Method and system for sensing and integrating monitoring information of silt dam

Abstract

The invention relates to the technical field of silt dam monitoring, and discloses a silt dam monitoring information sensing integration method and system, and the method comprises the steps: collecting the monitoring data of a silt dam, carrying out the short-time risk prediction at an edge node according to the monitoring data, triggering local early warning based on the short-time risk prediction, and starting a response mechanism; a self-adaptive cooperation mechanism is established between edge nodes, and sensor abnormity and local disaster chains are identified through exchange and comparison of monitoring data between the nodes; and the cloud integrates the monitoring data uploaded by the edge nodes, generates a global risk prediction model through dynamic data fitting, and performs hierarchical emergency response according to a risk prediction result. According to the method, the safety management efficiency of the silt dam is improved, and a scientific support and an intelligent decision foundation are provided for flood prevention and fighting.

Description

Technical Field

[0001] The present invention relates to the technical field of silt dam monitoring, and in particular to a silt dam monitoring and surveillance information perception integration method and system. Background Art

[0002] Warping dams are an important type of water conservancy facility, widely used in soil and water conservation and agricultural irrigation in mountainous areas. Their stable operation is directly related to regional ecological security and economic development. However, due to complex terrain, changeable climate and natural aging during long-term operation, warping dams face a variety of potential risks, such as rainwater erosion, dam landslides, and flood impacts. Especially in extreme weather and flood-prone seasons, the safe operation of warping dams has become a top priority in flood prevention and control.

[0003] Existing silt dam monitoring technology mainly relies on online monitoring equipment such as video surveillance, water level sensors and rainfall sensors, and uses Internet transmission technology to achieve sharing and centralized analysis of monitoring data. These technical means have played an important role in improving the perception of the operating status of silt dams. However, traditional monitoring systems usually rely on cloud processing architectures, and their monitoring and early warning functions are highly dependent on network transmission. Once communication is interrupted or delayed, monitoring data cannot be uploaded to the cloud for processing in a timely manner, and the generation of early warning information may be delayed, thereby affecting the decision-making efficiency and emergency response capabilities of flood control work.

[0004] In recent years, with the development of emerging technologies such as the Internet of Things (IoT), edge computing and artificial intelligence, new solutions have been provided for the monitoring and early warning system of silt dams. Among them, the application of edge computing technology has attracted particular attention. Edge computing reduces the dependence on network transmission by processing and analyzing data on the device side close to the data source, and significantly improves the real-time performance and ability to respond to emergencies of the system. This technical architecture can not only maintain the independent operation of the system in the event of communication interruption, but also generate preliminary warnings based on real-time monitoring data, providing a reliable basis for subsequent decision-making. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the existing technology is highly dependent on network transmission, and the monitoring data cannot be uploaded to the cloud for processing in time when the communication is poor, and there are deficiencies in real-time, reliability and robustness.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for sensing and integrating monitoring information of a silt dam, comprising: collecting monitoring data of the silt dam, performing short-term risk prediction at an edge node according to the monitoring data, triggering a local early warning based on the short-term risk prediction, and starting a response mechanism; Establish an adaptive coordination mechanism between edge nodes to identify sensor anomalies and local disaster chains through the exchange and comparison of monitoring data between nodes; The cloud integrates the monitoring data uploaded by the edge nodes, generates a global wind measurement model through dynamic data fitting, and implements hierarchical emergency response based on risk prediction results.

[0008] As a preferred solution of the method for integrating monitoring information perception of a silt dam described in the present invention, the monitoring data includes environmental data, hydrological data, dam body data, video image data and data surrounding the silt dam area.

[0009] As a preferred solution of the method for sensing and integrating the monitoring information of a silt dam described in the present invention, the short-term risk prediction includes: using the ARIMA model to establish a lightweight prediction model based on time series, using the data of the edge node to train in the cloud, and deploying the trained model to the corresponding edge node; The edge node collects monitoring data in real time and The rainfall and water level data are input into the lightweight prediction model to obtain the short-term rainfall and water level forecast values ​​in the future. The input data is updated every minute, and the subsequent short-term data is re-predicted to form a continuous prediction result. According to historical data, rainfall thresholds, water level thresholds and water level rise rate thresholds are set respectively. When the predicted value of the lightweight prediction model exceeds the set threshold, a local warning is triggered, and the risk level and prediction results are simultaneously reported to the cloud.

