Data monitoring and processing method based on dam stability

Abstract

The invention discloses a data monitoring and processing method based on dam stability, and relates to the technical field of dam monitoring, and the method comprises the following steps: arranging sensors: arranging high-precision sensors, such as a strain gauge, a displacement meter and the like, at key positions according to the structural characteristics of a dam; comprehensively analyzing the structure of the dam; according to the method, the high-precision sensors are arranged at key positions of the dam, real-time acquisition of dam stability parameters including strain, displacement, pressure, temperature and the like is achieved, the real-time data acquisition mechanism remarkably improves the monitoring frequency and continuity, the real-time performance and the accuracy of data are ensured, and compared with traditional regular manual inspection, the real-time monitoring accuracy is improved. According to the invention, more accurate and continuous monitoring data can be provided, so that the stability state of the dam can be evaluated more effectively.

Description

Technical Field

[0001] The invention relates to the technical field of dam monitoring, and in particular to a monitoring and processing method based on dam stability data. Background Art

[0002] In the existing dam stability monitoring field, traditional technologies mostly rely on regular manual inspections and simple automated monitoring systems. These methods often have problems such as low monitoring frequency, discontinuous data collection, and poor real-time performance, making it difficult to meet the high standards of dam safety management. Traditional monitoring technologies usually lack depth in data analysis and cannot fully utilize the collected data for in-depth analysis and prediction, resulting in limitations in the assessment of dam stability. With the development of technology, although automated monitoring technology has been widely used in dam safety monitoring, improving monitoring efficiency and realizing real-time data collection and processing, the monitoring system still faces quality problems such as low data accuracy, frequent equipment failures and limited monitoring range. These problems may affect the reliability of monitoring results, and then affect dam safety assessment and decision-making. Especially in the process of data collection, transmission and processing, how to ensure the accuracy, integrity and security of data is a technical problem that needs to be solved urgently.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of low data accuracy, poor real-time performance, insufficient analysis depth and limited intelligence level in the existing dam stability monitoring technology, and to propose a dam stability data monitoring and processing method.

[0005] The purpose of the present invention can be achieved through the following technical solutions: The dam stability data monitoring and processing method comprises the following steps: S1: Sensor deployment: According to the structural characteristics of the dam, corresponding sensors are deployed at different locations of the dam; S2: Data processing: collect and pre-process the data of the dam through the deployed sensors, and transmit the data after the pre-processing is completed; S3: Data monitoring, using data analysis algorithms to analyze the monitored data and identify the dam stability status; S4: Stability warning, generating warnings for abnormal monitored data; S5: Abnormal analysis, using machine learning and artificial intelligence technology to extract and analyze whether there are abnormal information from the monitoring data; S6: Maintenance planning: Perform sensor maintenance planning based on the results of sensor anomaly analysis and implement regular sensor self-inspection and remote maintenance strategies.

[0006] Furthermore, the specific steps of S5 are as follows: Collect sensor data from the dam and integrate it into a unified data set for subsequent anomaly analysis; Clean the data, remove noise and outliers, and standardize the data to ensure consistency of data from different sensors; Then perform multi-dimensional anomaly analysis on the preprocessed data; Build models to predict sensor life, analyze historical maintenance records, and determine maintenance cycles and failure modes; Analyze the impact of environmental factors on sensor performance and apply algorithms to predict sensor failure probability; Analyze the results of abnormality analysis, evaluate the impact of sensor abnormalities, classify abnormalities according to their severity and potential risks, and prioritize their processing; Record details of the anomaly, including description, cause, impact assessment and action plan, and notify relevant personnel promptly; Develop maintenance plans and strategies based on abnormal analysis results.

[0007] Furthermore, the specific operation steps of performing multi-dimensional abnormality analysis on the pre-processed data in S5 are as follows: Statistics and trend analysis: Calculate statistical indicators and check whether there are unexpected continuous increases or decreases; Threshold check: Set normal working thresholds according to sensor specifications and historical data, and check whether real-time data exceeds the preset thresholds; Cross-validation and consistency checking: Compare data from different sensors at the same location or under the same conditions to identify inconsistencies in the data set; Anomaly detection algorithms: Apply machine learning algorithms such as Isolation Forest or One-Class Support Vector Machines to identify abnormal patterns and train models to distinguish between normal and abnormal data; Signal analysis: Perform Fourier transform on the data to analyze the frequency components and identify abnormal frequencies caused by sensor failure; use time domain analysis methods such as autocorrelation and cross-correlation to check the stability and consistency of the signal.

