Urban underground pipe network digital graph construction and optimization method

Abstract

The invention discloses a method for constructing and optimizing a digital one-map of an urban underground pipe network, belongs to the technical field of urban infrastructure management, and aims at constructing and optimizing the digital one-map of the urban underground pipe network by integrating a digital twinborn technology, an artificial intelligence algorithm, big data analysis, a three-dimensional visualization technology and a data management technology. Comprehensive monitoring, analysis, early warning, evaluation and decision support of underground pipe networks for urban water supply, drainage, heat supply, gas and the like are realized; real-time updating and dynamic simulation of a pipe network state are realized through real-time data acquisition and integration in combination with three-dimensional modeling and digital twinborn model construction; deep analysis can be performed on pipe network data, potential risks can be predicted, a maintenance plan can be optimized, operation risks can be reduced, and resource configuration efficiency can be improved; the application of the method not only improves the management efficiency of the underground pipe network, but also enhances the toughness of the urban underground pipe network and the capability of coping with emergencies.

Description

Technical Field

[0001] This invention belongs to the field of urban infrastructure management technology, and in particular relates to a method for constructing and optimizing a digital map of urban underground pipe networks. Background Technology

[0002] Current situation abroad:

[0003] United States: Establishing a Comprehensive Management System: The United States has established a relatively complete system for the management of urban underground pipelines, achieving comprehensive control. Guided by laws and policies, the United States has developed a mature system for the planning, construction, and management of underground pipelines, effectively improving the efficiency and safety of pipeline management.

[0004] Switzerland: A Good Solution to the "Zip Chain" Problem

[0005] Switzerland has effectively addressed the issue of fragmented governance among different municipal departments by constructing underground utility tunnels. The construction of these tunnels not only reduces waste from redundant underground pipeline construction but also improves the utilization rate of public facilities, enhances the city's disaster prevention and mitigation capabilities, and beautifies the urban environment.

[0006] France: Legislation First, Technology Follows:

[0007] France has adopted a "legislation-first" approach to managing urban underground pipelines, regulating the responsibilities and obligations related to the planning, construction, operation, maintenance, and supervision of these pipelines through legislation. Simultaneously, France is planning to apply advanced technologies to urban underground pipeline management, such as the digital revolution and the application of electromagnetic induction technology in the location and construction of underground pipelines.

[0008] Current situation in China:

[0009] Digital Upgrade and Intelligent Management:

[0010] Digital upgrades and intelligent management are being gradually implemented in the management of urban underground pipelines. The concept of "resilient cities" has been proposed for the first time at the national level, emphasizing the digital management of urban buildings, public spaces, underground pipelines, etc., using a single map for unified management of urban operations.

[0011] Intelligent transformation and upgrading of underground pipeline networks: Many cities are carrying out a comprehensive survey of underground pipelines, focusing on pipeline data collection, production, database construction, and system development to create an underground pipeline database and promote a unified digital management system for underground pipelines. For example, Haining City in Zhejiang Province has achieved visualization of underground pipelines by constructing a BIM (Building Information Modeling) system for its underground pipeline network, saving on construction pipeline surveying costs.

[0012] Technology empowers efficient handling of potential hazards:

[0013] Some cities are leveraging digital technology to empower the maintenance and operation of underground utility tunnels, providing various departments with unified, real-time, and visualized operational information, and offering strong support for the efficient, precise management, and safe and reliable operation of these tunnels. For example, the Guangzhou New Urban Construction Pilot Joint Conference Office released outstanding pilot cases, including the "Guangzhou Ring Road Utility Tunnel Smart Operation and Management Platform."

[0014] Development Trends:

[0015] Reliance on Intelligent and Digital Technologies: Urban underground pipeline management will increasingly rely on intelligent and digital technologies. Through IoT sensors and real-time monitoring systems, remote monitoring and early warning of pipeline status can be achieved, effectively preventing accidents such as leaks and blockages. Simultaneously, AI-based fault prediction and maintenance strategies will optimize maintenance plans, reducing unnecessary excavation and interruptions.

[0016] The introduction of blockchain technology: The introduction of blockchain technology will ensure the immutability and traceability of pipeline data and enhance the trust in multi-party collaboration.

[0017] Whether abroad or in China, the digital management and intelligent upgrading of urban underground pipelines has become a global trend. Countries around the world are actively exploring and applying new technologies to improve the management efficiency and safety of urban underground pipelines.

[0018] Current problems:

[0019] Incomplete data and difficulties in information sharing: Due to historical reasons, lagging planning, and decentralized management, some underground pipeline systems suffer from serious loss of engineering records and incomplete data. Furthermore, the diverse types of underground pipelines and the storage of data in different departments, coupled with a lack of unified management and data sharing mechanisms, create information silos.

[0020] Low level of data visualization: Existing two-dimensional drawings or paper documents cannot intuitively display the three-dimensional attribute information of pipelines, which is not conducive to pipeline design, construction and subsequent management in complex road sections, affects the efficiency and accuracy of monitoring, and increases the risk of omissions or collisions.

[0021] Operational status is difficult to perceive: Underground pipelines involve multiple media, and traditional monitoring methods are lagging behind, making it impossible to obtain and analyze the operational status of the pipelines in real time and accurately, and making it difficult to detect potential safety hazards in advance.

[0022] Untimely accident response: The pipeline network has a complex structure and the causes of accidents are diverse, involving multiple departments and units. However, poor information communication and an imperfect coordination mechanism lead to low efficiency in accident response.

[0023] Construction technology issues: The construction technology of underground pipelines is highly demanding. If the equipment and technology used during construction are not advanced enough or are operated improperly, it may lead to substandard pipeline installation or problems such as leakage and blockage.

[0024] Material Risks: The quality of pipes and fittings used in the construction of urban underground pipeline networks is crucial. If the purchased materials do not meet the standards, it may affect the durability and safety of the pipeline network, increasing the frequency and cost of future maintenance.

[0025] Inadequate monitoring technology: Urban underground pipe networks require real-time monitoring during operation to ensure their safety and efficiency. Insufficient monitoring technology may lead to undetected potential problems, affecting the long-term stability of the pipe network.

