AI knowledge base construction method and system for smart water conservancy

Abstract

The present application relates to the field of data processing technology, and discloses a method and system for constructing an AI knowledge base for smart water conservancy. The method includes: forming a water conservancy intelligent perception data set by integrating hydrological monitoring data, engineering operation records and expert literature; identifying knowledge buds therefrom and generating water conservancy native knowledge units; extracting key features and decomposing knowledge elements, and constructing a knowledge evolution chain network through association analysis; screening based on historical application effects and expert evaluations to obtain preferred knowledge bodies; intelligently reconstructing scheduling rules, risk prevention experience and operation and maintenance methods to form an innovative knowledge base; and finally intelligently matching with real-time engineering needs to generate a smart water conservancy AI knowledge base. The present application establishes an adaptive knowledge acquisition, organization and application mechanism, so that the knowledge base can autonomously update knowledge content according to environmental changes, optimize the knowledge structure, and improve the level of intelligent operation and management of water conservancy projects.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and system for constructing an AI knowledge base for smart water conservancy. Background Art

[0002] The existing water conservancy knowledge base construction mainly adopts the method of manual experience summary and static knowledge storage. The traditional water conservancy knowledge base establishes a knowledge system including dispatching procedures, operating specifications and technical standards by collecting and organizing hydrological data, engineering cases and expert experience. These knowledge bases generally use relational databases to store structured data and realize knowledge query through keyword retrieval and classification catalogs. In actual applications, staff need to consult relevant procedures and standards according to specific circumstances and manually formulate dispatching plans and operating strategies. At the same time, some water conservancy projects have also established rule-based expert systems to encode dispatching experience into IF-THEN rules to provide decision support for water conservancy project operation and management.

[0003] However, the existing water conservancy knowledge base has problems such as lagging knowledge updates and inefficient knowledge application. First, the traditional knowledge base is mainly based on static storage, which makes it difficult to absorb new engineering experience and expert wisdom in a timely manner. Knowledge updates rely on manual maintenance and are inefficient. Secondly, the knowledge expression method is single, which makes it difficult to fully describe the complex spatiotemporal characteristics and multidimensional constraint relationships of water conservancy projects. Thirdly, the knowledge application process lacks adaptive capabilities, and the preset rules are difficult to cope with the complex and changeable operating environment of water conservancy projects. Finally, there are information islands between knowledge in different fields, and professional knowledge such as scheduling decisions, safety monitoring, and equipment operation and maintenance have not been effectively integrated, affecting the effect of knowledge application. Summary of the invention

[0004] This application provides an AI knowledge base construction method and system for smart water conservancy, which is used to realize the autonomous learning and evolutionary capabilities of the water conservancy knowledge base. By establishing an adaptive knowledge acquisition, organization and application mechanism, the knowledge base can autonomously update the knowledge content according to environmental changes, optimize the knowledge structure, and improve the intelligent operation and management level of water conservancy projects.

[0005] In the first aspect, the present application provides a method for constructing an AI knowledge base for smart water conservancy, which comprises: integrating data through an environmental change feature collector based on hydrological monitoring data, water conservancy project operation records and expert knowledge documents to form a water conservancy intelligent perception data set; according to the water conservancy intelligent perception data set, identifying knowledge germination of water conservancy project cases, hydrological law characteristics and expert experience to generate water conservancy native knowledge units; extracting project scheduling characteristics, flood prevention warning characteristics and equipment maintenance characteristics from the water conservancy native knowledge units, Decompose knowledge elements to obtain water conservancy knowledge characteristics, and encode and reorganize the water conservancy knowledge characteristics through water conservancy knowledge association analysis to construct a water conservancy knowledge evolution chain network; based on the water conservancy knowledge evolution chain network, combine historical engineering application effects and expert evaluation results to perform knowledge screening to obtain a water conservancy preferred knowledge body; based on the water conservancy preferred knowledge body, intelligently reconstruct the hydrological scheduling laws, engineering risk prevention experience and operation and maintenance guidance methods to form a water conservancy innovation knowledge base; intelligently match the water conservancy innovation knowledge base with real-time water conservancy project needs to generate an AI knowledge base for smart water conservancy.

[0006] In a second aspect, the present application provides an AI knowledge base construction system for smart water conservancy, and the AI ​​knowledge base construction system for smart water conservancy includes:

[0007] The integration module is used to integrate the hydrological monitoring data, water conservancy project operation records and expert knowledge documents through the environmental change feature collector to form a water conservancy intelligent perception data set;

[0008] The recognition module is used to identify knowledge buds of water conservancy project cases, hydrological law characteristics and expert experience according to the water conservancy intelligent perception data set, and generate water conservancy native knowledge units;

[0009] A coding module is used to extract engineering scheduling features, flood prevention warning features and equipment maintenance features from the water conservancy native knowledge unit, decompose knowledge elements, obtain water conservancy knowledge features, and encode and reorganize the water conservancy knowledge features through water conservancy knowledge association analysis to construct a water conservancy knowledge evolution chain network;

[0010] A screening module is used to screen the water conservancy knowledge based on the water conservancy knowledge evolution chain network, combined with the historical engineering application effects and expert evaluation results, to obtain the water conservancy optimal knowledge body;

[0011] A reconstruction module is used to intelligently reconstruct the hydrological regulation rules, engineering risk prevention experience and operation and maintenance guidance methods based on the water conservancy optimization knowledge body to form a water conservancy innovation knowledge base;

[0012] The matching module is used to intelligently match the water conservancy innovation knowledge base with real-time water conservancy project needs to generate an AI knowledge base for smart water conservancy.

[0013] In the technical solution provided in this application, the hydrological monitoring data, water conservancy project operation records and expert knowledge documents are integrated through the environmental change feature collector to form a water conservancy intelligent perception data set, which solves the problem of single data source and delayed update of the traditional water conservancy knowledge base; the knowledge budding recognition technology is used to analyze water conservancy project cases, hydrological law characteristics and expert experience to generate water conservancy native knowledge units, realizing the automatic extraction and standardized expression of knowledge; the knowledge characteristics are encoded and reorganized through water conservancy knowledge association analysis, and a water conservancy knowledge evolution chain network is constructed to establish the association relationship between knowledge units, thereby enhancing the systematicness and integrity of knowledge; knowledge screening is carried out based on historical engineering application effects and expert evaluation results to obtain water conservancy preferred knowledge bodies and ensure knowledge quality; the hydrological scheduling laws, engineering risk prevention experience and operation and maintenance guidance methods are intelligently reconstructed to form a water conservancy innovation knowledge base, which improves the practicality and innovation of knowledge; the water conservancy innovation knowledge base is combined with real-time water conservancy project needs through intelligent matching technology to generate an AI knowledge base for smart water conservancy, thereby realizing precise knowledge services. In specific applications, the knowledge bud recognition algorithm based on deep learning of this method significantly improves the accuracy of knowledge extraction, the knowledge association analysis algorithm based on graph neural network enhances the rationality of knowledge reorganization, the knowledge optimization mechanism based on reinforcement learning improves the scientific nature of knowledge screening, the knowledge reconstruction algorithm based on transfer learning enhances the knowledge innovation ability, and the intelligent matching algorithm based on attention mechanism improves the accuracy of knowledge services. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 This is a schematic diagram of an embodiment of a method for constructing an AI knowledge base for smart water conservancy in an embodiment of the present application;

[0016] Figure 2 Schematic diagram of the knowledge grouping system in the water conservancy project scheduling scenario in the embodiment of this application

[0017] Figure 3 A schematic diagram of a complete knowledge system for water conservancy project risk prevention and treatment is constructed in the embodiment of this application;

[0018] Figure 4 A schematic diagram of a systematic knowledge architecture for water conservancy project operation and maintenance management is described in the embodiments of this application;

[0019] Figure 5 This is a schematic diagram of an embodiment of an AI knowledge base construction system for smart water conservancy in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide a method and system for building an AI knowledge base for smart water conservancy. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the method for constructing an AI knowledge base for smart water conservancy includes:

[0022] Step S101: Based on the hydrological monitoring data, water conservancy project operation records and expert knowledge documents, the environmental change feature collector integrates the data to form a water conservancy intelligent perception data set;

[0023] Step S102: According to the water conservancy intelligent perception data set, knowledge budding is identified for water conservancy project cases, hydrological law characteristics and expert experience to generate water conservancy native knowledge units;

[0024] Step S103, extracting engineering scheduling features, flood prevention warning features and equipment maintenance features from the water conservancy native knowledge unit, decomposing the knowledge elements, obtaining water conservancy knowledge features, encoding and reorganizing the water conservancy knowledge features through water conservancy knowledge association analysis, and constructing a water conservancy knowledge evolution chain network;

[0025] Step S104: Based on the water conservancy knowledge evolution chain network, combined with the historical engineering application effects and expert evaluation results, knowledge screening is performed to obtain the water conservancy optimal knowledge body;

[0026] Step S105: Based on the water conservancy optimization knowledge body, intelligently reconstruct the hydrological regulation law, engineering risk prevention experience and operation and maintenance guidance method to form a water conservancy innovation knowledge base;

[0027] Step S106: Intelligently match the water conservancy innovation knowledge base with real-time water conservancy project needs to generate an AI knowledge base for smart water conservancy.

[0028] It is understandable that the execution subject of this application can be an AI knowledge base construction system for smart water conservancy, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, in the field of water conservancy, when the environmental change feature collector processes hydrological monitoring data, it mainly focuses on the collection of basic hydrological elements such as water level, flow, and rainfall. For the operation records of water conservancy projects, the focus is on extracting engineering parameters such as reservoir scheduling, gate opening and closing, and equipment operation status. For expert knowledge documents, the focus is on the structured extraction of professional knowledge such as flood prevention and rescue cases and engineering maintenance experience. These data are processed through spatiotemporal alignment to form a water conservancy intelligent perception data set, which contains related information in the three dimensions of hydrology, engineering, and management.

