Hydraulic engineering detection system and method for hydraulic engineering

Abstract

The invention discloses a hydraulic engineering detection system and method for hydraulic engineering, and belongs to the technical field of hydraulic engineering detection. The method comprises the following steps: step 1, carrying out real-time monitoring and data acquisition on key parameters of the water conservancy project; step 2, constructing an engineering state feature vector through a knowledge graph and semantic reasoning, and realizing preliminary identification and risk assessment of an abnormal state; step 3, performing multi-dimensional quantitative analysis on the identified abnormal state to realize accurate diagnosis of engineering diseases; step 4, based on the diagnosis result, automatically generating graded early warning information and matching a corresponding processing scheme; step 5, performing credibility evaluation on the diagnosis result, and realizing real-time dynamic adjustment of the early warning level and the processing suggestion; and step 6, displaying a detection diagnosis result through a visual interface, and automatically generating a standardized detection report according to an industrial standard.

Description

Technical Field

[0001] The present application relates to the field of water conservancy project detection technology, and more specifically, to a water conservancy project detection system and method. Background Art

[0002] As water conservancy projects grow in scale and technological complexity, the safety and stability of water conservancy facilities have become core issues for ensuring the effective use of water resources and disaster prevention and control. Current water conservancy project monitoring relies primarily on manual inspections, traditional sensor monitoring, and empirical judgment. These methods suffer from numerous shortcomings, including long monitoring cycles, limited coverage, and poor real-time performance. Consequently, the operation, maintenance, and management of water conservancy projects face significant challenges.

[0003] Traditional water conservancy project inspection methods typically rely on a small number of sensors to collect single data points, failing to comprehensively and in real time monitor the health of the entire project system. This approach not only leads to certain deviations in monitoring results, but also hinders rapid response and effective handling of emergencies, leading to increased project risks. Furthermore, current inspection technologies lack intelligent means for identifying, diagnosing, and assessing abnormal conditions, resulting in delayed problem detection and inaccurate handling, increasing the operation and maintenance costs of water conservancy facilities.

[0004] In order to ensure the safety and efficiency of water conservancy projects, it is necessary to introduce more intelligent and automated means to organically combine data collection, real-time monitoring and intelligent diagnosis to improve the detection accuracy, real-time performance and reliability of water conservancy projects.

[0005] To sum up, how to achieve comprehensive real-time monitoring of water conservancy projects through intelligent technology and how to combine multi-source data for accurate disease diagnosis and risk assessment have become technical problems that need to be solved urgently. Summary of the Invention

[0006] In order to overcome a series of defects in the prior art, the purpose of this application is to provide a water conservancy project detection method for the above-mentioned problems, comprising the following steps:

[0007] Step 1: Real-time monitoring and data collection of key parameters of water conservancy projects;

[0008] Step 2: Construct engineering status feature vectors through knowledge graph and semantic reasoning, and achieve preliminary identification and risk assessment of abnormal states;

[0009] Step 3: Conduct multi-dimensional quantitative analysis of the identified abnormal conditions to achieve accurate diagnosis of engineering defects;

[0010] Step 4: Based on the diagnosis results, automatically generate graded warning information and match the corresponding treatment plan;

[0011] Step 5: Conduct credibility assessment on the diagnostic results to achieve real-time dynamic adjustment of warning levels and treatment recommendations;

[0012] Step 6: Display the test and diagnosis results through a visual interface, and automatically generate a standardized test report based on industry standards.

[0013] Preferably, step 1 comprises the following steps:

[0014] Deploy multi-source sensor networks at key locations of water conservancy projects to monitor key parameters of the projects in real time. Install high-definition image acquisition equipment to capture real-time images and videos of the project structures.

[0015] Unified storage of data transmission from sensor networks and high-definition image acquisition devices to ensure data security and accessibility;

[0016] Perform standardized preprocessing on data, including standardization of data formats, synchronization of timestamps, and conversion of data units, to ensure that data from different sources can be compared and analyzed under the same standards, thereby improving data availability and consistency.

[0017] Perform noise reduction optimization on the pre-processed data to remove noise from the data and improve the quality and accuracy of the data;

[0018] Data is regularly backed up to multiple storage locations to prevent data loss, while encryption and access control measures are implemented to ensure data confidentiality and integrity.

[0019] Preferably, step 2 comprises the following steps:

[0020] Based on the domain ontology library, expert knowledge base and historical case library, a structured knowledge graph covering the core concepts, rules and cases of water conservancy projects is constructed;

[0021] Leveraging knowledge graphs and real-time monitoring data, we extract key features of the engineering status through semantic reasoning, construct quantitative feature vectors, and comprehensively characterize the engineering operation status.

[0022] The feature vector is compared with the abnormal patterns and historical cases in the knowledge graph to preliminarily identify the abnormal state, and the severity and potential impact of the abnormality are evaluated by combining expert knowledge and risk assessment models.

[0023] Preferably, step 3 comprises the following steps:

[0024] Multi-dimensional integration of identified abnormal state-related data, including sensor monitoring data, high-definition image data, historical case data, and environmental factor data;

[0025] Conduct quantitative analysis on the integrated data to extract key features related to abnormal conditions;

[0026] Based on the extracted feature data, an analytical model suitable for water conservancy project disease diagnosis is constructed to effectively capture the relationship between abnormal conditions and project diseases;

[0027] Real-time monitoring data is input into the trained analysis model to accurately diagnose engineering defects, output the defect type, location and severity, and interpret the diagnosis results in combination with domain knowledge.

[0028] Preferably, step 4 comprises the following steps:

[0029] Based on the disease diagnosis results, classify and grade abnormal conditions according to severity, scope of impact and urgency of treatment;

[0030] Based on the disease classification results, corresponding graded warning information is automatically generated, including disease type, location, severity, possible impact, and recommended initial response measures;

[0031] Match the generated warning information with the pre-built treatment solution library to quickly find the solution corresponding to the disease level and type;

[0032] Based on the matching results, a detailed treatment plan is generated, including operation steps, required resources, time schedule, and emergency response plan, to ensure that the plan is feasible and adaptable to the actual project situation;

[0033] The generated early warning information and detailed treatment plans will be pushed to relevant responsible personnel in a timely manner through multiple channels so that the treatment process can be quickly initiated.

