Water conservancy project risk point intelligent identification method and system based on Internet of Things

Abstract

The invention relates to the technical field of water conservancy risk identification, and discloses a water conservancy project risk point intelligent identification method and system based on the Internet of Things. The method comprises the following steps: acquiring sensing monitoring data, and transmitting the sensing monitoring data to an Internet of Things gateway; extracting data features related to hydraulic engineering risk factors from the to-be-analyzed data, labeling existing risk point data, and constructing a risk identification model; performing time sequence modeling on all the risk identification data to generate a risk prediction model, outputting a risk factor change result of any key monitoring area within prediction time, and screening out a first water conservancy risk point; and based on the relevance of the water conservancy project risk factors, quantitatively generating a causal relationship strength in the causal relationship graph, screening out a second water conservancy risk point in all the key monitoring areas, and summarizing the first water conservancy risk point and the second water conservancy risk point to generate a risk point list. According to the invention, the accuracy and comprehensiveness of intelligent identification of the risk points of the water conservancy project are improved.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy risk identification, and in particular to a method and system for intelligently identifying risk points in water conservancy projects based on the Internet of Things. Background Art

[0002] Water conservancy projects encompass multiple areas, including flood control, irrigation, water supply, and power generation. However, these projects face numerous risks during operation, including dam damage caused by floods, structural degradation of hydraulic structures, leakage, damage caused by natural disasters such as earthquakes, and equipment failures caused by human factors. Traditionally, risk identification for water conservancy projects relies primarily on manual inspections. While some projects have begun installing monitoring equipment such as water level gauges, rain gauges, and piezometers, these devices are often distributed and lack effective integration and unified management. Data from each monitoring device is stored and transmitted independently, making it difficult to form a comprehensive risk assessment system. Therefore, the integration of the Internet of Things (IoT) and artificial intelligence (AI) technologies is needed to provide new approaches and technical means for water conservancy project risk identification, effectively overcoming the limitations of traditional methods and enabling real-time, accurate, and intelligent risk identification for water conservancy projects.

[0003] A similar prior art includes a Chinese patent application with publication number CN119809327A, which discloses a method and system for automatically identifying risk points in water conservancy channel projects. The method relates to the technical field of engineering risk identification and includes an engineering information acquisition module, a construction personnel analysis module, a preliminary analysis module for engineering risk points, an environmental detection module, a comprehensive analysis module for engineering risk points, and a database. By acquiring and comprehensively analyzing the basic information and work information of construction personnel during the target water conservancy channel project, the engineering risk coefficient corresponding to the construction position of each construction personnel is obtained. Combined with the environmental factors and hydrological factors of the target water conservancy channel project, a comprehensive analysis is performed to obtain the confirmed engineering risk points corresponding to the target water conservancy channel project, and the engineering risk points of the target water conservancy channel project are effectively and quickly locked according to the environmental conditions and construction conditions. There is also a Chinese patent application with publication number CN119887028A, which discloses a water conservancy project risk assessment method and system based on artificial intelligence, involving the field of risk assessment management technology. The method includes: obtaining a feature parameter set; calling an information value quantification function to quantitatively analyze the predictive ability of the water conservancy project risk coefficient to obtain a first value coefficient; if the first value coefficient reaches a predetermined threshold, the first feature is added to the candidate feature factor list; activating the accident causal Bayesian network model to perform causal analysis on the candidate feature factor list to obtain a target causal chain and a target key feature factor list, dynamically collecting features of the target water conservancy project to obtain a real-time feature parameter set, performing normalized calculation and analysis, and obtaining a target risk coefficient of the target water conservancy project.

[0004] The shortcomings of existing technologies are mainly reflected in the single dimension of data collection and the failure to fully integrate multi-source heterogeneous data, which makes the identification results of engineering risk points single. In actual situations, it is necessary to accurately identify the risk points of water conservancy projects and the types of risk factors through the integration of monitoring of multiple sensor equipment. Summary of the Invention

[0005] The present application provides a method and system for intelligent identification of risk points in water conservancy projects based on the Internet of Things, which is used to improve the accuracy and comprehensiveness of intelligent identification of risk points in water conservancy projects based on the Internet of Things.

[0006] In a first aspect, the present application provides a method for intelligently identifying risk points in water conservancy projects based on the Internet of Things. The method comprises: Obtaining a regional distribution map of the water conservancy project, deploying multiple sensors in key monitoring areas of the water conservancy project based on the regional distribution map, wherein the sensors collect sensor monitoring data based on a preset collection method and transmit the sensor monitoring data to an Internet of Things gateway, wherein the Internet of Things gateway pre-processes the received sensor monitoring data and classifies and stores the sensor monitoring data in a cloud server based on the key monitoring areas and sensor types; The sensor monitoring data contained in any key monitoring area is set as data to be analyzed, data features related to water conservancy project risk factors are extracted from the data to be analyzed, existing risk point data are labeled based on the data features, a risk identification model is constructed based on the labeled data features, and the risk identification model is deployed to the cloud server; The cloud server receives real-time monitoring data of any of the key monitoring areas, the risk identification model outputs risk identification data based on the real-time monitoring data, performs time series modeling on all the risk identification data to generate a risk prediction model, the risk prediction model outputs the risk factor change results of any of the key monitoring areas within the prediction time, and screens out the first water conservancy risk point in all the key monitoring areas based on the risk factor change results; A causal relationship diagram of all the key monitoring areas is constructed based on the regional distribution map, and the causal relationship strength is quantified and generated in the causal relationship diagram based on the correlation of the water conservancy project risk factors. The second water conservancy risk point is screened out in all the key monitoring areas based on the causal relationship strength, and the first water conservancy risk point and the second water conservancy risk point are summarized based on the water conservancy project risk factors to generate a risk point list.

[0007] In combination with the first aspect, transmitting the sensor monitoring data to the Internet of Things gateway includes: Connecting all the sensors and the IoT gateway to the same IoT network, configuring communication parameters for the sensors, and setting unique node IDs for all the sensors and the IoT gateway; When collecting and generating the sensor monitoring data, the sensor checks whether there is valid routing information to reach the Internet of Things gateway. If so, the sensor monitoring data is transmitted to the Internet of Things gateway based on the arrival path of the valid routing information. If not, an optimal path is generated in the Internet of Things network, and the sensor monitoring data is transmitted to the Internet of Things gateway based on the optimal path.