[0026] Inadequate maintenance and management: The maintenance and management of underground pipelines is a crucial aspect of ensuring their safe operation. Inadequate maintenance and management may lead to frequent pipeline safety accidents, which could affect the progress of urban development. Summary of the Invention

[0027] The technical problem to be solved by this invention is to provide a method for constructing and optimizing a digital map of urban underground pipe networks, which addresses the shortcomings of the prior art. By constructing and optimizing a holographic map of urban underground pipe networks, this invention integrates technologies such as digital twins, artificial intelligence, big data analysis, and 3D visualization. It aims to solve the above-mentioned technical problems, improve the management efficiency and safety of underground pipe networks, reduce operational risks, optimize resource allocation, and enhance the resilience and ability to respond to emergencies of urban underground pipe networks.

[0028] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0029] A method for constructing and optimizing a digital map of urban underground pipe networks, specifically including the following steps;

[0030] Step 1: Use digital twin technology to establish a virtual model of the city's underground pipe network to achieve real-time monitoring and management of the network;

[0031] Step 2: Collect real-time operational data of the pipeline network through Internet of Things (IoT) technology and integrate this data into the digital twin model to achieve real-time synchronization between the digital twin and the physical entity; use computational fluid dynamics (CFD) and finite element analysis (FEA) simulation technologies to simulate the physical behavior of the pipeline network in the real world and provide a scientific basis for optimization decisions.

[0032] Step 3: Use WebGL technology and game engine technology to present highly realistic 3D models, enabling users to immerse themselves in the virtual environment to observe, interact and operate.

[0033] Step 4: Apply artificial intelligence (AI) and machine learning (ML) technologies to analyze pipeline network data, predict trends, optimize decision-making, and improve the intelligence level of the digital twin.

[0034] Step 5: Utilize technologies such as big data, SOA+ESB, and GIS+BIM to achieve intelligent and platform-based management of pipeline network operations;

[0035] Step 6: Integrate multiple functions such as monitoring, maintenance, engineering archives, and data analysis to achieve unified management and command of the pipeline network;

[0036] Step 7: By installing various sensors, monitor the internal environment and equipment status of the pipeline network in real time to ensure safe and stable operation;

[0037] Step 8: Build a comprehensive security protection system, including intrusion alarms and video surveillance.

[0038] Step 9: Establish a communication system and an automatic fire alarm system;

[0039] Step 10: Establish an operational and insurance plan: covering operational management, maintenance, and cost analysis to ensure the long-term stable operation of the project.

[0040] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0041] 1. This invention discloses a method for constructing and optimizing a digital map of urban underground pipe networks. By integrating digital twin technology, artificial intelligence algorithms, big data analysis, 3D visualization technology, and data governance technology, it achieves comprehensive monitoring, analysis, early warning, assessment, and decision support for urban underground pipe networks such as water supply, drainage, heating, and gas. This method achieves real-time updates and dynamic simulation of pipe network status through real-time data acquisition and integration, combined with 3D modeling and digital twin model construction. Utilizing artificial intelligence and machine learning technologies, this invention can perform in-depth analysis of pipe network data, predict potential risks, optimize maintenance plans, reduce operational risks, and improve resource allocation efficiency. Furthermore, through 3D visualization and interactive technology, this invention provides an intuitive and immersive pipe network management platform, enabling users to intuitively observe and operate the pipe network model. The application of this method not only improves the management efficiency of underground pipe networks but also enhances the resilience of urban underground pipe networks and their ability to respond to emergencies, providing strong technical support for urban planning, construction, and management. It has significant practical application value and broad market prospects.

[0042] 2. This invention enables comprehensive, real-time, and dynamic monitoring of urban underground pipe networks, improving the monitoring capabilities for underground pipe networks such as water supply, drainage, heating, and gas. By integrating digital twin technology, artificial intelligence algorithms, and big data analysis, it enhances the management efficiency and response speed of underground pipe networks. Through real-time monitoring and early warning systems, it reduces operational risks such as pipe network leaks and blockages, decreasing the probability of accidents. Utilizing data analysis and intelligent decision support, it optimizes resource allocation and improves resource utilization efficiency. It enhances the ability of urban underground pipe networks to cope with natural disasters and emergencies, improving the resilience of urban infrastructure. It establishes a unified data management and sharing mechanism, solving the problems of incomplete data and information silos. Through 3D visualization technology, it improves the visualization of pipe network data, facilitating design, construction, and management. It achieves real-time monitoring of the pipe network's operational status. This system has enhanced early warning capabilities for potential safety hazards; improved accident handling efficiency through a robust information communication and coordination mechanism; enhanced the technical level of pipeline construction and the quality of pipeline installation through the adoption of advanced construction technologies and equipment; reduced pipeline durability and safety risks caused by material quality issues through quality control and standard management; improved the long-term stability and safety of the pipeline network through real-time monitoring technology; reduced the frequency of pipeline safety accidents and ensured the progress of urban development through scientific maintenance and management; integrated environmental and equipment monitoring systems to achieve real-time monitoring of the internal environment and equipment status of the pipeline network; introduced a comprehensive safety protection system to improve the safety of the pipeline network; established a linkage mechanism between the communication system and the automatic fire alarm system to improve emergency response speed; and designed operation management and insurance plans to ensure the long-term stable operation of the project. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a diagram of the digital twin technology architecture of this invention;

[0045] Figure 2 This is a flowchart of the data acquisition and integration process of this invention;

[0046] Figure 3 This is a schematic diagram of the three-dimensional modeling technology of the present invention;

[0047] Figure 4 This is a flowchart of the digital twin model construction process of this invention;

[0048] Figure 5 This is a flowchart of the actual algorithm implementation of this invention;

[0049] Figure 6 This is a diagram of the application presentation layer development interface of this invention;

[0050] Figure 7 This is a comparison diagram of the technical architecture selection of this invention;

[0051] Figure 8 This is a flowchart of the real-time data integration and physical simulation process of the present invention;

[0052] Figure 9 This is a diagram of the three-dimensional visualization and interactive interface of the present invention;

[0053] Figure 10 This is a flowchart of the integration of artificial intelligence and machine learning in this invention;

[0054] Figure 11 This is the architecture diagram of the smart pipeline network big data cloud platform of this invention;

