Dam monitoring and early warning system based on cloud data

Abstract

The invention provides a dam monitoring and early warning system based on cloud data, relates to the technical field of dam monitoring and early warning, and aims to perform key monitoring on a high-risk area in a targeted manner by integrating historical data analysis, risk level quantification, scientific early warning threshold setting, data acquisition strategy optimization and a real-time monitoring and quick response mechanism. Monitoring resources are reasonably distributed, operation cost is reduced, intelligent data collection and management are achieved, an automatic multi-stage early warning mechanism is arranged, early warning accuracy and timeliness are improved, safety management and maintenance strategies are optimized, the dam safety management level is improved, potential safety threats are reduced, and the system has good application prospects. And the overall emergency response capability and the disaster prevention and control effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dam monitoring and early warning, and in particular to a dam monitoring and early warning system based on cloud data. Background Art

[0002] Traditional dam monitoring methods often rely on manual inspections and regular testing. This method is not only inefficient, but also difficult to achieve real-time and comprehensive monitoring of the dam status. Especially in the face of emergency situations such as sudden floods and geological disasters, it is often difficult to promptly discover and respond to potential safety hazards.

[0003] Specifically, current dam monitoring and management faces the following key issues:

[0004] Due to the lack of comprehensive assessment of dam safety risks, the allocation of monitoring resources often lacks scientific basis, resulting in insufficient monitoring of key areas and excessive monitoring of non-critical areas, resulting in a waste of resources;

[0005] In addition, existing technologies are often based on empirical judgments and lack scientific and systematic quantitative assessment methods, which leads to high subjectivity and low accuracy of risk assessment results.

[0006] Improper setting of warning thresholds may lead to frequent false alarms, often making it difficult to respond quickly and accurately, thereby increasing the losses caused by disasters.

[0007] Therefore, it is necessary to provide a dam monitoring and early warning system based on cloud data to solve the above technical problems. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a dam monitoring and early warning system based on cloud data to solve the problems of unreasonable allocation of monitoring resources, imperfect early warning mechanism and insufficient emergency response capability in the prior art.

[0009] The present invention provides a cloud data-based dam monitoring and early warning system, the monitoring and early warning system comprising:

[0010] The data acquisition module is used to collect monitoring data of historical safety accidents and identify abnormal parameters in the safety parameters of the monitoring data in historical safety accidents, where the safety parameters specifically include water level, structural stress, seepage, temperature and rainfall;

[0011] Analysis and identification module, used to identify the mapping relationship between abnormal parameters and safety accidents;

[0012] The evaluation and definition module is used to evaluate the types of dam safety parameters and quantify the risk level of each safety parameter type based on the evaluation results combined with the mapping relationship between abnormal parameters and safety accidents, so as to set the corresponding safety parameter type priority;

[0013] The threshold setting module is used to define the boundary between abnormal parameters and normal fluctuations to set the primary warning threshold, and to set the advanced warning threshold based on the mapping relationship between abnormal parameters and safety accidents;

[0014] A data collection module, used to set a corresponding collection frequency level based on the priority of the safety parameter type, and collect real-time monitoring data of the dam according to the collection frequency level;

[0015] The processing and judgment module is used to collect and pre-process the real-time monitoring data of the dam, and use the data analysis algorithm to compare and judge whether the value of the safety parameter in the pre-processed data exceeds the primary warning threshold or the advanced warning threshold;

[0016] The early warning notification module is used to generate a primary early warning notification or an advanced early warning notification in response to the value of the security parameter exceeding the primary early warning threshold or the advanced early warning threshold.

[0017] Preferably, the data acquisition module specifically includes:

[0018] The cleaning processing module is used to collect historical monitoring data of the dam and clean the historical monitoring data to remove duplicate, erroneous or invalid data points.

[0019] An extraction module is used to extract safety parameters from the cleaned historical monitoring data, including water level, structural stress, seepage, temperature and rainfall;

[0020] The anomaly identification module is used to identify abnormal parameters of safety parameters in historical safety accidents using statistical methods.