[0008] In combination with the first aspect, generating an optimal path in the Internet of Things network includes: The sensor transmits the routing request information to all neighboring nodes; After receiving the routing request information, the adjacent node checks whether the routing request information has passed through the current node. If the current node is the destination node corresponding to the IoT gateway, the response processing phase is entered. Otherwise, the ID tag of the current node is added to the request tag corresponding to the routing request information, and the routing request information is transmitted to the remaining adjacent nodes. After receiving the routing request information, the destination node generates routing response information and adds its own node ID to the routing response information. The routing response information is returned to the sensor in the opposite direction of the routing request information. After receiving the routing response information, the intermediate node extracts the label information, generates an extended label, adds the extended label to the routing response information, and continues to send the routing response information to the sensor. The intermediate node uses the received label information as the basic label and generates a new extended label for the next node; After receiving a plurality of the routing response messages, the sensor sets the path represented by the longest tag information as the optimal path based on the tag length.

[0009] In combination with the first aspect, extracting data features related to water conservancy project risk factors from the data to be analyzed includes: Extracting risk factor data from the data to be analyzed based on the water conservancy project risk factors, wherein the water conservancy project risk factors include water level factors, leakage factors, structural deformation factors, and flow rate factors; Establishing a physical model based on the sensor type, normalizing the risk factor data and outputting it to the physical model, calculating a ratio of a difference between adjacent time points of the risk factor data and a time interval, setting the ratio as a risk change rate, calculating a difference between a maximum value and a minimum value of the risk factor data within a preset time window, summarizing all the differences and setting them as a fluctuation value distribution, and setting the risk change rate and the fluctuation value distribution as physical features; Establishing a sequence analysis model based on the data type, wherein the sequence analysis model extracts time series features from the risk factor data, wherein the time series features include autocorrelation coefficient, partial autocorrelation coefficient and wavelet transform features; Establishing an image analysis model, inputting the risk factor data into the image analysis model based on the data type, and extracting image features, wherein the image features include texture features, edge detection features, and target monitoring features; The physical features, the time series features, and the image features are summarized based on the categories of the water conservancy project risk factors and set as the data features.

[0010] In combination with the first aspect, the step of constructing a risk identification model based on the labeled data features includes: If the data feature corresponding to any of the water conservancy project risk factors does not meet the preset threshold, the data to be analyzed corresponding to the data feature is set as the risk point data; Constructing a machine learning model, inputting the data features corresponding to all the key monitoring areas into the machine learning model for classification training, wherein the machine learning model outputs an identification result based on the risk point data, wherein the identification result includes a risk point category, a risk point indicator, and a risk point confidence level; The parameters of the machine learning model are adjusted based on the confidence of the risk point, and the adjusted machine learning model is set as the risk identification model.

[0011] In combination with the first aspect, the risk prediction model outputs the risk factor change results of any key monitoring area within the prediction time, including: Based on data correlation, the risk identification data corresponding to any of the key monitoring areas are sequentially combined to generate multiple risk sequence groups, and the risk sequence groups contained in all the key monitoring areas are pre-processed and classified into training sets and test sets; Using a deep learning framework to construct the risk prediction model, inputting the training set into the risk prediction model for training, using the test set to evaluate the trained risk prediction model, and calculating the prediction error; The risk category label of the risk sequence group is set based on the sensor type, the risk sequence group contained in any of the key monitoring areas is input into the risk prediction model, the change sequence within the prediction time is output based on the risk category label, the change sequence is corrected based on the prediction error and set as the risk factor change result.

[0012] In combination with the first aspect, if there are discrete points in the risk factor change results that are greater than the first preset value, any of the key monitoring areas is set as the first water conservancy risk point, and the risk category label corresponding to the discrete point is set as the risk factor.

[0013] In combination with the first aspect, screening out the second water conservancy risk point in all the key monitoring areas based on the strength of the causal relationship includes: Setting any of the key monitoring areas as a regional node, setting directed edges based on the operation layout between the nodes, and connecting all the regional nodes using the directed edges to generate the causal relationship graph; Splitting the causal relationship graph into multiple sub-graphs based on a directed connection method, if the regional node corresponding to the first water conservancy risk point exists in the sub-graph, establishing a Bayesian network structure based on the sub-graph, and estimating the conditional probability distribution of the Bayesian network structure using the real-time monitoring data corresponding to the regional node in the sub-graph to generate the causal relationship strength; If the causal relationship strength is greater than a second preset value, the regional sub-node of the first water risk point in the sub-relationship graph is set as the second water risk point.

[0014] In a second aspect, the present application provides an intelligent identification system for water conservancy project risk points based on the Internet of Things, the intelligent identification system for water conservancy project risk points based on the Internet of Things comprising: a data transmission module, configured to obtain a regional distribution map of the water conservancy project, deploy a plurality of sensors in key monitoring areas of the water conservancy project based on the regional distribution map, wherein the sensors collect sensor monitoring data based on a preset collection method and transmit the sensor monitoring data to an Internet of Things gateway, wherein the Internet of Things gateway pre-processes the received sensor monitoring data and classifies and stores the sensor monitoring data in a cloud server based on the key monitoring areas and sensor types; a risk identification module, configured to set the sensor monitoring data contained in any key monitoring area as data to be analyzed, extract data features related to water conservancy project risk factors from the data to be analyzed, annotate existing risk point data based on the data features, construct a risk identification model based on the annotated data features, and deploy the risk identification model to the cloud server; A risk prediction module is configured to receive real-time monitoring data of any of the key monitoring areas from the cloud server, output risk identification data based on the real-time monitoring data from the risk identification model, perform time series modeling on all of the risk identification data to generate a risk prediction model, output a risk factor change result for any of the key monitoring areas within a prediction time, and screen out a first water conservancy risk point from all of the key monitoring areas based on the risk factor change result; A summary module is used to construct a causal relationship diagram for all the key monitoring areas based on the regional distribution map, quantify the causal relationship strength in the causal relationship diagram based on the correlation of the water conservancy project risk factors, screen out the second water conservancy risk point in all the key monitoring areas based on the causal relationship strength, summarize the first water conservancy risk point and the second water conservancy risk point based on the water conservancy project risk factors, and generate a risk point list.