[0055] Figure 12 This is a diagram of the interface of the integrated pipeline network unified management and command information platform of this invention;

[0056] Figure 13 This is an architecture diagram of the environment and equipment monitoring system of the present invention;

[0057] Figure 14 This is a diagram of the security system architecture of this invention;

[0058] Figure 15 This is a flowchart of the communication system and automatic fire alarm system of the present invention;

[0059] Figure 16 This is a flowchart of the operation plan and insurance plan of this invention. Detailed Implementation

[0060] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings:

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become clearer. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0062] A method for constructing and optimizing a digital map of urban underground pipe networks, specifically including the following steps;

[0063] Step 1: Use digital twin technology to establish a virtual model of the city's underground pipe network to achieve real-time monitoring and management of the network;

[0064] Step 2: Collect real-time operational data of the pipeline network through Internet of Things (IoT) technology and integrate this data into the digital twin model to achieve real-time synchronization between the digital twin and the physical entity; use computational fluid dynamics (CFD) and finite element analysis (FEA) simulation technologies to simulate the physical behavior of the pipeline network in the real world and provide a scientific basis for optimization decisions.

[0065] Step 3: Use WebGL technology and game engine technology to present highly realistic 3D models, enabling users to immerse themselves in the virtual environment to observe, interact and operate.

[0066] Step 4: Apply artificial intelligence (AI) and machine learning (ML) technologies to analyze pipeline network data, predict trends, optimize decision-making, and improve the intelligence level of the digital twin.

[0067] Step 5: Utilize technologies such as big data, SOA+ESB, and GIS+BIM to achieve intelligent and platform-based management of pipeline network operations;

[0068] Step 6: Integrate multiple functions such as monitoring, maintenance, engineering archives, and data analysis to achieve unified management and command of the pipeline network;

[0069] Step 7: By installing various sensors, monitor the internal environment and equipment status of the pipeline network in real time to ensure safe and stable operation;

[0070] Step 8: Build a comprehensive security protection system, including intrusion alarms and video surveillance.

[0071] Step 9: Establish a communication system and an automatic fire alarm system;

[0072] Step 10: Establish an operational and insurance plan: covering operational management, maintenance, and cost analysis to ensure the long-term stable operation of the project.

[0073] Applications of digital twin technology: Digital twin technology is used to create virtual models of urban underground pipe networks, enabling real-time monitoring and management of these networks. Through 3D visualization technology, complex underground pipe networks are presented in a three-dimensional format, allowing people to intuitively observe and understand the various details of the model.

[0074] Digital twin technology architecture diagram as follows Figure 1 As shown, this illustrates the overall architecture and components of digital twin technology in urban underground pipe networks. The flowchart for building a digital twin model is as follows. Figure 4 The diagram illustrates the steps and process of building a digital twin model based on a 3D model.

[0075] Digital twin technology is a technique that models a physical object or system in the real world as a digital virtual counterpart. Through real-time data transmission and simulation analysis, it enables two-way interaction and real-time feedback with the actual object. Its core lies in collecting data from the physical system through sensors, IoT devices, etc., and then transmitting this data to the digital model. Through real-time analysis and simulation, it is possible to predict system behavior, optimize operational planning, improve efficiency, and conduct virtual testing.

[0076] The specific calculation model is as follows:

[0077] Coordinate transformation formula: P′=RP+T; where P is the original coordinates, P′ is the transformed coordinates, R is the rotation matrix, and T is the translation vector;

[0078] The equation of the straight line is: y = mx + b; where m is the slope and b is the y-intercept.

[0079] The equation of the plane is: ax + by + cz + d = 0; where a, b, and c are the normal vector components of the plane, and d is a constant.

[0080] Navier-Stokes equations: Where ρ is the fluid density, u is the velocity vector, p is the pressure, μ is the dynamic viscosity, and f is the external force vector;

[0081] For incompressible fluids, the continuity equation is used: ▽·u=0;

[0082] Structural analysis formula: Stress-strain relationship in finite element analysis (FEA): σ ij =C ijkl ε kl ; where σ ij It is the stress tensor, C ijkl It is the elastic constant tensor, ε kl It is the strain tensor;

[0083] Real-time data synchronization formula: Kalman filter: X k|k =X k|k-1 +K k (z k -HX k|k-1 ); where X k|k It is an estimated value, X k|k-1 It is the prior estimate, K k It is the Kalman gain, z k Here, H represents the observed values, and H is the observation matrix.

[0084] Optimization Algorithm: Linear Programming Where c are the coefficients of the objective function, A is the constraint matrix, and b are the constraint values;

[0085] Fitness function in genetic algorithms: Where f(x) is the fitness function, w i It is the weight, g i (X) is the objective function.

[0086] Data acquisition and integration flowchart as follows Figure 2 The diagram illustrates how pipeline network data is collected using sensors and other devices and integrated into a central processing system. Real-time data integration and physical simulation: Pipeline network operational data is collected in real-time using Internet of Things (IoT) technology and integrated into a digital twin model, achieving real-time synchronization between the digital twin and the physical entity. Computational fluid dynamics (CFD) and finite element analysis (FEA) simulation techniques are used to simulate the physical behavior of the pipeline network in the real world, providing a scientific basis for optimization decisions.

[0087] Technical Principle: The core of real-time data integration and physical simulation technology lies in utilizing Internet of Things (IoT) technology to collect real-time operational data of the pipeline network and integrate this data into a digital twin model, achieving real-time synchronization between the digital twin and the physical entity. This process involves the following key technologies:

[0088] Internet of Things (IoT) technology: By deploying a large number of sensors and IoT devices on physical entities, real-time data collection and transmission of physical entities are achieved, providing data support for digital twin models.

[0089] Big data and cloud computing technologies: Big data technology enables efficient storage, management, and analysis of massive, multi-source, and heterogeneous data, uncovering its potential value. Cloud computing technology provides powerful computing capabilities for digital twins, supporting large-scale model building, simulation analysis, and data processing.

[0090] Modeling and simulation technologies: Modeling technology can accurately model and describe physical entities, while simulation technology can simulate and predict the behavior and performance of physical entities in digital space, providing a basis for optimization decisions.

[0091] Artificial intelligence technology: Utilizing artificial intelligence technology to deeply mine and predict digital models, providing a scientific basis for the optimization and decision-making of physical entities.