[0034] Preferably, the generated warning information is matched with a pre-built treatment solution library to quickly find a solution corresponding to the disease level and type, including the following steps:

[0035] Normalize and extract features from received warning information, structure key information, and ensure consistency with the index field format in the processing solution library;

[0036] Construct a multi-dimensional feature vector of warning information, quantify the core elements into calculable values, and consider environmental conditions and historical data to form a complete feature space for subsequent similarity calculation;

[0037] Adopting the inverted index method, a mapping relationship between warning information features and treatment solutions is established to quickly locate the most similar solution combination;

[0038] Formulate similarity calculation rules, set weights based on the importance of each dimension, calculate the matching scores between warning information and candidate solutions, and sort and filter them according to the scores;

[0039] Conduct an adaptability assessment on the selected candidate solutions, and make a comprehensive judgment on the feasibility, economy and timeliness of the solutions based on the current actual situation to ensure that the recommended solutions are feasible and optimal.

[0040] Preferably, step 5 comprises the following steps:

[0041] Identify the uncertainty factors in the detection and diagnosis process, clarify their types and impact range, and provide input for fuzzy reasoning;

[0042] Based on domain knowledge and expert experience, a rule base containing fuzzy logic rules is constructed, and uncertainty is quantified through fuzzy sets and membership functions, providing a logical basis for reasoning;

[0043] Using fuzzy reasoning mechanism, combined with real-time data and rule base, the credibility of the diagnosis results is evaluated, the credibility score is calculated, and the reliability of the diagnosis results is quantified;

[0044] Dynamically adjust the warning level based on the credibility assessment results. When the credibility of the diagnosis result is low, the warning level is appropriately lowered to avoid false alarms; when the credibility is high, the warning level is maintained or increased.

[0045] Based on the adjusted warning level and credibility assessment results, optimize the treatment recommendations to improve their pertinence and practicality.

[0046] Preferably, step 6 comprises the following steps:

[0047] Design an intuitive and easy-to-use visual interface based on user needs and industry standards, supporting the display and operation of key information, so that users can quickly understand the test and diagnosis results;

[0048] The detection and diagnosis results are intuitively displayed in the form of charts, maps or heat maps to help users fully understand the project status;

[0049] Automatically generate standardized reports based on industry standards, including project overview, testing methods, diagnostic results, early warning information, and treatment recommendations;

[0050] Through template design and natural language generation technology, the test and diagnosis results are automatically filled into the report template to ensure the accuracy and completeness of the content, and users can customize the template to meet different needs;

[0051] Automatically review the report content, check data consistency, format standardization and logical rationality, and prompt users to make corrections or additions.

[0052] The purpose of this application is also to provide a water conservancy project detection system, which includes the following modules:

[0053] The sensor and data acquisition module monitors key parameters in real time and collects data from multiple sources, providing accurate data support for subsequent engineering status analysis and abnormality diagnosis;

[0054] The engineering status feature analysis module, based on real-time monitoring data and historical engineering cases, constructs engineering status feature vectors through knowledge graphs and semantic reasoning technology to identify abnormal conditions;

[0055] The abnormal state diagnosis module conducts in-depth diagnosis of identified abnormal states and combines multi-dimensional data analysis to achieve accurate diagnosis of diseases;

[0056] The early warning and treatment plan generation module automatically generates graded early warning information based on the diagnosis results and matches the appropriate treatment plan according to the disease type and severity;

[0057] Credibility assessment and dynamic adjustment module, which assesses the credibility of diagnostic results and dynamically adjusts warning levels and treatment recommendations based on the assessment results;

[0058] The visualization and report generation module displays the detection and diagnosis results through a visual interface and automatically generates standardized detection reports.

[0059] Compared with the prior art, this application has the following beneficial effects:

[0060] This application integrates multi-source data collection, knowledge graphs and semantic reasoning technologies to achieve real-time monitoring, accurate diagnosis, intelligent early warning and automatic generation of treatment plans for abnormal conditions of water conservancy projects, and dynamically adjusts the warning level and treatment suggestions in combination with credibility assessment. Finally, the detection results are displayed and standardized reports are generated through a visual interface, thereby improving the accuracy and response efficiency of water conservancy project detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a water conservancy project detection method disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0063] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0064] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0065] like Figure 1 As shown, a water conservancy project detection method for a water conservancy project includes the following steps:

[0066] Step 1: Real-time monitoring and data collection of key parameters of water conservancy projects;

[0067] Step 2: Construct engineering status feature vectors through knowledge graph and semantic reasoning, and achieve preliminary identification and risk assessment of abnormal states;

[0068] Step 3: Conduct multi-dimensional quantitative analysis of the identified abnormal conditions to achieve accurate diagnosis of engineering defects;

[0069] Step 4: Based on the diagnosis results, automatically generate graded warning information and match the corresponding treatment plan;

[0070] Step 5: Conduct credibility assessment on the diagnostic results to achieve real-time dynamic adjustment of warning levels and treatment recommendations;

[0071] Step 6: Display the test and diagnosis results through a visual interface, and automatically generate a standardized test report based on industry standards.

[0072] In this embodiment, step 1 is the basis of the detection method. Through real-time monitoring and data acquisition, dynamic data of key parameters of water conservancy projects can be obtained. These parameters generally include water level, flow, pressure, soil moisture, settlement displacement, etc. Water conservancy project monitoring is a key link in ensuring the safe operation of the project, especially under changing environmental conditions. Real-time data acquisition can enable project managers to grasp the health status of the project in a timely manner, discover potential risks at an early stage, and avoid accidents caused by human delays. In addition, real-time data monitoring can also provide accurate input data for subsequent diagnosis, risk assessment, early warning, etc., thereby ensuring the efficiency and accuracy of the detection method.

[0073] In this embodiment, step 2 analyzes and processes the collected data by constructing a knowledge graph and semantic reasoning. The construction of the knowledge graph can visualize and logicize the various indicators of the water conservancy project and their interrelationships to help understand and process complex engineering conditions. On this basis, semantic reasoning is used to extract valuable information from the data and derive the state feature vectors related to it. By analyzing these feature vectors, potential abnormal conditions can be quickly identified and a preliminary risk assessment can be performed. This process helps to efficiently screen out parts that may have problems, avoid errors in manual experience judgment, and make the identification of abnormal conditions more accurate and timely. Through data mining and pattern recognition technology, this stage can quickly provide actionable early warning information and lay the foundation for accurate diagnosis in the next step.