[0008] Furthermore, the specific operation steps of sorting and processing the exceptions in S5 are as follows: The processing priority of sensor abnormality is determined by comprehensively evaluating the evaluation parameters of sensor abnormality, where the evaluation parameters include: Outlier Deviation: Calculate the deviation between outliers and normal values; Rate of change: Measures the rate at which sensor readings change. Rapid changes or no change indicate an anomaly. Duration: The length of time the sensor abnormality lasts. Long-term abnormalities require higher priority processing. Historical failure rate: Evaluate the frequency of sensor failures based on historical data; Performance degradation rate: the rate at which sensor performance decreases as estimated by the sensor performance degradation model; Impact range: Evaluate the impact range of sensor anomalies on the overall performance of the dam stability monitoring system and quantify it by percentage, recorded as the impact ratio; After normalizing the obtained outlier deviation, change rate, duration, historical failure rate, performance degradation rate and shadow-norm ratio, substitute them into the following formula: To obtain the prior evaluation value XPZ, where yp, bh, cx, ll, xt and yf are the abnormal value deviation, change rate, duration, historical failure rate, performance degradation rate and impact ratio respectively. The preset weight coefficients are respectively the abnormal value deviation, change rate, duration, historical failure rate, performance degradation speed and shadow-norm ratio; and the obtained prior evaluation value is used as the evaluation criterion for measuring the priority of sensor abnormality processing; The prior evaluation values ​​obtained from all sensor anomalies are sorted by size, and the sorting order is used as the basis for the order of anomaly processing.

[0009] Furthermore, the specific operation steps of S6 are as follows: Combine the abnormal analysis results obtained by machine learning and artificial intelligence technology to evaluate the performance of the sensor. Based on the abnormal analysis results, diagnose whether the sensor is faulty or has performance degradation; Get the sensors in S5 that need maintenance or replacement, and the priority of processing; Develop maintenance plans, including maintenance schedules, required resources, and expected goals; Assign maintenance tasks to appropriate technicians or maintenance teams; Develop a self-diagnosis program for the sensor so that the sensor can perform self-diagnosis regularly; Establish a remote maintenance system to enable technicians to remotely diagnose and maintain sensors; Prepare the necessary spare parts and tools to quickly respond to maintenance needs, perform regular self-check procedures on sensors, and promptly detect and report potential problems; Implement remote maintenance for problems that can be solved remotely; For maintenance tasks applicable to on-site operations, arrange technicians to perform on-site maintenance; Record detailed information of all maintenance activities, including maintenance date, activity type and results, and optimize maintenance cycles and plans based on maintenance results and sensor performance; Update predictive maintenance models with the latest maintenance data, assess the impact of maintenance activities on dam stability monitoring, and manage potential risks; After maintenance is completed, continue to monitor sensor performance to ensure maintenance effectiveness; Establish a feedback loop from maintenance to exception analysis to continuously improve maintenance strategies.

[0010] Furthermore, the specific operation steps of S1 are as follows: Analyze the dam material, shape, stress distribution, and determine the key monitoring areas of the dam; Identify critical monitoring areas including upstream face, downstream face, foundation, joints and drainage systems; Determine the sensor type and sensor accuracy according to monitoring requirements to ensure data accuracy; Design sensor layout to achieve comprehensive coverage of all critical areas; Develop an installation plan including location, mode, and fixing method; Establish a communication network between sensors and data processing centers; Integration testing of sensors and communication networks before installation; Calibrate the sensor and conduct field verification tests after installation to ensure data accuracy; Record the installation information of the sensor and create files for query and maintenance.

[0011] Furthermore, the specific operation steps of S2 are as follows: Use sensors to collect the strain, displacement, pressure and temperature parameters of the dam in real time; Ensure the time synchronization of all sensor data and maintain data consistency; Clean the raw data, remove noise and outliers, standardize the data, and eliminate the differences in range and accuracy of different sensors; Integrate data from different sensors to form a unified data set, store pre-processed data in a database, and create an index for query; Compressing data reduces storage requirements, and encrypting ensures data security; Transmit data via wired or wireless networks; Label the data to provide feature identification for the analysis algorithm; Assess data quality to ensure it meets analytical requirements; Implement real-time monitoring, quickly respond to data collection issues, and regularly back up data to prevent loss.