[0015] In the technical solution provided by this application, first, a variety of sensors are deployed in the key monitoring areas of water conservancy projects using Internet of Things technology. The collected monitoring data is pre-processed through the Internet of Things gateway and stored in a cloud server in a classified manner. Through the interaction of routing request information and routing response information, the sensor can quickly find the optimal path to the Internet of Things gateway. The collaboration of adjacent nodes and the dynamic update of label information further optimize the path selection process, ensuring the stability of the network and the accuracy of data transmission. Then, data features are extracted from three dimensions: physical model, time series model and image model, which comprehensively reflects the dynamic changes, time correlation and spatial distribution of water conservancy project risk factors. By calculating the risk change rate and fluctuation value distribution, the change rate and fluctuation of risk factors are quantified, providing an important physical basis for risk identification. The autocorrelation coefficient, partial autocorrelation coefficient and wavelet transform features are extracted to reflect the long-term trend and periodic changes of risk factors, which helps to predict the development trend of risks. The texture features, edge detection features and target monitoring features are extracted to intuitively reflect the spatial distribution and morphological changes of risk factors. Different types of features are summarized according to the categories of water conservancy project risk factors to form a complete data feature set, which provides rich feature input for the subsequent risk identification model construction. Finally, a suitable machine learning model is selected to classify and identify risk points in water conservancy projects. It can learn the complex relationship between data features and risk points, realize automatic identification and classification of risk points, and use a deep learning framework to build a risk prediction model. It can learn the complex patterns and dynamic changes of risk sequence groups, improve the accuracy of risk prediction, and realize intelligent prediction of risk factors through deep learning models, reduce manual intervention, and improve the automation level of water conservancy project risk monitoring.

[0016] This application also constructs a causal relationship diagram to intuitively represent the mutual influence relationship between key monitoring areas, establishes a Bayesian network structure based on the sub-relationship diagram, and estimates the conditional probability distribution, which can quantify the causal relationship between variables, improve the scientificity and accuracy of risk point screening, calculate the causal relationship strength through the Bayesian network, quantify the degree of mutual influence between nodes in each area, and provide a clear quantitative standard for screening the second water conservancy risk point. The dynamic calculation of the causal relationship strength through the Bayesian network and real-time monitoring data can adapt to the dynamic changes in the operation of water conservancy projects and improve the intelligence level of risk monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a schematic diagram of an embodiment of a method for intelligently identifying risk points in water conservancy projects based on the Internet of Things in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of the process of extracting features from risk factor data in an embodiment of the present application; Figure 3 This is a schematic diagram of an embodiment of the risk identification model construction and evaluation process in the embodiment of the present application; Figure 4 This is a schematic diagram of an embodiment of the intelligent identification system for risk points of water conservancy projects based on the Internet of Things in the embodiment of this application. DETAILED DESCRIPTION

[0019] The embodiments of the present application provide a method and system for intelligent identification of risk points in water conservancy projects based on the Internet of Things. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the method for intelligently identifying risk points of water conservancy projects based on the Internet of Things includes: Step S101: Obtain a regional distribution map of the water conservancy project, and deploy multiple sensors in the key monitoring areas of the water conservancy project based on the regional distribution map. The sensors collect sensor monitoring data based on a preset collection method and transmit the sensor monitoring data to the Internet of Things gateway. The Internet of Things gateway pre-processes the received sensor monitoring data and classifies the sensor monitoring data based on the key monitoring area and sensor type and stores it in the cloud server.

[0021] It is understandable that the execution subject of this application can be an intelligent identification device for water conservancy project risk points based on the Internet of Things, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0022] Specifically, the regional distribution map includes key areas of the water conservancy project (such as dams, spillways, sluice gates, and diversion channels). Based on this regional distribution map, key monitoring areas are identified. Key monitoring areas are typically risk-prone areas of the water conservancy project, such as the dam body, foundation, and abutments, as well as the entrances and exits of the spillway. Sensors include, but are not limited to, water level sensors, seepage sensors, stress and strain sensors, and meteorological sensors. Sensors collect monitoring data according to preset collection methods (such as timed collection or event-triggered collection) and transmit the data to the IoT gateway via wireless or wired communication. Preprocessing includes operations such as data cleaning (removing noise and abnormal data) and data formatting (unifying data formats). Preprocessed monitoring data is categorized and stored in a cloud server based on key monitoring areas and sensor types. For example, within the same key monitoring area, water level sensor data is stored in the "water level monitoring data" category, while seepage sensor data is stored in the "seepage monitoring data" category. This categorized storage facilitates subsequent data analysis and processing.

[0023] Step S102: Set the sensor monitoring data contained in any key monitoring area as the data to be analyzed, extract data features related to the risk factors of water conservancy projects from the data to be analyzed, mark the existing risk point data based on the data features, build a risk identification model based on the marked data features, and deploy the risk identification model to the cloud server.

[0024] Specifically, risk factors for water conservancy projects include but are not limited to water level factors, leakage factors, structural deformation factors, and flow rate factors. Data features are extracted for the data to be analyzed according to different data categories, and the specific extraction methods are described later. For example, for dam seepage monitoring data, features such as seepage rate and seepage pressure can be extracted; for water level monitoring data, features such as water level change rate and water level peak can be extracted. Based on the extracted data features, the existing risk point data are annotated. For example, if the seepage rate in a key monitoring area exceeds the set threshold, it is marked as a potential seepage risk point. Then, a risk identification model is constructed based on the annotated data features and deployed to the cloud server so that it can receive real-time monitoring data of key monitoring areas in real time and output risk identification data, where risk identification data refers to data that contains risks in the real-time monitoring data, as well as the risk type.

[0025] Step S103: The cloud server receives the real-time monitoring data of any key monitoring area. The risk identification model outputs the risk identification data based on the real-time monitoring data. All the risk identification data are modeled as time series to generate a risk prediction model. The risk prediction model outputs the risk factor change results of any key monitoring area within the prediction time. Based on the risk factor change results, the first water conservancy risk point is screened out in all key monitoring areas.

