint features, facial features, and iris features. The fingerprint features are obtained by collecting the fingerprint data of the user through a fingerprint sensor and extracting them using the Minutiae point extraction algorithm. The facial features are obtained by collecting the facial image of the user through a front camera and extracting them after face detection. The iris features are obtained by acquiring the iris image of the user through a dedicated iris scanner and extracting them after detecting the iris boundary using the Hough transform. The building three-dimensional point cloud data is obtained by scanning the building with a laser scanner, and the building three-dimensional point cloud data is denoised and cleaned through point cloud denoising technology.

[0008] Further, the cross-verification includes user identity verification and spatial alignment verification. The user identity verification uses a comparison algorithm to compare the biometric data with the registration information in the government affairs database and outputs the biometric authentication information of the user.

[0009] Further, the spatial alignment verification is performed through point cloud registration. The point cloud registration includes coarse registration and fine registration. The coarse registration uses feature matching to initially align the positions of the building three-dimensional point cloud data and the building three-dimensional model, and outputs the building three-dimensional point cloud data after coarse registration. The fine registration performs registration on the building three-dimensional point cloud data after coarse registration through the ICP algorithm. The ICP algorithm defines an error metric and, through an iterative process based on a rigid transformation, the rigid transformation includes a rotation matrix and a displacement vector, to minimize the error metric. The formula is: Among them, p is a point in the 3D building point cloud data after rough registration; q is the point in the building 3D model closest to p ; n is the number of points; E is the error metric; Through the iterative process, minimize the error metric and obtain the optimal rotation matrix and displacement vector. The formula is: Among them, R is the optimal rotation matrix; t is the optimal displacement vector; is the minimized error metric; Update point p through the optimal rotation matrix and displacement vector. The formula is: Among them, is the point in the updated 3D building point cloud data; adjust the 3D building point cloud data according to the point in the updated 3D building point cloud data, and output the registered 3D building point cloud data; After completing the point cloud registration, perform accuracy verification. Use the point-to-point distance between the registered 3D building point cloud data and the building 3D model to calculate the overall error, and evaluate the registration accuracy through the root mean square error (PMSE). The formula is: When the overall error exceeds the threshold, re-perform the point cloud registration.

[0010] Furthermore, the environmental parameters are collected by a sensor array deployed around the building, and the user's biometric authentication information, the registered 3D building point cloud data, the building 3D model, and the environmental parameters are packaged to generate a standardized data packet.

[0011] Furthermore, the 3D pipe network model includes gas equipment entity nodes and topological connection edges, corresponding to the graph nodes and graph edges in the graph database, The basic theoretical path is calculated by the shortest path algorithm, including the definition of the constraint objective function and the calculation of the shortest path, The formula for defining the constraint objective function is: Among them, is the construction cost of the path ; is the actual laying distance of the path , that is, the objective of the shortest path problem; is the degree of conflict between the path and the existing underground facilities; is the weight coefficient of each constraint, used to adjust the importance of different constraints The shortest path calculation is used for the generation of the basic theoretical path, which is calculated using the Dijkstra algorithm. The optimal path is selected by calculating the weight of each path, and the weight of the path is determined by the constraint objective function. The formula is as follows: Among them, is the graph edge weight from graph node u to graph node v; The Dijkstra algorithm calculates the shortest path of each graph node through recursion until all graph nodes are traversed. The formula is as follows: Among them, is the shortest path length from the starting point to graph node v; is the shortest path length from the starting point to graph node u; is through the current graph node and the graph edge weight The newly calculated path length to reach graph node v; Through the Dijkstra algorithm, the shortest path of each graph node is continuously updated until the target point is reached, and the shortest path from the starting point to the target point is obtained , that is, the basic theoretical path. The formula is as follows: Among them, is the weight of each section of the path, and m is the total number of nodes; The three-dimensional space coordinates are detected by the Beidou Satellite Navigation System (BDS). By continuously tracking the three-dimensional space coordinates and detecting the deviation from the basic theoretical path, when the deviation exceeds the first threshold, the basic theoretical path adjustment mechanism is triggered to update the three-dimensional pipe network model and the basic theoretical path.