[0012] Furthermore, the specific operation steps of S3 are as follows: Receive pre-processed data and verify its integrity and consistency; Extract stability features, including displacement change rate and strain peak, and set thresholds; Analyze data in real time to monitor whether key features exceed safety thresholds; Conduct time series analysis to identify changing trends and unusual patterns; Combine multiple parameters for a comprehensive assessment to get a comprehensive view of dam stability; Apply machine learning and artificial intelligence algorithms to improve the accuracy of data analysis; Based on the results, the dam health status is evaluated and stability is determined; Assess the impact of natural hazards on dam stability; Generate alerts when data exceeds thresholds or identifies anomalies; Generate stability analysis reports regularly and present the results using data visualization techniques.

[0013] Furthermore, the specific operation steps of S4 are as follows: Determine the severity of the anomaly and set different warning levels based on the data analysis results; Convert warning signals into a format that includes level, time, location and impact; Deliver early warning information using SMS, email and push communication channels; Automatically or manually initiate emergency response mechanisms based on warning levels; Keep detailed records of all warning events and related information; Use charts and maps to display warning information in the monitoring system; Set up a manual confirmation process to avoid false alarms and ensure timely response; Collect feedback, evaluate the effectiveness of the early warning system, and optimize the early warning algorithm.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention realizes the real-time collection of dam stability parameters, including strain, displacement, pressure and temperature, by deploying high-precision sensors at key locations of the dam. This real-time data collection mechanism significantly improves the frequency and continuity of monitoring, ensuring the real-time and accuracy of data. Compared with traditional regular manual inspections, the present invention can provide more accurate and continuous monitoring data, thereby more effectively evaluating the stability status of the dam; (2) The present invention uses data analysis algorithms and machine learning techniques to conduct in-depth analysis of the collected monitoring data to identify the stability status of the dam. The system can analyze key features, such as displacement change rate and strain peak value, in real time to see whether they exceed preset safety thresholds, and use time series analysis and pattern recognition techniques to identify abnormal patterns or potential signs of instability in the data. It also includes a stability early warning module that can automatically generate different levels of early warnings when abnormal data is monitored, ensuring timely response to possible dam safety issues; (3) In the present invention, through the abnormal analysis module, the system can evaluate the performance status of the sensor, predict the failure probability of the sensor, and formulate maintenance plans and strategies accordingly. The maintenance planning module can implement regular sensor self-inspection and remote maintenance strategies to reduce the sensor failure rate and continuously improve the maintenance strategy through the feedback loop after maintenance. At the same time, it can also evaluate the impact of natural disasters on the stability of the dam, and optimize the maintenance cycle and plan through the predictive maintenance model to effectively manage potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0018] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0019] like Figure 1 As shown, the dam stability data monitoring and processing method includes the following steps: Sensor layout: According to the structural characteristics of the dam, high-precision sensors such as strain gauges and displacement gauges are deployed at key locations; First, a comprehensive analysis of the dam structure, including material properties, geometry, stress distribution, etc., is conducted to determine the key areas that may affect the stability of the dam. Based on the results of the dam structure analysis, key monitoring areas are identified, including the upstream face, downstream face, foundation, joints, drainage system, etc. of the dam body. The specific process is as follows: Comprehensive analysis of dam structure: Detailed analysis of the material properties, geometry, and stress distribution of the dam. Through the material of the dam, its physical and mechanical properties, such as strength and elastic modulus, are obtained; the analysis of geometry is used to determine the structural characteristics of the dam, such as the slope and height of the dam body. Dams of different shapes will have different stress distributions when subjected to stress; the study of stress distribution identifies stress concentration areas of the dam during normal operation and when subjected to external loads. These areas are key areas that need to be monitored; Identify critical monitoring areas based on structural analysis results: After completing a comprehensive analysis of the dam structure, critical areas that may affect the dam's stability are identified based on the analysis results; these areas include: Upstream and downstream surfaces: These are the parts of the dam that are in direct contact with