[0010] As a preferred solution of the method for sensing and integrating the monitoring information of a silt dam described in the present invention, the adaptive coordination mechanism includes establishing a disaster chain model in the cloud, the edge node standardizing the collected real-time monitoring data, updating the status of each factor in the disaster chain, and realizing the coordinated identification of the regional disaster chain; Each edge node sends core monitoring data and prediction results to adjacent edge nodes at regular intervals. After receiving the data, the adjacent edge nodes compare the timestamps. If the data is delayed or advanced, it is marked as a time anomaly, triggering a secondary check. The secondary verification calculates the time deviation between the data of adjacent edge nodes by comparing the transmission data with the local monitoring data. If the time deviation is greater than the set threshold, the compensation mechanism is triggered; The compensation mechanism interpolates and compensates for missing and abnormal data, verifies the compensation data using the disaster chain model, and re-updates the status of each factor in the disaster chain using the verified compensation data; An alarm is issued based on the data output in the disaster chain model. When the upstream edge node of the disaster chain triggers the alarm, a coordinated warning signal is sent to the middle and downstream edge nodes of the disaster chain to achieve coordinated warning.

[0011] As a preferred solution of the method for sensing and integrating the monitoring information of a silt dam according to the present invention, the establishment of a disaster chain model in the cloud includes determining the triggering factor, transmission factor and termination factor of the disaster chain according to the monitoring data in the cloud, and using correlation analysis to determine the strongly correlated variables between the data: ; in, express and The correlation between express Factor Monitoring data; express Factor Monitoring data; express The mean of the factors; express The mean of the factors; After determining the strongly correlated variables, a causal test is performed to determine whether one variable has predictive power for another variable through the lag relationship of the time series: ; in, Indicates the current time The target variable; A constant term representing the base level of the target variable; Indicates The value of the independent variable at the time lag period; express The regression coefficient of represents the lag order; express The regression coefficient of Indicates The target variable value after the time lag period; the variables that pass the causal test are used as chain nodes, and the direction of the causal chain is confirmed according to the time lag relationship; A disaster chain model is constructed using a Bayesian network. Nodes represent causal factors, and directed edges represent causal relationships. A conditional probability table is defined for each node to describe the impact of the parent node state on the child node state. Bayesian reasoning is used to update the conditional probability, and the constructed disaster chain model is sent to the edge nodes for application.

[0012] As a preferred solution of the method for sensing and integrating the monitoring information of a silt dam described in the present invention, the collaborative identification of the regional disaster chain includes: the edge node obtains the monitoring values ​​of the trigger factor, the transmission factor and the termination factor according to the monitoring data collected in real time; if the monitoring value of the trigger factor reaches the preset trigger factor threshold, the trigger factor is marked as a triggered state; based on the state of the trigger factor, the current state probability of the transmission factor is calculated by Bayesian reasoning: ; in, Indicates the trigger factor Under conditions that have been activated, the transfer factor Probability of being activated; It represents the probability of simultaneous activation of the trigger factor and the transmission factor; Indicates trigger factor The prior probability of ; Based on the status of the transmission factor, the risk level of the termination factor is calculated by Bayesian reasoning; Dynamically calculate the time delay of chain propagation according to the status of trigger factors and transmission factors; Each node calculates the risk score of the current area based on the calculated state probability: ; in, represents the risk score; represents the total number of causal factors; represents the weight of the causal factor; Indicates the current state probability of the causal factor; based on the risk score The value of determines the chain status; If all factors in the chain are activated, the chain is judged to be complete, triggering a regional warning. The upstream node of the disaster chain sends a coordinated warning signal to the downstream node of the disaster chain, and uploads the status to the cloud for global analysis.

[0013] As a preferred solution of the method for sensing and integrating the monitoring information of a silt dam described in the present invention, the global risk prediction model includes: the cloud regularly obtains real-time monitoring data from each edge node, extracts key features through time series analysis, and obtains key feature indicators; Historical operating data is used as training samples to establish a global risk prediction model. The cloud uses least squares to update the parameters of the global risk prediction model based on real-time monitoring data, obtains the global risk score, and conducts hierarchical emergency response based on the global risk score.

[0014] A silt dam monitoring and surveillance information perception integration system using any of the methods described in the present invention, wherein: a short-term prediction module collects monitoring data of the silt dam, performs short-term risk prediction at an edge node based on the monitoring data, triggers a local warning based on the short-term risk prediction, and starts a response mechanism; an edge collaboration module establishes an adaptive collaboration mechanism between edge nodes, identifies sensor anomalies and local disaster chains through the exchange and comparison of monitoring data between nodes; a global prediction module integrates the monitoring data uploaded by the edge nodes in the cloud, generates a global risk prediction model through dynamic data fitting, and performs hierarchical emergency response according to the risk prediction results.

