Standardized management method and management system applied to water conservancy project

Abstract

The invention provides a standardized management method and management system applied to a water conservancy project. The method specifically comprises the following steps: dividing a production area of a target water conservancy project into a plurality of unit grids; environment data and outdoor production operation data of an outdoor production area in the unit grid and indoor production operation data of an indoor production area are collected; constructing a multi-dimensional data graph network; determining an outdoor safety risk level of the outdoor production area according to the production risk association characteristics; determining an indoor safety risk level of the indoor production area based on the indoor production operation data; calculating to obtain a grid production safety risk level of the unit grid; summarizing the grid production safety risk levels of all the unit grids, and performing weighted calculation to obtain the engineering production safety risk level of the target water conservancy project; and determining a risk standardization management strategy corresponding to the target water conservancy project based on the project production safety risk level. The method has the effect of efficiently and objectively integrating and analyzing the multi-source production data.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy safety management, and in particular relates to a standardized management method and management system applied to water conservancy projects. Background Art

[0002] In the water conservancy industry, water conservancy projects, as an important part of infrastructure, have the characteristics of large scale, multiple professional categories, high safety requirements, and wide social and economic impact. At the same time, they are also facing the impact of complex geological and climatic conditions on project safety, water quality safety, and water supply safety. With the development of social economy and the increase in demand for infrastructure functions, the number and scale of built water conservancy projects have continued to expand, and the system integration capabilities have continued to increase, which has put forward higher requirements for high-level safe operation. Traditional safety management methods mostly rely on manual monitoring of production data, and the analysis and processing of production environment data often rely on human subjective judgment. However, traditional methods have been difficult to cope with the increasingly complex production environment and risk factors. Especially in large-scale, multi-regional water conservancy projects, there are a large amount of multi-source production monitoring data. How to efficiently and objectively integrate and analyze multi-source data, monitor the production process in real time, accurately assess production safety risks and implement standardized management has become the development direction of the water conservancy industry in the new stage. Summary of the invention

[0003] The present invention provides a standardized management method and management system applied to water conservancy projects to solve the problem that it is difficult to efficiently and objectively integrate and analyze multi-source data in large-scale, multi-regional water conservancy projects.

[0004] In a first aspect, the present invention provides a standardized management method applied to water conservancy projects, the method comprising the following steps:

[0005] Dividing the production area of ​​the target water conservancy project into a plurality of unit grids according to the project scope of the target water conservancy project;

[0006] For each of the unit grids, collecting environmental data and outdoor production operation data of the outdoor production area in the unit grid, and acquiring indoor production operation data of the indoor production area in the unit grid;

[0007] Constructing a multi-dimensional data graph network by combining the environmental data and the outdoor production operation data;

[0008] Extracting production risk association features between the environmental data and the outdoor production operation data based on the multidimensional data graph network, and determining the outdoor safety risk level of the outdoor production area according to the production risk association features;

[0009] Determine the indoor safety risk level of the indoor production area based on the indoor production operation data by combining BIM technology and image recognition technology;

[0010] Calculate the grid production safety risk level of the unit grid according to the outdoor safety risk level and the indoor safety risk level;

[0011] Summarizing the grid production safety risk levels of all the unit grids, and performing weighted calculation to obtain the engineering production safety risk level of the target water conservancy project;

[0012] The risk standardization management strategy corresponding to the target water conservancy project is determined based on the project production safety risk level.

[0013] Optionally, before combining the environmental data and the outdoor production and operation data to construct a multidimensional data graph network, the following steps are also included:

[0014] Crawling historical climate and environmental data, historical geological environment data, and historical outdoor production and operation data of projects of the same type as the target water conservancy project, and preprocessing the historical climate and environmental data, the historical geological environment data, and the historical outdoor production and operation data;

[0015] Extracting historical climate characteristics, historical geological characteristics and historical outdoor production characteristics from the historical climate environment data, the historical geological environment data and the historical outdoor production operation data respectively;

[0016] Constructing a first multiple regression analysis model in combination with the historical climate characteristics and the historical geological characteristics, and analyzing a first risk characteristic association matrix between the historical climate characteristics and the historical geological characteristics through the first multiple regression analysis model;

[0017] Building a second multiple regression analysis model in combination with the historical geological characteristics and the historical outdoor production characteristics, and analyzing a second risk characteristic association matrix between the historical geological characteristics and the historical outdoor production characteristics by using the second multiple regression analysis model;

[0018] Based on the historical geological characteristics, the first risk feature association matrix and the second risk feature association matrix are matrix-fused to obtain a fused risk feature association matrix;

[0019] A third multiple regression analysis model is constructed in combination with the historical climate characteristics and the historical outdoor production characteristics, and a third risk characteristic association matrix between the historical climate characteristics and the historical outdoor production characteristics is analyzed by the third multiple regression analysis model.

[0020] Optionally, the environmental data includes climate environmental data and geological environmental data, and the step of combining the environmental data with the outdoor production and operation data to construct a multidimensional data graph network includes the following steps:

[0021] Preprocessing the climate environment data, the geological environment data and the outdoor production operation data;

[0022] Extracting target climate characteristics, target geological characteristics and target outdoor production characteristics from the climate environment data, the geological environment data and the outdoor production operation data respectively;

[0023] The target climate feature, the target geological feature and the target outdoor production feature are uniformly projected to the same target feature space through a preset feature conversion function, and corresponding target climate feature nodes, target geological feature nodes and target outdoor production feature nodes are generated in the target feature space respectively;

[0024] Generating a first association node edge between the target geological feature node and the target outdoor production feature node in the target feature space based on the fused risk feature association matrix;

[0025] A second association node edge between the target climate node and the target outdoor production feature node is generated in the target feature space based on the third risk feature association matrix to obtain a multidimensional data graph network.

[0026] Optionally, extracting the production risk association features between the environmental data and the outdoor production operation data based on the multidimensional data graph network, and determining the outdoor safety risk level of the outdoor production area according to the production risk association features comprises the following steps:

[0027] According to the generation order of the target outdoor production feature nodes in the multidimensional data graph network, each of the target outdoor production feature nodes is marked as a central target node in turn, until all the target outdoor production feature nodes are marked as the central target node only once;

[0028] Whenever the target outdoor production feature node is marked as the central target node, a node risk association degree calculation step and a node community closeness calculation step are performed on the central target node to obtain a node risk association degree and a node community closeness of the central target node;

[0029] Calculate a production risk association value by combining all the node risk association degrees and the node community closeness, and use the production risk association value as a production risk association feature between the environmental data and the outdoor production operation data;

[0030] The outdoor safety risk level of the outdoor production area is determined based on the production risk association characteristics and based on preset risk level rules.

[0031] Optionally, the node risk association calculation step includes the following steps:

[0032] Calculate the geological node correlation between the central target node and each adjacent target geological feature node through the fusion risk feature correlation matrix;

[0033] Based on the first associated node edge, counting the number of first node edges between the adjacent target geological feature node and all other target outdoor production feature nodes;

[0034] The geological node degree of the adjacent target geological feature node is obtained by calculating the first associated node edge;

[0035] The geological node risk correlation degree of the central target node is calculated by combining the geological node correlation degree, the first node edge quantity and the geological node degree;

[0036] Calculate the climate node association degree between the central target node and each adjacent target climate feature node respectively through the third risk feature association matrix;

[0037] Calculating the climate node degree of the adjacent target climate feature node using the second associated node edge;

[0038] The climate node risk correlation degree of the central target node is calculated by combining the climate node correlation degree and the climate node degree;

[0039] The sum of the geological node risk correlation and the climate node risk correlation is calculated to obtain the node risk correlation of the central target node.

[0040] Optionally, the calculation formula of the geological node risk association is as follows:

[0041]

[0042] Where: Represents the i-th central target node V i The risk association degree of the geological node, m represents the central target node V i Adjacent target geological feature nodes The number of nodes, Represents the fusion risk feature association matrix A R The matrix element corresponding to the i-th row and j-th column in , ξ(·,·) represents the correlation calculation function, γ represents the geological weight parameter, L i，j Represents the central target node Vi The jth adjacent target geological feature node The number of edges of the first node, Represents the adjacent target geological feature node The degree of said geological node;

[0043] The calculation formula of the climate node risk correlation is as follows:

[0044]

[0045] Where: Represents the i-th central target node V i The climate node risk association degree, t represents the central target node V i Adjacent target climate feature nodes The number of nodes, Represents the third risk feature association matrix A 3 The matrix element corresponding to the i-th row and k-th column in ξ(·,·) represents the correlation calculation function, η represents the climate weight parameter, Represents the kth adjacent target geological feature node The degree of the geological node.

[0046] Optionally, the calculation formula for the node community closeness is as follows:

[0047]

[0048] Where: P i Represents the i-th central target node V i The node community closeness, M represents the total number of node edges in the multidimensional data graph network, d(V i ) represents the central target node V i The node degree of .

[0049] Optionally, the calculation formula of the production risk association value is as follows:

[0050]

[0051] Wherein: Y represents the production risk association value, n represents the number of nodes of the target outdoor production feature nodes, and δ represents the association value weight parameter.