water, and they bear external loads such as water pressure and wave impact force. Their stability is crucial to the overall safety of the dam. Foundation: The foundation of the dam bears the weight of the entire dam body, and transfers the various forces acting on the dam body to the foundation. The stability of the foundation is directly related to the safety of the dam. If the foundation has problems such as uneven settlement and sliding, the overall instability of the dam may result. Joints: The joints between different parts of the dam are the weak links in the structure, and are easily affected by factors such as temperature changes and deformation of the dam body. These may cause problems such as joint opening and dislocation, and thus affect the integrity and stability of the dam. Drainage system: It plays a key role in the seepage control of the dam. If the drainage system is not smooth, it may cause the water level in the dam body to rise, increase the seepage pressure of the dam body, and affect the stability of the dam body. Select the appropriate sensor type based on the characteristics of the key area and monitoring requirements. For example, strain gauges are used to measure stress changes, displacement gauges are used to monitor displacement, and osmometers are used to detect osmotic pressure, etc. Select high-precision sensors to ensure data accuracy. The accuracy should be determined based on monitoring needs and possible measurement errors. Design the sensor layout plan to ensure that the sensors can cover all key areas and the layout can provide comprehensive data to facilitate the analysis of the overall stability of the dam. Develop a detailed sensor installation plan, including installation location, installation method, fixing method, etc., to ensure the stability and long-term reliability of the sensors. Build a communication network between the sensor and the data processing center to ensure real-time and stable data transmission; perform integration testing before installing the sensor to verify the performance of the sensor and the reliability of the communication network; specifically verify through the following parameters: Error range: Calculate the deviation between the sensor measurement and the true value by comparing with a known standard value; Resolution: the smallest change that a sensor can detect. The higher the resolution, the better the sensor's ability to detect small changes. Long-term stability: The drift of the sensor output value over a period of time (such as months or years). By regularly measuring the standard value, observing the trend of the sensor output value, and calculating the drift rate; Short-term stability: The fluctuation of sensor output in a short period of time (such as a few hours or days) is evaluated by calculating the standard deviation of short-term measurement data. The smaller the standard deviation, the better the short-term stability. Repeatability: Repeatedly measure the same physical quantity multiple times, calculate the degree of difference between the measured values, and express it as repeatability error; Data transfer rate: measures the amount of data transmitted from the sensor to the data processing center per unit time; Packet loss rate: Statistics on the proportion of data packets lost during data transmission. By sending a certain number of test data packets, the number of data packets not received at the receiving end is counted to calculate the packet loss rate. Latency: The time interval from when the sensor sends data to when the data processing center receives the data, including transmission delay and processing delay, etc. It is measured by recording the timestamps at the sending and receiving ends and calculating the time difference between the two; Signal strength: reflects the strength of the communication network signal, and is tested using professional signal strength measurement equipment; Bit Error Rate: Indicates the proportion of erroneous bits in the data transmission process. By sending a data packet with known content, the receiving end compares the received data with the sent data and calculates the proportion of erroneous bits to the total number of transmitted bits. Then, according to the relative importance of sensor performance and communication network reliability in the entire system, a corresponding weight is assigned to each parameter, and the actual measured value of each parameter is converted into a value between 0 and 1 for unified calculation; then, a comprehensive evaluation score is obtained through weighted summation calculation, and the performance of the sensor and the reliability of the communication network are verified with this comprehensive evaluation score, and the obtained comprehensive evaluation score is compared with the preset score threshold. When the comprehensive evaluation score is less than the score threshold, it is judged that the performance of the sensor and the reliability of the communication network do not meet the standard, and further analysis is carried out to calibrate the installed sensor; otherwise, it is judged that the performance of the sensor and the reliability of the communication network have met the standard; After installation, conduct on-site verification tests on the sensors to ensure that the sensors are working properly and the data is accurate. Record the installation location, model, calibration data and other information of all sensors, and establish a sensor file to facilitate future query and maintenance.