[0015] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0016] A computer-readable storage medium stores a computer program, comprising: when the computer program is executed by a processor, the steps of implementing any one of the methods of the present invention are implemented.

[0017] Beneficial effects of the present invention: The present invention integrates multi-source monitoring data uploaded by edge nodes through the cloud, and uses dynamic data fitting and global risk prediction models to achieve accurate assessment of the operating status of the silt dam and hierarchical emergency response. Through the combination of dynamic risk prediction and multi-logical condition judgment, the present invention significantly improves the real-time perception capability of complex disaster chains, and can timely identify sudden risks and link regional warnings. At the same time, the dynamic update mechanism based on the model enhances the system's adaptability to environmental changes and effectively reduces the misjudgment and missed judgment rates. The overall design improves the safety management efficiency of the silt dam and provides scientific support and intelligent decision-making basis for flood prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 An overall flow chart of a method for integrating monitoring and surveillance information of a silt dam provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for sensing and integrating silt dam monitoring and surveillance information, comprising: S1: Collect monitoring data of the silt dam, make short-term risk predictions at edge nodes based on the monitoring data, trigger local warnings based on short-term risk predictions, and start response mechanisms.

[0022] Furthermore, in the silt dam monitoring system, edge nodes collect and integrate monitoring data through multi-source sensors. The monitoring data covers environmental data (such as rainfall intensity, wind speed), hydrological data (such as water level changes, flow velocity), dam body data (such as stress, displacement), video image data (such as real-time monitoring of dam body cracks), and data around the silt dam area (such as upstream water and downstream basin pressure). Each type of monitoring data is collected in real time through an independent sensor network, and a unified data format conversion and time synchronization are performed at the edge node.

[0023] Specifically, environmental data collection obtains real-time environmental information through rain gauges, anemometers and other equipment. The data units are such as mm / h or m / s, and dynamic trend analysis is performed after entering the system. Hydrological data is provided in real time by water level meters and flow meters. The sampling frequency is minute-level. The data is stored in a sequence to capture the dynamic changes in hydrology within the basin. The collection of dam data is based on stress sensors and displacement measurement devices. Through the sensor arrays arranged at key parts of the dam body, the internal stress field distribution, crack expansion and surface displacement amplitude of the dam body are monitored. The real-time data generated ensures comprehensive control of the safety of the dam structure. Video image data is collected by high-resolution cameras and combined with AI image analysis algorithms to monitor the appearance of the dam body in real time and quickly detect abnormal conditions. The data around the silt dam area is transmitted by upstream monitoring stations to dynamically capture the overall basin operation status.

[0024] Furthermore, an ARIMA lightweight prediction model based on time series is deployed on the edge nodes. The lightweight prediction model is trained by the cloud based on historical monitoring data and sent to each edge node for real-time short-term risk prediction. In actual operation, the edge node collects rainfall and water level data of the current time window every minute, inputs the data into the prediction model, predicts the rainfall and water level change trend in the next 10 minutes, and updates the input data every minute to form a continuous prediction result, ensuring dynamic control of short-term trends.

[0025] In short-term risk prediction, the system sets thresholds for rainfall, water level, and water level rise rate based on historical data as key conditions for triggering warnings. The threshold setting is based on historical disaster data and regional characteristics analysis. For example, the rainfall threshold may be set to 50mm / h. When the predicted value exceeds this value, it indicates that extreme rainfall events may occur; the water level threshold refers to the safety warning line (such as 10m), and the water level rise rate threshold is used to capture sudden flood phenomena. When the output value of the prediction model exceeds any threshold, the edge node immediately triggers a local warning, and reports the risk level and prediction results to the cloud through the communication module. The risk level is calculated by the edge node based on the magnitude of the predicted value exceeding the threshold, and is divided into low risk (close to the threshold), medium risk (exceeding the threshold by a certain range) and high risk (significantly exceeding the threshold).

[0026] When the local warning is triggered, the edge node starts the response mechanism, which includes increasing the frequency of collecting relevant monitoring data (such as increasing the original sampling frequency from one minute to once every 10 seconds), activating the backup sensor network simultaneously to ensure data collection redundancy, and sending risk signals to downstream related nodes to prepare for emergencies in advance. At the same time, the video surveillance data will enter a high-priority processing mode, and the system will use AI algorithms to analyze subtle changes in the video stream in real time, such as the expansion rate of cracks in the dam or abnormal vibrations, to provide more detailed risk information. Through the local computing power of the edge node, the response mechanism can be triggered and implemented in a very short time, buying more time for disaster prevention and mitigation.