[0030] In the knowledge budding identification stage, the focus of water conservancy project case processing is on the feature extraction of engineering construction records, operation status data and fault handling records. The hydrological law characteristics focus on the time series analysis of water level change relationship, flow cycle characteristics and rainfall distribution characteristics. The processing of expert experience focuses on the semantic analysis of flood control scheduling schemes, emergency response methods and emergency plans. Through scene mapping and association fusion, this information is organized into water conservancy native knowledge units. In the knowledge association analysis stage, the water conservancy native knowledge units are feature decomposed to extract engineering scheduling features, flood control warning features and equipment maintenance features. Then semantic annotation is performed and structured organization is carried out according to the hydrological scheduling sequence, engineering operation and maintenance sequence and emergency response sequence. Through the dynamic linking of nodes, the temporal relationship, spatial relationship and causal relationship are established to form a knowledge evolution path. Based on the node association strength, a two-way propagation analysis is performed, and the upstream and downstream relationships of the basin and the linkage relationship of water conservancy projects are combined to construct a water conservancy knowledge evolution chain network. In the knowledge screening link, the engineering scheduling sequence is extracted from the reservoir joint scheduling record, the dam group control strategy and the cascade power station operation plan. The effectiveness of water resource allocation, flood prevention and disaster reduction, and power generation efficiency indicators are classified and evaluated to generate engineering efficiency evaluation data. The expert evaluation results are converted into quantitative indicators for scheduling scheme scores, engineering operation suggestions, and risk warning opinions. Through correlation analysis, a comprehensive evaluation is conducted on the scheduling effect of the reservoir group, the benefits of cascade power stations, and the degree of ecological water demand guarantee, and a water conservancy optimization knowledge body is screened and formed.

[0031] The knowledge reconstruction stage focuses on the hydrological regulation rules, engineering risk prevention experience and operation and maintenance guidance methods. Reservoir regulation schemes, flood control plans and equipment maintenance plans are extracted from the water conservancy optimization knowledge body to perform parameter analysis and scenario matching. Through cross-domain integration, the regulation knowledge, risk prevention knowledge and operation and maintenance knowledge are organized in a coordinated manner to form a water conservancy innovation knowledge base.

[0032] In the final intelligent matching stage, the knowledge of reservoir group dispatching rules, flood warning parameters and engineering maintenance indicators in the water conservancy innovation knowledge base is first parsed. Combined with the hydrological forecast information, reservoir operation status and equipment monitoring parameters in the real-time water conservancy project needs, spatiotemporal organization and service configuration are carried out. By integrating the time-based knowledge response library and the regional knowledge response chain, an intelligent service response network is constructed to generate an AI knowledge service system for smart water conservancy.

[0033] The data processing process is explained in detail using a large reservoir group as a specific case: the reservoir group consists of three large reservoirs (upstream reservoir, midstream reservoir and downstream reservoir), and is equipped with 5 hydrological monitoring stations (located at the upstream inlet section, upstream outlet section, midstream inlet section, midstream outlet section and downstream control section respectively). Each monitoring station collects water level and flow data once an hour. In the original data, the water level is measured in meters, accurate to centimeters; the flow is measured in cubic meters per second, accurate to two decimal places. Due to equipment differences, the sampling time of the upstream station is at the hour, while the downstream station is 30 minutes after the hour. Standardization processing unifies all data into the hourly time series, and linear interpolation is used to fill in the non-integer data. The data is then processed for outliers. For example, if a negative value is found in the flow data of the upstream inlet section at a certain moment, which obviously violates the physical law, it is marked as an anomaly and corrected by the average value of the upper and lower moments.

[0034] The timing alignment of the operation records of water conservancy projects involves the dispatching process of three reservoirs. The daily dispatching records of the upstream reservoir contain water level, inflow, and outflow data for 24 time periods; the midstream reservoir records the gate opening change process, and each adjustment records the specific time and opening value; the downstream reservoir focuses on recording the start and stop time and output changes of the generator set. The timing alignment process unifies these discrete records into a time series on an hourly scale. For example, when it is found that the midstream reservoir has adjusted the gate at 9:30, the outflow change corresponding to this dispatching action is allocated to the two time periods of 9 and 10 to keep the total amount conserved.

[0035] The structured analysis of expert knowledge documents processed nearly 100 historical cases. This includes a typical flood control dispatch case during the flood season of 2020: the upstream reservoir encountered a rapid increase in inflow, which rose from 1,000 cubic meters per second to 3,500 cubic meters per second within 3 hours. At that time, the on-duty dispatcher emptied the flood control storage capacity in advance according to the forecast, and adopted a dispatching plan to increase the downstream flow in steps, successfully staggering the flood peak. Through structured analysis, key elements such as inflow threshold, warning time, and dispatching steps were extracted and converted into regularized knowledge units. The spatiotemporal correlation analysis focused on the joint dispatching characteristics of the reservoir group. Through comparative analysis, it was found that the dispatching action of the upstream reservoir would affect the inflow of the midstream reservoir about 2 hours later, and the water flow propagation time from the midstream to the downstream was about 1.5 hours. According to this spatiotemporal relationship, the dispatching sequences of the three reservoirs were aligned in time displacement to construct the joint dispatching response characteristics of the reservoir group.

[0036] The deep integration stage combines the project operation characteristics with expert experience. For each typical dispatching scenario, the hydrological conditions (such as the inflow process, the reservoir water level status), the project response (such as the gate opening adjustment sequence, the outflow change) and the expert decision-making points (such as the early warning judgment criteria, the step-by-step dispatching strategy) are extracted to form a knowledge feature spectrum.

[0037] In the embodiment of the present application, the hydrological monitoring data, water conservancy project operation records and expert knowledge documents are integrated through the environmental change feature collector to form a water conservancy intelligent perception data set, which solves the problem of single data source and delayed update of the traditional water conservancy knowledge base; the knowledge budding recognition technology is used to analyze the water conservancy project cases, hydrological law characteristics and expert experience to generate water conservancy native knowledge units, realizing the automatic extraction and standardized expression of knowledge; the knowledge characteristics are encoded and reorganized through water conservancy knowledge association analysis, and a water conservancy knowledge evolution chain network is constructed to establish the association relationship between knowledge units, thereby enhancing the systematicness and integrity of knowledge; knowledge screening is performed based on historical engineering application effects and expert evaluation results to obtain water conservancy preferred knowledge bodies and ensure knowledge quality; the hydrological scheduling laws, engineering risk prevention experience and operation and maintenance guidance methods are intelligently reconstructed to form a water conservancy innovation knowledge base, which improves the practicality and innovation of knowledge; the water conservancy innovation knowledge base is combined with real-time water conservancy project needs through intelligent matching technology to generate an AI knowledge base for smart water conservancy, thereby realizing precise knowledge services. In specific applications, the knowledge bud recognition algorithm based on deep learning of this method significantly improves the accuracy of knowledge extraction, the knowledge association analysis algorithm based on graph neural network enhances the rationality of knowledge reorganization, the knowledge optimization mechanism based on reinforcement learning improves the scientific nature of knowledge screening, the knowledge reconstruction algorithm based on transfer learning enhances the knowledge innovation ability, and the intelligent matching algorithm based on attention mechanism improves the accuracy of knowledge services.

[0038] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0039] (1) Collect water level data, flow data, and rainfall data from hydrological monitoring stations, and standardize the hydrological monitoring data through the data acquisition interface to obtain standardized hydrological data;

[0040] (2) Extract reservoir dispatching records, gate opening and closing records, and equipment operation status records from the water conservancy project management system, perform time-series alignment on the water conservancy project operation records, and obtain time-series project data;

[0041] (3) Retrieve flood prevention and rescue cases, engineering maintenance experience, and expert decision-making records from the water conservancy knowledge base, conduct structured analysis of expert knowledge documents, and obtain structured expert data;

[0042] (4) Temporally and spatially correlate the standardized hydrological data with the time-series engineering data to generate a water conservancy project operation feature sequence;

[0043] (5) Deeply integrate the water conservancy project operation feature sequence with structured expert data to obtain the knowledge characteristics of water conservancy projects;

[0044] (6) Identify environmental change patterns based on the knowledge characteristics of water conservancy projects and generate a water conservancy intelligent perception dataset.

[0045] Specifically, the data from the hydrological monitoring stations are standardized through the data acquisition interface to establish the hydrological state vector:

[0046] ;

[0047] in is the normalized water level, dimensionless; is the measured water level (m); and They are the lowest and highest water levels in history respectively (m). is the normalized flow rate, dimensionless; is the measured flow rate (m³ / s); and They are the historical minimum and maximum flow rates (m³ / s) respectively. is the standardized rainfall, dimensionless; is the measured rainfall (mm). For example, the measured water level of a reservoir is 86.5m (range 82-90m), the flow is 2500m³ / s (range 500-5000m³ / s), and the rainfall is 45mm / h (range 0-100mm / h). After standardization, the vector [0.56, 0.44, 0.45] is obtained.

[0048] Based on standardized hydrological data, the project operation records are aligned in time series to construct the project state vector:

[0049] ;

[0050] in For reservoir operation records, associated; To record the gate opening; This is the equipment operation status record. When it reached 0.56, the record showed that the gate opening was 40%, the discharge flow was 2000m³ / s, and the unit was operating normally.

[0051] The expert experience extracted from the knowledge base is adopted as vector express:

[0052] ;

[0053] in is the knowledge mapping function; is the flood prevention case vector; To maintain the experience vector; Record vectors for decisions; and is the weight coefficient. For example, when When the water level exceeds the warning level, the system automatically extracts similar flood prevention cases and weights them. Increased to 0.8.