[0092] 3D modeling technology diagram as follows Figure 3 The image shows how to use CAD drawings, oblique photography, laser point cloud technology, and other techniques to create a 3D model of a pipeline network.

[0093] 3D Visualization and Interaction: Using WebGL technology and game engine technology (such as Unity, Unreal Engine, etc.) to present highly realistic 3D models, enabling users to immerse themselves in the virtual environment to observe, interact and operate.

[0094] Technical Principle: The core of 3D visualization and interaction technology lies in using advanced graphics rendering techniques and interaction design to provide users with an intuitive and immersive virtual environment for observing, interacting with, and manipulating 3D models. This technology combines technological achievements from multiple fields such as computer graphics, human-computer interaction, virtual reality (VR), and augmented reality (AR).

[0095] WebGL technology: WebGL is an API for rendering 3D graphics in web browsers. It utilizes the HTML5 Canvas element and JavaScript API, allowing developers to create and render complex 3D graphics directly in the browser without relying on any plugins.

[0096] Game engine technology: Game engines such as Unity and Unreal Engine provide powerful 3D rendering capabilities, physics simulation, animation systems, and interactive features. They are widely used in game development, but are also increasingly being applied to fields such as architectural visualization, simulation training, and education.

[0097] The specific calculation model is as follows:

[0098] Equations of state: Where x(t) is the state vector, f() is the state equation, and u(t) is the control input;

[0099] The observation equation is: y(t) = h(x(t), u(t), t); where y(t) is the observation output and h() is the observation equation.

[0100] The system output equation is: z(t)=g(x(t),u(t),t); where z(t) is the system output and g() is the system equation.

[0101] In 3D visualization and interaction, the specific calculation formulas involved include:

[0102] 3D graphics rendering formula:

[0103] Pixel coloring formula in rasterization process: C = f(I d ·diffuse+I s ·specular+I a ); where C is the final pixel color, I d It is the intensity of the scattered light, I s It is the specular light intensity, I a It is the ambient light intensity;

[0104] Illumination model: L = ambient + diffuse (N·L) + specular (R·V) n ;

[0105] Where L is the final illumination, N is the surface normal, L is the light source direction, R is the reflection vector, V is the viewing direction, and n is the specular index;

[0106] 3D coordinate transformation formula: rotation matrix:

[0107] Where θ is the rotation angle;

[0108] Translation transformation: Among them, t x , t y , t z These are the components of the translation vector;

[0109] 3D Interaction Formula:

[0110] Ray picking: ray = camera + direction × distance; where camera is the camera position, direction is the ray direction, and distance is the ray length.

[0111] The simulation algorithm implementation block diagram is as follows: Figure 5 The diagram illustrates how real-time computing, the rules engine, and the AI ​​engine enable real-time calculation and synchronization of device status.

[0112] Application presentation layer development interface diagram as follows Figure 6 As shown: This demonstrates the user interface and interaction design of the application's presentation layer.

[0113] A comparison chart of technology architecture selections is shown below. Figure 7 As shown: A comparison of the technical architectures based on 3D engines (such as UE, Unity3D) and GIS software (such as Cesium, ArcGIS API for JS).

[0114] The flowchart of real-time data integration and physics simulation is as follows: Figure 8 As shown: This demonstrates how to collect pipeline network data in real time through IoT technology and integrate it into a digital twin model.

[0115] 3D visualization and interactive interface diagram as shown Figure 9 As shown: This displays 3D models and user interfaces rendered using WebGL or game engine technologies.

[0116] The flowchart of the integration of artificial intelligence and machine learning is as follows: Figure 10 The diagram illustrates the application process of AI and ML technologies in pipeline network data analysis and prediction.

[0117] Artificial intelligence and machine learning integration: Applying artificial intelligence (AI) and machine learning (ML) technologies to analyze pipeline network data, predict trends, optimize decision-making, and improve the intelligence level of digital twins.

[0118] Technical Principles: The application of artificial intelligence (AI) and machine learning (ML) technologies in urban underground pipeline network management is mainly reflected in the following aspects:

[0119] Data analysis and pattern recognition: AI and ML technologies can process and analyze the large amounts of data generated by pipeline networks, identify patterns and trends, and provide decision support for the operation and management of pipeline networks.

[0120] Prediction and Decision Optimization: Through machine learning models, the future state and behavior of the pipeline network can be predicted, optimizing the operation and maintenance decisions of the pipeline network and improving the efficiency and accuracy of decision-making.

[0121] Adaptive learning and optimization: Machine learning models can adaptively learn and optimize as time and environment change, maintaining the effectiveness and accuracy of decisions.

[0122] Reduced human intervention: The application of AI and ML technologies reduces the need for human intervention, improves work efficiency, and reduces the impact of human error and bias on decision-making.

[0123] In AI and ML technologies, the calculation formulas involved include:

[0124] Neural network weight update formula: w new =w old -η▽ θ J(θ); where w new It is the updated weight, w old These are the old weights, η is the learning rate, and ▽ θ J(θ) is the gradient of the loss function with respect to the weights;

[0125] Loss function: Where J(θ) is the loss function, m is the number of samples, and L() is the loss function for a single sample. It is the predicted value, y (i) This is the actual value;

[0126] Activation function: f(x) = max(0,x); where f(x) is the output of the ReLU activation function;

[0127] Backpropagation algorithm: Where, δ (l) σ' is the error of the l-th layer, and σ′ is the derivative of the activation function. The Hadamard product represents the element-wise product.

[0128] The architecture diagram of the smart pipeline network big data cloud platform is as follows: Figure 11 The following is a demonstration of the technical architecture and service components of the big data cloud platform: The Smart Pipeline Big Data Cloud Platform utilizes big data, SOA+ESB, and GIS+BIM technologies to achieve intelligent and platform-based management of pipeline operations.

[0129] Technical principle:

[0130] Big data technology: Utilizing big data technology to store, process, and analyze massive amounts of pipeline network data, uncovering patterns and trends behind the data, and providing a scientific basis for decision-making.

[0131] SOA+ESB technology: Service-oriented architecture (SOA) and enterprise service bus (ESB) technology are used to build a flexible and scalable enterprise application integration architecture to enable cross-departmental collaboration and information sharing.