[0027] S2: Establish an adaptive coordination mechanism between edge nodes to identify sensor anomalies and local disaster chains through the exchange and comparison of monitoring data between nodes.

[0028] The adaptive collaborative mechanism is a dynamic data interaction and analysis method based on distributed edge computing. Through real-time data sharing and collaborative processing between edge nodes, the system's ability to detect anomalies, correct data, and identify regional disaster chains is improved. In the silt dam monitoring scenario, the adaptive collaborative mechanism enables multiple edge nodes to form a dynamic, interdependent collaborative network, and uses the spatiotemporal correlation of data between nodes to make up for the monitoring blind spots or errors that may occur in a single node.

[0029] Furthermore, a disaster chain model is established in the cloud, and the edge nodes standardize the collected real-time monitoring data, update the status of each factor in the disaster chain, and realize the coordinated identification of regional disaster chains.

[0030] The cloud establishes a disaster chain model based on the data of the silt dam. The cloud obtains key data for establishing the disaster chain model from monitoring data and historical data, including rainfall, water level, dam displacement, microseismic signals, etc. in real-time data; records of floods and rainfall events in historical data, stress and deformation characteristics of the dam body, etc.; as well as geographic information data such as upstream and downstream watershed distribution, terrain slope, and dam body materials.

[0031] The cloud determines the disaster triggering factors (such as rainfall), transmission factors (such as water level changes) and termination factors (such as dam landslides) based on the monitoring data, and uses correlation analysis to determine the strongly correlated variables between the data: ; in, express and The correlation between express Factor Monitoring data; express Factor Monitoring data; express The mean of the factors; express The mean of the factors.

[0032] According to the calculated correlation coefficient ,like is greater than the set correlation threshold, e.g. , then it is believed that and There is a strong correlation. After determining the strongly correlated variables, a causal test is performed to determine whether one variable has predictive power for another variable through the lag relationship of the time series: ; in, Indicates the current time The target variable; A constant term representing the base level of the target variable; Indicates The value of the independent variable at the time lag period; express The regression coefficient of represents the lag order; express The regression coefficient of Indicates The target variable value after a time lag period is used; the variables that pass the causal test are used as chain nodes, and the direction of the causal chain is confirmed according to the time lag relationship.

[0033] A disaster chain model is constructed using a Bayesian network. Nodes represent causal factors, and directed edges represent causal relationships. A conditional probability table is defined for each node to describe the impact of the parent node state on the child node state. Bayesian reasoning is used to update the conditional probability, and the constructed disaster chain model is sent to the edge nodes for application.

[0034] It should be noted that the key factors of the disaster chain include triggering factors, transmission factors, and termination factors. The triggering factor is the starting point of the disaster chain, which refers to the external environmental variables that trigger the disaster process and is usually the driving factor that directly affects the downstream variables. The transmission factor is the middle link of the disaster chain, reflecting the degree of influence of the triggering factor on the spread and the trend of regional diffusion. The termination factor is the end link of the disaster chain, indicating the final impact of the disaster on the physical structure or ecological environment.

[0035] It should also be noted that the determination of the causal chain needs to be established according to the actual environment. The monitoring environment of the silt dam has obvious regional characteristics, including topography, hydrological conditions, climate change, dam structure and surrounding ecology, which will affect the causal chain of disaster transmission. Specifically, there are differences in the geographical environment and operating conditions of different silt dams. For example, the impact of rainfall on the water level of silt dams in mountainous areas is more direct; silt dams in plain areas may be more affected by flow velocity and sediment accumulation.

[0036] There are different types of disasters for silt dams, including but not limited to floods caused by rainfall, changes in dam pressure caused by silt accumulation, and deformation of dam bodies caused by micro-earthquakes or landslides.

[0037] In actual application, the cloud sends the established disaster chain model to the edge node. The edge node obtains the monitoring values ​​of the trigger factor, transmission factor and termination factor based on the real-time monitoring data collected. If the monitoring value of the trigger factor reaches the preset trigger factor threshold, the trigger factor is marked as triggered. Based on the state of the trigger factor, the current state probability of the transmission factor is calculated through Bayesian reasoning: ; in, Indicates the trigger factor Under conditions that have been activated, the transfer factor Probability of being activated; It represents the probability of simultaneous activation of the trigger factor and the transmission factor; Indicates trigger factor The prior probability of the termination factor is calculated by Bayesian reasoning based on the status of the transmission factor.