[0074] In this embodiment, step 3 conducts an in-depth analysis of these states and uses multi-dimensional quantitative analysis technology to accurately diagnose possible water conservancy project diseases. This process not only relies on further detailed analysis of abnormal data, but also combines statistics, mathematical modeling, and machine learning techniques to deconstruct abnormal conditions in detail. By establishing a complex analysis model and comparing the changing trend of each parameter with historical data, specific types of engineering diseases, such as cracks, leakage, and settlement, can be identified and their severity assessed. Multi-dimensional analysis takes into account the interaction of various factors to ensure the comprehensiveness and accuracy of the diagnosis. In this way, the diagnosis of water conservancy project diseases can shift from traditional empirical diagnosis to more accurate, data-driven intelligent diagnosis, greatly improving diagnostic efficiency and accuracy.

[0075] In this embodiment, step 4 converts the diagnostic results into specific action guidelines, ensuring that appropriate emergency measures can be taken for different levels of risk. By grading early warning information, managers can clearly understand the risk level of the current water conservancy project and respond promptly. For example, minor risks may only require regular inspections and attention, while serious risks may require immediate repairs or reinforcement measures. This automated process can save a considerable amount of time and manpower, improve decision-making efficiency, reduce the subjectivity of human decision-making, and make various risk management measures more standardized and regularized.

[0076] In this embodiment, the credibility assessment in step 5 is an important link in the entire detection system. Although the previous diagnostic results provide potential risk information, these diagnostic results need to undergo credibility assessment to ensure their scientificity and reliability. Step 5 uses a credibility assessment mechanism based on a data model to dynamically adjust the diagnostic results in real time to ensure the accuracy of the early warning information. This process is based on technical means such as backtracking of historical data, error analysis, and model calibration, and can evaluate the accuracy and credibility of the current diagnostic results. If there is uncertainty or potential error in the diagnostic results, the early warning level and treatment suggestions will be adjusted in real time to make them more in line with the actual situation. Through dynamic adjustment, it is possible to maintain a high level of responsiveness and accuracy under different environments and data changes.

[0077] In this embodiment, step 6 is to display the diagnostic results through a visual interface and automatically generate a standardized test report that complies with industry standards. The visual interface intuitively displays the real-time status and diagnostic results of the water conservancy project in various forms such as charts, graphs, and trend curves, allowing engineers, managers, or relevant personnel to quickly understand and judge the current risk situation. This not only improves the readability of the data, but also facilitates timely decision-making. In addition, the automatically generated test report complies with industry standards and can provide standardized and compliant technical documents for various regulatory agencies, auditors, etc. Such an automated report generation system reduces the errors and tediousness of manual operations and improves work efficiency and standardization.

[0078] Overall, this water conservancy project detection method integrates six closely coordinated steps, combining advanced sensor technology, data analysis, knowledge reasoning, and visualization techniques to create an efficient and intelligent water conservancy project status monitoring and diagnosis system. From data collection to diagnosis, early warning, and final report generation, the entire process forms a closed loop, capable of real-time monitoring of the operating status of water conservancy projects, promptly identifying potential risks, and automatically generating corresponding treatment plans based on the risk level. This intelligent detection method not only improves the efficiency of project management, but also significantly enhances the safety of water conservancy projects, greatly enhancing the ability to prevent and respond to emergencies.

[0079] Preferably, step 1 comprises the following steps:

[0080] Deploy multi-source sensor networks at key locations of water conservancy projects to monitor key parameters of the projects in real time. Install high-definition image acquisition equipment to capture real-time images and videos of the project structures.

[0081] Unified storage of data transmission from sensor networks and high-definition image acquisition devices to ensure data security and accessibility;

[0082] Perform standardized preprocessing on data, including standardization of data formats, synchronization of timestamps, and conversion of data units, to ensure that data from different sources can be compared and analyzed under the same standards, thereby improving data availability and consistency.

[0083] Perform noise reduction optimization on the pre-processed data to remove noise from the data and improve the quality and accuracy of the data;

[0084] Data is regularly backed up to multiple storage locations to prevent data loss, while encryption and access control measures are implemented to ensure data confidentiality and integrity.

[0085] In summary, the above steps, through the deployment of a multi-source sensor network and high-definition image acquisition equipment at key locations, enable real-time monitoring of key parameters and structural conditions of water conservancy projects, while ensuring data security and accessibility. Standardized preprocessing and noise reduction optimization ensure data consistency and accuracy. Furthermore, regular data backup and encryption measures enhance data reliability and confidentiality. This technology overall improves the monitoring and diagnostic capabilities of water conservancy projects, providing a solid foundation for project safety.

[0086] Preferably, step 2 comprises the following steps:

[0087] Based on the domain ontology library, expert knowledge base and historical case library, a structured knowledge graph covering the core concepts, rules and cases of water conservancy projects is constructed;

[0088] Leveraging knowledge graphs and real-time monitoring data, we extract key features of the engineering status through semantic reasoning, construct quantitative feature vectors, and comprehensively characterize the engineering operation status.

[0089] The feature vector is compared with the abnormal patterns and historical cases in the knowledge graph to preliminarily identify the abnormal state, and the severity and potential impact of the abnormality are evaluated by combining expert knowledge and risk assessment models.

[0090] In summary, the above steps build a structured knowledge graph and combine it with a domain ontology, expert knowledge base, and historical case library. Using semantic reasoning, they extract key features of the project status and construct a quantitative feature vector to comprehensively represent the project's operational status. This feature vector is compared with abnormal patterns and historical cases in the knowledge graph to initially identify abnormal conditions. Combined with expert knowledge and risk assessment models, the severity and potential impact of the abnormality are assessed, thereby improving the accuracy and reliability of abnormal condition identification in water conservancy projects and enhancing the efficiency of risk assessment and management.