[0052] Optionally, the indoor production operation data includes indoor production area drawing data and indoor image data of the indoor production area, the indoor production area drawing data is pre-marked with indoor dangerous areas, and the combination of BIM technology and image recognition technology and determining the indoor safety risk level of the indoor production area based on the indoor production operation data includes the following steps:

[0053] Generate an indoor engineering BIM model of the indoor production area based on the indoor production area drawing data and using BIM technology;

[0054] Generate a corresponding model danger zone in the indoor engineering BIM model according to the indoor danger zone pre-marked in the indoor production area drawing data;

[0055] Counting the area proportion of the model danger zone in the indoor engineering BIM model;

[0056] Identifying indoor risk points in the indoor image data using image recognition technology, and counting the number of danger sources at the indoor risk points;

[0057] The indoor safety risk level of the indoor production area is determined based on the area proportion and the number of hazard sources.

[0058] In a second aspect, the present invention also provides a standardized management system applied to water conservancy projects, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the standardized management method applied to water conservancy projects as described in the first aspect is implemented.

[0059] The beneficial effects of the present invention are:

[0060] By dividing the production area of ​​the target water conservancy project into multiple unit grids, the scope of risk assessment can be refined so that the specific risk factors of each grid can be accurately identified and quantified. The environmental data and outdoor production operation data of the outdoor production area, as well as the production data of the indoor production area, are collected to ensure the comprehensiveness and diversity of the data. Combining BIM technology and image recognition technology, the indoor and outdoor production environment can be fully monitored and analyzed, thereby improving the accuracy of risk assessment. By constructing a multidimensional data graph network, combining environmental data and production data, and extracting production risk association features, it is possible to systematically and objectively analyze multi-source data and identify potential production risks. This data-driven risk management method relies on a large amount of real-time data and historical data, and can dynamically adjust and optimize the risk assessment model to ensure the scientificity and effectiveness of risk management. Compared with traditional experience and manual monitoring methods, the data-driven approach significantly improves the efficiency of risk management, while also avoiding the influence of subjective factors on data processing accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a standardized management method applied to water conservancy projects in one embodiment of the present application.

[0062] Figure 2 A schematic diagram of a process for constructing a risk feature association matrix in one embodiment of the present application.

[0063] Figure 3 A schematic diagram of the process of constructing a multidimensional data graph network in one embodiment of the present application. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0065] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0066] The standardized management method for water conservancy projects disclosed in the present invention is specifically implemented and applied to water conservancy projects, which is a part of the standardized management of intelligent water conservancy projects. Promoting the informatization and intelligence of project management and building a standardized management system for project operation to promote high-quality development of water conservancy will be conducive to strengthening project safety management, eliminating major safety hazards, implementing management responsibilities, improving management systems, enhancing management capabilities, and establishing a long-term management mechanism for the sound operation of water conservancy projects.

[0067] The standardized management of intelligent water conservancy projects needs to meet the following requirements:

[0068] (I) Project status: The current status of the project meets the design standards and has no safety hazards; the main buildings and supporting facilities are operating normally, and the operating parameters meet the requirements of current specifications; the metal structures and electromechanical equipment are operating normally, safely and reliably; the monitoring and surveillance facilities are reasonably set up, intact and effective, and meet the needs of understanding the safety status of the project; the project appearance is intact, the management scope and environment are clean, and the signs and signboards are standardized and eye-catching.

[0069] (II) Safety Management: The project is registered in accordance with regulations, and the information is complete, accurate and updated in a timely manner; safety appraisals are carried out in accordance with regulations, and treatment measures are implemented in a timely manner; the project management and protection scope is delineated and announced, important boundary markers are complete and obvious, and there are no illegal buildings or activities that endanger the safety of the project; the safety management responsibility system is implemented, and the division of job responsibilities is clear; the flood prevention organization system is sound, the emergency plan is complete and feasible, the flood prevention material management is standardized, and measures for the project to safely pass the flood season are implemented.

[0070] (III) Operation and maintenance: The systems for project inspection, monitoring, operation, maintenance and biological control are complete, the behaviors are standardized, and the records are complete. Key systems and operating procedures are clearly posted on the wall; hidden dangers in the project are promptly checked and dealt with, and a closed-loop management system is implemented; the scheduling and operation procedures and plans are submitted for approval in accordance with the procedures and strictly implemented.

[0071] (IV) Management Guarantee: The management system is smooth, the property rights of the project are clear, and the management responsibilities are implemented; the personnel expenses and maintenance expenses are in place, and the use and management are standardized; the positions are reasonably set up, the responsibilities of the personnel are clear and they have the ability to perform their duties; the rules and regulations meet the management needs and are constantly improved, with complete content, clear requirements and strict implementation; the office facilities and equipment are complete, and the archives and materials are managed in an orderly manner.

[0072] (V) Information construction: Establish an engineering management information platform, ensure that the basic engineering information, monitoring information, management information and other data are complete and updated in a timely manner, and realize information integration, sharing and interconnection with platforms at all levels; integrate and access engineering information such as rainfall conditions and safety monitoring and control to achieve online supervision and automated control, apply intelligent inspection equipment, and enhance the ability to automatically identify, assess and warn of dangerous situations; improve the network security and data protection system, and perfect the protective measures.

[0073] Based on the above requirements, we will sort out the management matters in terms of project status, safety management, operation and maintenance, management guarantee and information construction according to the actual project operation management, determine the standardized management system in combination with relevant management systems and technical standards and specifications, and build a water conservancy project operation management standard system. Among them, the water conservancy project operation management standard system specifically includes a variety of different dimensional systems such as the technical standard system and the management standard system. For example, the technical standard system specifically involves multiple dimensions in the production and operation process of water conservancy projects, which can be summarized as the following dimensions:

[0074] 1. Technical dimensions of project operation and dispatching: including but not limited to hydraulic buildings (structures), metal structures, electromechanical equipment, main equipment, power generation (transmission) equipment and facilities, water and rainfall facilities, water quality monitoring systems, safety monitoring systems, video surveillance systems, water supply and power supply equipment and facilities, irrigation equipment and facilities, dispatching procedures and control plan reporting, flood control dispatching, irrigation dispatching, water supply dispatching, power generation dispatching, sediment dispatching, shipping dispatching, ecological dispatching, water release warning and other aspects.

[0075] 2. Repair and maintenance dimensions: including but not limited to hydraulic buildings (structures), metal structures, electromechanical equipment, host equipment, power generation (transmission) equipment and facilities, water and rainfall facilities, water quality monitoring systems, safety monitoring systems, video surveillance systems, water supply and power supply equipment and facilities, irrigation equipment and facilities, etc.

[0076] 3. Equipment and facility renovation and material dimensions: including but not limited to technical requirements for the selection of equipment and facilities involved, technical requirements for equipment and facility installation and commissioning, requirements for the implementation and acceptance of equipment and facility renovation, and technical requirements for the procurement of spare parts and materials.

[0077] 4. Inspection and identification dimensions: including but not limited to measurement methods, inspection methods, test methods, hydraulic and hydrological inspection, metal structure and electromechanical inspection, dispatching and operation inspection, water quality inspection, informatization inspection, energy-saving inspection, environmental protection inspection, power production inspection, water supply and irrigation inspection, inspection and calibration of measuring equipment and facilities, metal structure identification, safety monitoring system identification, safety identification, safety evaluation and other aspects.

[0078] 5. Water conservation and soil and water conservation dimensions: including but not limited to general standards, water resource conservation and intensive utilization, soil and water conservation, etc.

[0079] 6. Safety and occupational health dimension: including but not limited to general standards, engineering safety, flood prevention and rescue, production safety, operational safety, emergency response, prevention and control of biological and animal hazards such as termites, network security, prediction and early warning, accident analysis, accident investigation, occupational health and other aspects.

[0080] 7. Operation and production quality dimension: including but not limited to hydraulic buildings (structures), metal structures, electromechanical equipment, main equipment, power generation (transmission) equipment and facilities, water and rainfall facilities, water quality monitoring systems, safety monitoring systems, video surveillance systems, water supply and power supply equipment and facilities, irrigation equipment and facilities operation and maintenance indicator control, as well as flood control scheduling, irrigation scheduling, water supply scheduling, power generation scheduling, sediment scheduling, shipping scheduling, ecological scheduling, water release warning scheduling and other quality control aspects.

[0081] 8. Marketing and service dimensions: including but not limited to water supply quantity and quality indicators, voltage, frequency, harmonics, reliability and other performance and technical parameters of electric energy products.

[0082] 9. Informatization and network dimension: including but not limited to general standards, engineering management, science and technology, archives, information application, communication networks, digital twins and other aspects.

[0083] Figure 1 FIG. 1 is a flow chart of a standardized management method for water conservancy projects in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps in the above method may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps. Figure 1 As shown, a standardized management method for water conservancy projects disclosed in the present invention specifically includes the following steps:

[0084] S101. Divide the production area of ​​the target water conservancy project into a plurality of unit grids according to the project scope of the target water conservancy project.

[0085] Among them, first of all, according to the overall planning map and geographic information of the project, the geographic information system (GIS) technology can be used to divide the area. GIS technology can process and analyze spatial data, and generate a detailed regional division plan by inputting information such as the geographical location, terrain characteristics, and production planning of the production area into the system. The specific method can be to divide the entire production area into several grids. The size of each grid can be adjusted according to actual needs. Common grid sizes are 50 meters × 50 meters or 100 meters × 100 meters. When dividing the grid, multiple factors need to be considered, such as terrain changes, production difficulties, and environmental impacts. For example, in areas with complex terrain, the grid can be divided into smaller ones to collect data more accurately and conduct risk assessments; while in areas with flat terrain and stable environment, the grid can be appropriately enlarged to improve data collection efficiency. In addition, the production progress and production plan need to be considered, and the areas with faster production progress can be divided into independent grids for centralized management and data collection.