[0038] According to the status of the trigger factor and the transmission factor, the time delay of the chain propagation is dynamically calculated: ; in, represents the time delay of chain propagation; Indicates the delay time from trigger factor to transmission factor; Indicates the delay time from the transfer factor to the termination factor.

[0039] The delay time is analyzed in the cloud based on real-time data, and the time difference analysis is performed on the change of trigger factors (such as rainfall reaching a threshold) and the response of transfer factors (such as a significant increase in the rate of change of water level) in real-time data. The activation time of the trigger factor and the response time of the transfer factor are monitored, and the difference between the two times is the delay time from the trigger factor to the transfer factor.

[0040] Each node calculates the risk score of the current area based on the calculated state probability: ; in, represents the risk score; represents the total number of causal factors; represents the weight of the causal factor; Indicates the current state probability of the causal factor; based on the risk score The value of is used to determine the chain status; if all factors in the chain are activated, the chain is considered complete, triggering a regional warning, and the upstream node of the disaster chain sends a coordinated warning signal to the downstream node of the disaster chain, and uploads the status to the cloud for global analysis.

[0041] Furthermore, each edge node sends core monitoring data and prediction results to adjacent edge nodes at regular intervals. After receiving the data, the adjacent edge nodes compare the timestamps. If the data is delayed or advanced, it is marked as a time anomaly, triggering a secondary check.

[0042] The edge node regularly sends the current core monitoring data and short-term risk prediction results to the neighboring nodes through low-latency communication protocols (such as MQTT or CoAP) to ensure data synchronization in the region. The core monitoring data includes real-time collected environmental data, dam data, and the change trend of key factors in the short and medium term of the predicted value. The comparison of timestamps is achieved through high-precision clocks synchronized between nodes to ensure the timeliness and accuracy of the transmitted data. Lagging or leading time anomalies may indicate communication delays between nodes, clock drift, or hardware failures.

[0043] The secondary verification calculates the time deviation between the data of adjacent edge nodes by comparing the transmitted data with the local monitoring data. If the time deviation is greater than the set threshold, the compensation mechanism is triggered. The compensation mechanism interpolates and compensates for the missing and abnormal data, verifies the compensation data using the disaster chain model, and re-updates the status of each factor in the disaster chain using the verified compensation data.

[0044] An alarm is issued based on the data output in the disaster chain model. When the upstream edge node of the disaster chain triggers an alarm, a coordinated warning signal is sent to the downstream edge nodes of the disaster chain to achieve coordinated warning. By establishing a hierarchical linkage mechanism between the upstream and downstream edge nodes of the disaster chain, the downstream nodes can adjust their own monitoring strategies in real time according to the coordinated warning signals from the upstream to ensure unified perception and efficient response of risks in the region. The coordinated warning signal transmission adopts a redundant channel mechanism to ensure that the signal can still be transmitted through the backup path in the event of a single communication link failure.

[0045] S3: The cloud integrates the monitoring data uploaded by the edge nodes, generates a global risk prediction model through dynamic data fitting, and implements hierarchical emergency response based on the risk prediction results.

[0046] The cloud comprehensively integrates and analyzes the monitoring data transmitted from the edge nodes. Its design goal is to determine the safety status of the environment and facilities of the silt dam in real time, and make corresponding hierarchical emergency response decisions based on risk prediction results.

[0047] Furthermore, the cloud will regularly communicate with the edge nodes distributed on site. These edge nodes not only monitor traditional basic environmental and structural indicators such as rainfall, water level and dam pressure, but also collect multi-dimensional information including electromagnetic interference, noise power density, common mode noise voltage, vibration acceleration, temperature gradient change, etc. through multiple types of sensors. The cloud performs unified conversion and standardization on the received multi-source heterogeneous data, and through predefined correction and compensation strategies, eliminates outliers, fills in missing data, and ensures the accuracy and consistency of input data.

[0048] After the data quality is guaranteed, the cloud performs feature extraction and dynamic data fitting on the processed data set. Referring to the established disaster chain model and regional characteristic parameters, the cloud extracts key feature indicators from long-term historical data and short-term real-time data through time series analysis methods and model-driven technology. The time series analysis method can use sliding average models, long short-term memory networks, etc. Key features may include (but are not limited to) load change rate, stress change rate, equipment health state energy, dynamic trend of environmental interference power density, and steady-state and fluctuation characteristics of speed correction value.