[0054] The spatiotemporal correlation analysis uses the state transition matrix:

[0055] ;

[0056] Matrix Elements Indicates the correlation coefficient between hydrological elements and engineering parameters. =0.85 indicates that the water level is highly correlated with the gate opening.

[0057] Knowledge feature fusion uses deep attention network:

[0058] ;

[0059] Project Status With expert knowledge For example, the matching degree between the current working condition and the historical case is 0.92, indicating a high degree of similarity.

[0060] Environmental change recognition using deep learning models:

[0061] ;

[0062] Based on fusion features Determine the state of the environment, , is the weight matrix; , is the bias vector; is the activation function.

[0063] In the flood control operation of cascade reservoirs, the monitoring data is obtained after standardized processing. =[0.56, 0.44, 0.45], the engineering status record shows that the current gate opening is 40% and the discharge flow is 2000m³ / s. The system automatically matches similar cases and generates control suggestions: 1) Immediately adjust the opening of Gate 2 to 65% and increase the discharge flow to 2800m³ / s; 2) Notify the downstream reservoir to reserve 300 million m³ of flood control storage capacity; 3) Monitor water level changes every 5 minutes. If the decline rate is less than 0.2m / h, start the joint dispatch of Gate 3 and set the opening to 50%; 4) Coordinate the cascade power stations to adjust the output according to the 500MW-400MW-300MW gear to create conditions for gate dispatch; 5) The patrol team along the downstream river is deployed at intervals of 500 meters to monitor the safety of the embankment in real time.

[0064] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0065] (1) Extract construction records, operation status data, and fault handling records from water conservancy project cases, and obtain water conservancy project feature data through feature association processing;

[0066] (2) Extract the water level change relationship, flow cycle characteristics and rainfall distribution characteristics from the hydrological law characteristics, conduct time series analysis on the extraction results, and form hydrological evolution law data;

[0067] (3) Perform semantic analysis on flood control dispatching plans, emergency response measures and contingency plans in expert experience to generate expert decision-making feature data;

[0068] (4) Mapping the water conservancy project characteristic data with the hydrological evolution law data to obtain water conservancy project knowledge elements;

[0069] (5) Associating and integrating the water conservancy engineering knowledge elements and expert decision-making feature data to form a water conservancy knowledge feature vector;

[0070] (6) Construct knowledge concepts based on the water conservancy knowledge feature vectors and generate water conservancy native knowledge units.

[0071] Specifically, feature extraction and correlation analysis are performed on water conservancy project case data. By analyzing the project construction records, operation status data and fault handling records, the project feature vector is established:

[0072] ;

[0073] in It is the characteristic of engineering construction record, dimensionless; is the construction parameters (volume of earth and stone, etc.); is the operating status characteristic; is the operating parameters (equipment efficiency, etc.); is the fault characteristic; is the fault parameter (fault frequency, etc.). is the weight coefficient of the i-th construction parameter; is the weight coefficient of the jth operating parameter; is the weight coefficient of the mth fault parameter; k is the total number of construction parameters; l is the total number of operation parameters; p is the total number of fault parameters.

[0074] For example, the specific calculation process of a hydropower station: the engineering earthwork volume is 1 million m³ (weight 0.4), and the concrete volume is 500,000 m³ (weight 0.6). Then the construction characteristic value is ; Equipment operating efficiency is 90% (weight 0.5) and 85% (weight 0.5), then the operating characteristic value (90 0.5 + 85 0.5) / 100 = 0.875; the number of annual failures is 12 times (weight 0.3) and 8 times (weight 0.7), then the fault characteristic value (12 0.3 + 8 0.7) / 20 =0.46, and the engineering feature vector is [0.47, 0.875, 0.46].

[0075] Extract data from hydrological law characteristics and perform time series analysis to construct hydrological law vectors :

[0076] ;

[0077] in is the water level at time t (m); is the flow rate at time t (m³ / s); is the rainfall at time t (mm).

[0078] is the characteristic value of water level, in m; is the characteristic value of flow, the unit is m³ / s; is the characteristic value of rainfall, in mm; is the weight coefficient of the water level data at time t; is the weight coefficient of the flow data at time t; is the weight coefficient of rainfall data at time t; T is the total length of the time series (number of observation periods).

[0079] For example, the monthly data of a certain river basin is calculated as follows: water level 145m (weight 0.3), 150m (weight 0.4), 148m (weight 0.3), and the water level characteristic value is 147.9m; flow rate 2000m³ / s (weight 0.25), 2500m³ / s (weight 0.5), 1800m³ / s (weight 0.25), and the flow characteristic value is 2200m³ / s; rainfall 80mm (weight 0.2), 120mm (weight 0.5), 60mm (weight 0.3), and the rainfall characteristic value is 94mm, forming a hydrological law vector [147.9, 2200, 94].

[0080] Semantic analysis is performed on flood control dispatching schemes, emergency response measures and emergency plans in expert experience, and hierarchical weight calculation is adopted:

[0081] ;

[0082] in The weight of the flood control plan; The weight of emergency measures; is the weight of the plan elements. is the adjustment coefficient of the ith flood control scheme, which is used to fine-tune the weight; n is the total number of flood control schemes; is the adjustment coefficient of the jth emergency measure, which is used to fine-tune the weight; m is the total number of emergency measures; is the adjustment coefficient of the kth plan element, which is used to fine-tune the weight; l is the total number of plan elements.

[0083] For example, the expert experience is "When the water level exceeds 145m, the No. 2 flood discharge gate must be opened immediately to 45%, and the downstream coastal units must be notified. At the same time, the water level changes must be monitored every 10 minutes. If the water level continues to rise within 15 minutes, the No. 3 gate must be opened to 40%." The specific calculation process is: scheduling weight = (clear water level threshold × 0.4 + specific gate control × 0.5) = 0.9; emergency weight = (downstream notification mechanism × 0.3 + monitoring response measures × 0.4) = 0.7; plan weight = (tiered response mechanism × 0.4 + execution process specifications × 0.5) = 0.9, and the expert experience vector [0.9, 0.7, 0.9] is obtained.

[0084] The scene mapping of water conservancy project characteristic data and hydrological evolution law data adopts the association matrix:

[0085] ;

[0086] The correlation matrix between engineering and hydrology is obtained, and each element represents the correlation strength of the corresponding feature. For example, the first row and the first column =69.513 represents the correlation between construction characteristics and water level changes, and the larger the value, the stronger the correlation.

[0087] The final water conservancy knowledge feature vector is obtained through weighted fusion:

[0088] ;

[0089] in is the balance factor (take 0.6). Substitute the above case into the calculation: = 0.6×[69.513, 1034, 44.18] + 0.4×[0.9, 0.7, 0.9], and the final knowledge feature vector is [41.89, 620.82, 26.87]. Based on this feature vector, the system generates specific control suggestions: 1) The current water level is close to the warning value, and it is recommended to adjust the opening of Gate No. 2 to 45%, and increase the discharge flow to 2500m³ / s; 2) When the water level drops to 143m, the gate opening can be adjusted to 30%; 3) Continuously monitor the upstream water situation and record the water level changes every 10 minutes; 4) If any abnormality is found, promptly start the joint dispatching plan of Gate No. 3; 5) Notify the relevant units downstream to strengthen inspections to ensure the smooth flow of flood discharge.

[0090] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0091] (1) Contextual semantic annotation of water conservancy knowledge features, structural organization according to the hydrological dispatch sequence, engineering operation and maintenance sequence, and emergency response sequence, to form a water conservancy knowledge semantic map;

[0092] (2) Dynamically link the nodes in the water conservancy knowledge semantic graph according to temporal relationships, spatial relationships, and causal relationships to generate the evolution path of water conservancy knowledge;

[0093] (3) Based on the node association strength in the water conservancy knowledge evolution path, a two-way propagation analysis of knowledge links is performed to obtain the water conservancy knowledge transmission characteristics;

[0094] (4) Based on the characteristics of water conservancy knowledge transmission, combined with the upstream and downstream relationships of the basin and the linkage relationship of water conservancy projects, knowledge reconstruction is carried out to form a water conservancy knowledge association network;

[0095] (5) The water conservancy knowledge association network is grouped according to engineering scheduling scenarios, risk prevention and disposal scenarios, and operation and maintenance management scenarios to construct a water conservancy knowledge evolution chain network.

[0096] Specifically, the dynamic evolution of knowledge is achieved through the construction of the water conservancy knowledge evolution chain network. Features are extracted from the water conservancy native knowledge units, and a deep knowledge decomposition algorithm is used. The algorithm is expressed as:

[0097] ;

[0098] in, Indicates the characteristics of water conservancy knowledge, represents the engineering scheduling feature matrix, represents the flood warning feature matrix, represents the equipment maintenance feature matrix, Represent the weight coefficients of the three types of features, and n represents the feature dimension. The algorithm decomposes complex knowledge units into basic feature elements through feature decomposition and weight allocation. When performing contextual semantic annotation of water conservancy knowledge features, a semantic annotation method based on deep learning is adopted to perform structured processing on hydrological scheduling sequence, engineering operation and maintenance sequence, and emergency response sequence respectively. The annotation process includes three steps: feature vector construction, semantic category discrimination, and relationship extraction. Feature vector construction is based on the word vector model, which converts text features into numerical representations; semantic category discrimination classifies features through deep neural networks; relationship extraction uses dependency syntactic analysis to determine the semantic relationship between entities.

[0099] In the construction of the semantic graph of water conservancy knowledge, the dynamic links between nodes are realized through multidimensional relationship modeling, and the relationship strength calculation formula is:

[0100] ;

[0101] in, Indicates the strength of the relationship between nodes. Indicates timing correlation, represents the spatial distance function, represents the degree of causal relationship, is the weight coefficient of each dimension. The comprehensive correlation strength between nodes is calculated through this formula to form a dynamic evolution path.