[0086] In order to ensure that the divided grids are practical, the production planning map and topographic map can be superimposed in the GIS system to visualize the division results and make adjustments according to the actual situation. Different production projects can be divided into independent grids so that data can be collected and managed separately. After the grid division is completed, each grid needs to be numbered and marked to generate a detailed grid division map. This map can be used as a basic file for subsequent data collection and risk assessment to ensure that the data collection and management of each grid have a basis. In addition, the grid division map can be imported into the production management system, combined with the production schedule and production plan, to update and manage the production status of each grid in real time.

[0087] S102. For each unit grid, collect the environmental data and outdoor production operation data of the outdoor production area in the unit grid, and obtain the indoor production operation data of the indoor production area in the unit grid.

[0088] The outdoor production area may be any one or more of the following areas:

[0089] Water intake project area: includes water intakes, pumping stations, water diversion channels and other facilities used to obtain water resources from rivers, lakes or groundwater sources.

[0090] Water transfer project area: includes water transfer pipelines, water transfer tunnels, water transfer channels, etc., which are used to transport water from the water intake point to where it is needed.

[0091] Water storage project area: including reservoirs, regulating ponds, dams, etc., used to store and regulate water resources.

[0092] Drainage engineering area: including drainage channels, drainage pipes, drainage pumping stations, etc., used to discharge excess water and prevent waterlogging and flooding.

[0093] Irrigation engineering area: including irrigation canals, sprinkler systems, drip irrigation systems, etc., used for agricultural irrigation.

[0094] Flood control project area: including flood embankments, flood walls, spillways, etc., used to prevent flood disasters.

[0095] Hydropower project area: includes hydropower stations, generator sets, transmission lines, etc., used to generate electricity using water energy.

[0096] Water treatment facility area: including sedimentation tanks, filtration tanks, disinfection facilities, etc., used for water treatment and purification.

[0097] The indoor production area may specifically be any one or more of the following areas:

[0098] Control center area: used to monitor and control the operation of the entire water conservancy project, including monitoring equipment, control systems, communication equipment, etc.

[0099] Machine room area: including pump room, generator room, etc., where various mechanical equipment are installed and maintained.

[0100] Experimental area: used for water quality testing, material testing, etc., including various testing instruments and experimental equipment.

[0101] Office area: including offices for managers and technical personnel, conference rooms, etc.

[0102] Repair workshop area: used for repair and maintenance of equipment, including various tools and repair equipment.

[0103] Warehouse area: used to store various equipment, materials and spare parts.

[0104] Employee living area: including canteen, dormitory, lounge, etc., providing a place for employees to live and rest.

[0105] Training area: used for employee training and technical exchanges, including training equipment and materials.

[0106] For outdoor production areas, the collection of environmental data is a key step. These data include meteorological data (such as temperature, humidity, wind speed, rainfall, etc.), geological data (such as soil type, geological structure, etc.), and hydrological data (such as water level changes, flow rate, etc.). Meteorological data can be collected in real time through weather stations or sensors installed at the production site. These devices can automatically record and transmit data to the central database. Geological data can be obtained through on-site exploration and geological surveys, using geological drilling equipment and soil analysis instruments to record the geological characteristics of each grid in detail. Hydrological data can be obtained through hydrological monitoring equipment installed near water bodies such as rivers and lakes. These devices can monitor water level changes and flow rates in real time. Outdoor production operation data is equally important, including production progress at each production stage, production equipment usage, production personnel distribution, etc. Production progress data can be recorded through the production management system, and production progress can be updated every day. The use of production equipment can be monitored by sensors installed on the equipment, recording the operating time and working status of each equipment. Production personnel distribution data can be obtained through the personnel positioning system, using GPS or RFID technology to record the location and working status of each production personnel in real time.

[0107] For indoor production areas, it is necessary to collect indoor production operation data, including indoor building structure data, indoor production operation equipment data, production process data, etc. Building structure data can be obtained through BIM (Building Information Modeling) technology, which can provide detailed building structure and production process information, including the size, material, location, etc. of each component. Process flow data can be obtained through production site monitoring equipment and production logs to record the specific operations and completion of each process. In the process of data collection, it is necessary to ensure the accuracy and timeliness of the data. By establishing data collection standards and processes, it can be ensured that each data point has a clear collection method and recording method. For example, meteorological data needs to be recorded once an hour, geological data needs to be recorded immediately after each exploration, and production progress data needs to be updated once a day. In order to improve the efficiency and accuracy of data collection, automated data collection equipment and systems can be used to reduce manual intervention and errors.

[0108] S103. Construct a multi-dimensional data graph network by combining environmental data and outdoor production operation data.

[0109] Among them, the purpose of this step is to systematically represent the multidimensional data relationship of the production site through a multidimensional data graph network, so as to provide data support for subsequent risk assessment. A multidimensional data graph network is a graph structure that can represent the relationship between multidimensional data, in which nodes represent different data points (such as environmental data points, production data points, etc.), and edges represent the association relationship between these data points. The method of constructing a multidimensional data graph network can adopt graph database technology. First, the collected environmental data and production data are entered into the graph database as nodes. Each node contains detailed data attributes, such as meteorological data nodes contain attributes such as temperature, humidity, wind speed, etc., and production progress nodes contain attributes such as production progress percentage and production stage. Then, edges are constructed based on the association relationship between data (such as time, space, causal relationship, etc.). For example, meteorological data and production progress data in the same time period can be connected to represent the impact of meteorological conditions on production progress in the time period; geological data of adjacent grids can also be connected to represent the spatial association of geological conditions.

[0110] When constructing a multidimensional data graph network, it is necessary to consider the time dimension and space dimension of the data. The time dimension can record the collection time of each data point through a timestamp to ensure the time sequence of the data. The space dimension can record the location of each data point through coordinate information to ensure the spatial association of the data. For example, a meteorological data node can contain a timestamp and coordinate information to indicate the collection time and location of the data; a production progress node can contain a production stage and location to indicate the time and space information of the production operation process. Through this method of constructing a multidimensional data graph network, the multidimensional data relationship of the production site can be comprehensively and systematically represented, providing reliable data support for subsequent risk assessment. The multidimensional data graph network can not only intuitively display the complex relationship between data, but also deeply explore the potential associations and laws between data through the query and analysis functions of the graph database.

[0111] S104. Extract production risk association characteristics between environmental data and outdoor production operation data based on the multidimensional data graph network, and determine the outdoor safety risk level of the outdoor production area based on the production risk association characteristics.

[0112] Among them, production risk association characteristics refer to characteristic indicators that can reflect the impact of environmental conditions on production safety, such as the impact of high temperature weather on the health of production personnel, the impact of strong winds on the stability of production equipment, etc. The method of extracting these characteristics can use data mining and machine learning technology to identify important risk association characteristics by conducting in-depth analysis of the nodes and edges of the multidimensional data graph network. First, cluster analysis technology can be used to cluster similar production risk events together to identify common risk patterns. By performing cluster analysis on meteorological data and production accident data, the high-incidence time period of production accidents under high temperature weather can be identified, thereby determining the impact characteristics of high temperature weather on production safety. Cluster analysis can use algorithms such as K-means and hierarchical clustering to select appropriate algorithms and parameters based on the characteristics and distribution of the data.

[0113] Secondly, regression analysis technology can be used to establish a quantitative relationship model between environmental conditions and production safety risks. For example, by performing regression analysis on meteorological data and production progress data, a regression model of the impact of meteorological conditions on production progress can be established to quantify the changes in production progress under different meteorological conditions. Regression analysis can use models such as linear regression and logistic regression to select appropriate models and parameters based on the characteristics and relationships of the data. After extracting the production risk-related features, these features can be used to evaluate the safety risks of outdoor production areas and determine their outdoor safety risk levels. The risk level can be graded and scored according to the weights and impact levels of different risk features to calculate a comprehensive risk score and divide it into different risk levels (such as low risk, general risk, greater risk, major risk, etc.).

[0114] S105. Combine BIM technology and image recognition technology and determine the indoor safety risk level of the indoor production area based on indoor production operation data.

[0115] Among them, BIM (Building Information Modeling) technology can provide detailed information on building structures and production processes. By analyzing the BIM model, potential production safety risk points can be identified. In the BIM model, the design and installation of each component can be viewed, and problems such as structural weaknesses and unqualified production materials can be identified. In addition, BIM technology can also provide a visual display of production progress and production plans to help production managers fully understand the actual situation at the production site. Image recognition technology can monitor the production progress and the operating behavior of production personnel in real time through the analysis of production site images, and identify possible safety hazards. By analyzing the production site monitoring video, it can be identified whether the production personnel are wearing safety helmets and whether they comply with safety operating procedures. Image recognition technology can use deep learning algorithms to train a large number of production site images to establish a high-precision image recognition model to achieve real-time monitoring and risk identification of the production site.

[0116] Based on the analysis results of BIM technology and image recognition technology, a comprehensive assessment of the safety risks of indoor production areas can be conducted to determine the indoor safety risk level. The risk level can be determined by using a hierarchical scoring method. According to the weights and impact levels of different risk characteristics, a comprehensive risk score is calculated and divided into different risk levels (such as low risk, general risk, greater risk, major risk, etc.). For example, different weights can be assigned according to risk characteristics such as the stability of the building structure, the quality of production materials, and the operating behavior of production personnel, and then a comprehensive risk score is calculated and divided into three levels: low risk, general risk, greater risk, and major risk.