[0026] Specifically, time series modeling can employ LSTM neural networks to predict changes in risk factors in key monitoring areas over the forecast period. The risk prediction model outputs the changes in risk factors in key monitoring areas over the forecast period. Based on these results, the primary water conservancy risk point is identified across all key monitoring areas. For example, if the forecast indicates that the seepage rate in a particular area will continue to increase and exceed the safety threshold over the next period, it is selected as the primary water conservancy risk point, and "excessive seepage" is used as a risk factor.

[0027] Step S104: construct a causal relationship diagram for all key monitoring areas based on the regional distribution map, quantify the causal relationship strength in the causal relationship diagram based on the correlation of water conservancy project risk factors, screen out the second water conservancy risk point in all key monitoring areas based on the causal relationship strength, summarize the first water conservancy risk point and the second water conservancy risk point based on the water conservancy project risk factors, and generate a risk point list.

[0028] Specifically, the causal relationship diagram reflects the mutual influence relationship between each key monitoring area. For example, an increase in rainfall may cause the water level of the reservoir to rise, which in turn affects the seepage of the dam. The strength of the causal relationship refers to the probability value between the causal relationship. For example, if the rainfall in a certain area increases and the causal relationship strength between it and the downstream dam is high, the downstream dam will be screened as the second water conservancy risk point. If an area is screened as both the first water conservancy risk point (based on real-time monitoring data prediction) and the second water conservancy risk point (based on causal relationship analysis), it will be marked as a high-risk area when summarized. The generated risk point list includes information such as the location, risk factors, and risk level of all screened risk points, providing decision support for managers of water conservancy projects.

[0029] In a specific embodiment, transmitting sensor monitoring data to an Internet of Things gateway includes: (1) Connect all sensors and IoT gateways to the same IoT network, configure communication parameters for the sensors, and set unique node IDs for all sensors and IoT gateways.

[0030] (2) When collecting and generating sensor monitoring data, the sensor checks whether there is valid routing information to reach the IoT gateway. If so, the sensor monitoring data is transmitted to the IoT gateway based on the arrival path of the valid routing information. If not, an optimal path is generated in the IoT network, and the sensor monitoring data is transmitted to the IoT gateway based on the optimal path.

[0031] Specifically, the IoT network can be based on low-power wide-area network (LPWAN) technologies such as ZigBee, LoRa, and NB-IoT, or it can be based on Wi-Fi or 4G / 5G. The specific choice depends on the water conservancy project's location and monitoring requirements. Communication parameters, including communication frequency, data transmission rate, and encryption method, are configured for each sensor. These parameters need to be optimized based on the sensor type and network environment to ensure stable and secure data transmission. Each sensor and IoT gateway is assigned a unique node ID. The node ID uniquely identifies each device within the network, facilitating data transmission and routing management.

[0032] After collecting sensor monitoring data, the sensor first checks whether there is a valid route to the IoT gateway. Valid routing information refers to established path information that ensures the data is successfully transmitted to the IoT gateway. If no valid routing information exists, the sensor generates an optimal path within the IoT network.

[0033] In a specific embodiment, generating an optimal path in an Internet of Things network includes: (1) The sensor transmits the routing request information to all adjacent nodes.

[0034] (2) After receiving the routing request information, the adjacent node checks whether the routing request information has passed through the current node. If the current node is the destination node corresponding to the IoT gateway, it enters the response processing phase. Otherwise, it adds the ID tag of the current node to the request tag corresponding to the routing request information and transmits the routing request information to the remaining adjacent nodes.

[0035] (3) After receiving the routing request information, the destination node generates a routing response information and adds its own node ID to the routing response information. The routing response information is returned to the sensor in the opposite direction of the routing request information.

[0036] (4) After receiving the routing response information, the intermediate node extracts the label information, generates an extended label, adds the extended label to the routing response information, and continues to send the routing response information to the sensor. The intermediate node uses the received label information as the basic label and generates a new extended label for the next node.

[0037] (5) After receiving multiple routing response messages, the sensor sets the path represented by the longest tag information as the optimal path based on the tag length.

[0038] Specifically, the distribution coordinates corresponding to the node IDs are used to determine the distribution distance between nodes. Based on this distribution distance, neighboring nodes can be found. A route request message contains information such as the sensor's node ID (source node), the node ID of the target node (IoT gateway), a request tag (an empty tag list used to record the IDs of the nodes passed through), and a hop count (initial value 0, used to record the number of hops from the source node to the current node). The route request message is used to find a path to the IoT gateway, while the route response message returns this path information. Upon receiving a route request message, the neighboring node first checks whether the request tag in the route request message contains its own node ID. The response processing phase refers to the data transmission phase. By checking whether the request tag contains the current node's ID, the route request message is prevented from being forwarded circularly between nodes.

[0039] After receiving the routing request, the IoT gateway (destination node) generates a routing response, which includes the IoT gateway's node ID (source node), the sensor's node ID (destination node), and a response tag (including the IoT gateway's node ID). The routing response follows the same path as the routing request, but in the opposite direction.

[0040] The tag information is extracted from the request tag. The intermediate node generates an extended tag based on the tag information in the routing response message and adds the extended tag to the routing response message. By dynamically updating the tag information, the routing response message is ensured to be returned to the sensor along the correct path.

[0041] A sensor may receive multiple routing responses because multiple paths to the IoT gateway exist. The path represented by the routing response with the longest tag length is selected as the optimal path. A longer tag length indicates a more complete path, traversing more nodes. These steps optimize the transmission of sensor monitoring data, improving the reliability and efficiency of IoT networks in water conservancy project monitoring and providing more efficient data transmission support for intelligent identification of risk points in water conservancy projects.

[0042] In a specific embodiment, data features related to water conservancy project risk factors are extracted from the data to be analyzed, including: (1) Risk factor data are extracted from the data to be analyzed based on the risk factors of water conservancy projects. The risk factors of water conservancy projects include water level factors, leakage factors, structural deformation factors and flow rate factors.

[0043] (2) A physical model is established based on the sensor type. The risk factor data is standardized and output to the physical model. The ratio of the difference between the risk factor data at adjacent time points and the time interval is calculated, and the ratio is set as the risk change rate. The difference between the maximum and minimum values ​​of the risk factor data is calculated within the preset time window. All differences are summarized and set as the fluctuation value distribution. The risk change rate and the fluctuation value distribution are set as physical characteristics.