[0132] GIS+BIM technology: Geographic Information System (GIS) and Building Information Modeling (BIM) technologies are used to achieve precise spatial positioning and 3D modeling of pipeline networks, improving the accuracy and visualization level of pipeline network management.

[0133] The calculation formulas involved in the smart pipeline network big data cloud platform include:

[0134] Data mining algorithms: Utilizing machine learning algorithms, including decision trees, random forests, and support vector machines, for data classification and regression analysis. The calculation formulas involved include:

[0135] Splitting criteria for decision trees, such as Gini impurity: Where, p i It represents the proportion of the i-th type of sample in the node;

[0136] The optimization problem of support vector machines:

[0137] Where w is the weight vector, b is the bias term, and ξ is the bias term. i It is a slack variable;

[0138] GIS spatial analysis: This involves overlaying and buffer analysis of spatial data, and the calculation formulas involved include:

[0139] The formula for the distance from a point to a line: Where x1 and y1 are the coordinates of the points, and Ax1+By1+C=0 is the equation of the line;

[0140] BIM Collision Detection: Detecting collisions and conflicts in a 3D model. The calculation formulas involved include: algorithms for detecting whether two 3D objects intersect, including the separating axis theorem;

[0141] Vector dot product: a·b=ax b x +a y b y +a z b z Where a and b are two vectors, a x ,a y ,a z These are the components of vector a, b x ,b y ,b z These are the components of vector b; the dot product is used to calculate the angle and projection length between two vectors.

[0142] Projection interval calculation: For the projection of a polygon onto a specific axis, calculate the minimum and maximum projection values.

[0143] Among them, P n is the vertex of the polygon, n is the projection axis vector, and minproj and maxproj represent the minimum and maximum values ​​of the projection, respectively;

[0144] Separate axis detection: For two polygons A and B, and their vertex set a points and b points If there exists an axis n such that: maxproj A <minproj B or maxproj B <minproj A Then polygons A and B do not intersect, and n is a separating axis;

[0145] Projection overlap detection: For the projections of two polygons onto a certain axis, if their projection intervals overlap:

[0146] maxproj A ≤minproj B or maxproj B ≤minproj A If the projections of these two polygons onto this axis overlap, they may intersect.

[0147] The interface diagram of the integrated pipeline network unified management and command information platform is shown below. Figure 12 The image shows the user interface and functional modules of the integrated management platform. The unified management and command information platform for the integrated pipeline network integrates monitoring, maintenance, engineering archives, data analysis, and other functions to achieve unified management and command of the pipeline network.

[0148] Technical Principles: The technical principles of the integrated pipeline network unified management and command information platform involve the following key areas:

[0149] Big data technology: Utilizing big data technology to integrate and analyze pipeline operation data, uncovering patterns and trends behind the data, and providing a scientific basis for decision-making.

[0150] SOA+ESB architecture: It adopts service-oriented architecture (SOA) and enterprise service bus (ESB) technology to achieve service integration and information sharing across departments and systems.

[0151] GIS+BIM technology: Combining Geographic Information System (GIS) and Building Information Modeling (BIM) technologies, it enables precise spatial positioning and 3D modeling of pipeline networks, improving the accuracy and visualization level of pipeline network management.

[0152] Internet of Things (IoT) technology enables real-time monitoring and data collection of pipeline network equipment, improving response speed and operation and maintenance efficiency.

[0153] Cloud computing technology: Cloud computing technology provides powerful data processing and storage capabilities, supporting large-scale data analysis and model calculations.

[0154] The calculation formulas involved in the unified management and command information platform for integrated pipeline networks include:

[0155] Data correlation analysis formula: Where, x i and y i These are data points for two variables. and These are their means, used to calculate the correlation between two variables;

[0156] Cluster analysis formula: Where k is the number of clusters, S i It is the set of points in the i-th cluster, μ i is the center point of the i-th cluster, used to minimize the distance of each point to its cluster center;

[0157] Prediction model formula: y=β0+β1x+ε; where y is the dependent variable, x is the independent variable, β0 is the intercept, β1 is the slope, and ε is the error term, used to predict the operating status of the pipeline network.

[0158] The architecture diagram of the environment and equipment monitoring system is as follows: Figure 13 The diagram illustrates the sensor deployment and data processing flow of the environmental and equipment monitoring system. The environmental and equipment monitoring system uses various sensors to monitor the internal environment and equipment status of the pipeline network in real time, ensuring safe and stable operation.

[0159] Technical Principles: The technical principles of a Building Automation System (BAS) are mainly based on the following aspects:

[0160] Sensor technology: Real-time monitoring of the internal environment and equipment status of the pipeline network by installing various sensors, such as physical quantities like temperature, humidity, pressure, and flow rate.

[0161] Data acquisition technology: Data collected by sensors is collected and preprocessed through a data acquisition system and converted into digital signals for further analysis.

[0162] Internet of Things (IoT) technology: Enables remote monitoring and control of devices through IoT technology, improving the response speed and operational efficiency of monitoring systems.

[0163] Data processing and analysis technology: Using big data analytics to store, process, and analyze collected data to uncover patterns and trends behind the data.

[0164] Intelligent control technology: Based on the analysis results, it enables intelligent control and optimized management of pipeline equipment to ensure safe and stable operation.

[0165] In environmental and equipment monitoring systems, the calculation formulas involved include:

[0166] Temperature sensor acquisition conversion formula: R=R(0℃)[1+At+Bt(t-100℃)+Ct] 2 [(t-100℃)]; where R is the resistance value, t is the temperature, R(0℃) is the resistance value at 0℃, and A, B, and C are material constants;

[0167] Relationship between AD sampled values ​​and actual voltage: Among them, V out This is the actual voltage value, V. ad It is the AD sample value, V ref This is the reference voltage, and n is the number of bits in the AD converter;

[0168] Formula for calculating covariance matrix: Among them, C ij It is an element in the covariance matrix, x i and x j They are two random variables. and It is their mean;

[0169] Data acquisition power calculation formula: P=V×I; where P is power, V is operating voltage, and I is operating current.