[0021] Preferably, the analysis and identification module specifically includes:

[0022] Statistical analysis module, which is used to identify abnormal parameters that lead to safety accidents based on statistical techniques and domain knowledge;

[0023] The mapping identification module is used to determine the specific mapping relationship between the abnormal parameters and the safety accident through data mining technology according to the abnormal parameters that cause the safety accident.

[0024] Preferably, the evaluation and definition module specifically includes:

[0025] An evaluation module is used to identify the safety parameter type of the dam and evaluate each safety parameter type to obtain an evaluation result;

[0026] The quantification module is used to comprehensively quantify the risk level of each safety parameter type based on the mapping relationship between abnormal parameters and safety incidents and the evaluation results;

[0027] The weight module is used to set a corresponding priority for each security parameter type based on the risk level of each security parameter type.

[0028] Preferably, the threshold setting module specifically includes:

[0029] The first setting module is used to count and calculate the boundary between abnormal parameters and normal fluctuations of monitoring data change characteristics, so as to set the primary warning threshold;

[0030] The second setting module is used to set the advanced warning threshold according to the mapping relationship between abnormal parameters and safety accidents, and the fluctuation correlation between abnormal parameters.

[0031] Preferably, the data acquisition module specifically includes:

[0032] A collection parameter setting module, used to set the corresponding collection frequency level based on the priority of the security parameter type, wherein the collection frequency level specifically includes monthly collection, weekly collection and daily collection;

[0033] The collection and transmission module is used to collect the safety parameter data of the dam in real time according to the collection frequency level corresponding to the safety parameter type and transmit it through the cloud platform.

[0034] Preferably, the processing and judgment module specifically includes:

[0035] The preprocessing module is used to clean, format and process missing values ​​of the received security parameter data;

[0036] The judgment module is used to compare and judge whether the safety parameters in the pre-processed data exceed the primary warning threshold or the advanced warning threshold.

[0037] Preferably, the warning notification module specifically includes:

[0038] A response module, used to respond to the value of the security parameter exceeding the primary warning threshold or the advanced warning threshold, and determine the corresponding warning level;

[0039] The warning information generation module is used to generate corresponding warning information according to the warning level, including the warning type, time, location and impact range;

[0040] The warning module is used to send warning notifications to relevant personnel or organizations.

[0041] Compared with related technologies, the cloud data-based dam monitoring and early warning system provided by the present invention has the following beneficial effects:

[0042] The present invention integrates historical data analysis, risk level quantification, scientific warning threshold setting, optimized data collection strategy, and real-time monitoring and rapid response mechanism to carry out targeted key monitoring of high-risk areas, rationally allocate monitoring resources to reduce operating costs, realize intelligent data collection and management, and set up an automated multi-level warning mechanism to improve the accuracy and timeliness of warnings, optimize safety management and maintenance strategies. The system improves the level of dam safety management, reduces potential safety threats, and improves the overall emergency response capabilities and disaster prevention and control effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A system block diagram of a dam monitoring and early warning system based on cloud data provided by the present invention;

[0044] Figure 2 A system block diagram of a data acquisition module in an embodiment of the present invention;

[0045] Figure 3 A system block diagram of an analysis and identification module in an embodiment of the present invention;

[0046] Figure 4 A system block diagram of an evaluation and definition module in an embodiment of the present invention;

[0047] Figure 5 A system block diagram of a threshold setting module in an embodiment of the present invention;

[0048] Figure 6 A system block diagram of a data acquisition module in an embodiment of the present invention;

[0049] Figure 7 A system block diagram of a processing and judgment module in an embodiment of the present invention;

[0050] Figure 8 4 is a system block diagram of an early warning notification module in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0052] Example

[0053] See also Figure 1 This embodiment provides a dam monitoring and early warning system based on cloud data, and the monitoring and early warning system includes:

[0054] The data acquisition module 100 is used to collect monitoring data of historical safety accidents and identify abnormal parameters in the safety parameters of the monitoring data in historical safety accidents, wherein the safety parameters specifically include water level, structural stress, seepage, temperature and rainfall.