[0020] Data processing: collect and pre-process the dam data through the deployed sensors, and transmit the data after pre-processing; High-precision sensors placed at key locations on the dam are used to collect key parameters of the dam such as strain, displacement, pressure, and temperature in real time. The data collection time of all sensors is synchronized to facilitate data consistency in subsequent analysis. The collected raw data is cleaned to remove noise and outliers. The data is standardized or normalized to eliminate differences caused by different sensor ranges and accuracies. The data collected by different sensors are integrated to form a unified data set for comprehensive analysis. The pre-processed data is stored in the database and a data index is established for quick query and analysis. Compress the data to reduce the demand for storage space and speed up data transmission. Use encryption technology to ensure the security of data during transmission and storage. Transmit the pre-processed data to the central monitoring system or cloud platform through wired or wireless networks, label the data, and provide necessary feature identification for the data analysis algorithm. Evaluate the quality of the transmitted data to ensure that the data meets the requirements of subsequent analysis. The evaluation process is as follows: Integrity check: Verify that the data is intact and intact to ensure that no data is lost or damaged during transmission; consistency verification: Check whether the data is consistent with the expected format and structure to ensure data consistency; accuracy assessment: Evaluate the accuracy of the data to check whether the data reflects the actual situation without errors or deviations; outlier detection: Identify outliers or outliers in the data, which may be caused by sensor failure, data entry errors or other unexpected factors; data synchronization check: Ensure that the timestamps and order of all sensor data are synchronized, which is particularly important for time series analysis; data integrity test: Check whether there are missing values ​​in the data set and process these missing values, such as filling, deleting or interpolating; data range and unit verification: Verify whether the data is within a reasonable range, And whether all data units are consistent; data redundancy check: check whether there are duplicate records in the data set to reduce data redundancy; data comparison analysis: compare data from different sensors or at different time points to identify potential problems or trends; data quality indicator evaluation: evaluate data quality based on predefined data quality indicators (such as precision, recall rate, F1 score, etc.); real-time monitoring and feedback: implement a real-time monitoring system to quickly respond to problems that may arise during data collection and transmission, and provide feedback; data backup and recovery testing: regularly back up data in the database and test the recovery process to ensure rapid recovery when data is lost or damaged; implement real-time monitoring of data flows to quickly respond to problems that may arise during data collection; regularly back up data in the database to prevent data loss.

[0021] Data monitoring, using data analysis algorithms to analyze the monitored data and identify the dam stability status; Receive pre-processed data transmitted from sensor deployment points, verify the integrity and consistency of the received data to ensure data quality; extract features related to dam stability from the original data, such as displacement change rate, strain peak value, etc., set thresholds for each feature based on historical data analysis to determine the dam stability status; implement real-time data analysis to monitor whether key features exceed preset thresholds, perform time series analysis on data, and identify changing trends of dam stability parameters; Use pattern recognition technology to analyze abnormal patterns or potential signs of instability in the data, combine data from multiple parameters, and conduct a comprehensive assessment to obtain a comprehensive view of the dam's stability; apply machine learning and artificial intelligence algorithms, such as neural networks and support vector machines, to improve the accuracy and intelligence of data analysis, and based on the data analysis results, evaluate the health status of the dam and determine whether it is in a stable state; Assess risk factors that may affect dam stability, such as natural disasters such as earthquakes and floods. Generate early warning indicators when monitored data exceeds safety thresholds or identifies abnormal patterns. Generate dam stability analysis reports regularly, including changes in key parameters, early warning conditions, risk assessments, etc. Use data visualization technology to display monitoring data and analysis results in the form of graphics or charts to improve information readability.

[0022] Stability early warning: when the stability status of the dam is analyzed to be abnormal, an early warning is generated; Obtain early warning indicators and classify warnings into different levels, such as warning, severe warning, emergency, etc., according to the severity of the anomaly. When the monitoring data exceeds the threshold or the anomaly detection algorithm identifies an abnormal pattern, an early warning signal is automatically generated; the early warning signal is converted into an easy-to-understand format, including information such as the warning level, time, location, possible impact, etc., and the early warning information is quickly delivered to relevant personnel and institutions through appropriate communication channels, such as SMS, email, application push, etc.; According to the warning level, the corresponding emergency response mechanism and plan are automatically or manually triggered, and the detailed information of all warning events is recorded, including the warning time, parameters, level and measures taken; in the monitoring system, the warning information is displayed through visual means such as charts and maps. On the basis of automatic warning, a manual confirmation link is set up to avoid false alarms and ensure timely intervention; collect on-site feedback after the warning, evaluate the accuracy and response effect of the warning system, and continuously optimize the warning algorithm and threshold setting based on feedback and actual response.