[0117] S106. Calculate the grid production safety risk level of the unit grid according to the outdoor safety risk level and the indoor safety risk level.

[0118] Among them, after determining the outdoor safety risk level and indoor safety risk level of each unit grid, it is necessary to comprehensively calculate the two risk levels to obtain the grid production safety risk level of the unit grid. The purpose of this step is to determine the overall production safety risk level of each unit grid by comprehensively analyzing the risk characteristics of the outdoor production area and the indoor production area, so as to provide a basis for subsequent safety management. The comprehensive calculation method can adopt the weighted average method, assign different weights according to the risk characteristics and impact degree of the outdoor production area and the indoor production area, and then calculate the weighted average. When determining the weight, it is necessary to consider the risk characteristics and impact degree of the outdoor production area and the indoor production area. For example, for the outdoor production area, environmental factors such as meteorological conditions and geological conditions have a greater impact on production safety, and a higher weight can be assigned; for the indoor production area, factors such as building structure and production materials have a greater impact on production safety, and a higher weight can be assigned. In addition, it is also necessary to consider the production progress and production plan, and assign higher weights to areas with faster production progress to ensure that the key points and difficulties of safety management in the production process are fully paid attention to. After calculating the weighted average, the comprehensive risk level of the grid needs to be graded and divided into different risk levels (such as low risk, general risk, greater risk, major risk, etc.).

[0119] S107. Summarize the grid production safety risk levels of all unit grids, and perform weighted calculation to obtain the engineering production safety risk level of the target water conservancy project.

[0120] Among them, after calculating the production safety risk level of each unit grid, it is necessary to summarize the risk levels of all unit grids to calculate the overall production safety risk level of the target water conservancy project. The summary method can adopt a weighted calculation method, assign different weights according to the risk characteristics and impact degree of each grid, and then calculate the weighted average. For example, different weights can be set according to factors such as the area, production progress, and production difficulty of each grid, and then the weighted average is calculated based on these weights and the risk level of the grid to obtain the comprehensive risk level of the overall project. The method of dividing the comprehensive risk level can adopt a similar hierarchical scoring method to divide the weighted average into different risk levels (such as low risk, general risk, greater risk, major risk, etc.), so as to determine the overall production safety risk level of the target water conservancy project.

[0121] S108. Determine the risk standardization management strategy corresponding to the target water conservancy project based on the project production safety risk level.

[0122] Among them, after determining the overall production safety risk level of the target water conservancy project, it is necessary to formulate corresponding risk standardization management based on this risk level. Risk standardization management refers to the formulation of unified management specifications and operating guidelines for different risk levels to ensure production safety. The formulation of management strategies can refer to industry standards and best practices. For example, for projects with major risk levels, stricter safety management measures can be formulated, such as increasing the frequency of safety inspections, strengthening production personnel training, and equipping more safety equipment; for projects with general risk levels, appropriate safety management measures can be formulated, such as regular safety inspections, strengthening production site monitoring, and implementing safety operating procedures; for projects with low risk levels, basic safety management measures can be formulated, such as regular safety inspections and ensuring that production personnel comply with basic safety operating procedures. By formulating and implementing risk standardization management, production safety risks can be effectively reduced and the smooth progress of water conservancy projects can be guaranteed.

[0123] Specifically, in the same target water conservancy project, in addition to the risk standardization management generated by the analysis of project production safety, it also includes other risk standardization management in various dimensions. The purpose of implementing all risk standardization management is to meet the evaluation standards for standardized management of water conservancy projects. Specifically, if the target water conservancy project is a large or medium-sized reservoir project, the standardized management evaluation standards for large or medium-sized reservoir projects need to be met after implementing all risk standardization management. The following are some examples of standards:

[0124] 1. Project appearance and environment: The project is intact as a whole, with a neat appearance. The project management area is clean and orderly, with no garbage dumping. The project management area has a high degree of greening, good soil and water conservation, and a good water ecological environment.

[0125] 2. Water-retaining structures: the main dam and auxiliary dam are intact, the dam surface and slope protection are flat, the dam body deformation and seepage are normal; the wave-breaking wall, anti-filter body, corridor, and drainage ditch are intact; the deformation and seepage of the joints with both banks and other buildings are normal; there are no tall weeds, trees, or cave ant damage.

[0126] 3. Discharge structures: spillways and spillway tunnels are intact, inlets and outlets are unobstructed, gate chambers, bottom plates, side walls, and energy dissipators are structurally intact and function normally, and deformation and seepage conditions at the junction with the dam body and slope are normal.

[0127] 4. Water delivery (diversion) structures: the water intake tower and water delivery tunnel (culvert) are intact, the inlet and outlet structures are normal, the deformation and seepage conditions at the junction with the dam body and slope are normal, and there are no obvious hidden dangers in the buried culvert under the dam.

[0128] 5. Metal structures and electromechanical equipment: The gate and hoist facilities are intact and operate normally; the gate slots, wire ropes, screws, hydraulic components, supports, and water stops are normal; the electrical equipment and power supply are normal; the backup power supply conditions are good; the hoist room meets the operating requirements, and regular safety inspections and equipment grade assessments of gates and hoists are carried out.

[0129] If the target water conservancy project is a large or medium-sized sluice project, the standardized management of all risks needs to meet the standardized management evaluation standards for large or medium-sized sluice projects. The following are some examples of standards:

[0130] 1. Project appearance and environment: The project is intact as a whole, with a neat appearance, and the project management scope is neat and orderly; the project management scope has a high degree of greening, good soil and water conservation, and good water quality and water ecological environment.

[0131] 2. Lock chamber: The lock chamber structure (gate piers, bottom plate, side walls, etc.) and the connecting buildings on both sides are safe, without safety defects such as tilting, cracking, uneven settlement, etc.; the energy dissipation and anti-impact drainage facilities are complete and operate normally; the surface of the lock chamber structure is free of damage, exposed reinforcement, erosion, and cracking; there are no floating objects in the lock chamber, and there is no obvious siltation in the upstream and downstream connecting sections.

[0132] 3. Gate: The gate opens and closes smoothly, the water stopping is normal, the surface is clean and free of cracks, and there is no obvious deformation, jamming or rust. There are no defects in embedded parts, load-bearing components and walking support parts. The water-stop device is reliably sealed; the lifting ears are free of cracks or rust; safety inspections and equipment grade assessments are carried out as required; anti-freezing measures are taken for the gates during freezing periods.

[0133] 4. Gate hoists and electromechanical equipment: Gate hoists are clean and tidy, gate hoists run smoothly, without rust, oil leakage, damage, etc., wire ropes, screws or hydraulic components are normal, and protection and limit devices are effective; electromechanical equipment is intact and operates normally, and electrical equipment, indicating instruments, lightning protection facilities, grounding, etc. are inspected regularly as required, without safety hazards; lines are neat, firm and clearly marked; safety inspections and equipment grade assessments are carried out as required; and the backup power supply is reliable.

[0134] 5. Upstream and downstream river channels and embankments: There is no obvious siltation or erosion in the upstream and downstream river channels; the embankments on both sides are complete and intact.

[0135] If the target water conservancy project is a large or medium-sized irrigation and drainage pump station project, then after implementing all risk standardization management, it is necessary to meet the standardization management evaluation standards for large or medium-sized irrigation and drainage pump station projects. The following are some examples of standards:

[0136] 1. Safety facilities and equipment management: Carry out daily inspections and regular checks on engineering safety facilities and equipment (including fire-fighting equipment), and regularly perform maintenance, testing and replacement to ensure that they are complete and in good condition. Labor protection supplies are equipped to meet safety production requirements.

[0137] 2. Dispatching and control management: Formulate the operation dispatching and control operation plan of the pump station (including joint dispatching with other water conservancy projects). The relevant contents related to flood control and drought relief work shall be submitted for approval or filing in accordance with regulations. Strictly implement the operation dispatching instructions and control operation plan, and keep complete records. Achieve safe, efficient and economical operation of the pump station.

[0138] 3. Construction of information platform: Establish an information platform for project operation and management to realize operation automation, management informatization and online monitoring of projects; timely and dynamic update of project and operation information.

[0139] 4. Automated monitoring and early warning: The management information platform operates reliably, the equipment is in good condition, and the utilization rate is high. Key information such as construction conditions, water and rainfall conditions, operation monitoring, safety monitoring, video monitoring, etc. are connected to the information platform to achieve dynamic management; when the monitoring data is abnormal, it can automatically identify the dangerous situation and issue a timely forecast and early warning.

[0140] If the target water conservancy project is a levee project, then after implementing standardized management of all risks, it is necessary to meet the standardized management evaluation standards for levee projects. The following are some examples of standards:

[0141] 1. Embankment: The cross-section of the embankment and the width of the embankment protection area shall maintain the design or final acceptance scale; the embankment shoulder line shall be straight and circular, and the embankment slope shall be smooth; the embankment body shall be free of cracks, gullies, holes, and debris and garbage accumulation; the boundary of the embankment protection area shall be clear.

[0142] 2. Embankment roads: The embankment roads are unobstructed and meet the requirements for flood control and emergency traffic; the road surface on the top of the embankment (back embankment, flood control road) is complete and flat, without potholes, obvious depressions and undulations, and no water accumulation after rain; the intersection line between the upper embankment auxiliary road and the embankment slope is straight and regular, and the embankment body is not eroded.

[0143] 3. Embankment protection project: The embankment protection project (slope protection, revetment, groin, foot protection, etc.) has no defects, no collapse, no looseness; the embankment surface is flat; the slope protection is smooth.