[0091] Preferably, the knowledge graph and real-time monitoring data are used to extract key features of the engineering status through semantic reasoning, and a quantitative feature vector is constructed to comprehensively characterize the engineering operation status, including the following steps:

[0092] The key feature set F required to clarify the project operation status is F = {f1, f2, ..., f n}, including monitoring data, structural health information and environmental factors, where F is the key feature set of the project operation status, representing all feature items that need to be monitored and analyzed; f1,f2,…,f n is a single key feature of the engineering state; n is the number of key features, that is, the number of features in the set F;

[0093] Construct a knowledge graph K={k1,k2,…,k m}, and fill in expert knowledge and historical cases, where k1, k2, …, k m are the nodes in the knowledge graph; m is the number of nodes in the knowledge graph K;

[0094] Through semantic reasoning, combined with real-time data D = {d1, d2, ..., d q} and knowledge graph K, extracting key features related to engineering status; D is the real-time monitoring data set, representing the data collected by all sensors and image acquisition devices; d1, d2, ..., d q is a single data point of real-time monitoring data; q is the number of real-time monitoring data, that is, the number of data points in set D;

[0095] The extracted features are quantized into feature vectors v = [v1, v2, ..., v n ], each v i It is obtained through weighted calculation to reflect the current engineering status. The formula is: i =∑ j＝1 p α ij ·r j ·d i , where v is the characteristic vector of the engineering state, obtained through reasoning and quantification, and represents the combination of key features; v1, v2,…, v n Each dimension of the feature vector represents a specific state feature; p is the number of dimensions of the feature vector, which is equal to n; α ij is the feature f i The weight in the feature vector indicates the importance of the feature to the overall engineering status; r j is the feature value obtained through knowledge graph reasoning, which is used to quantify a certain feature state; d i is a specific data point from the real-time monitoring data D, used to calculate the feature vector;

[0096] Based on the quantitative feature vector v, the current project status is evaluated, anomalies are identified and risks are quantified, and the severity and potential impact of the project are analyzed;

[0097] As real-time data is updated, the feature vector v is dynamically adjusted, and the warning level and processing plan are adjusted through fuzzy reasoning and credibility assessment to ensure efficient response.

[0098] In summary, by leveraging a knowledge graph and real-time monitoring data, combined with semantic reasoning, key features of the project status are extracted and a quantified feature vector is constructed to comprehensively characterize the project's operational status. First, the key feature set F required for the project's operational status is defined, including monitoring data, structural health information, and environmental factors. This set encompasses all features that need to be monitored and analyzed and is broken down into individual key features. Next, a knowledge graph K is constructed, encompassing core concepts, failure modes, and rules. This is populated with expert knowledge and historical cases to form a knowledge network encompassing all nodes and relationships. Through semantic reasoning, the real-time monitoring data set D is combined with the knowledge graph K to extract key features relevant to the project's status. In this step, set D includes data collected by all sensors and image acquisition devices, with each data point representing a specific monitoring value. Using semantic reasoning techniques, this real-time data is matched and correlated with the information in the knowledge graph to extract the features most relevant to the project's status. These extracted features are then quantified into feature vectors v, each derived through a weighted calculation to reflect the current project status. Based on the quantified feature vectors v, the current project status can be assessed, anomalies identified, and risks quantified. This assessment process analyzes the severity and potential impact of a project, providing managers with detailed risk assessment results. In practical applications, the feature vector v is dynamically adjusted as real-time data is continuously updated. Through fuzzy reasoning and credibility assessment, warning levels and treatment plans can be adjusted in real time, ensuring efficient response and handling of abnormal conditions. By integrating knowledge graphs, real-time monitoring data, and semantic reasoning, accurate monitoring and assessment of the operational status of water conservancy projects is achieved. By constructing a quantitative feature vector to comprehensively characterize the project's operational status and dynamically adjust warning and treatment plans, the accuracy of abnormal condition identification and the reliability of risk assessment are improved. Ultimately, this ensures efficient response and safe management of water conservancy projects, providing solid technical support for the long-term and stable operation of the projects. This technology has important application value in project management and risk control, effectively improving the management level and safety assurance capabilities of water conservancy projects.

[0099] Preferably, the feature vector is compared with the abnormal patterns and historical cases in the knowledge graph to preliminarily identify the abnormal state, and the severity and potential impact of the abnormality are evaluated by combining expert knowledge and risk assessment models, including the following steps:

[0100] Compare the feature vector v with the predefined abnormal pattern m in the knowledge graph r =[m r1 ,m r2 ,…,m rg] and use cosine similarity to measure similarity: S(v,m r )=(v·m r ) / (‖v‖·‖m r ‖), when the similarity S(v,m r ) is higher than the set threshold θ, indicating that the engineering status is different from the abnormal mode m r Match, so as to preliminarily identify the abnormal state, where m r Represents the rth abnormal pattern in the knowledge graph, including the feature vector of the abnormal state; m r1 ,m r2 ,…,m rg Indicates abnormal mode m r The specific feature elements in are used to describe the different dimensions of abnormal patterns; g represents the number of abnormal patterns, that is, the total number of all abnormal patterns in the knowledge graph;

[0101] Based on the identified abnormal state, combined with the risk score Rcase in the historical case library s , calculate the severity score S of the abnormal state based on the similarity between the abnormal pattern and the case severity , the formula is: S severity =∑ s＝1 S w s Rcase s ·S(v,m s ), where S represents the number of abnormal states identified during the detection process; S(v,m s ) represents the characteristic vector v and the abnormal pattern m s The similarity between s Represents the weight of historical cases, which is used to calculate the risk score;

[0102] Using expert knowledge and risk assessment model, through the function f(S severity ) Calculate the potential impact value I of the abnormal state;

[0103] According to the severity of the abnormality assessed S severity and potential impact I, classify abnormal states into different risk levels, and automatically generate priority processing plans.

[0104] In summary, by comparing feature vectors with anomaly patterns and historical cases in the knowledge graph, anomaly conditions are initially identified. Expert knowledge and risk assessment models are then combined to assess the severity and potential impact of the anomalies. This process involves the following steps. First, the feature vectors are compared with predefined anomaly patterns in the knowledge graph, and cosine similarity is used to measure similarity. When the similarity exceeds a set threshold, the project status matches the anomaly pattern, thus preliminarily identifying an anomaly condition. Then, based on the identified anomaly condition and the risk scores in the historical case database, a severity score is calculated based on the similarity between the anomaly pattern and the case. Leveraging expert knowledge and the risk assessment model, a function is used to calculate the potential impact of the anomaly condition. Based on the assessed severity and potential impact, the anomaly condition is classified into different risk levels, and prioritized treatment plans are automatically generated. This technical approach, through comprehensive analysis of feature vectors, knowledge graphs, historical cases, and expert knowledge, can accurately identify and assess abnormal conditions in water conservancy projects, quantify risks, and provide scientific treatment recommendations. Its advantages include improved accuracy in anomaly identification and reliability in risk assessment, ensuring efficient response and safe management of water conservancy projects. This technical approach is not only intelligent and automated, but also dynamically adjusts warning levels and response plans, providing solid technical support and assurance for the safe operation of water conservancy projects. The application of this comprehensive approach will help improve the management and emergency response capabilities of water conservancy projects, and has significant practical and dissemination value.