[0012] Furthermore, the pipe network laying path is generated based on the three-dimensional pipe network model and the basic theoretical path, combined with real-time constraint conditions. The real-time constraint conditions include real-time meteorological data and municipal underground pipeline data. According to the pipe network laying path, combined with the three-dimensional geographical information of the construction site, the three-dimensional visualization construction plan is generated, including the display of the pipe network path, the installation position of the pipeline, and the construction steps and sequence.

[0013] Furthermore, the real-time construction data is collected in real time through Internet of Things sensors and positioning systems, and the real-time construction data is compared with the pipe network laying path to calculate the deviation value. The formula is as follows: Among them, Are the coordinate points of real-time construction data; Are the coordinate points of the pipeline network laying path; Is the deviation value, representing the path deviation; When the deviation value exceeds the second threshold, a deviation alarm is triggered, and the credible evidence record is generated, including the time stamp, operator information, and deviation parameters. The credible evidence record is encrypted and stored through blockchain technology. The credible evidence record will also be fed back to the construction plan generation module, and according to the credible evidence record, the pipeline network laying path is recalculated.

[0014] A method for intelligent gas installation management based on the Internet of Things, characterized in that the method includes: S1. Collect the biometric data of the user, simultaneously scan and obtain the three-dimensional point cloud data of the building, and collect the environmental parameters in real time through the sensor array; through cross-verification, compare the biometric data with the government affairs database to verify the user identity, and through the point cloud registration technology, spatially align and verify the three-dimensional point cloud data of the building with the three-dimensional building model, and generate a standardized data packet in combination with the environmental parameters; S2. Based on the standardized data packet, construct a three-dimensional pipeline network model through the graph database, and generate the basic theoretical path for equipment installation. When the deviation between the three-dimensional spatial coordinates of the construction equipment uploaded and the basic theoretical path exceeds the first threshold, update the three-dimensional pipeline network model, and output the updated three-dimensional pipeline network model and the basic theoretical path; S3. Based on the updated three-dimensional pipeline network model and the basic theoretical path, combine the real-time meteorological data and the municipal underground pipeline data to generate the pipeline network laying path, and output a three-dimensional visual construction plan; S4. Carry out construction according to the three-dimensional visual construction plan, and collect real-time construction data. Based on the real-time construction data, perform compliance verification of the construction path. When it is detected that the deviation between the real-time construction data and the pipeline network laying path exceeds the second threshold, generate a credible evidence record, and feed the credible evidence record back to the construction plan generation module for adjustment of the pipeline network laying path.

[0015] Compared with the prior art, the advantages of the present invention are: 1. High-precision identity verification: Through multiple biometric authentications such as fingerprint, face, and iris, the accuracy of user identity verification is improved, and the potential safety hazards that may be brought by the single verification method in the traditional technology are avoided.

[0016] 2. Dynamic update of the pipeline network model: Based on the three-dimensional point cloud data and real-time construction data, the three-dimensional pipeline network model can be updated in a timely manner, ensuring that the construction plan is consistent with the actual construction site, and avoiding the problem of lagging pipeline network planning in the traditional method.

[0017] 3. Real-time monitoring during construction: Through the holographic monitoring and safety traceability module, the construction progress and path deviation are monitored in real time, construction deviations are promptly detected and adjusted to ensure that the construction proceeds along the planned path, improving the safety and efficiency of construction. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 It is the flowchart of the intelligent gas installation management system of the present invention; Figure 2 It is the flowchart of the multi-source perception and intelligent verification module of the present invention; Figure 3 It is the flowchart of the theoretical planning module of the present invention. Detailed Embodiments