[0144] 4. Buildings crossing the dike: There are no major hidden dangers in the dike section of the buildings crossing the dike; the buildings crossing the dike (bridges, culverts, various pipelines, etc.) meet the requirements for safe operation; the metal structures and opening and closing equipment are well maintained and operate flexibly; the concrete is not aged or damaged; the connection between the dike body and the building is reliable, and there are no hidden dangers at the joints, and no uneven settlement cracks, gaps, or leakage.

[0145] 5. Biological protection project: Within the scope of project management, trees and grasses are planted reasonably, and a biological protection system is formed in areas suitable for planting protective forests; the turf on the embankment (dam) slope is neat and free of tall weeds; the turf on the embankment shoulder (except for embankment shoulder ridges) is more than 0.5m wide on each side; the tree defect rate is less than 5%, and there are no diseases and insect pests.

[0146] 6. Project drainage system: The project drainage is unobstructed; all types of project drainage ditches, pressure relief wells, and drainage ditches are complete and unobstructed as required, and weeds and debris in the ditches are cleaned up in a timely manner without blockage or damage.

[0147] If the target water conservancy project is a water diversion project, then after implementing standardized management of all risks, it is necessary to meet the standardized management evaluation standards for water diversion projects. The following are some examples of standards:

[0148] 1. Engineering facilities: All buildings are in good condition, without tilt, collapse, cracks, sliding, uneven settlement, etc., and have a neat appearance. All equipment is in good condition, without obvious oil leakage, rust, etc., and operates normally. The project has the conditions for repair and maintenance, with reasonable maintenance space and complete maintenance facilities.

[0149] 2. Monitoring infrastructure: All types of hydrological monitoring stations are reasonably set up, the buildings are stable and intact, and the equipment is in good condition; the hydrological monitoring system is operating normally, and the technical indicators such as data measurement accuracy, frequency and timeliness meet the requirements. The water quality and water environment monitoring facilities are reasonably set up, the buildings are stable and intact, and the equipment is in good condition; the water quality monitoring system is operating normally, and the technical indicators such as data measurement accuracy, frequency and timeliness meet the requirements. All types of engineering safety monitoring projects are reasonably set up, and the monitoring facilities and equipment are in good condition. All types of monitoring data are compiled in a timely, complete and accurate manner to meet the requirements.

[0150] If the target water conservancy project is a canal (aqueduct) project, then after implementing standardized management of all risks, it is necessary to meet the standardized management evaluation standards for canal (aqueduct) projects. The following are some examples of standards:

[0151] 1. Project appearance and environment: The project is intact, safe and in normal operation, and the water supply is smooth. There is no debris piled up, no siltation, no illegal buildings and no activities that endanger the safety of the project within the management scope.

[0152] 2. Canal (dike) project: the canal (dike) top, canal (dike) shoulder, and road crossing should be flat, solid, and free of weeds and debris. The canal (dike) slope should maintain the designed slope, with a smooth slope surface, no rain ditch, steep slope, caves, pits, debris, etc.; the platform (platform) should maintain the designed width, the platform surface should be flat, and the height difference between the inner and outer edges of the platform should meet the design requirements. The slope protection should maintain a smooth slope surface, the concrete or block structure should be intact, the masonry joints should be tight, there should be no loosening, collapse, falling off, overhead, etc., there should be no weeds or debris, and the slope surface should be kept clean and intact; the canal (dike) slope should be free of burrows and traces of activities of harmful animals. The bottom plate should be free of cracks and damage, and drainage facilities such as drainage ditches and check valves should be intact and unobstructed; the protective layer of anti-seepage facilities should be intact. The boundary of the dike protection area is clear, the ground is flat, and there is no debris. Buildings passing through and across the canal meet the requirements for safe operation, the canal body and the building are reliably connected, and there are no hidden dangers at the joint.

[0153] 3. Aqueduct project: The concrete structures of the inlet and outlet and the body of the aqueduct are intact, without cracks, erosion, leakage, carbonization, etc.; there is no exposed steel bar or rust; the water flows smoothly, without debris accumulation, the flow state is stable, and there is no abnormal scouring; the joint water stop and anti-seepage structures are intact and without leakage. The concrete structures of the piers and abutments are intact, without cracks, erosion, leakage, carbonization, etc.; there is no exposed steel bar or rust; there is no serious scouring of the piers, and there is no subsidence or cavity in the backfill soil around the foundation. The bearings are intact, without deformation or dislocation. The working bridge, railings and protective doors are intact.

[0154] 4. Biological protection project: The canal (embankment) slope grass is neat and intact, free of weeds, and remains intact and beautiful. The tree species, layout and survival rate within the project management scope meet the requirements. Appropriate protective measures should be taken in areas with plant and animal risks that are prone to engineering hidden dangers.

[0155] If the target water conservancy project is a pipe culvert (tunnel, inverted siphon) project, then after implementing all risk standardization management, it is necessary to meet the pipe culvert (tunnel, inverted siphon) project standardization management evaluation standards. The following are some examples of standards:

[0156] 1. Project appearance and environment: The project is intact, safe and in normal operation, and water supply is smooth. There is no debris piled up within the management scope, no illegal buildings and activities that endanger the safety of the project.

[0157] 2. Pipe culvert project: The protection facilities on the top of the pipe (culvert) are intact, and there is no accumulation of large amounts of slag, stone piles, etc. The fill soil near the pipe (culvert) body is intact, without wetting, collapse, or scouring pits. There is no water seepage in the pipe (culvert) body or structural joints, and there is no broken wire in the PCCP pipeline; the movement and dislocation of adjacent pipe (culvert) sections are within the allowable range, and the anti-seepage and anti-corrosion materials are intact. The vents, inspection holes, etc. are intact, and the park walls or isolation nets such as water retention weirs, connecting wells, inspection holes, and vents are intact.

[0158] 3. Tunnel engineering: The entrance and exit slopes are stable and intact. The tunnel support structure is intact, the drainage system is intact, the drainage is normal, and there is no obvious deformation, seepage, water gushing or landslide on the surface and the entrance slope; the sealing body is intact without cracks, deformation and obvious seepage. There are no natural or human activities along the tunnel (culvert) that cause obvious changes in geology and landforms.

[0159] 4. Underpass Project: The settlement, displacement and inclination of the inlet and outlet wing walls meet the requirements. The inlet and outlet platforms are intact without sinking or cracking. The connection between the inlet and outlet and the canal embankment is intact without scouring, hollowing or collapse. The drainage pipes of the inlet and outlet wing walls are intact and unobstructed. The inlet and outlet connection sections are unobstructed without siltation and there is no water leakage inside the pipe body. There is no uneven settlement inside the adjacent pipe sections of the pipe body section, and there is no collapse of the backfill soil near the pipe body section.

[0160] 5. Metal structures and electromechanical equipment: The gate and hoist facilities are intact and operate normally. The gate slots, wire ropes, screws, hydraulic components, supports, and water stops are normal, the electrical equipment and power supply are normal, and the backup power supply is in good condition. The hoist room meets the operating requirements, and gate and hoist safety inspections and equipment grade assessments are carried out regularly.

[0161] In one embodiment, referring to Figure 2 , before step S103, the following steps are also included:

[0162] Crawl the historical climate and environment data, historical geological environment data, and historical outdoor production and operation data of the same type of projects as the target water conservancy project, and pre-process the historical climate and environment data, historical geological environment data, and historical outdoor production and operation data;

[0163] The historical climate characteristics, historical geological characteristics and historical outdoor production characteristics are extracted from the historical climate environment data, historical geological environment data and historical outdoor production operation data respectively;

[0164] A first multiple regression analysis model is constructed by combining historical climate characteristics and historical geological characteristics, and a first risk characteristic correlation matrix between historical climate characteristics and historical geological characteristics is analyzed and constructed through the first multiple regression analysis model;

[0165] A second multiple regression analysis model is constructed by combining historical geological characteristics and historical outdoor production characteristics, and a second risk characteristic correlation matrix between historical geological characteristics and historical outdoor production characteristics is analyzed and constructed through the second multiple regression analysis model;

[0166] Based on the historical geological characteristics, the first risk feature correlation matrix and the second risk feature correlation matrix are matrix-fused to obtain a fused risk feature correlation matrix;

[0167] A third multiple regression analysis model is constructed by combining historical climate characteristics and historical outdoor production characteristics. The third risk characteristic association matrix between historical climate characteristics and historical outdoor production characteristics is analyzed and constructed through the third multiple regression analysis model.

[0168] In this embodiment, it is first necessary to obtain these data from multiple data sources, which may include government meteorological departments, geological exploration agencies, engineering production record databases, etc. By writing a web crawler program, relevant data can be automatically extracted from these data sources. For example, using Python's BeautifulSoup library and Scrapy framework, historical climate data on web pages can be effectively captured, including information such as temperature, air pressure, precipitation, wind speed, humidity, evaporation, solar radiation, and sunshine intensity. For geological environment data, it can be extracted from geological survey reports and geological exploration data, including information such as soil type, geological structure, and groundwater level. Outdoor production and operation data can be obtained from the engineering project management system, including production progress, production technology, equipment usage, etc.