[0105] Preferably, step 3 comprises the following steps:

[0106] Multi-dimensional integration of identified abnormal state-related data, including sensor monitoring data, high-definition image data, historical case data, and environmental factor data;

[0107] Conduct quantitative analysis on the integrated data to extract key features related to abnormal conditions;

[0108] Based on the extracted feature data, an analytical model suitable for water conservancy project disease diagnosis is constructed to effectively capture the relationship between abnormal conditions and project diseases;

[0109] Real-time monitoring data is input into the trained analysis model to accurately diagnose engineering defects, output the defect type, location and severity, and interpret the diagnosis results in combination with domain knowledge.

[0110] In summary, by integrating data related to identified abnormal conditions across multiple dimensions, including sensor monitoring data, high-definition image data, historical case data, and environmental factor data, comprehensive information related to abnormal conditions can be obtained, providing a foundation for subsequent quantitative analysis and diagnosis. Quantitative analysis of the integrated data extracts key features associated with abnormal conditions, accurately capturing the characteristics of engineering defects. Based on this, an analytical model for water conservancy project defect diagnosis is constructed, effectively establishing the relationship between abnormal conditions and engineering defects. This model, incorporating domain knowledge and historical data, uses real-time monitoring data as input to accurately diagnose engineering defects, outputting the type, location, and severity of the defect and providing an explanation for the diagnostic results. This process enables rapid and accurate identification and diagnosis of abnormal conditions in water conservancy projects, providing detailed defect information and providing managers with a sound basis for decision-making. This improves the accuracy and reliability of diagnosis, ensures timely detection and resolution of engineering defects, and enhances the safety and operational efficiency of water conservancy projects. This method not only optimizes the process of data integration and analysis, but also realizes automated and intelligent disease diagnosis, providing strong technical support for the management and maintenance of water conservancy projects, and has broad application prospects and promotion value.

[0111] Preferably, quantitative analysis of the integrated data to extract key features related to the abnormal state includes the following steps:

[0112] Clean and preprocess the integrated data, handle missing values, outliers, and duplicate values, and standardize them to ensure data quality and consistency;

[0113] Calculate the statistical characteristics of various indicators and analyze the distribution patterns and changing trends of data through visualization;

[0114] Conduct time series and trend analysis to identify periodic fluctuations, mutation points, and trend changes in time series data, and use exponential smoothing to extract long-term trends and eliminate seasonal effects;

[0115] Use correlation analysis and principal component analysis to calculate the correlation between indicators, identify strong correlation combinations, and reduce the dimension to extract the main feature vectors;

[0116] Build an anomaly detection model to identify abnormal samples and analyze their common characteristics, and extract key indicators to distinguish normal samples.

[0117] In summary, by quantitatively analyzing the integrated data and extracting key features related to abnormal conditions, comprehensive assessment and monitoring of the operational status of water conservancy projects can be achieved. First, through data cleaning and preprocessing, missing values, outliers, and duplicate values ​​are addressed, and data is standardized to ensure data quality and consistency. This process ensures data reliability and usability, laying the foundation for subsequent analysis. Second, by calculating the statistical characteristics of various indicators and using visualization techniques to analyze the distribution patterns and changing trends of the data, data characteristics and changes can be intuitively displayed. Time series and trend analysis can identify cyclical fluctuations, breakpoints, and trend changes in time series data. Exponential smoothing methods are used to extract long-term trends, eliminate seasonal effects, and provide a basis for long-term trend forecasting. Correlation analysis and principal component analysis can identify strongly correlated combinations between indicators. Dimensionality reduction is used to extract the main feature vectors, thereby simplifying the data dimension and improving analysis efficiency. Based on these key features, an anomaly detection model is constructed to identify abnormal samples and analyze their common characteristics to extract key indicators that distinguish normal samples. This series of steps can systematically identify and quantify abnormal conditions, improving the accuracy and reliability of engineering disease diagnosis. This approach enables real-time monitoring and accurate diagnosis of water conservancy projects, promptly identifying potential problems, reducing risks, and ensuring safe project operation. Overall, systematic data processing and analysis improves the intelligence level of water conservancy project monitoring and diagnosis, providing a scientific basis and support for project management and maintenance.

[0118] Preferably, step 4 comprises the following steps:

[0119] Based on the disease diagnosis results, classify and grade abnormal conditions according to severity, scope of impact and urgency of treatment;

[0120] Based on the disease classification results, corresponding graded warning information is automatically generated, including disease type, location, severity, possible impact, and recommended initial response measures;

[0121] Match the generated warning information with the pre-built treatment solution library to quickly find the solution corresponding to the disease level and type;

[0122] Based on the matching results, a detailed treatment plan is generated, including operation steps, required resources, time schedule, and emergency response plan, to ensure that the plan is feasible and adaptable to the actual project situation;

[0123] The generated early warning information and detailed treatment plans will be pushed to relevant responsible personnel in a timely manner through multiple channels so that the treatment process can be quickly initiated.

[0124] In summary, by classifying and grading the disease diagnosis results, systematic management of abnormal conditions is achieved. Based on the diagnosis results, abnormal conditions are classified and graded according to severity, scope of impact, and urgency of treatment. Based on these grading results, corresponding graded warning information is automatically generated, including the disease type, location, severity, potential impact, and recommended initial response measures. This warning information is then matched with a pre-built treatment solution library to quickly identify solutions corresponding to the disease level and type. Based on the matching results, a detailed treatment plan is generated, including operational steps, required resources, timelines, and contingency plans, ensuring the plan's feasibility and adaptability to the project's actual conditions. Finally, the generated warning information and detailed treatment plan are promptly distributed to relevant responsible personnel through various channels to quickly initiate the treatment process. Through automation and intelligent means, full-process management of disease diagnosis and emergency response is achieved, significantly improving the efficiency and accuracy of abnormal condition response. The innovation of step 4 lies in optimizing the emergency response process, ensuring the scientific and rationality of the treatment plan, and providing a solid guarantee for the safe operation of the water conservancy project.