[0020] To achieve the above objectives, the present invention is realized through the following technical solutions. The present invention provides a system for intelligent gas installation management based on the Internet of Things, and the system includes: M1. The multi-source perception and intelligent verification module collects the biometric data of users through mobile terminal devices, simultaneously scans and obtains the three-dimensional point cloud data of the building, and collects the environmental parameters in real time through the sensor array; through cross-verification, the biometric data is compared with the government affairs database to verify the user's identity, and the three-dimensional point cloud data of the building is spatially aligned and verified with the three-dimensional building model through point cloud registration technology, and a standardized data packet is generated in combination with the environmental parameters. The biometric data includes fingerprint features, facial features, and iris features. A fingerprint sensor is used to collect the fingerprint data of users. After the collected fingerprint data is preprocessed, the fingerprint features are extracted using the Minutiae point extraction algorithm; the front camera is used to collect the facial images of users, and after face detection, the key features are extracted; the iris images of users are obtained through a dedicated iris scanner, the iris boundary is detected using the Hough transform, and the unique texture features are extracted. All biometric data is transmitted to the backend verification port through an encrypted channel. Using a comparison algorithm, the biometric data is compared with the registration information in the government affairs database to ensure the authenticity of the identity. If multiple biometric information matches successfully, the user is determined to be a legitimate user; if the match fails, the system notifies the user to conduct an identity review.

[0021] Use a laser scanner to perform three-dimensional point cloud scanning on a building, generating three-dimensional coordinate data of the building's facade, structure, and internal space. The acquisition accuracy of the point cloud data reaches the millimeter level, enabling the capture of detailed information about the building. After the three-dimensional point cloud data is acquired, denoise and clean the point cloud data through point cloud denoising technology; subsequently, perform spatial alignment verification. Spatial alignment verification is carried out through point cloud registration technology. Point cloud registration includes coarse registration and fine registration. Randomly select an initial point pair from two groups of point clouds. Coarse registration uses feature matching to estimate the initial alignment position. Perform fine registration on the coarsely registered point cloud data through the ICP algorithm. The ICP algorithm finds a set of rigid transformations through iteration. The rigid transformation includes a rotation matrix and a displacement vector, minimizing the distance between the points in the three-dimensional point cloud data and the points in the building three-dimensional model. During the fine registration process, it is necessary to define an error metric to evaluate the matching quality between point clouds. The error metric is the Euclidean distance between points, that is, for a point in the three-dimensional point cloud , find the point in the building three-dimensional model point cloud that is closest to it , the formula is: where is the error metric; n is the number of points; In each iteration process, use the least squares method to find the optimal rotation matrix and displacement vector , such that after the three-dimensional point cloud undergoes a rigid transformation, the error metric is minimized , the formula is: Through the calculated rotation matrix and displacement vector transform the source point cloud to obtain a new registration result and update the position of the three-dimensional point cloud. The formula is: where is the updated three-dimensional point cloud; repeat the iteration process until the threshold is reached; After completing point cloud registration, it is necessary to perform accuracy verification to ensure the accuracy of registration. Use the point-to-point distance between the registered three-dimensional point cloud data and the building three-dimensional model to calculate the overall error, and evaluate the registration accuracy through the root mean square error (PMSE). The formula is: When the overall error exceeds the threshold, re-perform point cloud registration; In practical applications, through visual comparison, the registered point cloud and the building three-dimensional model can be overlapped and displayed to visually check the registration effect. In spatial alignment verification, the building 3D model provides a known standardized coordinate system. The registered point cloud data will be compared with this model. Through error analysis and visual comparison of the registration results, it can be ensured that the building 3D point cloud data is spatially aligned with the building 3D model; By deploying a sensor array around the building to collect the environmental parameters of the construction site, the sensors transmit real-time data to the central management platform through wireless communication to ensure the timeliness and accuracy of the data. After being standardized, the user's biometric data, building 3D point cloud data, and environmental parameters are packaged into a standardized data packet, including the user's biometric authentication information, the registered building 3D model, point cloud data, and environmental parameters.