[0169] After data crawling is completed, the data needs to be preprocessed. The preprocessing steps include data cleaning, data format conversion, and data standardization. Data cleaning refers to removing noise and outliers in the data, such as deleting missing values ​​and correcting erroneous data records. Data format conversion is to convert data from different sources into a unified format for subsequent analysis. Data standardization is to normalize data of different dimensions so that they can be compared and analyzed on the same scale. The Z-score data standardization method can be used to subtract the mean of each feature from its data and divide it by the standard deviation to make it conform to the standard normal distribution. Through these preprocessing steps, the quality and consistency of historical climate and environmental data, historical geological environmental data, and historical outdoor production and operation data can be ensured, laying a solid foundation for subsequent feature extraction and model building.

[0170] Extracting features from preprocessed historical data is a key step in data analysis. First, extract climate features from historical climate and environmental data. Climate features can include annual average temperature, annual precipitation, frequency of extreme weather events, etc. These climate features can be obtained through statistical analysis methods, such as calculating mean, variance, and extreme value. Next, extract geological features from historical geological environmental data. Geological features can include the distribution of soil types, the type and distribution of geological structures, changes in groundwater levels, etc. These geological features can be extracted through spatial analysis methods of geological data such as geological profile analysis and geological model construction. Then extract production features from historical outdoor production operation data. Production features can include production progress, production process, equipment usage, accident rate, etc. These production features can be obtained through statistical analysis of production record data. Through these feature extraction steps, complex raw data can be converted into feature data that is easy to analyze and understand. These feature data provide the necessary input for the construction of subsequent multivariate regression analysis models.

[0171] The purpose of constructing the first multiple regression analysis model is to analyze the relationship between historical climate characteristics and historical geological characteristics, and to construct a risk characteristic association matrix between them. First, the extracted historical climate characteristics and historical geological characteristics need to be used as input data. The multiple regression analysis model is a statistical method used to study the linear relationship between multiple independent variables and dependent variables. Here, historical climate characteristics can be used as independent variables and historical geological characteristics as dependent variables. The specific steps include: First, the historical climate characteristics and historical geological characteristics data are stored in matrices X and Y respectively. Then, the linear regression model is used to fit the data to obtain the regression coefficient. The regression coefficient reflects the degree of influence of each climate feature on the geological feature. By fitting the model, a regression coefficient matrix can be obtained, which reflects the degree of influence of each climate feature on each geological feature. Organizing these regression coefficients into an association matrix is ​​the first risk characteristic association matrix. This matrix can help analyze the potential impact of climate change on the geological environment and provide a scientific basis for risk assessment and management.

[0172] The purpose of constructing the second multiple regression analysis model is to analyze the relationship between historical geological characteristics and historical outdoor production characteristics, and to construct a risk characteristic association matrix between them. Similar to the previous process, historical geological characteristics are used as independent variables, and historical outdoor production characteristics are used as dependent variables. The steps for constructing the multiple regression analysis model are also similar to the previous process. First, the historical geological characteristics and historical outdoor production characteristics data are stored in matrices X and Y respectively. Then, the linear regression model is used to fit the data to obtain the regression coefficient. The regression coefficient reflects the degree of influence of each geological feature on the production characteristics. By fitting the model, a regression coefficient matrix can be obtained, which reflects the degree of influence of each geological feature on each production feature. Organizing these regression coefficients into an association matrix is ​​the second risk characteristic association matrix. This matrix can help analyze the potential impact of the geological environment on the production process and provide a scientific basis for production risk assessment and management.

[0173] Matrix fusion combines the first risk feature association matrix and the second risk feature association matrix to obtain a comprehensive risk feature association matrix. First, it is necessary to ensure that the dimensions of the two matrices are consistent, that is, their rows and columns represent the same features. Through matrix fusion, the information of the two matrices can be combined to obtain a more comprehensive risk feature association matrix. This fused matrix can reflect the complex relationship between climate characteristics, geological characteristics and production characteristics, and provide a more comprehensive basis for risk assessment and management.

[0174] The purpose of constructing the third multivariate regression analysis model is to analyze the relationship between historical climate characteristics and historical outdoor production characteristics, and to construct a risk characteristic association matrix between them. First, the extracted historical climate characteristics and historical outdoor production characteristics need to be used as input data. Similar to the previous example, historical climate characteristics are used as independent variables and historical outdoor production characteristics are used as dependent variables. The steps for constructing the multivariate regression analysis model are similar to the previous ones. First, the historical climate characteristics and historical outdoor production characteristics data are stored in matrices X and Y respectively. Then, the linear regression model is used to fit the data to obtain the regression coefficients. The regression coefficients reflect the degree of influence of each climate characteristic on the production characteristics. By fitting the model, a regression coefficient matrix can be obtained, which reflects the degree of influence of each climate characteristic on each production characteristic. Organizing these regression coefficients into an association matrix is ​​the third risk characteristic association matrix. This matrix can help analyze the potential impact of climate change on the production process and provide a scientific basis for production risk assessment and management.

[0175] In one embodiment, the environmental data includes climate environmental data and geological environmental data. Figure 3 , step S103 specifically includes the following steps:

[0176] Pre-process climate and environmental data, geological environment data, and outdoor production and operation data;

[0177] Extract target climate characteristics, target geological characteristics and target outdoor production characteristics from climate environment data, geological environment data and outdoor production operation data respectively;

[0178] The target climate characteristics, target geological characteristics and target outdoor production characteristics are uniformly projected into the same target feature space through a preset feature conversion function, and corresponding target climate characteristic nodes, target geological characteristic nodes and target outdoor production characteristic nodes are generated in the target feature space respectively;

[0179] Generating a first association node edge between a target geological feature node and a target outdoor production feature node in a target feature space based on the fused risk feature association matrix;

[0180] Based on the third risk feature association matrix, a second association node edge between the target climate node and the target outdoor production feature node is generated in the target feature space to obtain a multidimensional data graph network.

[0181] In the present embodiment, in the process of pre-processing climate environment data, geological environment data and outdoor production and operation data, it is first necessary to clean, standardize and normalize these three types of data. The purpose of cleaning data is to remove noise and outliers to ensure the accuracy and consistency of data. Statistical analysis methods can be used to identify and process outliers, such as using box plots (Box Plot) to detect and process outliers. In the standardization process, data of different units and dimensions need to be converted to the same scale for subsequent analysis and processing. This can be achieved by mean-variance standardized data method (Z-score normalization), that is, each data point is subtracted from the mean and divided by the standard deviation. Normalization is to scale the data to a specific range (such as 0 to 1), which can be achieved by minimum-maximum normalization (Min-Maxnormalization), that is, each data point is subtracted from the minimum and divided by the difference between the maximum and the minimum. After the above-mentioned pre-processing steps, climate environment data, geological environment data and outdoor production and operation data become more standardized and consistent, laying a solid foundation for subsequent data analysis and feature extraction.

[0182] In the process of extracting target climate features, target geological features and target outdoor production features from climate and environmental data, geological environment data and outdoor production and operation data, it is necessary to first determine the most representative and relevant features in each type of data. For climate and environmental data, the target climate features that can be extracted include temperature, humidity, precipitation, wind speed, etc. These features can be extracted through time series analysis and statistical methods, such as using sliding average and smoothing techniques to eliminate short-term fluctuations and extract long-term trend features. For geological environment data, the target geological features that can be extracted include soil type, geological structure, groundwater level, etc. These features can be extracted and analyzed through geological exploration and geophysical measurement data. For outdoor production and operation data, the target outdoor production features that can be extracted include production progress, production equipment status, number of production personnel, etc. These features can be extracted and analyzed through real-time monitoring data and production logs at the production site. The target features extracted by the above method can fully reflect the actual situation of climate, geology and production, and provide rich information for subsequent feature conversion and analysis.

[0183] In the process of uniformly projecting the target climate features, target geological features, and target outdoor production features to the same target feature space through a preset feature conversion function, it is necessary to design and apply a specific feature conversion function to ensure that different types of features can be compared and analyzed in the same feature space. The feature conversion function can be implemented by linear transformation, nonlinear transformation, or deep learning model. Linear transformation can be implemented by matrix multiplication, such as multiplying the feature vector by the conversion matrix to obtain the projected feature vector. Nonlinear transformation can be implemented by kernel function, such as using Gaussian kernel function to map the feature vector to a high-dimensional space. Deep learning model can learn feature conversion function by training neural network, such as using autoencoder to encode input features into low-dimensional representation. In the target feature space after uniform projection, corresponding target climate feature nodes, target geological feature nodes, and target outdoor production feature nodes can be generated. These nodes have the same dimension and scale in the feature space, which is convenient for subsequent association analysis and graph network construction.

[0184] In the process of generating the first associated node edge between the target geological feature node and the target outdoor production feature node in the target feature space based on the fusion risk feature association matrix, it is necessary to first construct the fusion risk feature association matrix. The matrix can be generated by calculating the correlation or similarity between the target geological feature and the target outdoor production feature. The correlation can be calculated using the Pearson Correlation Coefficient, that is, calculating the ratio of the covariance of the two feature vectors to the standard deviation. The similarity can be calculated using the cosine similarity, that is, calculating the ratio of the dot product of the two feature vectors to their modulus. The strength of the association between the target geological feature node and the target outdoor production feature node can be determined by calculating the correlation or similarity matrix obtained. According to the magnitude of the association strength, the first associated node edges can be generated in the target feature space, which represent the potential risk association relationship between the geological features and the production features. In this way, the impact of the geological environment on production activities can be identified and analyzed, providing an important basis for production risk management.