[0125] Preferably, the generated warning information is matched with a pre-built treatment solution library to quickly find a solution corresponding to the disease level and type, including the following steps:

[0126] Normalize and extract features from received warning information, structure key information, and ensure consistency with the index field format in the processing solution library;

[0127] Construct a multi-dimensional feature vector of warning information, quantify the core elements into calculable values, and consider environmental conditions and historical data to form a complete feature space for subsequent similarity calculation;

[0128] Adopting the inverted index method, a mapping relationship between warning information features and treatment solutions is established to quickly locate the most similar solution combination;

[0129] Formulate similarity calculation rules, set weights based on the importance of each dimension, calculate the matching scores between warning information and candidate solutions, and sort and filter them according to the scores;

[0130] Conduct an adaptability assessment on the selected candidate solutions, and make a comprehensive judgment on the feasibility, economy and timeliness of the solutions based on the current actual situation to ensure that the recommended solutions are feasible and optimal.

[0131] In summary, by matching generated warning information with a pre-built library of treatment solutions, solutions corresponding to the disease level and type can be quickly identified, enabling efficient and accurate emergency response. First, the received warning information undergoes normalization and feature extraction to ensure that key information is structured and consistent with the index field format in the treatment solution library. Next, a multidimensional feature vector of the warning information is constructed, quantifying the core elements into calculable values. Environmental conditions and historical data are also considered to form a complete feature space for similarity calculation. An inverted index method is used to establish a mapping relationship between warning information features and treatment solutions, enabling rapid identification of the most similar solution combinations. Similarity calculation rules are developed, with weights assigned based on the importance of each dimension. The matching scores between the warning information and candidate solutions are calculated and sorted by score. Finally, the selected candidate solutions undergo an adaptability assessment, and a comprehensive assessment of their feasibility, cost-effectiveness, and timeliness is made based on the current situation to ensure that the recommended solutions are feasible and optimal. Through multiple steps such as standardized processing, quantitative analysis, rapid matching and comprehensive evaluation, the automatic generation and efficient matching of emergency response plans are achieved, which significantly improves the efficiency and accuracy of responding to emergencies and ensures the safe operation of water conservancy projects.

[0132] Preferably, step 5 comprises the following steps:

[0133] Identify the uncertainty factors in the detection and diagnosis process, clarify their types and impact range, and provide input for fuzzy reasoning;

[0134] Based on domain knowledge and expert experience, a rule base containing fuzzy logic rules is constructed, and uncertainty is quantified through fuzzy sets and membership functions, providing a logical basis for reasoning;

[0135] Using fuzzy reasoning mechanism, combined with real-time data and rule base, the credibility of the diagnosis results is evaluated, the credibility score is calculated, and the reliability of the diagnosis results is quantified;

[0136] Dynamically adjust the warning level based on the credibility assessment results. When the credibility of the diagnosis result is low, the warning level is appropriately lowered to avoid false alarms; when the credibility is high, the warning level is maintained or increased.

[0137] Based on the adjusted warning level and credibility assessment results, optimize the treatment recommendations to improve their pertinence and practicality.

[0138] In summary, by identifying uncertainties in the detection and diagnosis process, clarifying their types and impact, and providing input for fuzzy reasoning, the accuracy and reliability of diagnosis are ensured. Based on domain knowledge and expert experience, a rule base containing fuzzy logic rules is constructed. Fuzzy sets and membership functions are used to quantify uncertainty, providing a logical foundation for reasoning. Utilizing fuzzy reasoning mechanisms, combined with real-time data and the rule base, the credibility of diagnostic results is assessed and a credibility score is calculated to quantify the reliability of the diagnostic results. Based on the credibility assessment results, the warning level is dynamically adjusted. When the credibility of the diagnostic result is low, the warning level is appropriately lowered to avoid false alarms; when the credibility is high, the warning level is maintained or increased. Combining the adjusted warning level with the credibility assessment results, treatment recommendations are optimized to improve their relevance and practicality. By comprehensively analyzing uncertainties and fuzzy logic, the warning level and treatment recommendations are dynamically adjusted, ensuring efficient and accurate response to abnormal conditions in water conservancy projects and enhancing the level of intelligent project management and maintenance.

[0139] Preferably, based on domain knowledge and expert experience, a rule base containing fuzzy logic rules is constructed, and uncertainty is quantified through fuzzy sets and membership functions, including the following steps:

[0140] Combining the domain ontology library and expert knowledge base, we organize the core concepts and rules of water conservancy projects to form a structured knowledge base;

[0141] Define fuzzy sets for monitoring data and set membership functions for each state to map actual data into fuzzy sets to quantify uncertainty;

[0142] Design fuzzy reasoning rules based on domain knowledge and expert experience for reasoning and judgment;

[0143] By combining fuzzy reasoning with real-time data and rule base, the reasoning result is calculated and expressed as: Among them, z is the output variable of the fuzzy reasoning result, which represents a fuzzy value derived from the input data and rules; H represents the number of rules in the fuzzy rule base; Indicates that the input variable x is in the fuzzy set A h The degree of membership in ; Indicates that the input variable y is in the fuzzy set B h The degree of membership in ;

[0144] Combined with uncertainty factors, fuzzy logic is used to evaluate the credibility of the reasoning results and dynamically adjust the warning level; combined with the reasoning results and credibility, the warning level is automatically generated and the corresponding treatment plan is matched to ensure that the response measures are reasonable and timely. The formula is expressed as: defuzzified =(∫z·μ B (z)dz) / (∫μ B(z)dz), where z defuzzified Indicates the final output result of the fuzzy inference result after defuzzification; μ B (z) represents the membership function of the defuzzified result z in the fuzzy set B; dz represents the small increment of the integral during the defuzzification process.