[0022] M2. Based on the standardized data packet, a 3D pipeline network model is constructed through a graph database, and a basic theoretical path is generated. When the 3D spatial coordinates uploaded by the construction equipment deviate from the basic theoretical path by more than the first threshold, the 3D pipeline network model is updated, and an update record is generated. The 3D pipeline network model is constructed through a graph database, including gas equipment entity nodes and topological connection edges, corresponding to the graph nodes and graph edges in the graph database. The graph nodes represent each device in the gas network, including valves, regulators, pipe joints, branch points, etc. Each entity node has spatial coordinate information and relevant attributes of the device, including device type, flow rate, pressure, pipe diameter, etc. The graph edge represents the physical connection relationship between graph nodes, which is represented as the physical connection between pipelines, connecting pipelines, or other gas facilities in this embodiment. Each edge also contains information such as the size, type, length, and pressure rating of the pipeline. After constructing the 3D pipeline network model, basic theoretical path planning is carried out. The basic theoretical path refers to the theoretically optimal pipeline laying path without any external interference, calculated through the shortest path algorithm, including the definition of the constraint objective function and the calculation of the shortest path. The definition of the constraint objective function is to determine the optimal path by considering multiple constraints. The most important factors are the cost of the path and the degree of conflict with existing underground facilities. The definition formula of the constraint objective function is: Among them, is the construction cost of the path ; is the actual laying distance of the path , that is, the objective of the shortest path problem; is the degree of conflict between the path and existing underground facilities; is the weight coefficient of each constraint, used to adjust the importance of different constraints; The constrained objective function weights and synthesizes these factors to generate an optimizable path; The shortest path calculation is used to generate the basic theoretical path, which is calculated using the Dijkstra algorithm. The optimal path is selected by calculating the weights of each path, and the weight of the path is determined by the constrained objective function, that is: Among them, is the edge weight from node u to node v, The Dijkstra algorithm calculates the shortest path of each node through recursion until all nodes are traversed. The formula is: Among them, is the shortest path length from the starting point to node v; is the shortest path length from the starting point to node u; is the new path length to reach node v calculated through the current node and the edge weight ; Through the Dijkstra algorithm, the shortest path of each node is continuously updated until the target node is reached. Finally, the algorithm returns the shortest path from the starting point to the target point , that is, the basic theoretical path. The formula is: Among them, is the weight of each path segment, and m is the total number of nodes.

[0023] The construction equipment conducts three-dimensional spatial positioning of the building through the Beidou Satellite Navigation System (BDS). By real-time tracking the spatial coordinates of the construction equipment, the deviation from the basic theoretical path is detected. When the deviation exceeds the first threshold, the basic theoretical path adjustment mechanism will be triggered to update the three-dimensional pipe network model and the basic theoretical path, ensuring that the basic theoretical path can correspond to the three-dimensional space of the construction site. This update process ensures that the three-dimensional pipe network model can maintain consistency with the construction site during the construction process and adapt to on-site changes. Through deviation update, the three-dimensional pipe network model is continuously optimized, reducing errors and deviations in subsequent actual construction processes.

[0024] M3, the construction plan generation module, based on the updated three-dimensional pipe network model, combines real-time meteorological data and municipal underground pipeline data to generate a pipe network laying path and outputs a three-dimensional visualization construction plan. This module receives the updated 3D pipeline network model and the basic theoretical path. This model has incorporated the real-time 3D coordinate information of the construction site, reflecting the latest 3D pipeline network model under actual construction deviations. Compared with the basic theoretical path generated by the 3D pipeline network model, this module further generates a pipeline network laying path that can be used to guide actual construction operations. The pipeline network laying path is generated by further combining real-time constraint conditions on the basis of the basic theoretical path. The real-time constraint conditions include real-time meteorological data and municipal underground pipeline data. The main purpose is to optimize and adjust the basic theoretical path to cope with the complex environment and safety requirements in actual construction. Based on the 3D pipeline network model, taking the basic theoretical path as the initial input, an optimization problem is constructed. The goal is to obtain an optimized pipeline network laying path on the premise of meeting multiple actual construction constraints. , According to the pipeline network laying path, combined with the 3D geographic information of the construction site, a 3D visual construction plan is generated, including pipeline network path display, pipeline installation positions, and construction steps and sequences. Specifically, The pipeline network path display can visualize information such as the start and end points, pipeline orientation, slope, and pipeline specifications of each section of the pipeline according to the pipeline network laying path. The pipeline installation positions will accurately mark the pipeline installation positions in the 3D model of the building to ensure that the pipeline installation matches the building structure. The construction steps and sequences display the construction steps in 3D space, indicating the start and end positions of each stage, as well as the corresponding construction periods, the distribution of construction equipment, and the operation areas.