[0102] In the bidirectional propagation analysis of knowledge links, an improved graph neural network algorithm is used to calculate the knowledge transfer characteristics:

[0103] ;

[0104] in, represents the knowledge transfer feature, represents the hidden layer state matrix, represents the node feature matrix, represents the message passing matrix, Represents the number of propagation layers. The algorithm iteratively updates the node status to achieve bidirectional propagation of knowledge in the network.

[0105] When reconstructing knowledge based on the upstream and downstream relationships of the river basin and the linkage relationships of water conservancy projects, knowledge graph fusion technology is used to integrate knowledge from different sources.

[0106] like Figure 2 As shown in the figure, the knowledge grouping system under the water conservancy project scheduling scenario is demonstrated, which mainly includes three core sub-scenarios: flood control scheduling, water supply scheduling and power generation scheduling. In terms of flood control scheduling, the focus is on flood control limit water level control, flood discharge flow regulation and joint scheduling of cascade reservoir groups; water supply scheduling focuses on ecological downstream flow guarantee, reasonable allocation of water demand and overall balance of water scheduling; power generation scheduling focuses on precise control of power generation flow, optimization of unit operation efficiency and dynamic regulation of power grid load. This grouping structure effectively supports the scientific scheduling decision-making of water conservancy projects.

[0107] like Figure 3 As shown in the figure, a complete knowledge system for water conservancy project risk prevention and mitigation has been constructed, which consists of three parts: early warning monitoring, emergency response and post-disaster assessment. The early warning monitoring section covers real-time monitoring elements such as water situation monitoring, construction situation monitoring and risk warning; the emergency response section includes rapid response mechanisms such as emergency plan activation, rescue team dispatch and material and equipment deployment; the post-disaster assessment section integrates systematic assessment contents such as disaster statistics, loss assessment and recovery plan. This grouping framework provides knowledge support for the whole process of risk prevention and mitigation of water conservancy projects.

[0108] like Figure 4 As shown in the figure, the systematic knowledge structure of water conservancy project operation and maintenance management is explained, which is divided into three major sections: equipment maintenance, engineering maintenance and resource allocation. The equipment maintenance module focuses on daily maintenance work such as equipment status monitoring, preventive maintenance and fault diagnosis and processing; the engineering maintenance section integrates standardized maintenance processes such as regular inspections, maintenance plan formulation and quality control; and the resource allocation link systematically manages core resource elements such as personnel, materials and equipment. This grouping structure ensures the safe and stable operation of water conservancy projects.

[0109] Specifically, the fusion process includes three steps: entity alignment, relationship mapping, and conflict resolution. Entity alignment calculates similarity through entity name, attribute, and context information; relationship mapping establishes relationship correspondence based on a predefined ontology model; conflict resolution uses rule reasoning methods to resolve contradictions in knowledge merging. When grouping the water conservancy knowledge association network according to different scenarios, a clustering method based on scenario semantics is adopted. The clustering process takes into account node attributes, relationship types, and spatiotemporal characteristics, and automatically groups nodes by calculating the semantic similarity between them.

[0110] For example, in the knowledge evolution of joint dispatching of a reservoir group, dispatching rules, early warning indicators, equipment status and other features are extracted from the original knowledge units. For example, the dispatching features of a reservoir include parameters such as flood control limit water level, ecological discharge flow, and power generation flow, which are converted into standardized feature vectors through feature decomposition algorithm. Then, these features are semantically annotated, and concepts such as "flood season dispatching" and "dry season dispatching" are associated with corresponding data sequences. When constructing the semantic graph, a knowledge link network is formed by calculating the temporal correlation between water level changes and gate openings, the spatial correlation between upstream and downstream reservoirs, and the causal relationship between dispatching operations and water regime changes. For example, the increase in upstream water inflow of a reservoir triggers a rise in water level, resulting in the pre-emption of storage capacity in the downstream reservoir. This causal relationship is expressed through dynamic links between nodes. Through two-way propagation analysis, the impact transmission process of different dispatching strategies in the reservoir group is calculated, and key control nodes and transmission paths are identified. In the knowledge reconstruction link, the dispatching rules of a single reservoir are integrated with the flood control dispatching requirements of the basin to form a coordinated dispatching strategy network. The knowledge network is divided into different scenarios such as flood control scheduling, water supply scheduling, and power generation scheduling to support intelligent scheduling decisions.

[0111] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0112] (1) Extract reservoir joint dispatching records, sluice dam group control strategies, and cascade power station operation plans from the water conservancy knowledge evolution chain network, classify and organize them according to historical application scenarios, and obtain the water conservancy project dispatching sequence;

[0113] (2) Classify and summarize the water resources allocation results, flood control and disaster reduction effects, and power generation benefit indicators in the historical engineering application effects to generate engineering benefit evaluation data;

[0114] (3) Quantify the dispatching scheme scores, engineering operation suggestions and risk warning opinions in the expert evaluation results to form an expert evaluation index table;

[0115] (4) Based on the water conservancy project scheduling sequence, combined with the specific scenario characteristics of dry season scheduling, flood control period scheduling and daily operation scheduling, generate a scheduling knowledge scoring table;

[0116] (5) Based on the project benefit evaluation data and expert evaluation index table, the correlation analysis is conducted on the reservoir group scheduling effect, cascade power station benefits and ecological water demand guarantee degree to obtain the knowledge application evaluation results;

[0117] (6) Based on the dispatching knowledge scoring table and knowledge application evaluation results, the knowledge units are selected and optimized to obtain the water conservancy optimal knowledge body.

[0118] Specifically, feature data are extracted from the water conservancy knowledge evolution chain network, including reservoir joint dispatching records, sluice dam group control strategies and cascade power station operation plans. Reservoir joint dispatching records include reservoir group dispatching plans, flood discharge rules, flood control dispatching processes, etc.; sluice dam group control strategies include gate opening control, discharge flow regulation and water level control, etc.; cascade power station operation plans include unit start-stop rules, power generation flow distribution and output optimization, etc. Through data cleaning and standardization processing, these historical data are classified and sorted according to application scenarios to generate standardized water conservancy project dispatching sequences. In the analysis of historical project application effects, the effectiveness of water resource allocation, flood control and disaster reduction effects and power generation benefit indicators are analyzed in depth. The effectiveness of water resource allocation mainly considers indicators such as water supply guarantee rate, water use efficiency, and ecological flow compliance rate; the flood control and disaster reduction effect mainly focuses on flood peak reduction rate, flood control reservoir capacity utilization rate, downstream safety guarantee degree, etc.; power generation benefit indicators include power generation, unit utilization rate, peak regulation capacity, etc. Through data statistics and analysis, a complete engineering benefit evaluation data set is formed.

[0119] When standardizing the expert evaluation results, it is necessary to convert qualitative evaluation into quantitative indicators. The scheduling scheme is scored using a multi-level scoring system, including technical feasibility, economic rationality, safety and reliability, etc.; engineering operation suggestions are extracted through semantic analysis of key control parameters and operation points; risk warning opinions are quantified according to risk level, impact scope and disposal measures. Through multi-dimensional quantification, a structured expert evaluation indicator table is formed.

[0120] When generating the scheduling knowledge scoring table, a multidimensional scoring model is used:

[0121] ;

[0122] in, represents the scheduling knowledge score, represents the dry season scheduling effect matrix, represents the flood control period dispatch effect matrix, Represents the daily operation effect matrix, Represent the weight coefficients of the three scenarios respectively. The model generates standardized scoring results by comprehensively evaluating the performance in different scheduling scenarios.

[0123] When constructing the water conservancy optimization knowledge body, the knowledge optimization evaluation model is adopted:

[0124] ;

[0125] in, represents the knowledge optimization score, represents the scheduling knowledge scoring matrix, represents the application evaluation matrix, represents the evaluation weight, and m represents the evaluation dimension. The model achieves optimal selection of knowledge by integrating scheduling knowledge scoring and application evaluation results.

[0126] For example, in the knowledge optimization of joint dispatching of a group of reservoirs in a certain river basin, dispatching records for many years are collected, including flood season dispatching schemes, ecological dispatching rules, power generation dispatching strategies, etc. These data are classified according to dispatching objectives and application scenarios, such as flood control dispatching, water supply dispatching, power generation dispatching, etc. Then, historical application effect data are collected, and various benefit indicators are calculated by analyzing the actual operation data of the reservoir. For example, the contribution of reservoir dispatching to downstream flood control is analyzed, and the flood peak reduction effect is calculated by comparing the natural water inflow and the actual downstream flow; the water supply guarantee is analyzed, and the water supply reliability is evaluated by comparing the actual water supply with the planned water supply; the power generation benefit is analyzed, and the power generation dispatching effect is evaluated by calculating the group utilization hours and power generation indicators. At the same time, expert evaluation opinions are collected, and the qualitative evaluation of experts is converted into quantitative indicators. For example, the feasibility evaluation of the flood control dispatching scheme is converted into a scoring indicator, and the control parameters in the operation suggestion are extracted as specific thresholds. In the actual evaluation process, scores are given for different scenario characteristics. For example, in the dry season, the focus is on evaluating ecological flow guarantee and water supply reliability, in the flood season, the focus is on evaluating flood control reservoir capacity utilization and flood control capabilities, and during normal operation, the focus is on evaluating power generation benefits and water resource utilization efficiency. Through comprehensive scoring and screening, the optimal scheduling knowledge is selected as the core content of the knowledge base.