[0145] In summary, based on domain knowledge and expert experience, a rule base containing fuzzy logic rules is constructed. Fuzzy sets and membership functions are used to quantify uncertainty, enabling uncertainty assessment and dynamic adjustment of warning levels for water conservancy project monitoring data. First, the core concepts and rules of water conservancy projects are organized by combining the domain ontology and expert knowledge base, forming a structured knowledge base. This knowledge base provides a solid foundation for the subsequent definition of fuzzy sets and design of inference rules. Next, fuzzy sets are defined for the monitoring data, and membership functions are assigned to each state. This maps the real-world data into fuzzy sets to quantify uncertainty. Finally, fuzzy inference rules are designed based on domain knowledge and expert experience. Fuzzy inference combines real-time data with the rule base through fuzzy inference to calculate the inference result. The output variable z represents the fuzzy value derived from the input data and the rules. Fuzzy logic is used to assess the credibility of the inference result, taking into account uncertainty factors, and the warning level is dynamically adjusted. When the credibility of the diagnosis result is low, the warning level is appropriately lowered to avoid false alarms; when the credibility is high, the warning level is maintained or increased. Finally, the inference result and credibility are combined to automatically generate a warning level and match the corresponding treatment plan, ensuring appropriate and timely response. This technical method achieves efficient assessment and accurate early warning of abnormal conditions of water conservancy projects through the combination of systematic fuzzy logic and expert knowledge, significantly improving the intelligence level and emergency response capabilities of water conservancy project management, and providing solid technical support and guarantee for the safe operation of the project.

[0146] Preferably, step 6 comprises the following steps:

[0147] Design an intuitive and easy-to-use visual interface based on user needs and industry standards, supporting the display and operation of key information, so that users can quickly understand the test and diagnosis results;

[0148] The detection and diagnosis results are intuitively displayed in the form of charts, maps or heat maps to help users fully understand the project status;

[0149] Automatically generate standardized reports based on industry standards, including project overview, testing methods, diagnostic results, early warning information, and treatment recommendations;

[0150] Through template design and natural language generation technology, the test and diagnosis results are automatically filled into the report template to ensure the accuracy and completeness of the content, and users can customize the template to meet different needs;

[0151] Automatically review the report content, check data consistency, format standardization and logical rationality, and prompt users to make corrections or additions.

[0152] In summary, by designing an intuitive and easy-to-use visual interface, key information is presented according to user needs and industry standards, helping users quickly understand test and diagnosis results. Test and diagnosis results are intuitively displayed through charts, maps, and heat maps, enabling users to fully understand the project status. The system automatically generates standardized reports based on industry standards, including a project overview, test methods, diagnostic results, warning information, and action suggestions. Leveraging template-based design and natural language generation technology, the system automatically populates test and diagnosis results into report templates, ensuring accuracy and completeness, and supports user-defined templates to meet diverse needs. Report content is automatically reviewed to ensure data consistency, formatting compliance, and logical rationality, prompting users to make corrections or additions. This technical approach significantly improves the efficiency and accuracy of information transmission and optimizes the display and report generation process for test and diagnosis results. Its technical benefits are reflected in improved user understanding and utilization of test and diagnosis results, ensuring the standardization and reliability of report content, and providing strong technical support for the management and maintenance of water conservancy projects. Ultimately, this approach not only enhances the visualization and report generation of test and diagnosis results but also implements automated and intelligent workflows, promising broad application prospects.

[0153] This embodiment also provides a water conservancy project detection system for a water conservancy project, which is used to implement the above-mentioned water conservancy project detection method, including the following modules:

[0154] The sensor and data acquisition module monitors key parameters in real time and collects data from multiple sources, providing accurate data support for subsequent engineering status analysis and abnormality diagnosis;

[0155] The engineering status feature analysis module, based on real-time monitoring data and historical engineering cases, constructs engineering status feature vectors through knowledge graphs and semantic reasoning technology to identify abnormal conditions;

[0156] The abnormal state diagnosis module conducts in-depth diagnosis of identified abnormal states and combines multi-dimensional data analysis to achieve accurate diagnosis of diseases;

[0157] The early warning and treatment plan generation module automatically generates graded early warning information based on the diagnosis results and matches the appropriate treatment plan according to the disease type and severity;

[0158] Credibility assessment and dynamic adjustment module, which assesses the credibility of diagnostic results and dynamically adjusts warning levels and treatment recommendations based on the assessment results;

[0159] The visualization and report generation module displays the detection and diagnosis results through a visual interface and automatically generates standardized detection reports.

[0160] In summary, this water conservancy project inspection system integrates multiple functional modules to achieve intelligent management across the entire process, from data acquisition, status analysis, anomaly diagnosis, to warning generation, and credibility assessment. First, the sensor and data acquisition module monitors key parameters in real time and collects multi-source data, providing accurate data support for subsequent project status analysis and anomaly diagnosis. Next, the project status feature analysis module utilizes knowledge graphs and semantic reasoning techniques to construct project status feature vectors based on real-time monitoring data and historical project cases, identifying anomalies. The anomaly diagnosis module further diagnoses identified anomalies, combining multi-dimensional data analysis to achieve accurate disease diagnosis. Based on the diagnostic results, the warning and treatment plan generation module automatically generates graded warning information and matches appropriate treatment plans, improving response speed and accuracy. The credibility assessment and dynamic adjustment module assesses the credibility of diagnostic results and dynamically adjusts warning levels and treatment recommendations to ensure the reliability of warning information. Finally, the visualization and report generation module presents inspection and diagnosis results through an intuitive visual interface and automatically generates standardized inspection reports, improving information transmission efficiency and the accuracy of management decisions. Overall, the system has significantly improved the monitoring, diagnosis and emergency response capabilities of water conservancy projects through intelligent management of the entire process, ensuring the safe and stable operation of the projects.

[0161] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that modifications may be made to the technical solutions described in the above embodiments, or that some of the technical features may be replaced with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A water conservancy project detection method, characterized in that: The following steps are involved: Step 1: Real-time monitoring and data collection of key parameters of water conservancy projects; Step 2: Construct engineering status feature vectors through knowledge graph and semantic reasoning, and achieve preliminary identification and risk assessment of abnormal states; Step 3: Conduct multi-dimensional quantitative analysis of the identified abnormal conditions to achieve accurate diagnosis of engineering defects; Step 4: Based on the diagnosis results, automatically generate graded warning information and match the corresponding treatment plan; Step 5: Conduct credibility assessment on the diagnostic results to achieve real-time dynamic adjustment of warning levels and treatment recommendations; Step 6: Display the test and diagnosis results through a visual interface, and automatically generate a standardized test report based on industry standards.