[0025] M4, the holographic monitoring and safety traceability module, constructs the construction according to the 3D visual construction plan and collects the real-time construction data of the construction site. Based on the real-time construction data, the compliance of the construction path is verified. When it is detected that the deviation between the real-time construction path and the pipeline network laying path exceeds the second threshold, a credible evidence record containing the timestamp, the operator, and the deviation parameters is generated, and the credible evidence record is fed back to the dynamic pipeline network modeling module for model update. By using augmented reality (AR) technology to superimpose the 3D visual construction plan on the construction site, after wearing AR devices on site, construction workers can clearly see the real-time path of pipeline laying, understand the construction steps and target positions, avoid manual errors and construction path deviations, and construction workers can directly see information such as the pipeline network path, terrain changes, and construction progress in the device, helping construction workers complete their work more efficiently and accurately. The dynamic situation of the construction site is monitored in real time through Internet of Things sensors and positioning systems, and real-time construction data is collected. The collected construction data is uploaded through a wireless network to ensure that all data of construction operations can be fed back to the background in real time for monitoring and analysis. Compare the deviation between the real-time construction data and the pipeline network laying path. The formula is: where is the coordinate point of the real-time construction data; is the coordinate point of the pipeline network laying path; is the deviation value, representing the path deviation; When the deviation between the real-time construction data and the theoretical path exceeds the set second threshold, trigger a deviation alarm and generate a credible evidence record, including the time stamp, operator information, and deviation parameters. Specifically, The time stamp is the specific time when the path deviation occurs; the operator information records the information of the construction personnel participating in this construction link; the deviation parameters include the deviation magnitude, the specific location where the deviation occurs, and the status of the construction equipment; To ensure the credibility and immutability of the data, all credible evidence records will be encrypted and stored through blockchain technology. Once the evidence data is generated, it cannot be tampered with, ensuring the transparency and traceability of the construction process; The credible evidence record will also be fed back to the construction plan generation module. The construction plan generation module recalculates the pipeline network laying path based on the credible evidence record and makes necessary path adjustments to ensure that subsequent construction can follow the planned path more accurately.

[0026] A method for intelligent gas installation management based on the Internet of Things, characterized in that the method includes: S1. Collect the biometric data of the user, simultaneously scan and obtain the three-dimensional point cloud data of the building, and collect the environmental parameters in real time through the sensor array; through cross-verification, compare the biometric data with the government affairs database to verify the user's identity, and use the point cloud registration technology to spatially align and verify the three-dimensional point cloud data of the building with the three-dimensional building model, and generate a standardized data packet in combination with the environmental parameters; S2. Based on the standardized data packet, construct a three-dimensional pipeline network model through a graph database and generate the basic theoretical path for equipment installation. When the deviation between the three-dimensional spatial coordinates of the construction equipment uploaded and the basic theoretical path exceeds the first threshold, update the three-dimensional pipeline network model and output the updated three-dimensional pipeline network model and the basic theoretical path; S3. Based on the updated three-dimensional pipeline network model and the basic theoretical path, combine the real-time meteorological data and the municipal underground pipeline data to generate the pipeline network laying path and output a three-dimensional visual construction plan; S4. Perform construction according to the three-dimensional visualization construction plan, collect real-time construction data, perform compliance verification of the construction path based on the real-time construction data. When it is detected that the deviation between the real-time construction data and the pipe network laying path exceeds the second threshold, generate a credible evidence record, and feedback the credible evidence record to the construction plan generation module for adjusting the pipe network laying path.

[0027] The present invention proposes a method and system for intelligent gas installation management based on the Internet of Things. By the multi-source perception and intelligent verification module, the biological characteristic data of users, the three-dimensional building point cloud data and environmental parameters are collected in real time. Using the point cloud registration technology and the three-dimensional pipe network model, the intelligentization of user identity verification, pipe network planning and construction management is realized; the basic theoretical path is generated through the graph database and the shortest path algorithm, and the optimization adjustment of the pipe network laying path is carried out in combination with the real-time construction data; the holographic monitoring and safety traceability module ensures the compliance of the construction path and adjusts the construction plan in real time to ensure the safety and accuracy of the construction.

[0028] In summary, through the innovative combination of Internet of Things technology, three-dimensional modeling and real-time data collection, the present invention provides a more accurate, efficient and safe solution for intelligent gas installation management, and has high application value and promotion prospects.