[0049] The cloud side realizes real-time updating and fitting of risk scores by inputting key characteristic indicators into global risk prediction models, such as Logistic regression models, random forest models or support vector machines (SVM), thereby dynamically reflecting the comprehensive operational risks of the silt dam system during the continuous data input process.

[0050] After generating the preliminary risk score, the cloud will not directly make a final decision, but will make further judgments and screening through multiple logical condition judgment modules. To this end, the cloud has built-in multiple sets of logical judgment criteria, covering operation specifications of different dimensions and time scales. For example, if the rate of decrease of system load, the rate of change of steam pressure, the status of equipment tripping signal and other key parameters in the past 30 minutes meet the preset "planned operation" standards, the risk level will not be easily raised to high risk. On the contrary, if any of the logical conditions is not met or a sudden tripping signal occurs, the cloud will judge that the risk level has increased significantly and adjust the risk level accordingly.

[0051] After the above multi-level dynamic assessment, the cloud finally forms a clear risk level classification result. When the system is judged to be low risk, the cloud maintains the regular monitoring and data analysis frequency without special intervention. When the system enters a medium-risk state, the cloud will automatically increase the frequency of data reporting, strengthen the monitoring density of specific subsystems, and require edge nodes to increase the frequency of correcting sensor data to ensure that potential problems can be discovered before the risk further expands. If the system enters a high-risk state, the cloud will immediately issue a coordinated early warning instruction to the downstream node, and closely monitor the data changes during the emergency process after the protective measures are implemented, and notify the management personnel or relevant decision-makers. This coordinated early warning signal not only requires downstream nodes to implement stricter monitoring strategies, but may also trigger automated protection devices (such as flood gate adjustment) and link the on-site emergency team to prepare for response.

[0052] This embodiment also provides an integrated system for monitoring and surveillance information perception of a silt dam, including a short-term prediction module, which collects monitoring data of the silt dam, performs short-term risk prediction at the edge node based on the monitoring data, triggers local warnings based on the short-term risk prediction, and starts a response mechanism; an edge collaboration module, which establishes an adaptive collaboration mechanism between edge nodes, identifies sensor anomalies and local disaster chains through the exchange and comparison of monitoring data between nodes; and a global prediction module, which integrates the monitoring data uploaded by the edge nodes in the cloud, generates a global risk prediction model through dynamic data fitting, and implements hierarchical emergency responses based on the risk prediction results.

[0053] Example 2, the following is an embodiment of the present invention, which provides a method for integrating information perception of a silt dam monitoring and supervision. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0054] This embodiment is verified and optimized based on the field test conditions of the silt dam monitoring and early warning system to prove that through the adaptive coordination mechanism and short-term risk prediction model between edge nodes, regional disaster chains can be efficiently identified, abnormal data can be compensated, and the accuracy of early warning can be improved. The test environment selected a medium-sized mountain silt dam with complex terrain. The upstream catchment area is mostly valley terrain, and the downstream is a relatively flat irrigated farmland area. In order to restore the real working conditions, multiple edge nodes will be deployed around the silt dam: upstream nodes (denoted as A), midstream nodes (B), and downstream nodes (C).

[0055] During the experiment, A, B, and C collected environmental and hydrological data of the current time window from their respective sensors every minute, and used the lightweight ARIMA prediction model deployed on B to make short-term predictions for the received rainfall and water level for the next 10 minutes. The model parameters were trained and optimized by the cloud based on the historical monitoring data of the past year before the experiment to ensure the adaptability of the model to the current season, climate conditions and hydrological characteristics of the dam. B, as the core prediction node, updates the prediction data every minute under normal conditions. If the predicted value exceeds the preset threshold (such as rainfall >50mm / h or water level >10m), B immediately triggers a local warning and increases the collection frequency of related sensors (from every minute to every 10 seconds). At the same time, it reports the risk level and prediction results to the cloud and sends a risk signal to C. After receiving the coordinated warning signal from the upstream node, C can adjust its own monitoring strategy in advance, appropriately increase the monitoring density of sensitive areas or activate backup sensors to ensure that the downstream area has more time to respond to possible flood peaks or dam abnormalities.