[0023] Abnormal analysis uses machine learning and artificial intelligence technology to extract useful information from monitoring data to analyze whether there are abnormalities in the sensor; Collect data from high-precision sensors placed at key locations on the dam, integrate data from different sensors, and prepare a unified data set for analysis; clean the data, remove noise and outliers, standardize the data, and adjust the data from different sensors to the same range; then perform multi-dimensional anomaly analysis on the processed data, including: Statistics and trend analysis: calculate key statistical indicators, such as mean and standard deviation, analyze data trends, and check whether there is an unexpected continuous increase or decrease; threshold check: set normal working thresholds according to sensor specifications and historical data, and check whether real-time data exceeds the preset threshold; cross-validation and consistency check: compare data from different sensors at the same location or under similar conditions to identify inconsistencies in the data set; anomaly detection algorithm: apply machine learning algorithms such as isolation forest or single-class support vector machine to identify abnormal patterns and train models to distinguish normal and abnormal data; signal analysis: perform Fourier transform on data, analyze frequency components, identify abnormal frequencies that may be caused by sensor failure, and use time domain analysis methods such as autocorrelation and cross-correlation to check signal stability and consistency; Performance degradation assessment: Establish a sensor performance degradation model to predict the service life and replacement time of the sensor; Maintenance record analysis: Analyze the historical maintenance records of the sensor to determine the maintenance cycle and failure mode; Environmental factor analysis: Analyze the impact of environmental factors on sensor performance, such as temperature, humidity, pressure, etc.; Predictive maintenance algorithm applies predictive maintenance algorithms, such as hidden Markov models or deep learning models, to predict the failure probability of sensors.

[0024] Analyze the results of the abnormality analysis, evaluate the impact of the sensor abnormality, classify the abnormalities according to the severity and potential risks of the abnormalities, and sort the processing priorities. Specifically, the processing priority of the sensor abnormality is determined by comprehensively evaluating the judgment parameters of the sensor abnormality, where the judgment parameters include: Abnormal value deviation: calculate the deviation between abnormal value and normal value; change rate: measure the rate of change of sensor readings, rapid change or no change indicates abnormality; duration: the length of time the sensor abnormality lasts, long-term abnormality requires higher priority processing; historical failure rate: based on historical data, evaluate the frequency of sensor failure; performance degradation rate: the speed of sensor performance degradation estimated by the sensor performance degradation model; impact range: evaluate the impact range of sensor abnormalities on the overall performance of the dam stability monitoring system, and quantify it by percentage, recorded as the impact ratio; After normalizing the obtained outlier deviation, change rate, duration, historical failure rate, performance degradation rate and shadow-norm ratio, substitute them into the following formula: To obtain the prior evaluation value XPZ, where yp, bh, cx, ll, xt and yf are the abnormal value deviation, change rate, duration, historical failure rate, performance degradation rate and impact ratio respectively. They are preset weight coefficients for abnormal value deviation, change rate, duration, historical failure rate, performance degradation speed and shadow-norm ratio, and their values ​​are set between 1.12-1.28, 0.91-0.95, 1.02-1.18, 0.93-0.98, 1.03-1.05 and 0.85-0.92 respectively; and the obtained priority value XPZ is used as the evaluation criterion for measuring the priority of sensor abnormality processing; the priority values ​​XPZ obtained for all sensor abnormalities are sorted according to size, and the order of sorting is used as the basis for the order of abnormality processing.

[0025] Record specific exception analysis including exception description, cause, impact assessment and recommended action plan; notify the technical team, maintenance personnel and decision makers of the exception report in a timely manner.

[0026] Maintenance planning: perform sensor maintenance planning based on the results of sensor anomaly analysis, and implement regular sensor self-inspection and remote maintenance strategies to reduce sensor failure rates; Integrate the abnormal analysis results obtained by machine learning and artificial intelligence technology to evaluate the performance status of the sensor. Based on the abnormal analysis results, diagnose whether the sensor is faulty or has performance degradation; determine the sensors that need maintenance or replacement, and the maintenance priority; develop a detailed maintenance plan, including the maintenance schedule, required resources, and expected goals; assign maintenance tasks to the corresponding technicians or maintenance teams; Develop self-check procedures for sensors to enable them to perform self-diagnosis regularly; establish a remote maintenance system to enable technicians to remotely diagnose and maintain sensors; prepare the required spare parts and tools to facilitate rapid response to maintenance needs, perform regular self-check procedures for sensors, and promptly detect and report potential problems; For problems that can be solved remotely, remote maintenance is implemented; for maintenance tasks that require on-site operations, technicians are arranged to perform on-site maintenance; detailed information of all maintenance activities, including maintenance dates, activity types and results, is recorded, and maintenance cycles and plans are optimized based on maintenance results and sensor performance; the predictive maintenance model is updated with the latest maintenance data, the impact of maintenance activities on dam stability monitoring is evaluated, and potential risks are managed; after maintenance is completed, sensor performance continues to be monitored to ensure maintenance effectiveness; a feedback loop from maintenance to abnormality analysis is established to continuously improve maintenance strategies.