[0055] Specifically, the data acquisition module 100 is used to collect detailed monitoring data when safety accidents occurred in historical dams, including key safety parameters such as water level, structural stress, seepage, temperature and rainfall, and to identify abnormal parameters in these safety accidents through data analysis technology, specifically statistical methods or machine learning algorithms, so as to more accurately locate potential risk points and provide a basis for subsequent risk assessment and warning threshold setting.

[0056] In the specific implementation process, Figure 2 As shown, the data acquisition module 100 specifically includes:

[0057] The cleaning processing module is used to collect the historical monitoring data of the dam and clean the historical monitoring data to remove duplicate, erroneous or invalid data points.

[0058] Specifically, the cleaning processing module is used to check whether there are exactly the same records in the data set, delete duplicates, use data verification to detect errors in the data, check missing values ​​in the data, and fill them in according to actual conditions.

[0059] The extraction module is used to extract safety parameters from the cleaned historical monitoring data, including water level, structural stress, seepage, temperature and rainfall.

[0060] Specifically, the extraction module is used to map corresponding fields, specifically water level, structural stress, seepage, temperature and rainfall, etc., to variables of safety parameters according to the format and structure of the data.

[0061] For example, assume that the cleaned historical monitoring data is stored in the form of a CSV file, each row represents a monitoring record at a time point, and each column represents a monitoring indicator. Read this file, extract the data of the "water level", "structural stress", "seepage", "temperature" and "rainfall" columns, and store them in corresponding variables for subsequent analysis and processing.

[0062] The anomaly identification module is used to identify abnormal parameters of safety parameters in historical safety accidents using statistical methods.

[0063] Specifically, the anomaly identification module is used to identify abnormal parameters of safety parameters in historical safety accidents by using mean comparison, variance analysis and time series analysis.

[0064] For example, suppose that in a dam breach accident, the system found that the seepage data suddenly increased significantly in the few days before the accident, and far exceeded the historical average level of the same period. By comparing the mean and variance of the seepage data before and after the accident, it can be judged that seepage is an abnormal parameter, and its abnormal change may be related to the dam breach. Therefore, in the subsequent early warning system, seepage will be regarded as a safety parameter that needs to be monitored.

[0065] The analysis and identification module 200 is used to identify the mapping relationship between abnormal parameters and safety accidents.

[0066] Specifically, the analysis and identification module 200 is used to deeply analyze the correlation between each abnormal parameter and a specific type of safety accident, and to clarify which abnormal parameters appear or change as factors leading to which type of safety accident through statistical analysis or causal reasoning methods. This helps to more accurately predict and judge the types of risks that the dam may face, and provides strong support for the subsequent setting of warning thresholds and formulation of warning strategies.

[0067] In the specific implementation process, Figure 3 As shown, the analysis and identification module 200 specifically includes:

[0068] The statistical analysis module is used to identify abnormal parameters that lead to safety accidents based on statistical techniques and domain knowledge.

[0069] Specifically, the statistical analysis module is used to use statistical tools and techniques, such as hypothesis testing, correlation analysis, and regression analysis, to analyze the relationship between various safety parameters and safety accidents in historical monitoring data, and to combine professional knowledge in the field of dam engineering to assist in judgment and comprehensively determine which abnormal changes in safety parameters are the key factors leading to safety accidents.

[0070] For example, suppose that in historical monitoring data, statistical analysis shows that water level and seepage parameters have undergone significant abnormal changes before multiple dam safety accidents. At the same time, experts in the field also pointed out that when the water level exceeds a certain threshold and the seepage increases, the stability of the dam will be seriously affected, thereby increasing the risk of dam breach. Therefore, water level and seepage can be clearly identified as abnormal parameters that lead to safety accidents.

[0071] The mapping identification module is used to determine the specific mapping relationship between the abnormal parameters and the safety accident through data mining technology according to the abnormal parameters that cause the safety accident.

[0072] Specifically, the mapping identification module is used to determine the thresholds of abnormal parameters, the rate of change, and the time delay between them and the occurrence of safety accidents, and to analyze and determine the specific mapping relationship between abnormal parameters and safety accidents through classification algorithms or association rule mining algorithms.

[0073] The evaluation and definition module 300 is used to evaluate the dam safety parameter types, and based on the evaluation results combined with the mapping relationship between abnormal parameters and safety accidents, quantify the risk level of each safety parameter type to set the corresponding safety parameter type priority.