[0127] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0128] (1) Extract reservoir dispatching schemes, flood control plans, and equipment maintenance plans from the water conservancy optimization knowledge body, perform parameter analysis according to project characteristics, and generate a water conservancy basic knowledge table;

[0129] (2) Select the low-water season scheduling strategy, the flood control period joint scheduling scheme and the ecological scheduling criteria from the hydrological scheduling law, perform data reorganization and processing, and obtain the water conservancy scheduling knowledge set;

[0130] (3) Match the risk warning indicators, emergency response procedures and rescue decision-making points in the engineering risk prevention experience to form a risk prevention knowledge system;

[0131] (4) Conduct correlation analysis on the equipment operating parameters, fault handling solutions and maintenance requirements in the operation and maintenance guidance method to obtain the engineering operation and maintenance knowledge chain;

[0132] (5) Cross-domain integration of water conservancy dispatching knowledge set, disaster prevention knowledge system and engineering operation and maintenance knowledge chain to generate a comprehensive water conservancy knowledge network;

[0133] (6) Based on the comprehensive water conservancy knowledge network, hydrology, engineering and management knowledge are collaboratively organized to form a water conservancy innovation knowledge base.

[0134] Specifically, the formation process of the water conservancy innovation knowledge base in the AI ​​knowledge base construction method for smart water conservancy begins with knowledge extraction from the water conservancy preferred knowledge body. Reservoir scheduling plans, flood control plans and equipment maintenance plans are extracted from the water conservancy preferred knowledge body. The text content is analyzed by a knowledge extraction algorithm, in which the knowledge extraction algorithm adopts a sequence annotation model based on BiLSTM-CRF (bidirectional long short-term memory-conditional random field), which identifies key entities and relationships in the text by bidirectional scanning of the text sequence. For the reservoir scheduling plan, focus on extracting parameters such as scheduling period, control water level, and discharge flow; for the flood control plan, extract elements such as warning water level, warning flow, and response level; for the equipment maintenance plan, extract maintenance cycle, inspection items, maintenance requirements and other information. The extracted parameters are classified and sorted according to the characteristics of water conservancy projects to generate a structured basic knowledge table of water conservancy.

[0135] In the data processing of hydrological regulation law, a time series pattern mining algorithm is used. This algorithm is based on the principle of dynamic time warping (DTW) and discovers regular characteristics by calculating the similarity between different time series. For the low-water season regulation strategy, historical water inflow, water demand and reservoir operation data are analyzed to identify the characteristic periods of the dry season and the corresponding regulation mode; for the joint regulation scheme during the flood control period, flood control regulation decision rules are extracted based on flood process analysis and cascade reservoir group joint regulation experience; the ecological regulation criteria form ecologically friendly regulation constraints by analyzing the downstream ecological water demand process and water quality requirements. Through data reorganization processing, these regulation laws and decision rules are systematically organized to form a complete set of water conservancy regulation knowledge. In the process of processing engineering risk prevention experience, a scene matching method based on knowledge graph is adopted. A risk prevention knowledge graph is constructed, and the risk warning indicators, emergency response processes and rescue decision points are represented as nodes in the graph. The relationship between nodes represents the logical association between the elements. Knowledge matching in different scenarios is achieved through graph reasoning algorithms. The risk warning indicators include the warning thresholds of monitoring data such as water level, flow, and rainfall. The emergency response process describes the standard response steps for various types of risks. The key points of emergency decision-making summarize the key judgment basis and response principles. These risk prevention experience elements are organized in scenarios according to different disaster types and risk levels to build a systematic risk prevention knowledge system.

[0136] The association analysis of the engineering operation and maintenance guidance method adopts multi-dimensional association rule mining technology. Multi-level analysis is performed on data such as equipment operating parameters, fault handling solutions and maintenance requirements. Equipment operating parameters include operating status indicators of various types of electromechanical equipment, fault handling solutions record the diagnosis methods and treatment measures of equipment abnormalities, and maintenance requirements stipulate the cycle and standards of daily maintenance of equipment. Through association rule mining, the association between equipment status, fault type and maintenance measures is discovered to form a complete engineering operation and maintenance knowledge chain. The cross-domain fusion process of water conservancy scheduling knowledge set, risk prevention knowledge system and engineering operation and maintenance knowledge chain adopts knowledge graph fusion technology. The semantic association between knowledge in different fields is established through ontology mapping, and then the entity alignment technology is used to identify entities describing the same object in different knowledge bases. Finally, the association between knowledge is expanded through relational reasoning. The integrated water conservancy comprehensive knowledge network realizes the interconnection and interoperability of knowledge in multiple fields such as scheduling, risk prevention, and operation and maintenance.

[0137] In the final collaborative organization stage, a knowledge reasoning method based on graph neural network is used to organize hydrology-engineering-management knowledge at multiple levels. The deep associations between knowledge nodes are captured through graph convolutional networks, which enables collaborative expression and reasoning of cross-domain knowledge and forms a structured water conservancy innovation knowledge base.

[0138] Taking the operation process of water conservancy projects as an example, the specific data obtained by the above method include: the water level is controlled below the flood control limit water level during daily reservoir scheduling, and flood control storage capacity is actively reserved during the flood season; the operating parameters of the unit equipment are operating status data such as rated speed, vibration value, and bearing temperature; the hazard warning adopts a graded warning mechanism based on rainfall intensity and water level increase; and the equipment maintenance stipulates different levels of maintenance requirements such as daily inspection, periodic test, and annual maintenance. After multi-dimensional correlation analysis, it is found that there is a significant correlation between abnormal equipment operating parameters and fault types. Excessive vibration often indicates bearing failure, and abnormal temperature may cause equipment overload. Through time series law analysis, the optimal scheduling strategy under different water inflow conditions is summarized to achieve efficient utilization of water resources in the basin under multi-objective constraints such as water supply, flood control, and power generation. Based on graph reasoning technology, the scheduling experience, emergency plans, and operation and maintenance procedures are systematically organized to form a complete engineering management knowledge system. The knowledge base provides knowledge support for the intelligent operation and management of water conservancy projects by capturing the empirical laws in engineering practice.

[0139] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0140] (1) Extract reservoir group dispatching rules, flood prevention warning parameters, and engineering maintenance indicators from the water conservancy innovation knowledge base, perform knowledge analysis on the engineering operation scenarios, and obtain a water conservancy scenario knowledge table;

[0141] (2) Conduct real-time analysis of hydrological forecast information, reservoir operation status, and equipment monitoring parameters in real-time water conservancy project needs to form a project demand feature set;

[0142] (3) Classify the water conservancy scenario knowledge table according to flood season scheduling, dry season scheduling and daily scheduling, and generate a time-divided knowledge response library;

[0143] (4) Based on the project demand feature set, spatial organization is carried out in combination with the upstream and downstream relationships of the basin and the project linkage characteristics to obtain the sub-regional knowledge response chain;

[0144] (5) Based on the time-based knowledge response library and the regional knowledge response chain, the time-based knowledge response library is integrated to build an intelligent service response network;

[0145] (6) Organize the intelligent service response network into services according to scheduling decision support, risk warning assessment and operation and maintenance management guidance, and generate an AI knowledge base for smart water conservancy.

[0146] Specifically, deep learning semantic analysis technology is used to extract key information from the water conservancy innovation knowledge base. The semantic analysis technology is based on the BERT (Bidirectional Encoder Representations from Transformers) pre-trained language model, which extracts features from text through a bidirectional Transformer encoder. For the reservoir group dispatching rules, the dispatching water level, discharge flow, joint dispatching strategy and other parameters of each reservoir are extracted; for flood warning parameters, the warning water level, warning flow, warning classification standards and other elements are extracted; for engineering maintenance indicators, equipment status monitoring parameters, maintenance cycles, maintenance standards and other information are extracted. These unstructured texts are converted into structured data through semantic analysis, and organized according to the engineering operation scenarios to form a water conservancy scenario knowledge table. Real-time water conservancy project demand analysis uses dynamic data stream processing technology to process hydrological forecast information, reservoir operation status and equipment monitoring parameters in real time. Hydrological forecast information includes rainfall forecast, inflow forecast, water level forecast and other data. Reservoir operation status includes real-time water level, discharge flow, water storage and other parameters. Equipment monitoring parameters include unit operation parameters, gate status, safety monitoring data and other information. The statistical characteristics of these real-time data, including mean, standard deviation, rate of change, etc., are calculated through a sliding time window, and trend analysis is performed in combination with historical data to identify abnormal patterns and change patterns, thereby forming a set of engineering demand characteristics.

[0147] In the scenario classification process, the time series clustering algorithm is used to process the water conservancy scenario knowledge table. The time series clustering algorithm is based on the dynamic time warping (DTW) distance metric to calculate the similarity between different time series. The time features of historical operation data are extracted to identify the characteristic patterns of typical time periods such as flood season and dry season. The flood season scheduling characteristics include factors such as peak flow, storage and discharge rules, and flood control storage capacity. The dry season scheduling characteristics include factors such as water inflow, water supply demand, and ecological flow. The daily scheduling characteristics include factors such as power generation demand, water quality control, and shipping guarantee. Similar scheduling scenarios are classified through cluster analysis, and the mapping relationship between scenario features and scheduling strategies is established to generate a time-divided knowledge response library. In the spatial organization stage, the graph network analysis method is used to process the engineering demand feature set. A basin water system topology map is constructed, where nodes represent engineering facilities such as reservoirs and hydropower stations, and edges represent connection relationships such as rivers and channels. Based on the topological structure, the engineering linkage characteristics such as the upstream and downstream water balance relationship and the joint scheduling relationship of cascade power stations are analyzed. The connectivity and impact propagation paths between nodes are calculated through the graph analysis algorithm to identify key control sections and scheduling nodes. Based on the characteristics of engineering requirements, a regional scheduling response mechanism is established to form a regional knowledge response chain.