[0029] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A system for intelligent gas installation management based on the Internet of Things, characterized in that, The system includes: M1, a multi-source perception and intelligent verification module, which collects the biometric data of users through mobile terminal devices, simultaneously scans and obtains the three-dimensional point cloud data of the building, and collects environmental parameters in real time through a sensor array; through cross-verification, compares the biometric data with the government affairs database to verify the user's identity, aligns the three-dimensional point cloud data of the building with the three-dimensional building model through point cloud registration technology, and generates a standardized data packet in combination with the environmental parameters; M2, a theoretical planning module, based on the standardized data packet, constructs a three-dimensional pipe network model through a graph database and generates a basic theoretical path for equipment installation. When the deviation between the three-dimensional spatial coordinates of the construction equipment uploaded and the basic theoretical path exceeds the first threshold, updates the three-dimensional pipe network model and outputs the updated three-dimensional pipe network model and the basic theoretical path; M3, a construction plan generation module, based on the updated three-dimensional pipe network model and the basic theoretical path, combines real-time meteorological data and municipal underground pipeline data to generate a pipe network laying path and outputs a three-dimensional visual construction plan; M4, a holographic monitoring and safety traceability module, constructs according to the three-dimensional visual construction plan and collects the real-time construction data of the construction site. Based on the real-time construction data, conducts compliance verification of the construction path. When it is detected that the deviation between the real-time construction data and the pipe network laying path exceeds the second threshold, generates a credible evidence record and feeds the credible evidence record back to the construction plan generation module for adjustment of the pipe network laying path.

2. The system for intelligent gas installation management based on the Internet of Things according to claim 1, wherein The biometric data includes fingerprint features, facial features and iris features. The fingerprint features are obtained by collecting the fingerprint data of users through a fingerprint sensor and extracting them using the Minutiae point extraction algorithm. The facial features are obtained by collecting the facial images of users through a front camera and extracting them after face detection. The iris features are obtained by acquiring the iris images of users through a dedicated iris scanner and extracting them after detecting the iris boundary using the Hough transform. The three-dimensional point cloud data of the building is obtained by scanning the building with a laser scanner, and the three-dimensional point cloud data of the building is denoised and cleaned through point cloud denoising technology.

3. The system for intelligent gas installation management based on the Internet of Things according to claim 2, characterized in that, The cross-verification includes user identity verification and spatial alignment verification. The user identity verification compares the biometric data with the registered information in the government affairs database through a comparison algorithm and outputs the biometric authentication information of the user.

4. The system for intelligent gas installation management based on the Internet of Things according to claim 3, characterized in that, The spatial alignment verification is performed through point cloud registration. The point cloud registration includes coarse registration and fine registration. The coarse registration uses feature matching to initially align the positions of the three-dimensional point cloud data of the building and the three-dimensional building model, and outputs the three-dimensional point cloud data of the building after coarse registration. The fine registration performs registration on the three-dimensional point cloud data of the building after coarse registration through the ICP algorithm. The ICP algorithm defines an error metric and, through an iterative process based on a rigid transformation, the rigid transformation includes a rotation matrix and a displacement vector, to minimize the error metric. The formula is: Among them, p is a point in the 3D point cloud data of the building after rough registration; q is the point in the 3D building model closest to p ; n is the number of points; E is the error metric; Through the iterative process, minimize the error metric and obtain the optimal rotation matrix and displacement vector. The formula is: where R is the optimal rotation matrix; t is the optimal displacement vector; is the minimized error metric; Update point p using the optimal rotation matrix and displacement vector, with the formula: Among them, is a point of the updated 3D building point cloud data; adjusting the 3D building point cloud data according to the points of the updated 3D building point cloud data, and outputting the registered 3D building point cloud data; After completing the point cloud registration, perform accuracy verification. Use the point-to-point distance between the registered 3D building point cloud data and the 3D building model to calculate the overall error, and evaluate the registration accuracy through the root mean square error (PMSE), with the formula: When the overall error exceeds the threshold, re-perform the point cloud registration.

5. The system for intelligent gas installation management based on the Internet of Things according to claim 4, characterized in that, Collect the environmental parameters through a sensor array deployed around the building, and package the user's biometric authentication information, the registered 3D building point cloud data, the 3D building model, and the environmental parameters to generate a standardized data packet.