[0185] In the process of generating the second association node edge between the target climate node and the target outdoor production feature node in the target feature space based on the third risk feature association matrix, it is necessary to first construct the third risk feature association matrix. The matrix can be generated by calculating the correlation or similarity between the target climate feature and the target outdoor production feature. The correlation can be calculated using the Spearman Rank Correlation Coefficient, that is, calculating the ratio of the rank difference of two feature vectors to their standard deviation. The similarity can be calculated using the Jaccard Similarity Coefficient, that is, calculating the ratio of the intersection and union of two feature sets. The strength of the association between the target climate feature node and the target outdoor production feature node can be determined by calculating the correlation or similarity matrix obtained. According to the magnitude of the association strength, the second association node edges can be generated in the target feature space, which represent the potential risk association relationship between the climate feature and the production feature. In this way, the impact of the climate environment on production activities can be identified and analyzed, providing an important basis for production risk management.

[0186] Finally, the multidimensional data graph network generated by the above steps unifies the climate characteristics, geological characteristics and production characteristics in a comprehensive feature space, and reflects the risk association relationship between each feature through the associated node edges. The multidimensional data graph network can not only fully reflect the actual climate, geology and production conditions, but also conduct in-depth risk analysis and prediction through graph structure analysis methods (such as graph convolutional networks). In this way, it can provide a scientific basis for risk management and decision-making of production projects, and improve production safety and efficiency.

[0187] In one implementation manner, step S104 specifically includes the following steps:

[0188] According to the generation order of the target outdoor production feature nodes in the multidimensional data graph network, each target outdoor production feature node is marked as a central target node in turn, until all target outdoor production feature nodes are marked as central target nodes only once;

[0189] Whenever the target outdoor production feature node is marked as a central target node, a node risk association degree calculation step and a node community closeness calculation step are performed on the central target node to obtain the node risk association degree and the node community closeness of the central target node;

[0190] The production risk association value is calculated by combining the risk association degree of all nodes and the node community closeness, and the production risk association value is used as the production risk association feature between environmental data and outdoor production operation data;

[0191] The outdoor safety risk level of the outdoor production area is determined based on the production risk association characteristics and based on the preset risk level rules.

[0192] In this embodiment, in the multidimensional data graph network, the generation order of the target outdoor production feature nodes can be determined based on a timestamp or other specific rules. First, traverse each target outdoor production feature node in turn according to the generation order, and mark the currently traversed node as the central target node. Ensure that during the traversal process, each node can be marked only once to avoid repeated marking or omissions. Whenever the target outdoor production feature node is marked as a central target node, the node risk correlation calculation step and the node community closeness calculation step are performed on the central target node to obtain the node risk correlation and node community closeness of the central target node. The node risk correlation calculation step is intended to evaluate the risk correlation strength between the central target node and other nodes. This can be achieved by calculating the edge weights or correlation coefficients between the central target node and other nodes.

[0193] The node community closeness calculation step is intended to evaluate the closeness between the central target node and its first-order neighbor nodes. In this implementation, the calculation formula for the node community closeness is as follows:

[0194]

[0195] Where: P i Represents the i-th central target node V i The node community density, M represents the total number of node edges in the multidimensional data graph network, d(V i ) represents the central target node V i The node degree of .

[0196] The production risk association value is calculated by combining all node risk associations and node community closeness, and the production risk association value is used as the production risk association feature between environmental data and outdoor production operation data. In this embodiment, the calculation formula of the production risk association value is as follows:

[0197]

[0198] Where: Y represents the production risk association value, n represents the number of target outdoor production feature nodes, and δ represents the association value weight parameter.

[0199] The production risk association value reflects the overall risk association characteristics between environmental data and outdoor production operation data, and can be used as an important indicator for production risk assessment. In this way, the complex association relationship in the multidimensional data graph network can be converted into a single risk characteristic value, which is convenient for subsequent risk level assessment.

[0200] The outdoor safety risk level of the outdoor production area is determined based on the production risk association characteristics and the preset risk level rules. The risk level rules can be formulated based on production safety standards and actual experience, and usually include several risk levels (such as low risk, general risk, greater risk, and major risk) and their corresponding threshold ranges. First, the production risk association value is compared with the preset risk level threshold to determine the risk level to which it belongs. For example, if the production risk association value is less than a certain threshold, it is judged as a low risk; if it is between two thresholds, it is judged as a general risk; if it is greater than a certain threshold, it is judged as a major risk. In this way, the complex production risk association characteristics can be converted into simple and clear risk levels, providing an intuitive reference for production safety management. Finally, through the above steps, the safety risk level of the outdoor production area can be systematically evaluated, which can help production managers identify potential risks, formulate corresponding prevention and response measures, and improve production safety and efficiency.

[0201] In one embodiment, the node risk correlation calculation step includes the following steps:

[0202] The geological node correlation between the central target node and each adjacent target geological feature node is calculated by fusing the risk feature correlation matrix;

[0203] Based on the first associated node edge, the number of first node edges between the adjacent target geological feature node and all other target outdoor production feature nodes is counted;

[0204] The geological node degree of the adjacent target geological feature node is obtained by calculating the first associated node edge;

[0205] The geological node risk correlation of the central target node is calculated by combining the geological node correlation, the number of first node edges and the geological node degree;

[0206] The climate node correlation between the central target node and each adjacent target climate feature node is calculated through the third risk feature correlation matrix;

[0207] The climate node degree of the adjacent target climate feature node is calculated using the second associated node edge;

[0208] The climate node risk correlation of the central target node is calculated by combining the climate node correlation and climate node degree;

[0209] The node risk correlation of the central target node is obtained by calculating the sum of the geological node risk correlation and the climate node risk correlation.

[0210] In this implementation, the calculation formula for the geological node risk association is as follows:

[0211]

[0212] Where: Represents the i-th central target node V i The risk correlation degree of geological nodes, m represents the central target node V i Adjacent target geological feature nodes The number of nodes, Represents the fusion risk feature association matrix A R The matrix element corresponding to the i-th row and j-th column in , ξ(·,·) represents the correlation calculation function, γ represents the geological weight parameter, L i，j Represents the central target node V i The jth adjacent target geological feature node The number of first node edges, Indicates adjacent target geological feature nodes The degree of geological nodes;

[0213] The calculation formula of climate node risk correlation is as follows:

[0214]

[0215] Where: Represents the i-th central target node V i The climate node risk correlation degree, t represents the central target node V i Adjacent target climate feature nodes The number of nodes, Represents the third risk feature association matrix A 3 The matrix element corresponding to the i-th row and k-th column in ξ(·,·) represents the correlation calculation function, η represents the climate weight parameter, Represents the kth adjacent target geological feature node The degree of geological nodes.

[0216] In one embodiment, the indoor production operation data includes indoor production area drawing data and indoor image data of the indoor production area, and the indoor production area drawing data is pre-marked with indoor dangerous areas. Step S105 specifically includes the following steps:

[0217] Generate the indoor engineering BIM model of the indoor production area based on the indoor production area drawing data and using BIM technology;

[0218] Generate corresponding model danger zones in the indoor engineering BIM model according to the indoor danger zones pre-marked in the indoor production area drawing data;

[0219] Statistical model danger zone ratio in the indoor engineering BIM model;

[0220] Use image recognition technology to identify indoor risk points in indoor image data and count the number of danger sources at indoor risk points;

[0221] Determine the indoor safety risk level of the indoor production area based on the area ratio and the number of hazardous sources.

[0222] In this embodiment, the indoor production area drawing data generally includes detailed building structure, room layout, pipeline layout and other information. These drawing data can be two-dimensional drawings in CAD format or design files in other digital formats. Using BIM (Building Information Modeling) technology, these two-dimensional drawing data are converted into three-dimensional digital models. BIM technology can integrate the geometric information, physical properties and functional characteristics of the building to generate a highly accurate three-dimensional model. During specific implementation, it is first necessary to import the two-dimensional drawing data into BIM software (such as Revit, Archicad, etc.), and then gradually build a three-dimensional model based on the size, position and other information in the drawing data. In this process, it is necessary to pay attention to the accuracy and details of each component to ensure that the generated BIM model is consistent with the actual building.

[0223] Indoor production area drawing data usually mark some potential dangerous areas, such as high temperature areas, flammable and explosive areas, electrical equipment areas, etc. The information of these dangerous areas is imported into the indoor engineering BIM model, and the corresponding model dangerous areas are generated in the BIM model. In specific implementation, the information of dangerous areas can be attached to the corresponding model components through the marking and annotation functions in the BIM software. For example, mark a certain area as a high temperature area in the BIM model, and set corresponding warning signs and color codes. In this way, the location and range of each dangerous area can be intuitively seen in the three-dimensional model.

[0224] Next, the area ratio of the model danger zone in the indoor engineering BIM model is calculated. The area ratio is an important indicator for evaluating the proportion of the danger zone in the entire indoor engineering. In specific implementation, the area or volume of the model danger zone can be calculated through the measurement and calculation functions in the BIM software, and then compared with the total area or total volume of the entire indoor engineering BIM model to calculate the proportion of the danger zone. In this way, the proportion of the danger zone in the entire indoor engineering can be quantified, providing a quantitative basis for subsequent risk assessment. The statistical results of the area ratio can intuitively reflect the distribution and density of the danger zone.