[0044] (3) A sequence analysis model is established based on the data type. The sequence analysis model extracts time series features from the risk factor data. The time series features include autocorrelation coefficient, partial autocorrelation coefficient and wavelet transform features.

[0045] (4) Establish an image analysis model, input risk factor data into the image analysis model based on the data type, and extract image features. Image features include texture features, edge detection features, and target monitoring features.

[0046] (5) Based on the categories of water conservancy project risk factors, physical characteristics, time series characteristics and image characteristics are summarized and set as data features.

[0047] Specifically, Figure 2 Flowchart for feature extraction of risk factor data. Water level factors refer to information such as reservoir and river levels; seepage factors refer to information such as dam seepage rate and seepage pressure; structural deformation factors refer to information such as dam displacement and settlement; and flow rate factors refer to information such as water flow velocity and flood discharge velocity. Based on sensor type and key monitoring areas, data related to these risk factors, i.e., risk factor data, is extracted from the data to be analyzed. For example, water level data is extracted from water level sensors, and seepage data is extracted from seepage sensors.

[0048] The sensor type refers to the sensor category. A physical model is established based on the sensor type. For example, for a water level sensor, a physical model of water level changes is established; for a seepage sensor, a physical model of seepage changes is established. Data normalization is the process of normalizing risk factor data to eliminate the influence of different dimensions and magnitudes. For example, all data is normalized to the interval [0, 1]. The formula for calculating the risk change rate P1 is: ,in, is the risk factor data at the current time point, Risk factor data at the previous adjacent time point, By setting the risk change rate and fluctuation value distribution as physical characteristics, we can clearly obtain the physical fluctuation changes contained in the risk factor data.

[0049] Sequence analysis models refer to time series analysis models. For example, the ARIMA model can be used for water level data, while the wavelet transform model can be used for seepage data. The autocorrelation coefficient reflects the correlation of data at different time lags. The partial autocorrelation coefficient reflects the correlation of data after removing the influence of intermediate lags. By performing a wavelet transform on risk factor data and extracting the wavelet coefficients, we can reflect the characteristics of the data at different frequencies. Therefore, sequence analysis models can extract time series features from risk factor data that contain temporal characteristics.

[0050] Risk factor data may contain images. We use a convolutional neural network (CNN) to build an image analysis model and extract image features from risk factor data. We can use methods like the gray-level co-occurrence matrix to extract texture features, edge detection algorithms like Canny to extract edge features, and object detection algorithms (such as YOLO) to extract target features in images. For example, to monitor dam cracks, these features can be used as image features.

[0051] Based on the types of water conservancy project risk factors (such as water level factors and leakage factors), physical characteristics, time series characteristics, and image characteristics are aggregated to form a complete data feature set. For example, for water level factors, its risk change rate, fluctuation value distribution, autocorrelation coefficient, texture characteristics, etc. are summarized.

[0052] In a specific embodiment, a risk identification model is constructed based on the annotated data features, including: (1) If the data characteristics corresponding to any water conservancy project risk factor do not meet the preset threshold, the data to be analyzed corresponding to the data characteristics will be set as risk point data.

[0053] (2) Construct a machine learning model and input the data features corresponding to all key monitoring areas into the machine learning model for classification training. The machine learning model outputs recognition results based on the risk point data, where the recognition results include risk point category, risk point indicator, and risk point confidence.

[0054] (3) Adjust the parameters of the machine learning model based on the confidence level of the risk points, and set the adjusted machine learning model as the risk identification model.

[0055] Specifically, Figure 3 Flowchart for risk identification model construction and evaluation. Before building the risk identification model, it is first necessary to conduct a preliminary screening of data features to determine which data are risk point data. For each water conservancy project risk factor (for example, water level factors, leakage factors, structural deformation factors, etc.), set the corresponding preset threshold. For example, for the water level factor, the threshold of the water level change rate is set to 0.5 meters per hour; for the leakage factor, the threshold of the seepage rate is set to 0.1 cubic meters per hour. If the data characteristics corresponding to a water conservancy project risk factor (such as the risk change rate, fluctuation value distribution, etc.) do not meet the preset threshold, the data to be analyzed corresponding to the data characteristics will be marked as risk point data. For example, if the water level change rate in a key monitoring area exceeds 0.5 meters per hour, the data corresponding to the water level change sequence in the key monitoring area will be marked as risk point data.

[0056] A machine learning model is constructed using the random forest algorithm, and the data features corresponding to all key monitoring areas are input into the machine learning model for classification training. These data features include physical features, time series features, and image features. During the training process, samples marked as risk point data are used as positive samples, and other normal data are used as negative samples to train the model to identify risk points. The identification results are output based on the input data features. Among them, risk point categories include but are not limited to water level anomalies, leakage anomalies, structural deformation anomalies, etc. Risk point indicators include but are not limited to water level change rate, seepage rate, displacement, etc. The risk point confidence refers to the confidence of the machine learning model in the identification result, which is usually a value between 0 and 1, indicating the reliability of the machine learning model in the identification result.

[0057] Analyze the confidence level of the risk points output by the machine learning model. If the confidence level is low (for example, below a certain threshold), the model's identification of the risk point is unreliable. For the machine learning model corresponding to the random forest, parameters such as the number of trees and tree depth can be adjusted. The adjusted machine learning model is set as the final risk identification model for real-time risk identification of monitoring data.

[0058] In a specific embodiment, the risk prediction model outputs the risk factor change results of any key monitoring area within the prediction time, including: (1) Based on data correlation, the risk identification data corresponding to any key monitoring area are sequentially combined to generate multiple risk sequence groups. The risk sequence groups contained in all key monitoring areas are preprocessed and classified into training sets and test sets.

[0059] (2) Use a deep learning framework to build a risk prediction model, input the training set into the risk prediction model for training, use the test set to evaluate the trained risk prediction model, and calculate the prediction error.

[0060] (3) Set the risk category label of the risk sequence group based on the sensor type, input the risk sequence group contained in any key monitoring area into the risk prediction model, output the change sequence within the prediction time based on the risk category label, correct the change sequence based on the prediction error and set it as the risk factor change result.