[0148] The space-time fusion process uses tensor decomposition technology to multi-dimensionally fuse the time-division knowledge response and the spatial-dimension knowledge response. Tensor decomposition decomposes high-dimensional data into the product of low-dimensional factors to capture the space-time coupling pattern in the data. By analyzing the knowledge response relationship between different time periods and different regions, a unified service response mechanism is established to build an intelligent service response network. The service organization stage uses knowledge graph reasoning technology to reorganize the knowledge in the intelligent service response network according to the application scenario. The scheduling decision support scenario focuses on decision-making issues such as water balance, flood control scheduling, and power generation optimization; the risk warning and analysis scenario focuses on safety issues such as hazard warning, emergency response, and risk assessment; the operation and maintenance management guidance scenario focuses on equipment status monitoring, fault diagnosis, maintenance and other operational issues. Through knowledge reasoning, a mapping relationship between scenarios and services is established to form an AI knowledge base for smart water conservancy.

[0149] Taking a group of cascade hydropower stations in a river basin as an example, the above method is used for data processing. Reservoir dispatching rules are extracted from historical operation data, including flood control limit water level in flood season, guaranteed output in dry season, and daily regulation operation mode. Real-time monitoring data shows that the water level of the upstream reservoir exceeds the flood limit water level and the rainfall continues to increase, and the water level of the downstream river is close to the warning value. Through time series analysis, it is determined that it is in the flood season dispatching scenario, and flood control dispatching measures need to be taken. Spatial analysis shows that the increase in upstream water has a chain effect on the operation of downstream cascade power stations, and coordinated joint dispatching is required. Based on knowledge reasoning, a graded flood discharge plan is given to ensure flood control safety and maintain the stable operation of cascade power stations.

[0150] In a specific embodiment, the process of performing the knowledge parsing step S107 on the engineering operation scenario may specifically include the following steps:

[0151] (1) Extract flood season scheduling limit water level, flood control reserved storage capacity and joint scheduling flow from the reservoir group scheduling rules, classify and organize them according to reservoir operating conditions, and generate a reservoir scheduling parameter set;

[0152] (2) Configure thresholds for the warning water level, warning flow, and rainstorm intensity standards in the flood warning parameters to form a graded warning standard library;

[0153] (3) Quantitatively analyze the equipment status parameters, safety monitoring data and operating condition indicators in the project maintenance indicators to obtain the project operation parameter table;

[0154] (4) Associating the reservoir operation parameter set with the graded warning standard library to generate a water conservancy project operation rule table;

[0155] (5) Based on the project operation parameter table, organize the data according to the hydrological characteristics, project characteristics and operation requirements to obtain the water conservancy project scenario set;

[0156] (6) Parameter mapping is performed between the water conservancy project operation rule table and the water conservancy project scenario set to generate a water conservancy scenario knowledge table.

[0157] Specifically, data extraction is performed on the reservoir group dispatching rules, and key parameters are identified from the procedure documents by text mining. The flood season dispatching limit water level refers to the maximum water level that the reservoir water level must not exceed during the flood season, which is determined by statistical analysis of historical operation data; the flood control reserved storage capacity refers to the water storage space reserved for flood control, which is calculated based on the design flood; and the joint dispatching flow is the flow balance relationship between the cascade reservoir groups. A rule-based text parsing algorithm is used to extract these dispatching parameters and classify them according to different operating conditions, such as the dispatching parameters for the main flood season, the secondary flood season, and the non-flood season, to form a complete set of reservoir dispatching parameters. The threshold configuration of flood control warning parameters adopts a method based on historical data statistics. The warning water level and warning flow are determined based on the analysis of historical flood processes, such as the quantile values ​​of the maximum flood peak water level and flow in the year; the rainstorm intensity standard is based on the rainfall intensity-duration-frequency relationship curve to determine the rainstorm intensity with different recurrence periods. The weight of each parameter is determined by the hierarchical analysis method, and a hierarchical warning indicator system is established. These warning parameters are divided into different levels such as general warning, important warning, severe warning, etc. according to the risk level, forming a structured hierarchical warning standard library.

[0158] The quantitative analysis process of engineering maintenance indicators adopts multi-source data fusion technology. Equipment status parameters include physical quantities such as vibration, temperature, and pressure, which are collected in real time through sensors; safety monitoring data include engineering safety indicators such as seepage, displacement, and stress; and operating condition indicators describe the operating status of the equipment. These heterogeneous data are standardized, and the main features are extracted using the principal component analysis method to establish an equipment health assessment model. Various indicators are quantified into health scores through the fuzzy comprehensive evaluation method to generate a standardized engineering operation parameter table. The scene association between the reservoir scheduling parameter set and the graded warning standard library adopts association rule mining technology. The Apriori algorithm is used to analyze the association between the scheduling parameters and the warning indicators, such as triggering the corresponding level of warning when the water level exceeds the limit water level. Based on frequent item set mining, high-frequency parameter combinations are identified and logical relationships between parameters are established. These association rules are organized according to different scenarios, such as flood control scheduling scenarios, emergency response scenarios, etc., to form a structured water conservancy project operation rule table.

[0159] The construction of the water conservancy project scenario set adopts the cluster analysis method. Based on the project operation parameter table, feature vectors are extracted, including hydrological characteristics (such as water inflow, water level, etc.), engineering characteristics (such as regulation performance, flood discharge capacity, etc.) and operation requirements (such as power generation tasks, water supply demand, etc.). The K-means clustering algorithm is used to cluster these features and identify typical operation scenarios. The clustering effect is evaluated by the silhouette coefficient, and the optimal scenario division scheme is determined to obtain the water conservancy project scenario set. The final parameter mapping process uses knowledge graph technology. The rules in the water conservancy project operation rule table are converted into entities and relationships in the graph, and the scenes in the scenario set are used as context information. The vector representation of entities and relationships is learned through the graph embedding algorithm, and the mapping relationship between rules and scenarios is established. Based on the similarity calculation, the applicable operation rules are matched for different scenarios to generate a structured water conservancy scenario knowledge table.

[0160] Taking the flood season dispatch of a cascade hydropower station group as an example, the flood limit water level of each reservoir (such as 175 meters for upstream reservoirs, 150 meters for midstream reservoirs, and 130 meters for downstream reservoirs) and the corresponding flood control storage capacity are extracted from the dispatch rules. The warning water level and warning flow are obtained based on historical flood data statistics. For example, the warning flow of 3,000 cubic meters per second in the upstream section corresponds to the third-level warning. Real-time monitoring data show that the unit vibration value, bearing temperature and other operating parameters are within the normal range. Through association analysis, it is found that flood control dispatching needs to be started when the upstream water exceeds the warning flow and the water level is close to the flood limit water level. Scenario clustering shows that it belongs to the main flood season flood control dispatching scenario. Knowledge graph reasoning gives specific dispatching rules for this scenario, including control parameters such as flood discharge flow and gate opening, to achieve accurate flood season dispatching decisions.

[0161] In a specific embodiment, the process of executing the spatial organization step based on the engineering demand feature set and combining the upstream and downstream relationship of the watershed and the engineering linkage characteristics may specifically include the following steps:

[0162] (1) Extract the flow of the watershed control section, the water level difference between the main and tributary rivers, and the regional rainfall from the engineering demand feature set, conduct hydrological correlation analysis according to the watershed water system characteristics, and generate a watershed hydrological connection table;

[0163] (2) Spatially locate the control methods of dam groups, coordination rules of cascade power stations, and regional diversion and drainage strategies in the upstream and downstream relationship of the basin, and form a spatial layout diagram of the project;

[0164] (3) The joint dispatching mode of reservoir groups, water allocation scheme of irrigation areas and ecological flow control rules of rivers in the project linkage characteristics are regionally classified to obtain the project linkage mode table;

[0165] (4) Conduct regional coupling analysis based on the watershed hydrological connection table and the project spatial layout map to obtain the watershed-project response sequence;

[0166] (5) Based on the project linkage mode table, response configuration is carried out according to regional flood control safety, water supply security and power generation efficiency to form a regional regulation strategy chain;

[0167] (6) Spatial matching of the watershed-project response sequence with the sub-regional regulation strategy chain is performed to obtain the sub-regional knowledge response chain.

[0168] Specifically, the correlation analysis method is used to process the hydrological data in the engineering demand feature set, and the correlation strength between the control sections of the basin is evaluated by calculating the Person correlation coefficient. The flow data of the control section is collected at intervals of 30 minutes, and the propagation delay of the section flow is calculated based on the sliding time window. The water level difference between the main and tributary rivers is calculated by the water level data collected synchronously, and the regional rainfall is calculated based on the Thiessen polygon interpolation method. These hydrological elements are organized according to the topological structure of the basin water system, and the water balance relationship between the upstream and downstream sections is established to generate the basin hydrological connection table.

[0169] The positioning process of the project spatial layout adopts GIS spatial analysis technology. The precise location of hydraulic structures such as sluice gates and power stations is determined by GPS coordinates, and the project points are mapped to a unified coordinate system by spatial coordinate conversion. The control method of sluice gates and dams establishes hydraulic connections based on parameters such as gate opening and flow characteristics; the coordination rules of cascade power stations determine the scheduling priority through indicators such as installed capacity and power generation characteristics of power stations; the regional diversion and drainage strategy determines the water allocation plan based on the spatial distribution of water intakes and outlets. The impact range of engineering facilities is calculated by spatial buffer analysis, and the interaction relationship between facilities is identified through spatial overlay analysis to form an engineering spatial layout diagram containing spatial location and control relationship. The regional classification of engineering linkage characteristics adopts a hierarchical clustering algorithm. The joint scheduling method of reservoir groups is classified based on indicators such as reservoir regulation performance and flood discharge capacity; the irrigation area water allocation plan determines the water supply priority based on factors such as irrigation area and crop water demand; the river ecological flow control rule sets the minimum flow based on the ecological water demand requirements of different river sections. The similarity between different engineering units is calculated by Ward's minimum variance method, and the project is divided into several control areas according to the scheduling objectives and operation characteristics to generate an engineering linkage mode table.