[0225] Image recognition technology is used to identify indoor risk points in indoor image data. Indoor risk points specifically refer to possible danger sources in the room, and the number of danger sources at indoor risk points is counted. Image recognition technology is a technology that uses computer vision and machine learning algorithms to identify and classify objects from images. In specific implementation, indoor image data can be obtained through a camera or other image acquisition device, and then the image recognition algorithm (such as convolutional neural network CNN) is used to process and analyze the image to identify the danger sources in the image. For example, an image recognition model can be trained to identify flammable items, chemicals, electrical equipment and other danger sources in the image. After the danger sources are identified, their number can be counted. In this way, the number of indoor risk points can be automatically identified and counted to provide data support for subsequent risk assessment. The application of image recognition technology can greatly improve the efficiency and accuracy of danger source identification and reduce the workload and errors of manual identification.

[0226] The indoor safety risk level of the indoor production area is determined by combining the area proportion and the number of hazardous sources. The indoor safety risk level is an important indicator for comprehensively evaluating the potential risks of indoor production areas. In specific implementation, the area proportion and the number of hazardous sources can be comprehensively calculated according to the preset risk level rules to determine the risk level of the indoor production area. For example, the preset risk level rules may include several levels (such as low risk, general risk, greater risk, major risk) and their corresponding threshold ranges. First, the area proportion and the number of hazardous sources are standardized to ensure that their values ​​are between 0 and 1. Then, according to the preset weight parameters, the area proportion and the number of hazardous sources are weighted averaged to obtain a comprehensive risk score.

[0227] The present invention also discloses a standardized management system applied to water conservancy projects, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the standardized management method applied to water conservancy projects described in any one of the above-mentioned embodiments is implemented.

[0228] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0229] Among them, the memory can be an internal storage unit of a computer device, such as a hard disk or memory of a computer device, or an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the computer device, etc., and the memory can also be a combination of an internal storage unit and an external storage device of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0230] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0231] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the protection scope of the present application.

Claims

1. A standardized management method applied to water conservancy projects, characterized in that: The steps include: Dividing the production area of ​​the target water conservancy project into a plurality of unit grids according to the project scope of the target water conservancy project; For each of the unit grids, collecting environmental data and outdoor production operation data of the outdoor production area in the unit grid, and acquiring indoor production operation data of the indoor production area in the unit grid; Constructing a multi-dimensional data graph network by combining the environmental data and the outdoor production operation data; Extracting production risk association features between the environmental data and the outdoor production operation data based on the multidimensional data graph network, and determining the outdoor safety risk level of the outdoor production area according to the production risk association features; Determine the indoor safety risk level of the indoor production area based on the indoor production operation data by combining BIM technology and image recognition technology; Calculate the grid production safety risk level of the unit grid according to the outdoor safety risk level and the indoor safety risk level; Summarizing the grid production safety risk levels of all the unit grids, and performing weighted calculation to obtain the engineering production safety risk level of the target water conservancy project; The risk standardization management strategy corresponding to the target water conservancy project is determined based on the project production safety risk level.

2. The standardized management method for water conservancy projects according to claim 1 is characterized in that: Before combining the environmental data and the outdoor production and operation data to construct a multidimensional data graph network, the following steps are also included: Crawling historical climate and environmental data, historical geological environment data, and historical outdoor production and operation data of projects of the same type as the target water conservancy project, and preprocessing the historical climate and environmental data, the historical geological environment data, and the historical outdoor production and operation data; Extracting historical climate characteristics, historical geological characteristics and historical outdoor production characteristics from the historical climate environment data, the historical geological environment data and the historical outdoor production operation data respectively; Constructing a first multiple regression analysis model in combination with the historical climate characteristics and the historical geological characteristics, and analyzing and constructing a first risk characteristic association matrix between the historical climate characteristics and the historical geological characteristics through the first multiple regression analysis model; A second multiple regression analysis model is constructed by combining the historical geological characteristics and the historical outdoor production characteristics, and a second risk characteristic association matrix between the historical geological characteristics and the historical outdoor production characteristics is analyzed and constructed by the second multiple regression analysis model; Based on the historical geological characteristics, the first risk feature association matrix and the second risk feature association matrix are matrix-fused to obtain a fused risk feature association matrix; A third multivariate regression analysis model is constructed in combination with the historical climate characteristics and the historical outdoor production characteristics, and a third risk characteristic association matrix between the historical climate characteristics and the historical outdoor production characteristics is analyzed and constructed through the third multivariate regression analysis model.

3. The standardized management method applied to water conservancy projects according to claim 2 is characterized in that: The environmental data includes climate environmental data and geological environmental data, and the construction of a multidimensional data graph network by combining the environmental data and the outdoor production and operation data includes the following steps: Preprocessing the climate environment data, the geological environment data and the outdoor production operation data; Extracting target climate characteristics, target geological characteristics and target outdoor production characteristics from the climate environment data, the geological environment data and the outdoor production operation data respectively; The target climate feature, the target geological feature and the target outdoor production feature are uniformly projected to the same target feature space through a preset feature conversion function, and corresponding target climate feature nodes, target geological feature nodes and target outdoor production feature nodes are generated in the target feature space respectively; Generating a first association node edge between the target geological feature node and the target outdoor production feature node in the target feature space based on the fused risk feature association matrix; A second association node edge between the target climate node and the target outdoor production feature node is generated in the target feature space based on the third risk feature association matrix to obtain a multidimensional data graph network.

4. The standardized management method applied to water conservancy projects according to claim 3 is characterized in that: The extracting the production risk association features between the environmental data and the outdoor production operation data based on the multidimensional data graph network, and determining the outdoor safety risk level of the outdoor production area according to the production risk association features comprises the following steps: According to the generation order of the target outdoor production feature nodes in the multidimensional data graph network, each of the target outdoor production feature nodes is marked as a central target node in turn, until all the target outdoor production feature nodes are marked as the central target node only once; Whenever the target outdoor production feature node is marked as the central target node, a node risk association degree calculation step and a node community closeness calculation step are performed on the central target node to obtain a node risk association degree and a node community closeness of the central target node; Calculate a production risk association value by combining all the node risk association degrees and the node community closeness, and use the production risk association value as a production risk association feature between the environmental data and the outdoor production operation data; The outdoor safety risk level of the outdoor production area is determined based on the production risk association characteristics and based on preset risk level rules.

5. The standardized management method applied to water conservancy projects according to claim 4 is characterized in that: The node risk association calculation step comprises the following steps: Calculate the geological node correlation between the central target node and each adjacent target geological feature node through the fusion risk feature correlation matrix; Based on the first associated node edge, counting the number of first node edges between the adjacent target geological feature node and all other target outdoor production feature nodes; The geological node degree of the adjacent target geological feature node is obtained by calculating the first associated node edge; The geological node risk correlation degree of the central target node is calculated by combining the geological node correlation degree, the first node edge quantity and the geological node degree; Calculate the climate node association degree between the central target node and each adjacent target climate feature node respectively through the third risk feature association matrix; Calculating the climate node degree of the adjacent target climate feature node using the second associated node edge; The climate node risk correlation degree of the central target node is calculated by combining the climate node correlation degree and the climate node degree; The sum of the geological node risk correlation and the climate node risk correlation is calculated to obtain the node risk correlation of the central target node.

6. The standardized management method for water conservancy projects according to claim 5 is characterized in that: The calculation formula of the geological node risk association is as follows: Where: Represents the i-th central target node V i The risk association degree of the geological node, m represents the central target node V i Adjacent target geological feature nodes The number of nodes, Represents the fusion risk feature association matrix A R The matrix element corresponding to the i-th row and j-th column in , ξ(·,·) represents the correlation calculation function, γ represents the geological weight parameter, L i，j Represents the central target node V i The jth adjacent target geological feature node The number of edges of the first node, Represents the adjacent target geological feature node The degree of said geological node; The calculation formula of the climate node risk correlation is as follows: Where: Represents the i-th central target node V i The climate node risk association degree, t represents the central target node V i Adjacent target climate feature nodes The number of nodes, Represents the third risk feature association matrix A 3 The matrix element corresponding to the i-th row and k-th column in ξ(·,·) represents the correlation calculation function, η represents the climate weight parameter, Represents the kth adjacent target geological feature node The degree of the geological node.

7. The standardized management method for water conservancy projects according to claim 6 is characterized in that: The calculation formula of the node community closeness is as follows: Where: P i Represents the i-th central target node V i The node community closeness, M represents the total number of node edges in the multidimensional data graph network, d(V i ) represents the central target node V i The node degree of .

8. The standardized management method for water conservancy projects according to claim 7 is characterized in that: The calculation formula of the production risk association value is as follows: Wherein: Y represents the production risk association value, n represents the number of nodes of the target outdoor production feature nodes, and δ represents the association value weight parameter.

9. The standardized management method for water conservancy projects according to claim 1 is characterized in that: The indoor production operation data includes indoor production area drawing data and indoor image data of the indoor production area, and the indoor dangerous area is pre-marked in the indoor production area drawing data. The indoor safety risk level of the indoor production area is determined based on the indoor production operation data by combining BIM technology and image recognition technology, and includes the following steps: Generate an indoor engineering BIM model of the indoor production area based on the indoor production area drawing data and using BIM technology; Generate a corresponding model danger zone in the indoor engineering BIM model according to the indoor danger zone pre-marked in the indoor production area drawing data; Counting the area proportion of the model danger zone in the indoor engineering BIM model; Identifying indoor risk points in the indoor image data using image recognition technology, and counting the number of danger sources at the indoor risk points; The indoor safety risk level of the indoor production area is determined based on the area proportion and the number of hazard sources.

10. A standardized management system applied to water conservancy projects, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the standardized management method applied to water conservancy projects as described in any one of claims 1 to 9 is implemented.

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