[0170] Security system architecture diagram as follows Figure 14 The diagram illustrates the subsystem integration and workflow of the security system. The security system includes intrusion alarms, video surveillance, and other features, forming a comprehensive security protection system.

[0171] Technical Principles: A Security Protection System (SPS) is a comprehensive system that integrates multiple subsystems, such as intrusion alarm systems, video surveillance systems, and access control systems, to achieve the goal of preventing losses and crime. The technical principles involve the following aspects:

[0172] Sensor technology: Intrusion alarm systems utilize sensor technology to detect unauthorized intrusions, including technologies such as infrared, microwave, or electronic sensing.

[0173] Video surveillance technology: Video security monitoring systems use cameras, recorders, and display devices to record and transmit video in real time in the monitored area.

[0174] Access control technology: Access control systems use technologies such as access control systems, facial recognition, and fingerprint recognition to control and manage the entrances and exits of buildings or areas.

[0175] Cloud computing and Internet of Things (IoT) technologies: Utilizing cloud computing, IoT, and other technologies to achieve remote monitoring and emergency response, as well as interconnectivity of devices.

[0176] Big data analytics: Using big data analytics techniques to store, process, and analyze collected data to uncover patterns and trends behind the data.

[0177] The flowchart of the communication system and the automatic fire alarm system is as follows: Figure 15 The diagram illustrates the working principles and processes of the communication system and the automatic fire alarm system. The communication system and automatic fire alarm system establish communication subsystems such as landline telephones and wireless intercoms to ensure smooth information flow. This enables early fire detection and alarm, improving emergency response speed.

[0178] Technical Principles: The technical principles of communication systems and automatic fire alarm systems (CFS) involve the following aspects:

[0179] Fire detection technology: The system uses fire detectors such as smoke detectors, temperature detectors, and light detectors to detect early characteristics of fires and convert these physical quantities into electrical signals.

[0180] Communication technology: The system utilizes wired or wireless communication technologies, such as LoRa wireless communication technology, to enable communication between the fire alarm controller and detectors and actuators.

[0181] Alarm and linkage control technology: Once a fire is detected, the system will issue an alarm through the sound and light alarm device and link with the fire-fighting equipment (such as sprinkler system and smoke exhaust system) to carry out emergency response.

[0182] Cloud computing and big data technologies: The system may integrate a cloud platform for real-time monitoring, data processing, and decision analysis, thereby improving the system's intelligence level.

[0183] In security systems, the calculation formulas involved include:

[0184] Video surveillance storage capacity calculation formula: Used to calculate the storage requirements of the video stream, where the video stream size is in Mbps and the storage days are in units of days;

[0185] Formula for calculating the average length of a cable:

[0186]

[0187] Actual average cable length = average cable length × 1.1 + termination tolerance;

[0188] The termination tolerance is typically taken as 6 meters when calculating the cable length requirement.

[0189] Power cable calculation formula:

[0190]

[0191] Used to calculate the total demand for power cables.

[0192] In communication systems and automatic fire alarm systems, the calculation formulas involved include:

[0193] Formula for calculating the number of fire detectors: Where N is the number of detectors to be set, S is the protection area, K is the correction factor, which is 0.8 to 0.9, and A is the protection area of ​​a single detector;

[0194] Formula for calculating cable laying length: Where L is the cable laying length, and the termination tolerance is usually 6 meters;

[0195] Formula for calculating bandwidth of a communication system: Where B is the required bandwidth, D is the data rate, N is the number of concurrent communication devices, and T is the time.

[0196] The flowcharts for the operation plan and insurance plan are as follows: Figure 16 The diagram illustrates the design and implementation flow of operations management and insurance plans.

[0197] Operational and insurance plans: covering operation management, maintenance, cost analysis, etc., to ensure the long-term stable operation of the project;

[0198] Technical Principles: The technical principles of the operational and insurance plans are mainly based on the following aspects:

[0199] Operations Management: By establishing a comprehensive operations management process, including daily monitoring, maintenance, and fault response, we ensure the long-term stable operation of the project.

[0200] Cost analysis: Using financial tools such as cost-benefit analysis, the operating costs of a project are assessed and controlled in order to maximize economic benefits.

[0201] Risk assessment: Using safety risk assessment systems and methods to identify, assess and control the risks that may be encountered during project operation.

[0202] Insurance plan design: Based on the project characteristics and risk assessment results, design corresponding insurance plans to transfer risks and reduce potential losses.

[0203] The calculation formulas involved in the operational and insurance plans include:

[0204] Cost-benefit analysis formula: Among them, C t t is the net cash flow in period t, r is the discount rate, and n is the total number of periods;

[0205] Risk assessment formula: Risk value = Expected loss + Unexpected loss; used to assess the maximum loss a project may suffer at a certain confidence level.

[0206] Insurance cost calculation formula: Insurance cost = Insured amount × Insurance rate; used to calculate the insurance cost required for the project.

[0207] Example 1: Digital Management of Drainage Pipeline Network in Jiangbei District, Ningbo

[0208] Background: As a rapidly developing urban area, Ningbo Jiangbei District has a complex and critical drainage network system that requires efficient management and maintenance to cope with the rainy season and urban flooding problems.

[0209] Implementation steps:

[0210] 1. Data Acquisition and Integration: Smart sensors, such as flow meters, water level sensors, and water quality monitors, are installed at key nodes of the drainage network in Jiangbei District to collect real-time operational data of the drainage system. The data is then transmitted to a central processing system via a wireless network.

[0211] 2. 3D Modeling: Laser scanning and oblique photogrammetry techniques were used to create a 3D model of the drainage network in Jiangbei District, resulting in an accurate network map.

[0212] Integrate model data into the digital twin platform.

[0213] 3. Digital twin model construction: A digital twin model is built on the basis of the 3D model to update the pipeline network status in real time.

[0214] 4. Simulation algorithm implementation: Develop AI algorithms to simulate drainage behavior under different rainfall conditions and predict areas at risk of urban flooding.

[0215] 5. Application Presentation Layer Development: Develop a user-friendly interface to display the pipeline network status and prediction results for use by city management personnel.

[0216] 6. Real-time data integration and physical simulation: Utilize CFD to simulate the dynamics of water flow in the pipe network and optimize drainage design.