[0074] Specifically, the evaluation and definition module 300 is used to quantify the risk level of each security parameter type based on the evaluation results of the security parameter type and the mapping relationship between abnormal parameters and security incidents, and set the corresponding security parameter type priority according to the quantified risk level. By quantifying the risk level and setting the priority, it can be ensured that in the case of limited resources, those security parameters with higher risks and more critical are given priority attention and processing, thereby improving the efficiency and accuracy of the early warning system.

[0075] In the specific implementation process, Figure 4 As shown, the evaluation and definition module 300 specifically includes:

[0076] The evaluation module is used to identify the safety parameter type of the dam, and evaluate each safety parameter type to obtain the evaluation result.

[0077] Specifically, the evaluation module is used to identify the types of parameters that affect the safety of the dam based on the structural characteristics, operating environment and historical experience of the dam, including water level, structural stress, seepage, temperature and rainfall, and to evaluate whether the current status of each parameter is within the normal range and remains normal through statistical analysis or expert scoring methods.

[0078] For example, water level is identified as an important type of safety parameter, and current water level data is collected. By comparing the design water level and the actual water level, the evaluation results may indicate that the current water level is within the normal range, but is close to the warning line and requires close attention. Taking the water level as an example, assuming that the design water level of the dam is 10 meters, the currently monitored water level is 9.5 meters.

[0079] The quantification module is used to comprehensively quantify the risk level of each safety parameter type based on the mapping relationship between abnormal parameters and safety accidents and the evaluation results.

[0080] Specifically, the quantification module is used to combine the evaluation results of each safety parameter with the mapping relationship, analyze the impact of the current state of the parameter on the safety of the dam, and assign a risk level to each safety parameter type based on the analysis results. In this embodiment, different colors such as red, yellow, and green are used to represent high risk, medium risk, and low risk levels.

[0081] The weight module is used to set a corresponding priority for each security parameter type based on the risk level of each security parameter type.

[0082] Specifically, the weight module is used to sort all security parameter types from low to high according to risk level, and assign a priority to each security parameter type based on the sorting result. For example, a parameter type with a higher risk level will receive a higher priority.

[0083] The threshold setting module 400 is used to define the boundary between abnormal parameters and normal fluctuations to set the primary warning threshold, and to set the advanced warning threshold based on the mapping relationship between abnormal parameters and safety accidents.

[0084] Specifically, the threshold setting module 400 is used to set the primary warning threshold according to the boundary definition of abnormal parameters and normal fluctuations. At the same time, based on the mapping relationship between abnormal parameters and safety accidents, the advanced warning threshold is further set to determine whether the safety parameters in the real-time monitoring data have reached or exceeded the dangerous level.

[0085] In the specific implementation process, Figure 5 As shown, the threshold setting module 400 specifically includes:

[0086] The first setting module is used to count and calculate the boundary between abnormal parameters and normal fluctuations of monitoring data change characteristics, so as to set the primary warning threshold.

[0087] Specifically, the first setting module is used to analyze the distribution characteristics of each monitoring data by utilizing statistical methods, specifically the mean and standard deviation, to determine the normal fluctuation range of the parameters in each monitoring data, and to set the primary warning threshold based on the statistically determined normal fluctuation range. Specifically, when the monitoring parameter value exceeds this range, a primary warning is triggered.

[0088] The second setting module is used to set the advanced warning threshold according to the mapping relationship between abnormal parameters and safety accidents, and the fluctuation correlation between abnormal parameters.

[0089] Specifically, the second setting module is used to analyze the correlation between different abnormal parameters according to the mapping relationship between abnormal parameters and safety accidents, identify the causal relationship or covariation relationship between them, and set the advanced warning threshold by setting the combination conditions of multiple parameters. In this embodiment, it is assumed that the water level, seepage and dam deformation are represented by W, S, and D respectively, and each parameter has a corresponding weight, which is α, β, and γ (and α+β+γ=1), and then the comprehensive warning index (CPI) can be expressed as: CPI=αW+βS+γ*D, where W, S, and D can be the ratio of the actual value of the parameter to the upper limit of their normal fluctuation range.