6. The system for intelligent gas installation management based on the Internet of Things according to claim 5, wherein, The 3D pipe network model includes gas equipment entity nodes and topological connection edges, corresponding to the graph nodes and graph edges in the graph database. The basic theory path is calculated through the shortest path algorithm, including the definition of the constraint objective function and the calculation of the shortest path. The formula for defining the constraint objective function is: Among them, is the construction cost of path ; is the actual laying distance of path , that is, the goal of the shortest path problem; is the conflict degree between path and existing underground facilities; is the weight coefficient of each constraint, used to adjust the importance of different constraints The shortest path calculation is used to generate the basic theory path. Use the Dijkstra algorithm for calculation, and select the optimal path by calculating the weight of each path. The weight of the path is determined by the constraint objective function, with the formula: Among them, is the graph edge weight from graph node u to graph node v; The Dijkstra algorithm calculates the shortest path of each graph node through recursion until all graph nodes are traversed, with the formula: Among them, is the shortest path length from the starting point to graph node v; is the shortest path length from the starting point to graph node u; is through the current graph node and the graph edge weight The newly calculated path length to reach graph node v; Through the Dijkstra algorithm, continuously update the shortest path of each graph node until the target point is reached, and obtain the shortest path from the starting point to the target point , that is, the basic theoretical path, and the formula is: Among them, is the weight of each path segment, and m is the total number of nodes; The 3D space coordinates are detected by the Beidou Satellite Navigation System (BDS). By continuously tracking the 3D space coordinates and detecting the deviation from the basic theory path, when the deviation exceeds the first threshold, trigger the basic theory path adjustment mechanism to update the 3D pipe network model and the basic theory path.

7. The system for intelligent gas installation management based on the Internet of Things according to claim 6, characterized in that, The pipe network laying path is generated based on the 3D pipe network model and the basic theory path, combined with real-time constraint conditions, where the real-time constraint conditions include real-time meteorological data and municipal underground pipeline data. According to the pipe network laying path, combined with the 3D geographical information of the construction site, generate the 3D visualization construction plan, including the display of the pipe network path, the installation position of the pipeline, and the construction steps and sequence.

8. The system for intelligent gas installation management based on the Internet of Things according to claim 7, wherein, Collect the real-time construction data through IoT sensors and positioning systems in real time, and compare the real-time construction data with the pipe network laying path to calculate the deviation value, with the formula: Among them, are the coordinate points of real-time construction data; are the coordinate points of the pipe network laying path; is the deviation value, representing the path deviation; When the deviation value exceeds the second threshold, trigger a deviation alarm and generate the credible evidence record, including the timestamp, operator information, and deviation parameters. The credible evidence record is encrypted and stored through blockchain technology. The credible evidence record will also be fed back to the construction plan generation module, and based on the credible evidence record, recalculate the pipe network laying path.

9. A method for intelligent gas installation management based on the Internet of Things, characterized in that, This method includes: S1. Collect the user's biometric data, simultaneously scan and obtain the 3D building point cloud data, and collect the environmental parameters in real time through a sensor array; through cross-verification, compare the biometric data with the government affairs database to verify the user's identity, use the point cloud registration technology to perform spatial alignment verification between the 3D building point cloud data and the 3D building model, and generate a standardized data packet in combination with the environmental parameters. S2. Based on the standardized data packet, construct a three-dimensional pipe network model through a graph database, and generate a basic theoretical path for equipment installation. When the deviation between the building three-dimensional space coordinates uploaded by the construction equipment and the basic theoretical path exceeds the first threshold, update the three-dimensional pipe network model, and output the updated three-dimensional pipe network model and the basic theoretical path; S3. Based on the updated three-dimensional pipe network model and the basic theoretical path, combine the real-time meteorological data and the municipal underground pipeline data to generate a pipe network laying path, and output a three-dimensional visualization construction plan; S4. Carry out construction according to the three-dimensional visualization construction plan, collect real-time construction data, and perform compliance verification on the construction path based on the real-time construction data. When it is detected that the deviation between the real-time construction data and the pipe network laying path exceeds the second threshold, generate a credible evidence record, and feedback the credible evidence record to the construction plan generation module for adjustment of the pipe network laying path.

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