[0061] Specifically, the correlation between risk identification data can be analyzed. The data correlation can be calculated using the Pearson correlation coefficient, for example, the relationship between water level changes and seepage rate, the relationship between structural deformation and rainfall, etc. The data with correlation are combined into multiple risk sequence groups. For example, water level change data and seepage rate data are combined into one risk sequence group, and structural deformation data and rainfall data are combined into another risk sequence group. Preprocessing refers to data processing processes such as data cleaning and normalization. Usually, the training set is used for model training, and the test set is used for model evaluation. For example, the data corresponding to 70% of the risk sequence groups can be used as the training set, and the data corresponding to 30% of the risk sequence groups can be used as the test set.

[0062] Select a deep learning framework suitable for time series forecasting, such as a combination of a convolutional neural network (CNN) and LSTM (CNN-LSTM). Use the backpropagation algorithm to adjust model parameters and optimize performance, such as the learning rate, number of hidden layer nodes, and regularization parameters. Input the risk series from the test set into the trained risk prediction model to evaluate the model's predictive performance. Calculate the difference between the predicted and actual values, which is the prediction error. Common error metrics include mean squared error (MSE) and mean absolute error (MAE).

[0063] A risk category label is assigned to each risk sequence group based on the sensor type. For example, the risk category label for a water level sensor is "water level anomaly," and the risk category label for a seepage sensor is "seepage anomaly." Based on the risk category label, the risk prediction model outputs a change sequence within the predicted timeframe. For example, a change sequence for the water level or seepage rate over the next 24 hours is output. The change sequence is corrected based on the prediction error. If the prediction error is large, the accuracy of the prediction can be improved by adjusting model parameters or introducing an error correction mechanism.

[0064] In a specific embodiment, if there are discrete points greater than a first preset value in the risk factor change results, any key monitoring area is set as the first water risk point, and the risk category label corresponding to the discrete point is set as the risk factor.

[0065] Specifically, discrete points often indicate potential sudden risks, such as dam leakage or a sharp rise in water levels. By detecting discrete points, these anomalies can be promptly identified to prevent further escalation of risk. If a discrete point is detected, the key monitoring area is identified as the primary water conservancy risk point, and the specific risk factors at that point are identified, allowing water conservancy project managers to quickly locate the problem and take targeted measures.

[0066] In a specific embodiment, the second water conservancy risk points are screened out in all key monitoring areas based on the strength of the causal relationship, including: (1) Set any key monitoring area as a regional node, set directed edges based on the operation layout between nodes, use directed edges to connect all regional nodes, and generate a causal relationship graph.

[0067] (2) The causal relationship graph is split into multiple sub-graphs based on directed connections. If there is a regional node corresponding to the first water conservancy risk point in the sub-graph, a Bayesian network structure is established based on the sub-graph. The conditional probability distribution of the Bayesian network structure is estimated using the real-time monitoring data corresponding to the regional nodes in the sub-graph to generate the causal relationship strength.

[0068] (3) If the causal relationship strength is greater than the second preset value, the regional subnode of the first water risk point in the sub-relationship graph is set as the second water risk point.

[0069] Specifically, each key monitoring area is designated as a regional node. For example, the dam's body, foundation, spillway, and sluice gates are each considered a different regional node. A directed edge represents the direction of influence from one regional node on another. For example, if increased rainfall (regional node A) could cause a rise in reservoir water level (regional node B), a directed edge would be drawn from regional node A to regional node B. Directed edges are used to connect all regional nodes to generate a complete causal relationship graph. This graph reflects the causal relationships between the key monitoring areas in a water conservancy project.

[0070] The causal relationship graph is split into multiple sub-graphs. Each sub-graph contains a set of interconnected regional nodes and directed edges. For example, a sub-graph might include rainfall nodes, reservoir water level nodes, and dam seepage nodes for different regional nodes. If a sub-graph contains a primary water conservancy risk point (for example, a region has been identified as high risk), further analysis is performed on that sub-graph.

[0071] A Bayesian network structure is established using the regional nodes and directed edges in the sub-relationship graph. A Bayesian network is a graphical representation method based on a probability model that is used to describe the conditional dependency relationship between variables. Using the real-time monitoring data corresponding to the regional nodes in the sub-relationship graph, the conditional probability distribution of the Bayesian network structure is estimated. For example, the probability of a reservoir water level rising when rainfall increases is calculated. The numerical value corresponding to the conditional probability is set as the causal relationship strength, which is used to represent the degree of influence of one regional node on another regional node. For example, the influence of rainfall on reservoir water level and the influence of reservoir water level on dam seepage are calculated.

[0072] Based on the specific circumstances and experience of water conservancy projects, a threshold (second preset value) is set to determine whether the causal relationship strength is sufficiently strong. For example, the second preset value is set to 0.7, indicating that if the causal relationship strength is greater than 0.7, the impact is considered significant. If the causal relationship strength in a sub-relationship graph is greater than the second preset value, the regional subnode (i.e., the affected regional node) of the first water conservancy risk point in the sub-relationship graph is set as the second water conservancy risk point. For example, if the causal relationship strength of increased rainfall leading to a rise in reservoir water level is greater than 0.7, the reservoir water level node is set as the second water conservancy risk point.

[0073] The above describes the method for intelligent identification of water conservancy project risk points based on the Internet of Things in the embodiment of the present application. The following describes the intelligent identification system for water conservancy project risk points based on the Internet of Things in the embodiment of the present application. Figure 4 In the embodiment of the present application, an embodiment of the intelligent identification system for water conservancy project risk points based on the Internet of Things includes: The data transmission module 201 is used to obtain the regional distribution map of the water conservancy project, and deploy multiple sensors in the key monitoring areas of the water conservancy project based on the regional distribution map. The sensors collect sensor monitoring data based on a preset collection method and transmit the sensor monitoring data to the Internet of Things gateway. The Internet of Things gateway pre-processes the received sensor monitoring data and classifies the sensor monitoring data based on the key monitoring area and sensor type and stores it in the cloud server.