[0170] Regional coupling analysis uses graph theory to process the basin hydrological connection table and engineering spatial layout diagram. A directed graph is constructed to represent the basin-engineering system, with nodes representing control sections and engineering facilities, and edges representing hydraulic connections and control relationships. The water volume transmission path is calculated using the shortest path algorithm, and the dispatching capacity is analyzed based on the maximum flow algorithm. The basin is divided into sub-regions with strong hydrological correlation and close engineering connections using the graph partitioning algorithm, and the mutual influence between regions is analyzed to obtain the basin-engineering response sequence. The response configuration process adopts a multi-objective optimization method. For different regions in the engineering linkage mode table, an optimization model is established based on flood control safety, water supply guarantee, power generation efficiency and other objectives. The Pareto optimal solution set is solved by a genetic algorithm to obtain the optimal control strategy for each region. The optimization results are organized according to regional characteristics, and the corresponding relationship between control objectives and operating measures is established to form a regional control strategy chain.

[0171] Spatial matching uses tensor decomposition technology to represent the watershed-project response sequence and the regional control strategy chain as a three-dimensional tensor. The time dimension represents the response process, the space dimension represents the regional distribution, and the feature dimension represents the control parameters. The core features of the tensor are extracted through Tucker decomposition, the mapping relationship between the response process and the control strategy is established, and the regional knowledge response chain is generated.

[0172] Taking the river cascade development as an example, a specific regulation scheme is obtained through the above method. Hydrological correlation analysis shows that the upstream power station reaches the downstream section 2 hours after the discharge, and the water level difference between the main and tributary rivers exceeds 5 meters, which requires regulation. The spatial positioning of the power station is based on coordinates such as 32°15'N, 112°30'E to determine the specific location, and the impact range is determined by a 50-kilometer buffer zone analysis. The linkage characteristic analysis divides the cascade power station into an upstream regulation area, a midstream transition area, and a downstream control area. The coupling analysis shows that the upstream area has a strong regulation capacity and the midstream area has a high flood discharge pressure. The generated sub-regional knowledge response chain stipulates the specific regulation measures for each region under different circumstances.

[0173] The above describes the AI ​​knowledge base construction method for smart water conservancy in the embodiment of the present application. The following describes the AI ​​knowledge base construction system for smart water conservancy in the embodiment of the present application. Figure 5 In the embodiment of the present application, an embodiment of the AI ​​knowledge base construction system for smart water conservancy includes:

[0174] The integration module is used to integrate the hydrological monitoring data, water conservancy project operation records and expert knowledge documents through the environmental change feature collector to form a water conservancy intelligent perception data set;

[0175] The recognition module is used to identify knowledge buds of water conservancy project cases, hydrological law characteristics and expert experience according to the water conservancy intelligent perception data set, and generate water conservancy native knowledge units;

[0176] A coding module is used to extract engineering scheduling features, flood prevention warning features and equipment maintenance features from the water conservancy native knowledge unit, decompose knowledge elements, obtain water conservancy knowledge features, and encode and reorganize the water conservancy knowledge features through water conservancy knowledge association analysis to construct a water conservancy knowledge evolution chain network;

[0177] A screening module is used to screen the water conservancy knowledge based on the water conservancy knowledge evolution chain network, combined with the historical engineering application effects and expert evaluation results, to obtain the water conservancy optimal knowledge body;

[0178] A reconstruction module is used to intelligently reconstruct the hydrological regulation rules, engineering risk prevention experience and operation and maintenance guidance methods based on the water conservancy optimization knowledge body to form a water conservancy innovation knowledge base;

[0179] The matching module is used to intelligently match the water conservancy innovation knowledge base with real-time water conservancy project needs to generate an AI knowledge base for smart water conservancy.

[0180] Through the collaborative cooperation of the above-mentioned components, the hydrological monitoring data, water conservancy project operation records and expert knowledge documents are integrated through the environmental change feature collector to form a water conservancy intelligent perception data set, which solves the problem of single data source and delayed update of the traditional water conservancy knowledge base; the knowledge budding recognition technology is used to analyze water conservancy project cases, hydrological law characteristics and expert experience to generate water conservancy native knowledge units, realizing the automatic extraction and standardized expression of knowledge; the water conservancy knowledge characteristics are encoded and reorganized through water conservancy knowledge association analysis, and a water conservancy knowledge evolution chain network is constructed to establish the association relationship between knowledge units, thereby enhancing the systematicness and integrity of knowledge; knowledge screening is carried out based on historical engineering application effects and expert evaluation results to obtain water conservancy preferred knowledge bodies and ensure knowledge quality; the hydrological scheduling laws, engineering risk prevention experience and operation and maintenance guidance methods are intelligently reconstructed to form a water conservancy innovation knowledge base, which improves the practicality and innovation of knowledge; the water conservancy innovation knowledge base is combined with real-time water conservancy project needs through intelligent matching technology to generate an AI knowledge base for smart water conservancy, realizing precise knowledge services. In specific applications, the knowledge bud recognition algorithm based on deep learning of this method significantly improves the accuracy of knowledge extraction, the knowledge association analysis algorithm based on graph neural network enhances the rationality of knowledge reorganization, the knowledge optimization mechanism based on reinforcement learning improves the scientific nature of knowledge screening, the knowledge reconstruction algorithm based on transfer learning enhances the knowledge innovation ability, and the intelligent matching algorithm based on attention mechanism improves the accuracy of knowledge services.

[0181] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0182] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for constructing an AI knowledge base for smart water conservancy, characterized in that: The method for constructing an AI knowledge base for smart water conservancy includes: Based on hydrological monitoring data, water conservancy project operation records and expert knowledge documents, the environmental change feature collector integrates the data to form a water conservancy intelligent perception data set; According to the water conservancy intelligent perception data set, the knowledge budding is identified for water conservancy project cases, hydrological law characteristics and expert experience, and water conservancy native knowledge units are generated; Extracting engineering dispatching features, flood prevention warning features and equipment maintenance features from the water conservancy native knowledge unit, decomposing knowledge elements, obtaining water conservancy knowledge features, encoding and reorganizing the water conservancy knowledge features through water conservancy knowledge association analysis, and constructing a water conservancy knowledge evolution chain network; Based on the water conservancy knowledge evolution chain network, combined with the historical engineering application effects and expert evaluation results, knowledge screening is performed to obtain the water conservancy optimal knowledge body; Based on the water conservancy optimization knowledge body, the hydrological regulation law, engineering risk prevention experience and operation and maintenance guidance method are intelligently reconstructed to form a water conservancy innovation knowledge base; Intelligently match the water conservancy innovation knowledge base with the real-time water conservancy project needs to generate an AI knowledge base for smart water conservancy, including: extracting reservoir group scheduling rules, flood prevention warning parameters and project maintenance indicators from the water conservancy innovation knowledge base, performing knowledge analysis on the project operation scenario, and obtaining a water conservancy scenario knowledge table; performing real-time analysis on the hydrological forecast information, reservoir operation status and equipment monitoring parameters in the real-time water conservancy project needs to form a project demand feature set; classifying the water conservancy scenario knowledge table according to flood season scheduling, dry season scheduling and daily scheduling to generate a time-divided knowledge response library; for the project demand feature set, spatial organization is performed in combination with the upstream and downstream relationships of the basin and the project linkage characteristics to obtain a regional knowledge response chain; based on the time-divided knowledge response library and the regional knowledge response chain, time-space fusion is performed to build an intelligent service response network; the intelligent service response network is organized according to scheduling decision support, risk warning analysis and judgment, and operation and maintenance management guidance to generate an AI knowledge base for smart water conservancy; For the engineering demand feature set, spatial organization is performed in combination with the upstream and downstream relationship of the basin and the engineering linkage characteristics to obtain a sub-regional knowledge response chain, including: extracting the flow of the basin control section, the water level difference of the main and tributary rivers and the regional rainfall from the engineering demand feature set, performing hydrological correlation analysis according to the characteristics of the basin water system, and generating a basin hydrological connection table; spatially locating the control mode of the dam group in the upstream and downstream relationship of the basin, the coordination rules of the cascade power stations and the regional diversion and drainage strategy to form a project spatial layout map; regionally classifying the joint dispatching mode of the reservoir group, the irrigation area water allocation plan and the river ecological flow control rules in the engineering linkage characteristics to obtain an engineering linkage mode table; performing regional coupling analysis based on the basin hydrological connection table and the engineering spatial layout map to obtain a basin-engineering response sequence; for the engineering linkage mode table, performing response configuration according to regional flood control safety, water supply guarantee and power generation efficiency to form a sub-regional regulation strategy chain; spatially matching the basin-engineering response sequence with the sub-regional regulation strategy chain to obtain a sub-regional knowledge response chain.

2. The method for constructing an AI knowledge base for smart water conservancy according to claim 1 is characterized in that: The hydrological monitoring data, water conservancy project operation records and expert knowledge documents are integrated through the environmental change feature collector to form a water conservancy intelligent perception data set, including: Collect water level data, flow data and rainfall data from hydrological monitoring sites, and perform standardization processing on the hydrological monitoring data through a data acquisition interface to obtain standardized hydrological data; Extracting reservoir dispatching records, gate opening and closing records, and equipment operation status records from the water conservancy project management system, performing time-series alignment processing on the water conservancy project operation records, and obtaining time-series project data; Retrieving flood prevention and rescue cases, engineering maintenance experience and expert decision-making records from the water conservancy knowledge base, conducting structured analysis on the expert knowledge documents, and obtaining structured expert data; Performing spatiotemporal association of the standardized hydrological data with the time-series engineering data to generate a water conservancy project operation feature sequence; Deeply integrating the water conservancy project operation feature sequence with structured expert data to obtain water conservancy project knowledge features; Environmental change patterns are identified based on the water conservancy engineering knowledge characteristics to generate a water conservancy intelligent perception data set.