2. A water conservancy project detection method according to claim 1, characterized in that: Step 1 includes the following steps: Deploy multi-source sensor networks at key locations of water conservancy projects to monitor key parameters of the projects in real time. Install high-definition image acquisition equipment to capture real-time images and videos of the project structures. Unified storage of data transmission from sensor networks and high-definition image acquisition devices to ensure data security and accessibility; Perform standardized preprocessing on data, including standardization of data formats, synchronization of timestamps, and conversion of data units, to ensure that data from different sources can be compared and analyzed under the same standards, thereby improving data availability and consistency. Perform noise reduction optimization on the pre-processed data to remove noise from the data and improve the quality and accuracy of the data; Data is regularly backed up to multiple storage locations to prevent data loss, while encryption and access control measures are implemented to ensure data confidentiality and integrity.

3. A water conservancy project detection method according to claim 1, characterized in that: Step 2 includes the following steps: Based on the domain ontology library, expert knowledge base and historical case library, a structured knowledge graph covering the core concepts, rules and cases of water conservancy projects is constructed; Leveraging knowledge graphs and real-time monitoring data, we extract key features of the engineering status through semantic reasoning, construct quantitative feature vectors, and comprehensively characterize the engineering operation status. The feature vector is compared with the abnormal patterns and historical cases in the knowledge graph to preliminarily identify the abnormal state, and the severity and potential impact of the abnormality are evaluated by combining expert knowledge and risk assessment models.

4. A water conservancy project detection method according to claim 1, characterized in that: Step 3 includes the following steps: Multi-dimensional integration of identified abnormal state-related data, including sensor monitoring data, high-definition image data, historical case data, and environmental factor data; Conduct quantitative analysis on the integrated data to extract key features related to abnormal conditions; Based on the extracted feature data, an analytical model suitable for water conservancy project disease diagnosis is constructed to effectively capture the relationship between abnormal conditions and project diseases; Real-time monitoring data is input into the trained analysis model to accurately diagnose engineering defects, output the defect type, location and severity, and interpret the diagnosis results in combination with domain knowledge.

5. A water conservancy project detection method according to claim 1, characterized in that: Step 4 includes the following steps: Based on the disease diagnosis results, classify and grade abnormal conditions according to severity, scope of impact and urgency of treatment; Based on the disease classification results, corresponding graded warning information is automatically generated, including disease type, location, severity, possible impact, and recommended initial response measures; Match the generated warning information with the pre-built treatment solution library to quickly find the solution corresponding to the disease level and type; Based on the matching results, a detailed treatment plan is generated, including operation steps, required resources, time schedule, and emergency response plan, to ensure that the plan is feasible and adaptable to the actual project situation; The generated early warning information and detailed treatment plans will be pushed to relevant responsible personnel in a timely manner through multiple channels so that the treatment process can be quickly initiated.

6. A water conservancy project detection method according to claim 5, characterized in that: Match the generated warning information with the pre-built treatment solution library to quickly find the solution corresponding to the disease level and type, including the following steps: Normalize and extract features from received warning information, structure key information, and ensure consistency with the index field format in the processing solution library; Construct a multi-dimensional feature vector of warning information, quantify the core elements into calculable values, and consider environmental conditions and historical data to form a complete feature space for subsequent similarity calculation; Adopting the inverted index method, a mapping relationship between warning information features and treatment solutions is established to quickly locate the most similar solution combination; Formulate similarity calculation rules, set weights based on the importance of each dimension, calculate the matching scores between warning information and candidate solutions, and sort and filter them according to the scores; Conduct an adaptability assessment on the selected candidate solutions, and make a comprehensive judgment on the feasibility, economy and timeliness of the solutions based on the current actual situation to ensure that the recommended solutions are feasible and optimal.

7. A water conservancy project detection method according to claim 1, characterized in that: Step 5 includes the following steps: Identify the uncertainty factors in the detection and diagnosis process, clarify their types and impact range, and provide input for fuzzy reasoning; Based on domain knowledge and expert experience, a rule base containing fuzzy logic rules is constructed, and uncertainty is quantified through fuzzy sets and membership functions, providing a logical basis for reasoning; Using fuzzy reasoning mechanism, combined with real-time data and rule base, the credibility of the diagnosis results is evaluated, the credibility score is calculated, and the reliability of the diagnosis results is quantified; Dynamically adjust the warning level based on the credibility assessment results. When the credibility of the diagnosis result is low, the warning level is appropriately lowered to avoid false alarms; when the credibility is high, the warning level is maintained or increased. Based on the adjusted warning level and credibility assessment results, optimize the treatment recommendations to improve their pertinence and practicality.

8. A water conservancy project detection method according to claim 1, characterized in that: Step 6 includes the following steps: Design an intuitive and easy-to-use visual interface based on user needs and industry standards, supporting the display and operation of key information, so that users can quickly understand the test and diagnosis results; The detection and diagnosis results are intuitively displayed in the form of charts, maps or heat maps to help users fully understand the project status; Automatically generate standardized reports based on industry standards, including project overview, testing methods, diagnostic results, early warning information, and treatment recommendations; Through template design and natural language generation technology, the test and diagnosis results are automatically filled into the report template to ensure the accuracy and completeness of the content, and users can customize the template to meet different needs; Automatically review the report content, check data consistency, format standardization and logical rationality, and prompt users to make corrections or additions.

9. A water conservancy project detection system for a water conservancy project, used to implement a water conservancy project detection method according to any one of claims 1 to 8, characterized in that: Includes the following modules: The sensor and data acquisition module monitors key parameters in real time and collects data from multiple sources, providing accurate data support for subsequent engineering status analysis and abnormality diagnosis; The engineering status feature analysis module, based on real-time monitoring data and historical engineering cases, constructs engineering status feature vectors through knowledge graphs and semantic reasoning technology to identify abnormal conditions; The abnormal state diagnosis module conducts in-depth diagnosis of identified abnormal states and combines multi-dimensional data analysis to achieve accurate diagnosis of diseases; The early warning and treatment plan generation module automatically generates graded early warning information based on the diagnosis results and matches the appropriate treatment plan according to the disease type and severity; Credibility assessment and dynamic adjustment module, which assesses the credibility of diagnostic results and dynamically adjusts warning levels and treatment recommendations based on the assessment results; The visualization and report generation module displays the detection and diagnosis results through a visual interface and automatically generates standardized detection reports.

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