[0074] The risk identification module 202 is used to set the sensor monitoring data contained in any key monitoring area as the data to be analyzed, extract data features related to the risk factors of water conservancy projects from the data to be analyzed, mark the existing risk point data based on the data features, build a risk identification model based on the marked data features, and deploy the risk identification model to the cloud server.

[0075] The risk prediction module 203 is used for the cloud server to receive the real-time monitoring data of any key monitoring area. The risk identification model outputs the risk identification data based on the real-time monitoring data, performs time series modeling on all risk identification data, and generates a risk prediction model. The risk prediction model outputs the risk factor change results of any key monitoring area within the prediction time, and screens out the first water conservancy risk point in all key monitoring areas based on the risk factor change results.

[0076] The summary module 204 is used to construct a causal relationship diagram for all key monitoring areas based on the regional distribution map, quantify the causal relationship strength in the causal relationship diagram based on the correlation of water conservancy project risk factors, screen out the second water conservancy risk point in all key monitoring areas based on the causal relationship strength, summarize the first water conservancy risk point and the second water conservancy risk point based on the water conservancy project risk factors, and generate a risk point list.

[0077] Through the collaborative efforts of the aforementioned components, the system first utilizes IoT technology to deploy multiple sensors in key monitoring areas of water conservancy projects. The collected monitoring data is preprocessed by an IoT gateway and stored in a classified manner on a cloud server. Through the interaction of route request and response information, sensors can quickly find the optimal path to the IoT gateway. Collaboration between adjacent nodes and the dynamic updating of label information further optimize the path selection process, ensuring network stability and data transmission accuracy. Data features are then extracted from three dimensions: physical, time series, and image models. These features comprehensively reflect the dynamic changes, temporal correlations, and spatial distribution of water conservancy project risk factors. By calculating the risk change rate and fluctuation value distribution, the rate of change and fluctuation of risk factors are quantified, providing an important physical basis for risk identification. Extracted autocorrelation coefficients, partial autocorrelation coefficients, and wavelet transform features reflect the long-term trends and cyclical changes of risk factors, facilitating risk prediction. Texture features, edge detection features, and target monitoring features intuitively reflect the spatial distribution and morphological changes of risk factors. Different types of features are aggregated according to the categories of water conservancy project risk factors, forming a comprehensive data feature set that provides rich input for the subsequent construction of risk identification models. Finally, a suitable machine learning model is selected to classify and identify risk points in water conservancy projects. It can learn the complex relationship between data features and risk points, realize automatic identification and classification of risk points, and use a deep learning framework to build a risk prediction model. It can learn the complex patterns and dynamic changes of risk sequence groups, improve the accuracy of risk prediction, and realize intelligent prediction of risk factors through deep learning models, reduce manual intervention, and improve the automation level of water conservancy project risk monitoring.

[0078] This application also constructs a causal relationship diagram to intuitively represent the mutual influence relationship between key monitoring areas, establishes a Bayesian network structure based on the sub-relationship diagram, and estimates the conditional probability distribution, which can quantify the causal relationship between variables, improve the scientificity and accuracy of risk point screening, calculate the causal relationship strength through the Bayesian network, quantify the degree of mutual influence between nodes in each area, and provide a clear quantitative standard for screening the second water conservancy risk point. The dynamic calculation of the causal relationship strength through the Bayesian network and real-time monitoring data can adapt to the dynamic changes in the operation of water conservancy projects and improve the intelligence level of risk monitoring.

[0079] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or 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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0081] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent identification method for risk points in water conservancy projects based on the Internet of Things, characterized in that: The method for intelligently identifying risk points in water conservancy projects based on the Internet of Things includes: Obtaining a regional distribution map of the water conservancy project, deploying multiple sensors in key monitoring areas of the water conservancy project based on the regional distribution map, wherein the sensors collect sensor monitoring data based on a preset collection method and transmit the sensor monitoring data to an Internet of Things gateway, wherein the Internet of Things gateway pre-processes the received sensor monitoring data and classifies and stores the sensor monitoring data in a cloud server based on the key monitoring areas and sensor types; The sensor monitoring data contained in any key monitoring area is set as data to be analyzed, data features related to water conservancy project risk factors are extracted from the data to be analyzed, existing risk point data are labeled based on the data features, a risk identification model is constructed based on the labeled data features, and the risk identification model is deployed to the cloud server; The cloud server receives real-time monitoring data of any of the key monitoring areas, the risk identification model outputs risk identification data based on the real-time monitoring data, performs time series modeling on all the risk identification data to generate a risk prediction model, the risk prediction model outputs the risk factor change results of any of the key monitoring areas within the prediction time, and screens out the first water conservancy risk point in all the key monitoring areas based on the risk factor change results; A causal relationship diagram of all the key monitoring areas is constructed based on the regional distribution map, and the causal relationship strength is quantified and generated in the causal relationship diagram based on the correlation of the water conservancy project risk factors. The second water conservancy risk point is screened out in all the key monitoring areas based on the causal relationship strength, and the first water conservancy risk point and the second water conservancy risk point are summarized based on the water conservancy project risk factors to generate a risk point list.

2. The method for intelligently identifying risk points of water conservancy projects based on the Internet of Things according to claim 1 is characterized in that: The transmitting the sensor monitoring data to the Internet of Things gateway includes: Connecting all the sensors and the IoT gateway to the same IoT network, configuring communication parameters for the sensors, and setting unique node IDs for all the sensors and the IoT gateway; When collecting and generating the sensor monitoring data, the sensor checks whether there is valid routing information to reach the Internet of Things gateway. If so, the sensor monitoring data is transmitted to the Internet of Things gateway based on the arrival path of the valid routing information. If not, an optimal path is generated in the Internet of Things network, and the sensor monitoring data is transmitted to the Internet of Things gateway based on the optimal path.