[0217] 7. 3D Visualization and Interaction: Using WebGL technology to display 3D pipeline models on web pages, allowing users to interactively query and analyze them.

[0218] 8. Integration of Artificial Intelligence and Machine Learning: Apply machine learning techniques to analyze historical data and predict maintenance needs and potential bottlenecks.

[0219] 9. Smart Pipeline Network Big Data Cloud Platform: Establish a cloud platform to integrate all data and management functions of the drainage pipeline network to achieve intelligent management.

[0220] 10. Operation and Insurance Plans: Design operation and management processes and insurance plans to ensure the long-term stable operation of the drainage system.

[0221] Implementation Results: By implementing the above steps, the efficiency of drainage network management in Ningbo Jiangbei District has been significantly improved, urban flooding incidents have been reduced, and urban drainage safety has been guaranteed.

[0222] Example 2: Intelligent Optimization of Yining City's Water Supply Network System

[0223] Background: Yining City is located in an arid region, and the efficient management and optimization of the water supply network system is crucial to ensuring the city's water supply.

[0224] Implementation steps:

[0225] 1. Data Acquisition and Integration: Pressure sensors and flow meters are installed at key nodes of the water supply network to monitor the network's operational status in real time. The data is then transmitted to a central processing system via Internet of Things (IoT) technology.

[0226] 2. 3D Modeling: Using GIS data and field measurement data, a 3D model of the water supply network in Yining City was established.

[0227] 3. Digital twin model construction: A digital twin model is constructed based on the 3D model to realize real-time monitoring of the pipeline network status.

[0228] 4. Simulation algorithm implementation: Develop simulation algorithms to simulate water pressure and flow distribution, and optimize water pump operation plans.

[0229] 5. Application Presentation Layer Development: Develop an interactive interface to display pipeline network operation data and optimization suggestions.

[0230] 6. Real-time data integration and physical simulation: Using finite element analysis to simulate the stress and deformation of the pipeline network under different working conditions to ensure the safety of the pipeline network.

[0231] 7. 3D Visualization and Interaction: Use game engine technology to display the pipeline model and provide an immersive interactive experience.

[0232] 8. Integration of Artificial Intelligence and Machine Learning: Apply AI technology to analyze water consumption data, predict changes in demand, and optimize water supply scheduling.

[0233] 9. Smart Pipeline Network Big Data Cloud Platform: Establish a cloud platform to integrate all data and management functions of the water supply network to achieve intelligent management.

[0234] 10. Operation and Insurance Plans: Design operation and management processes and insurance plans to ensure the long-term stable operation of the water supply system.

[0235] Implementation Results: By implementing the above steps, Yining City's water supply network system has achieved intelligent management, improved water supply efficiency, reduced network maintenance costs, and effectively guaranteed urban water supply security.

Claims

1. A method for constructing and optimizing a digital map of urban underground pipe networks, characterized in that: Specifically, it includes the following steps; Step 1: Use digital twin technology to establish a virtual model of the city's underground pipe network to achieve real-time monitoring and management of the network; Step 2: Collect real-time operational data of the pipeline network through Internet of Things (IoT) technology and integrate this data into the digital twin model to achieve real-time synchronization between the digital twin and the physical entity; use computational fluid dynamics (CFD) and finite element analysis (FEA) simulation technologies to simulate the physical behavior of the pipeline network in the real world and provide a scientific basis for optimization decisions. Step 3: Use WebGL technology and game engine technology to present highly realistic 3D models, enabling users to immerse themselves in the virtual environment to observe, interact and operate. Step 4: Apply artificial intelligence (AI) and machine learning (ML) technologies to analyze pipeline network data, predict trends, optimize decision-making, and improve the intelligence level of the digital twin. Step 5: Utilize technologies such as big data, SOA+ESB, and GIS+BIM to achieve intelligent and platform-based management of pipeline network operations; Step 6: Integrate multiple functions such as monitoring, maintenance, engineering archives, and data analysis to achieve unified management and command of the pipeline network; Step 7: By installing various sensors, monitor the internal environment and equipment status of the pipeline network in real time to ensure safe and stable operation; Step 8: Build a comprehensive security protection system, including intrusion alarms and video surveillance. Step 9: Establish a communication system and an automatic fire alarm system; Step 10: Establish an operational and insurance plan: covering operational management, maintenance, and cost analysis to ensure the long-term stable operation of the project.

2. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 1, a technology for two-way interaction and real-time feedback with the actual object is achieved through real-time data transmission and simulation analysis. Data is collected from the physical system through sensors, IoT devices, etc., and transmitted to the digital model. Real-time analysis and simulation are used to predict the system's behavior, optimize operational planning, improve efficiency, and conduct virtual testing. The specific calculation model is as follows: Coordinate transformation formula: P′=RP+T; where P is the original coordinates, P′ is the transformed coordinates, R is the rotation matrix, and T is the translation vector; The equation of the straight line is: y = mx + b; where m is the slope and b is the y-intercept. The equation of the plane is: ax + by + cz + d = 0; where a, b, and c are the normal vector components of the plane, and d is a constant. Navier-Stokes equations: Where ρ is the fluid density, u is the velocity vector, p is the pressure, μ is the dynamic viscosity, and f is the external force vector; For incompressible fluids, the continuity equation is used: Structural analysis formula: Stress-strain relationship in finite element analysis (FEA): σ ij =C ijkl ε kl ; where σ ij It is the stress tensor, C ijkl It is the elastic constant tensor, ε kl It is the strain tensor; Real-time data synchronization formula: Kalman filter: X k|k =X k|k-1 +K k (z k -HX k|k-1 ); where X k|k It is an estimated value, X k|k-1 It is the prior estimate, K k It is the Kalman gain, z k Here, H represents the observed values, and H is the observation matrix. Optimization Algorithm: Linear Programming Where c are the coefficients of the objective function, A is the constraint matrix, and b are the constraint values; Fitness function in genetic algorithms: Where f(x) is the fitness function, w i It is the weight, g i (X) is the objective function.

3. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 2, the calculation model is as follows: Equations of state: Where x(t) is the state vector, f() is the state equation, and u(t) is the control input; The observation equation is: y(t) = h(x(t), u(t), t); where y(t) is the observation output and h() is the observation equation. The system output equation is: z(t)=g(x(t),u(t),t); where z(t) is the system output and g() is the system equation.

4. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 3, the calculation formulas involved in 3D visualization and interaction specifically include: 3D graphics rendering formula: Pixel coloring formula in rasterization process: C = f(I d ·diffuse+I s ·specular+I a ); where C is the final pixel color, I d It is the intensity of the scattered light, I s It is the specular light intensity, I a It is the ambient light intensity; Illumination model: L = ambient + diffuse (N·L) + specular (R·V) n ; Where L is the final illumination, N is the surface normal, L is the light source direction, R is the reflection vector, V is the viewing direction, and n is the specular index; 3D coordinate transformation formula: rotation matrix: Where θ is the rotation angle; Translation transformation: Among them, t x , t y , t z These are the components of the translation vector; 3D Interaction Formula: Ray picking: ray = camera + direction × distance; where camera is the camera position, direction is the ray direction, and distance is the ray length.

5. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 4, the calculation formulas involved in AI and ML technologies include: Neural network weight update formula: Among them, w new It is the updated weight, w old These are the old weights, and η is the learning rate. It is the gradient of the loss function with respect to the weights; Loss function: Where J(θ) is the loss function, m is the number of samples, and L() is the loss function for a single sample. It is the predicted value, y (i) This is the actual value; Activation function: f(x) = max(0,x); where f(x) is the output of the ReLU activation function; Backpropagation algorithm: Where, δ (l) σ' is the error of the l-th layer, and σ′ is the derivative of the activation function. The Hadamard product represents the element-wise product.

6. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 5, the calculation formulas involved in the smart pipeline network big data cloud platform include: Data mining algorithms: Utilizing machine learning algorithms, including decision trees, random forests, and support vector machines, for data classification and regression analysis. The calculation formulas involved include: Splitting criteria for decision trees, such as Gini impurity: Where, p i It represents the proportion of the i-th type of sample in the node; The optimization problem of support vector machines: Where w is the weight vector, b is the bias term, and ξ is the bias term. i It is a slack variable; GIS spatial analysis: This involves overlaying and buffer analysis of spatial data, and the calculation formulas involved include: The formula for the distance from a point to a line: Where x1 and y1 are the coordinates of the points, and Ax1+By1+C=0 is the equation of the line; BIM Collision Detection: Detecting collisions and conflicts in a 3D model. The calculation formulas involved include: algorithms for detecting whether two 3D objects intersect, including the separating axis theorem; Vector dot product: a·b=a x b x +a y b y +a z b z Where a and b are two vectors, a x ,a y ,a z These are the components of vector a, b x ,b y ,b z These are the components of vector b; the dot product is used to calculate the angle and projection length between two vectors. Projection interval calculation: For the projection of a polygon onto a specific axis, calculate the minimum and maximum projection values. Among them, P n is the vertex of the polygon, n is the projection axis vector, and minproj and maxproj represent the minimum and maximum values ​​of the projection, respectively; Separate axis detection: For two polygons A and B, and their vertex set a points and b points If there exists an axis n such that: maxproj A <minproj B or maxproj B <minproj A Then polygons A and B do not intersect, and n is a separating axis; Projection overlap detection: For the projections of two polygons onto a certain axis, if their projection intervals overlap: maxproj A ≤minproj B or maxproj B ≤minproj A If the projections of these two polygons onto this axis overlap, they may intersect.

7. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 6, the calculation formulas involved in the integrated pipeline network unified management and command information platform include: Data correlation analysis formula: Where xi and yi are data points for two variables. and These are their means, used to calculate the correlation between two variables; Cluster analysis formula: Where k is the number of clusters, S i It is the set of points in the i-th cluster, μ i is the center point of the i-th cluster, used to minimize the distance of each point to its cluster center; Prediction model formula: y=β0+β1x+ε; where y is the dependent variable, x is the independent variable, β0 is the intercept, β1 is the slope, and ε is the error term, used to predict the operating status of the pipeline network.

8. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 7, the calculation formulas involved in the environmental and equipment monitoring system include: Temperature sensor acquisition conversion formula: R=R(0℃)[1+At+Bt(t-100℃)+Ct] 2 [(t-100℃)]; where R is the resistance value, t is the temperature, R(0℃) is the resistance value at 0℃, and A, B, and C are material constants; Relationship between AD sampled values ​​and actual voltage: Among them, V out This is the actual voltage value, V. ad It is the AD sample value, V ref This is the reference voltage, and n is the number of bits in the AD converter; Formula for calculating covariance matrix: Among them, C ij It is an element in the covariance matrix, x i and x j They are two random variables. and It is their mean; Data acquisition power calculation formula: P=V×I; where P is power, V is operating voltage, and I is operating current.

9. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 8, the calculation formulas involved in the security system include: Video surveillance storage capacity calculation formula: Used to calculate the storage requirements of the video stream, where the video stream size is in Mbps and the storage days are in units of days; Formula for calculating the average length of a cable: Actual average cable length = average cable length × 1.1 + termination tolerance; The termination tolerance is typically taken as 6 meters when calculating the cable length requirement. Power cable calculation formula: Used to calculate the total demand for power cables.

10. The method for constructing and optimizing a digital map of urban underground pipe networks according to claim 1, characterized in that: In step 9, the calculation formulas involved in the communication system and the automatic fire alarm system include: Formula for calculating the number of fire detectors: Where N is the number of detectors to be set, S is the protection area, K is the correction factor, which is 0.8 to 0.9, and A is the protection area of ​​a single detector; Formula for calculating cable laying length: Where L is the cable laying length, and the termination tolerance is usually 6 meters; Formula for calculating bandwidth of a communication system: Where B is the required bandwidth, D is the data rate, N is the number of concurrent communication devices, and T is the time; In step 10, the calculation formulas involved in the operation plan and insurance plan include: Cost-benefit analysis formula: Among them, C t t is the net cash flow in period t, r is the discount rate, and n is the total number of periods; Risk assessment formula: Risk value = Expected loss + Unexpected loss; used to assess the maximum loss a project may suffer at a certain confidence level. Insurance cost calculation formula: Insurance cost = Insured amount × Insurance rate; used to calculate the insurance cost required for the project.

Images