[0056] To verify the adaptive coordination mechanism between edge nodes, A and B, B and C send core monitoring data and prediction results to each other every 2 minutes. When the adjacent nodes receive the data, they will compare it according to the timestamp of the data. If there is an obvious lag or advance (such as a deviation of more than 100ms), it will be marked as a time anomaly and trigger a secondary check. In this experiment, the rainfall, water level, and water level rise rate under different rainfall conditions will be analyzed, and the data interaction between nodes, time deviation, and changes in prediction accuracy before and after compensation will be recorded. By collecting multiple sets of experimental data, it can be proved that the method of the present invention can effectively improve data quality and warning accuracy, and compare the shortcomings of the existing single-node independent analysis mode. According to the obtained experimental data, analysis is performed, and a set of data used for analysis is shown in Table 1.

[0057] Table 1 Experimental data table

[0058] A, B, and C in the table data represent a complete local disaster chain. The compensation data reflects the unified result after interpolation compensation for abnormal or missing monitoring data under the coordination mechanism between nodes. It is the value after correcting the time deviation or data deviation of the original monitoring data between different nodes.

[0059] It should be noted that in actual applications, the compensation data is calculated through the adjacent nodes of the edge node, and is not necessarily the edge node data of the disaster chain. In this embodiment, in order to simplify the display, the average value is calculated through the disaster chain node data, which is only used to reflect the beneficial effects of the edge node coordination mechanism.

[0060] From the table data, we can see that the rainfall data of the upstream, midstream and downstream nodes are 42 mm / h, 45 mm / h and 43 mm / h respectively, indicating that there are certain differences between the monitoring points, but the deviation is not large. The existence of a time difference of 85 ms may cause data delay. After compensation, the rainfall is adjusted to 44 mm / h, and the weighted average of the three-point data is taken. This compensation mechanism ensures the overall consistency of regional rainfall and avoids the interference of single-node data anomalies on system decision-making.

[0061] The water level monitored by the midstream node was 9.7 m, close to the warning line, and the water level rise rate of 0.52 m / h exceeded the threshold, indicating that the regional hydrological situation was in a high-risk state. After compensation, the water level and rate were adjusted to 9.65 m and 0.51 m / h, respectively, which were consistent with the actual critical conditions. This adjustment avoided the possibility of misjudging high or low risks. In the end, the comprehensive risk score was 0.68, and the risk level was medium risk, providing a scientific and reasonable basis for emergency response for downstream nodes and management.

[0062] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0064] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0065] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for sensing and integrating silt dam monitoring and surveillance information, characterized in that: include: Collect monitoring data of the silt dam, make short-term risk predictions at edge nodes based on the monitoring data, trigger local warnings based on short-term risk predictions, and initiate response mechanisms; Establish an adaptive coordination mechanism between edge nodes to identify sensor anomalies and local disaster chains through the exchange and comparison of monitoring data between nodes; The cloud integrates the monitoring data uploaded by the edge nodes, generates a global risk prediction model through dynamic data fitting, and implements hierarchical emergency response based on the risk prediction results.

2. A method for sensing and integrating silt dam monitoring and surveillance information according to claim 1, characterized in that: The monitoring data includes environmental data, hydrological data, dam body data, video image data and data around the silt dam area.

3. A method for sensing and integrating silt dam monitoring and surveillance information according to claim 2, characterized in that: The short-term risk prediction includes using the ARIMA model to establish a lightweight prediction model based on time series, using the data of the edge node for training in the cloud, and deploying the trained model to the corresponding edge node; The edge node collects monitoring data in real time and The rainfall and water level data are input into the lightweight prediction model to obtain the short-term rainfall and water level forecast values ​​in the future. The input data is updated every minute, and the subsequent short-term data is re-predicted to form a continuous prediction result. According to historical data, rainfall thresholds, water level thresholds and water level rise rate thresholds are set respectively. When the predicted value of the lightweight prediction model exceeds the set threshold, a local warning is triggered, and the risk level and prediction results are simultaneously reported to the cloud.

4. A method for sensing and integrating silt dam monitoring and surveillance information according to claim 3, characterized in that: The adaptive collaborative mechanism includes establishing a disaster chain model in the cloud, where the edge nodes standardize the collected real-time monitoring data, update the status of each factor in the disaster chain, and realize the collaborative identification of regional disaster chains; Each edge node sends core monitoring data and prediction results to adjacent edge nodes at regular intervals. After receiving the data, the adjacent edge nodes compare the timestamps. If the data is delayed or advanced, it is marked as a time anomaly, triggering a secondary check. The secondary verification calculates the time deviation between the data of adjacent edge nodes by comparing the transmission data with the local monitoring data. If the time deviation is greater than the set threshold, the compensation mechanism is triggered; The compensation mechanism interpolates and compensates for missing and abnormal data, verifies the compensation data using the disaster chain model, and re-updates the status of each factor in the disaster chain using the verified compensation data; An alarm is issued based on the data output in the disaster chain model. When the upstream edge node of the disaster chain triggers the alarm, a coordinated warning signal is sent to the middle and downstream edge nodes of the disaster chain to achieve coordinated warning.