[0027] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A dam stability data monitoring and processing method, characterized in that: The following steps are involved: S1: Sensor deployment: According to the structural characteristics of the dam, corresponding sensors are deployed at different locations of the dam; S2: Data processing: collect and pre-process the data of the dam through the deployed sensors, and transmit the data after the pre-processing is completed; S3: Data monitoring, using data analysis algorithms to analyze the monitored data and identify the dam stability status; S4: Stability warning, generating warnings for abnormal monitored data; S5: Abnormal analysis, using machine learning and artificial intelligence technology to extract and analyze whether there are abnormal information from the monitoring data; S6: Maintenance planning: Perform sensor maintenance planning based on the results of sensor anomaly analysis and implement regular sensor self-inspection and remote maintenance strategies.

2. The dam stability data monitoring and processing method according to claim 1 is characterized in that: The specific steps of S5 are as follows: Collect sensor data from the dam and integrate it into a unified data set for subsequent anomaly analysis; Clean the data, remove noise and outliers, and standardize the data to ensure consistency of data from different sensors; Then perform multi-dimensional anomaly analysis on the preprocessed data; Build models to predict sensor life, analyze historical maintenance records, and determine maintenance cycles and failure modes; Analyze the impact of environmental factors on sensor performance and apply algorithms to predict sensor failure probability; Analyze the results of abnormality analysis, evaluate the impact of sensor abnormalities, classify abnormalities according to their severity and potential risks, and prioritize their processing; Record details of the anomaly, including description, cause, impact assessment and action plan, and notify relevant personnel promptly; Develop maintenance plans and strategies based on abnormal analysis results.

3. The dam stability data monitoring and processing method according to claim 2 is characterized in that: The specific operation steps of performing multi-dimensional abnormal analysis on the pre-processed data in S5 are as follows: Statistics and trend analysis: Calculate statistical indicators and check whether there are unexpected continuous increases or decreases; Threshold check: Set normal working thresholds according to sensor specifications and historical data, and check whether real-time data exceeds the preset thresholds; Cross-validation and consistency checking: Compare data from different sensors at the same location or under the same conditions to identify inconsistencies in the data set; Anomaly detection algorithms: Apply the isolation forest learning algorithm or the one-class support vector machine machine learning algorithm for abnormal pattern recognition and train the model to distinguish between normal and abnormal data; Signal analysis: Perform Fourier transform on the data to analyze the frequency components and identify abnormal frequencies caused by sensor failure; use time domain analysis methods such as autocorrelation and cross-correlation to check the stability and consistency of the signal.

4. The dam stability data monitoring and processing method according to claim 2 is characterized in that: The specific operation steps for sorting and processing the exceptions in S5 are as follows: The processing priority of sensor abnormality is determined by comprehensively evaluating the evaluation parameters of sensor abnormality, where the evaluation parameters include: Outlier Deviation: Calculate the deviation between outliers and normal values; Rate of change: Measures the rate at which sensor readings change. Rapid changes or no change indicate an anomaly. Duration: The length of time the sensor abnormality lasts; Historical failure rate: Evaluate the frequency of sensor failures based on historical data; Performance degradation rate: the rate at which sensor performance decreases as estimated by the sensor performance degradation model; Impact range: Evaluate the impact range of sensor anomalies on the overall performance of the dam stability monitoring system and quantify it by percentage, recorded as the impact ratio; After normalizing the obtained abnormal value deviation, change rate, duration, historical failure rate, performance degradation speed and shadow-norm ratio, a prior evaluation value is obtained by weighted summation, and the obtained prior evaluation value is used as an evaluation criterion for measuring the priority of sensor abnormality processing; The prior evaluation values ​​obtained from all sensor anomalies are sorted by size, and the sorting order is used as the basis for the order of anomaly processing.