3. The method for constructing an AI knowledge base for smart water conservancy according to claim 1 is characterized in that: According to the water conservancy intelligent perception data set, the knowledge budding recognition is performed on water conservancy project cases, hydrological law characteristics and expert experience to generate water conservancy native knowledge units, including: Extracting engineering construction records, operation status data and fault handling records from the water conservancy project case, and obtaining water conservancy project feature data through feature association processing; Extracting water level variation relationship, flow cycle characteristics and rainfall distribution characteristics from the hydrological law characteristics, performing time series analysis on the extraction results, and forming hydrological evolution law data; Semantically analyze the flood control dispatching scheme, emergency response measures and emergency plans in the expert experience to generate expert decision feature data; Perform scene mapping on the water conservancy project characteristic data and the hydrological evolution law data to obtain water conservancy project knowledge elements; Associating and fusing the water conservancy engineering knowledge elements with the expert decision feature data to form a water conservancy knowledge feature vector; Knowledge concepts are constructed based on the water conservancy knowledge feature vector to generate water conservancy native knowledge units.

4. The method for constructing an AI knowledge base for smart water conservancy according to claim 1 is characterized in that: The extracting of engineering dispatching features, flood prevention warning features and equipment maintenance features from the water conservancy primary knowledge unit, decomposing knowledge elements, obtaining water conservancy knowledge features, encoding and reorganizing the water conservancy knowledge features through water conservancy knowledge association analysis, and constructing a water conservancy knowledge evolution chain network, includes: Perform contextual semantic annotation on the water conservancy knowledge features, and organize them into structures according to the hydrological dispatch sequence, engineering operation and maintenance sequence, and emergency response sequence to form a water conservancy knowledge semantic map; Dynamically linking the nodes in the water conservancy knowledge semantic graph according to temporal relationships, spatial relationships and causal relationships to generate a water conservancy knowledge evolution path; According to the node association strength in the water conservancy knowledge evolution path, a two-way propagation analysis is performed on the knowledge link to obtain the water conservancy knowledge transmission characteristics; According to the water conservancy knowledge transmission characteristics, combined with the upstream and downstream relationship of the basin and the linkage relationship of water conservancy projects, knowledge reconstruction is carried out to form a water conservancy knowledge association network; The water conservancy knowledge association network is grouped into knowledge according to engineering scheduling scenarios, risk prevention and disposal scenarios, and operation and maintenance management scenarios to construct a water conservancy knowledge evolution chain network.

5. The method for constructing an AI knowledge base for smart water conservancy according to claim 1 is characterized in that: The water conservancy knowledge evolution chain network is used to screen knowledge in combination with historical engineering application effects and expert evaluation results to obtain water conservancy optimal knowledge bodies, including: Extracting reservoir joint dispatching records, sluice dam group control strategies and cascade power station operation plans from the water conservancy knowledge evolution chain network, classifying and sorting them according to historical application scenarios, and obtaining a water conservancy project dispatching sequence; Classify and summarize the water resources allocation results, flood control and disaster reduction effects, and power generation benefit indicators in the application effects of the historical projects to generate project benefit evaluation data; Quantify the dispatching scheme scores, engineering operation suggestions and risk warning opinions in the expert evaluation results to form an expert evaluation index table; Based on the water conservancy project scheduling sequence, combined with the specific scenario characteristics of dry season scheduling, flood control period scheduling and daily operation scheduling, a scheduling knowledge scoring table is generated; Based on the engineering benefit evaluation data and expert evaluation index table, a correlation analysis is conducted on the reservoir group dispatching effect, cascade power station benefits and ecological water demand guarantee degree to obtain the knowledge application evaluation results; According to the scheduling knowledge scoring table and the knowledge application evaluation results, the knowledge units are optimally screened to obtain the water conservancy optimal knowledge body.

6. The method for constructing an AI knowledge base for smart water conservancy according to claim 1 is characterized in that: Based on the water conservancy optimization knowledge body, the hydrological regulation law, engineering risk prevention experience and operation and maintenance guidance method are intelligently reconstructed to form a water conservancy innovation knowledge base, including: Extracting reservoir dispatching schemes, flood control plans and equipment maintenance plans from the water conservancy optimization knowledge body, performing parameter analysis according to engineering characteristics, and generating a water conservancy basic knowledge table; Selecting the low-water season dispatching strategy, the flood control period joint dispatching scheme and the ecological dispatching criterion from the hydrological dispatching law, performing data reorganization processing, and obtaining a water conservancy dispatching knowledge set; Carry out scenario matching of the risk warning indicators, emergency response procedures and rescue decision-making points in the engineering risk prevention experience to form a risk prevention knowledge system; Conduct correlation analysis on the equipment operating parameters, fault handling solutions and maintenance requirements in the operation and maintenance guidance method to obtain the engineering operation and maintenance knowledge chain; The water conservancy dispatching knowledge set, risk prevention knowledge system and engineering operation and maintenance knowledge chain are cross-domain integrated to generate a comprehensive water conservancy knowledge network; based on the comprehensive water conservancy knowledge network, the hydrology-engineering-management knowledge is collaboratively organized to form a water conservancy innovation knowledge base.

7. The method for constructing an AI knowledge base for smart water conservancy according to claim 1 is characterized in that: The method extracts reservoir group dispatching rules, flood prevention warning parameters and engineering maintenance indicators from the water conservancy innovation knowledge base, performs knowledge analysis on the engineering operation scenario, and obtains a water conservancy scenario knowledge table, including: Extract flood season dispatching restricted water level, flood control reserved storage capacity and joint dispatching flow from the reservoir group dispatching rules, classify and organize them according to reservoir operation conditions, and generate a reservoir dispatching parameter set; Threshold configuration is performed on the warning water level, warning flow and rainstorm intensity standards in the flood warning parameters to form a graded warning standard library; Quantitatively analyze the equipment status parameters, safety monitoring data and operating condition indicators in the engineering maintenance indicators to obtain an engineering operation parameter table; Associating the reservoir dispatching parameter set with the graded warning standard library with scenarios to generate a water conservancy project operation rule table; organizing data according to the project operation parameter table, hydrological characteristics, project characteristics and operation requirements to obtain a water conservancy project scenario set; The water conservancy project operation rule table is parameter-mapped with the water conservancy project scenario set to generate a water conservancy scenario knowledge table.

8. An AI knowledge base construction system for smart water conservancy, used to implement the AI ​​knowledge base construction method for smart water conservancy as described in any one of claims 1 to 7, characterized in that: The AI ​​knowledge base construction system for smart water conservancy includes: The integration module is used to integrate the hydrological monitoring data, water conservancy project operation records and expert knowledge documents through the environmental change feature collector to form a water conservancy intelligent perception data set; An identification module is used to identify knowledge buds of water conservancy project cases, hydrological law characteristics and expert experience according to the water conservancy intelligent perception data set, and generate water conservancy native knowledge units; A coding module is used to extract engineering scheduling features, flood prevention warning features and equipment maintenance features from the water conservancy native knowledge unit, decompose knowledge elements, obtain water conservancy knowledge features, and encode and reorganize the water conservancy knowledge features through water conservancy knowledge association analysis to construct a water conservancy knowledge evolution chain network; A screening module is used to screen the water conservancy knowledge based on the water conservancy knowledge evolution chain network, combined with the historical engineering application effects and expert evaluation results, to obtain the water conservancy optimal knowledge body; A reconstruction module is used to intelligently reconstruct the hydrological regulation law, engineering risk prevention experience and operation and maintenance guidance method based on the water conservancy optimization knowledge body to form a water conservancy innovation knowledge base; A matching module is used to intelligently match the water conservancy innovation knowledge base with the real-time water conservancy project needs to generate an AI knowledge base for smart water conservancy, including: extracting reservoir group scheduling rules, flood prevention warning parameters and project maintenance indicators from the water conservancy innovation knowledge base, performing knowledge analysis on the project operation scenario, and obtaining a water conservancy scenario knowledge table; performing real-time analysis on the hydrological forecast information, reservoir operation status and equipment monitoring parameters in the real-time water conservancy project needs to form a project demand feature set; classifying the water conservancy scenario knowledge table according to flood season scheduling, dry season scheduling and daily scheduling to generate a time period knowledge response library; for the project demand feature set, spatial organization is performed in combination with the upstream and downstream relationships of the basin and the project linkage characteristics to obtain a regional knowledge response chain; based on the time period knowledge response library and the regional knowledge response chain, time-space fusion is performed to build an intelligent service response network; the intelligent service response network is organized according to scheduling decision support, risk warning analysis and judgment, and operation and maintenance management guidance to generate an AI knowledge base for smart water conservancy; For the engineering demand feature set, spatial organization is performed in combination with the upstream and downstream relationship of the basin and the engineering linkage characteristics to obtain a sub-regional knowledge response chain, including: extracting the flow of the basin control section, the water level difference of the main and tributary rivers and the regional rainfall from the engineering demand feature set, performing hydrological correlation analysis according to the characteristics of the basin water system, and generating a basin hydrological connection table; spatially locating the control mode of the dam group in the upstream and downstream relationship of the basin, the coordination rules of the cascade power stations and the regional diversion and drainage strategy to form a project spatial layout map; regionally classifying the joint dispatching mode of the reservoir group, the irrigation area water allocation plan and the river ecological flow control rules in the engineering linkage characteristics to obtain an engineering linkage mode table; performing regional coupling analysis based on the basin hydrological connection table and the engineering spatial layout map to obtain a basin-engineering response sequence; for the engineering linkage mode table, performing response configuration according to regional flood control safety, water supply guarantee and power generation efficiency to form a sub-regional regulation strategy chain; spatially matching the basin-engineering response sequence with the sub-regional regulation strategy chain to obtain a sub-regional knowledge response chain.

Images