5. A method for sensing and integrating silt dam monitoring and surveillance information according to claim 4, characterized in that: The establishment of the disaster chain model in the cloud includes determining the triggering factor, transmission factor and termination factor of the disaster chain in the cloud according to the monitoring data, and using correlation analysis to determine the strongly correlated variables between the data: ； in, express and The correlation between express Factor Monitoring data; express Factor Monitoring data; express The mean of the factors; express The mean of the factors; After determining the strongly correlated variables, a causal test is performed to determine whether one variable has predictive power for another variable through the lag relationship of the time series: ； in, Indicates the current time The target variable; A constant term representing the base level of the target variable; Indicates The value of the independent variable at the time lag period; express The regression coefficient of represents the lag order; express The regression coefficient of Indicates The target variable value after the time lag period; the variables that pass the causal test are used as chain nodes, and the direction of the causal chain is confirmed according to the time lag relationship; A disaster chain model is constructed using a Bayesian network. Nodes represent causal factors, and directed edges represent causal relationships. A conditional probability table is defined for each node to describe the impact of the parent node state on the child node state. Bayesian reasoning is used to update the conditional probability, and the constructed disaster chain model is sent to the edge nodes for application.

6. A method for sensing and integrating silt dam monitoring and surveillance information according to claim 5, characterized in that: The collaborative identification of the regional disaster chain includes that the edge node obtains the monitoring values ​​of the trigger factor, the transmission factor and the termination factor according to the real-time collected monitoring data. If the monitoring value of the trigger factor reaches the preset trigger factor threshold, the trigger factor is marked as a triggered state. Based on the state of the trigger factor, the current state probability of the transmission factor is calculated by Bayesian reasoning: ； in, Indicates the trigger factor Under conditions that have been activated, the transfer factor Probability of being activated; It represents the probability of simultaneous activation of the trigger factor and the transmission factor; Indicates trigger factor The prior probability of ; Based on the status of the transmission factor, the risk level of the termination factor is calculated by Bayesian reasoning; Dynamically calculate the time delay of chain propagation according to the status of trigger factors and transmission factors; Each node calculates the risk score of the current area based on the calculated state probability: ； in, represents the risk score; represents the total number of causal factors; represents the weight of the causal factor; Indicates the current state probability of the causal factor; based on the risk score The value of determines the chain status; If all factors in the chain are activated, the chain is judged to be complete, triggering a regional warning. The upstream node of the disaster chain sends a coordinated warning signal to the downstream node of the disaster chain, and uploads the status to the cloud for global analysis.

7. A method for sensing and integrating silt dam monitoring and surveillance information according to claim 6, characterized in that: The global risk prediction model includes: the cloud periodically obtains real-time monitoring data from each edge node, extracts key features through time series analysis, and obtains key feature indicators; The key characteristics include load change rate, stress change rate and equipment health status; Historical operating data is used as training samples to establish a global risk prediction model. The cloud uses least squares to update the parameters of the global risk prediction model based on real-time monitoring data, obtains the global risk score, and conducts hierarchical emergency response based on the global risk score.

8. A silt dam monitoring and surveillance information perception integration system, applied to a silt dam monitoring and surveillance information perception integration method according to any one of claims 1 to 7, characterized in that: include, The short-term prediction module collects monitoring data of the silt dam, performs short-term risk prediction at the edge node based on the monitoring data, triggers local warnings based on the short-term risk prediction, and starts the response mechanism; The edge collaboration module establishes an adaptive collaboration mechanism between edge nodes, and identifies sensor anomalies and local disaster chains through the exchange and comparison of monitoring data between nodes; The global prediction module integrates the monitoring data uploaded by the edge nodes in the cloud, generates a global risk prediction model through dynamic data fitting, and implements hierarchical emergency response based on the risk prediction results.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of a method for integrating silt dam monitoring and surveillance information perception are implemented as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for sensing and integrating silt dam monitoring and supervision information are implemented as described in any one of claims 1 to 7.

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