5. The dam stability data monitoring and processing method according to claim 1 is characterized in that: The specific operation steps of S6 are as follows: Combine the abnormal analysis results obtained by machine learning and artificial intelligence technology to evaluate the performance of the sensor. Based on the abnormal analysis results, diagnose whether the sensor is faulty or has performance degradation; Get the sensors in S5 that need maintenance or replacement, and the priority of processing; Develop maintenance plans, including maintenance schedules, required resources, and expected goals; Assign maintenance tasks to appropriate technicians or maintenance teams; Develop a self-diagnosis program for the sensor so that the sensor can perform self-diagnosis regularly; Establish a remote maintenance system to enable technicians to remotely diagnose and maintain sensors; Prepare the necessary spare parts and tools to quickly respond to maintenance needs, perform regular self-check procedures on sensors, and promptly detect and report potential problems; Implement remote maintenance for problems that can be solved remotely; For maintenance tasks applicable to on-site operations, arrange technicians to perform on-site maintenance; Record detailed information of all maintenance activities, including maintenance date, activity type and results, and optimize maintenance cycles and plans based on maintenance results and sensor performance; Update predictive maintenance models with the latest maintenance data, assess the impact of maintenance activities on dam stability monitoring, and manage potential risks; After maintenance is completed, continue to monitor sensor performance to ensure maintenance effectiveness; Establish a feedback loop from maintenance to exception analysis to continuously improve maintenance strategies.

6. The method for monitoring and processing dam stability data according to claim 1 is characterized in that: The specific operation steps of S1 are as follows: Analyze the dam material, shape, stress distribution, and determine the key monitoring areas of the dam; Identify critical monitoring areas including upstream face, downstream face, foundation, joints and drainage systems; Determine the sensor type and sensor accuracy according to monitoring requirements to ensure data accuracy; Design sensor layout to achieve comprehensive coverage of all critical areas; Develop an installation plan including location, mode, and fixing method; Establish a communication network between sensors and data processing centers; Integration testing of sensors and communication networks before installation; Calibrate the sensor and conduct field verification tests after installation to ensure data accuracy; Record the installation information of the sensor and create files for query and maintenance.

7. The dam stability data monitoring and processing method according to claim 1 is characterized in that: The specific operation steps of S2 are as follows: Use sensors to collect the strain, displacement, pressure and temperature parameters of the dam in real time; Ensure the time synchronization of all sensor data and maintain data consistency; Clean the raw data, remove noise and outliers, standardize the data, and eliminate the differences in range and accuracy of different sensors; Integrate data from different sensors to form a unified data set, store pre-processed data in a database, and create an index for query; Compressing data reduces storage requirements, and encrypting ensures data security; Transmit data via wired or wireless networks; Label the data to provide feature identification for the analysis algorithm; Assess data quality to ensure it meets analytical requirements; Implement real-time monitoring, quickly respond to data collection issues, and regularly back up data to prevent loss.

8. The dam stability data monitoring and processing method according to claim 1 is characterized in that: The specific operation steps of S3 are as follows: Receive pre-processed data and verify its integrity and consistency; Extract stability features, including displacement change rate and strain peak, and set thresholds; Analyze data in real time to monitor whether key features exceed safety thresholds; Conduct time series analysis to identify changing trends and unusual patterns; Combine multiple parameters for a comprehensive assessment to get a comprehensive view of dam stability; Apply machine learning and artificial intelligence algorithms to improve the accuracy of data analysis; Based on the results, the dam health status is evaluated and stability is determined; Assess the impact of natural hazards on dam stability; Generate alerts when data exceeds thresholds or identifies anomalies; Generate stability analysis reports regularly and present the results using data visualization techniques.

9. The method for monitoring and processing dam stability data according to claim 1, characterized in that: The specific operation steps of S4 are as follows: Determine the severity of the anomaly and set different warning levels based on the data analysis results; Convert warning signals into a format that includes level, time, location and impact; Deliver early warning information using SMS, email and push communication channels; Automatically or manually initiate emergency response mechanisms based on warning levels; Keep detailed records of all warning events and related information; Use charts and maps to display warning information in the monitoring system; Set up a manual confirmation process to avoid false alarms and ensure timely response; Collect feedback, evaluate the effectiveness of the early warning system, and optimize the early warning algorithm.

Images