3. The method for intelligently identifying risk points of water conservancy projects based on the Internet of Things according to claim 2 is characterized in that: Generating an optimal path in the Internet of Things network includes: The sensor transmits the routing request information to all neighboring nodes; After receiving the routing request information, the adjacent node checks whether the routing request information has passed through the current node. If the current node is the destination node corresponding to the IoT gateway, the response processing phase is entered. Otherwise, the ID tag of the current node is added to the request tag corresponding to the routing request information, and the routing request information is transmitted to the remaining adjacent nodes. After receiving the routing request information, the destination node generates routing response information and adds its own node ID to the routing response information. The routing response information is returned to the sensor in the opposite direction of the routing request information. After receiving the routing response information, the intermediate node extracts the label information, generates an extended label, adds the extended label to the routing response information, and continues to send the routing response information to the sensor. The intermediate node uses the received label information as the basic label and generates a new extended label for the next node; After receiving a plurality of the routing response messages, the sensor sets the path represented by the longest tag information as the optimal path based on the tag length.

4. The method for intelligently identifying risk points of water conservancy projects based on the Internet of Things according to claim 1 is characterized in that: The step of extracting data features related to water conservancy project risk factors from the data to be analyzed includes: Extracting risk factor data from the data to be analyzed based on the water conservancy project risk factors, wherein the water conservancy project risk factors include water level factors, leakage factors, structural deformation factors, and flow rate factors; Establishing a physical model based on the sensor type, normalizing the risk factor data and outputting it to the physical model, calculating a ratio of a difference between adjacent time points of the risk factor data and a time interval, setting the ratio as a risk change rate, calculating a difference between a maximum value and a minimum value of the risk factor data within a preset time window, summarizing all the differences and setting them as a fluctuation value distribution, and setting the risk change rate and the fluctuation value distribution as physical features; Establishing a sequence analysis model based on the data type, wherein the sequence analysis model extracts time series features from the risk factor data, wherein the time series features include autocorrelation coefficient, partial autocorrelation coefficient and wavelet transform features; Establishing an image analysis model, inputting the risk factor data into the image analysis model based on the data type, and extracting image features, wherein the image features include texture features, edge detection features, and target monitoring features; The physical features, the time series features, and the image features are summarized based on the categories of the water conservancy project risk factors and set as the data features.

5. The method for intelligently identifying risk points of water conservancy projects based on the Internet of Things according to claim 1 is characterized in that: The step of constructing a risk identification model based on the labeled data features includes: If the data feature corresponding to any of the water conservancy project risk factors does not meet the preset threshold, the data to be analyzed corresponding to the data feature is set as the risk point data; Constructing a machine learning model, inputting the data features corresponding to all the key monitoring areas into the machine learning model for classification training, wherein the machine learning model outputs an identification result based on the risk point data, wherein the identification result includes a risk point category, a risk point indicator, and a risk point confidence level; The parameters of the machine learning model are adjusted based on the confidence of the risk point, and the adjusted machine learning model is set as the risk identification model.

6. The method for intelligently identifying risk points in water conservancy projects based on the Internet of Things according to claim 1 is characterized in that: The risk prediction model outputs the risk factor change results of any key monitoring area within the prediction time, including: Based on data correlation, the risk identification data corresponding to any of the key monitoring areas are sequentially combined to generate multiple risk sequence groups, and the risk sequence groups contained in all the key monitoring areas are pre-processed and classified into training sets and test sets; Using a deep learning framework to construct the risk prediction model, inputting the training set into the risk prediction model for training, using the test set to evaluate the trained risk prediction model, and calculating the prediction error; The risk category label of the risk sequence group is set based on the sensor type, the risk sequence group contained in any of the key monitoring areas is input into the risk prediction model, the change sequence within the prediction time is output based on the risk category label, the change sequence is corrected based on the prediction error and set as the risk factor change result.

7. The method for intelligently identifying risk points of water conservancy projects based on the Internet of Things according to claim 6 is characterized in that: If there are discrete points greater than the first preset value in the risk factor change results, any of the key monitoring areas is set as the first water conservancy risk point, and the risk category label corresponding to the discrete point is set as the risk factor.

8. The method for intelligently identifying risk points in water conservancy projects based on the Internet of Things according to claim 1 is characterized in that: The step of screening out the second water conservancy risk point in all the key monitoring areas based on the strength of the causal relationship includes: Setting any of the key monitoring areas as a regional node, setting directed edges based on the operation layout between the nodes, and connecting all the regional nodes using the directed edges to generate the causal relationship graph; Splitting the causal relationship graph into multiple sub-graphs based on a directed connection method, if the regional node corresponding to the first water conservancy risk point exists in the sub-graph, establishing a Bayesian network structure based on the sub-graph, and estimating the conditional probability distribution of the Bayesian network structure using the real-time monitoring data corresponding to the regional node in the sub-graph to generate the causal relationship strength; If the causal relationship strength is greater than a second preset value, the regional sub-node of the first water risk point in the sub-relationship graph is set as the second water risk point.

9. An intelligent identification system for water conservancy project risk points based on the Internet of Things, characterized by: The IoT-based intelligent identification system for water conservancy project risk points includes: a data transmission module, configured to obtain a regional distribution map of the water conservancy project, deploy a plurality of sensors in key monitoring areas of the water conservancy project based on the regional distribution map, wherein the sensors collect sensor monitoring data based on a preset collection method and transmit the sensor monitoring data to an Internet of Things gateway, wherein the Internet of Things gateway pre-processes the received sensor monitoring data and classifies and stores the sensor monitoring data in a cloud server based on the key monitoring areas and sensor types; a risk identification module, configured to set the sensor monitoring data contained in any key monitoring area as data to be analyzed, extract data features related to water conservancy project risk factors from the data to be analyzed, annotate existing risk point data based on the data features, construct a risk identification model based on the annotated data features, and deploy the risk identification model to the cloud server; A risk prediction module is configured to receive real-time monitoring data of any of the key monitoring areas from the cloud server, output risk identification data based on the real-time monitoring data from the risk identification model, perform time series modeling on all of the risk identification data to generate a risk prediction model, output a risk factor change result for any of the key monitoring areas within a prediction time, and screen out a first water conservancy risk point from all of the key monitoring areas based on the risk factor change result; A summary module is used to construct a causal relationship diagram for all the key monitoring areas based on the regional distribution map, quantify the causal relationship strength in the causal relationship diagram based on the correlation of the water conservancy project risk factors, screen out the second water conservancy risk point in all the key monitoring areas based on the causal relationship strength, summarize the first water conservancy risk point and the second water conservancy risk point based on the water conservancy project risk factors, and generate a risk point list.

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