[0090] Exemplarily, if the CPI exceeds 0.8, a high-level warning signal is issued.

[0091] The data collection module 500 is used to set a corresponding collection frequency level based on the priority of the safety parameter type, and collect real-time monitoring data of the dam according to the collection frequency level.

[0092] Specifically, the data acquisition module 500 is used to set the corresponding data acquisition frequency level according to the priority of the security parameter type. For security parameters with high priority, the frequency of data acquisition is increased to ensure that potential risks can be discovered and responded to in a timely manner. By setting different acquisition frequencies according to priority, resource allocation can be optimized while ensuring the sensitivity of the early warning system, and unnecessary data acquisition and processing costs can be reduced.

[0093] In the specific implementation process, Figure 6 As shown, the data acquisition module 500 specifically includes:

[0094] The collection parameter setting module is used to set the corresponding collection frequency level based on the priority of the security parameter type, wherein the collection frequency level specifically includes monthly collection, weekly collection and daily collection.

[0095] The collection and transmission module is used to collect the safety parameter data of the dam in real time according to the collection frequency level corresponding to the safety parameter type and transmit it through the cloud platform.

[0096] The processing and judgment module 600 is used to collect and pre-process the real-time monitoring data of the dam, and use the data analysis algorithm to compare and judge whether the value of the safety parameter in the pre-processed data exceeds the primary warning threshold or the advanced warning threshold.

[0097] Specifically, the processing and judgment module 600 is used to collect real-time monitoring data of the dam according to the set collection frequency, and perform pre-processing operations such as cleaning, formatting, and missing value processing, and use the data analysis algorithm to compare the values ​​of the safety parameters in the pre-processed data with the primary warning threshold and the advanced warning threshold in turn to determine whether they exceed the primary warning threshold or the advanced warning threshold to obtain a judgment result. By acquiring and analyzing the safety parameter data of the dam in real time, potential safety hazards can be discovered and responded to in a timely manner to ensure the safe and stable operation of the dam.

[0098] In the specific implementation process, Figure 7 As shown, the processing and judging module 600 specifically includes:

[0099] The preprocessing module is used to clean, format and process missing values ​​of the received security parameter data.

[0100] The judgment module is used to compare and judge whether the safety parameters in the pre-processed data exceed the primary warning threshold or the advanced warning threshold.

[0101] The warning notification module 700 is used to generate a primary warning notification or an advanced warning notification in response to the value of the security parameter exceeding the primary warning threshold or the advanced warning threshold.

[0102] Specifically, the early warning notification module 700 is used to generate corresponding early warning notifications according to preset rules and processes when the value of the security parameter exceeds the primary or advanced early warning threshold. Through the early warning notification, relevant departments and personnel can respond quickly and take effective countermeasures to prevent the occurrence of security accidents or mitigate their impact.

[0103] In the specific implementation process, Figure 8 As shown, the early warning notification module 700 specifically includes:

[0104] The response module is used to respond to the value of the security parameter exceeding the primary warning threshold or the advanced warning threshold and determine the corresponding warning level.

[0105] Specifically, the response module is used to compare the values ​​of the monitored security parameters with the primary warning threshold and the advanced warning threshold. Specifically: if the value of a certain security parameter exceeds the primary warning threshold but does not reach the advanced warning threshold, the warning level is determined to be a primary warning; if a certain parameter value exceeds the advanced warning threshold, or multiple parameters exceed the primary warning threshold at the same time, the warning level is determined to be an advanced warning.

[0106] The warning information generation module is used to generate corresponding warning information according to the warning level, including the warning type, time, location and impact range.

[0107] Specifically, the warning information generation module is used to determine the specific type of warning according to the warning level and the parameters that trigger the warning, and generate corresponding warning information, including the warning type, time, location and impact range.

[0108] The warning module is used to send warning notifications to relevant personnel or organizations.

[0109] Specifically, according to the warning type and scope of impact, determine the personnel or organizations that need to receive the warning notification, and select the notification method, including text messages, telephone, email, radio, television, etc., to ensure that the notification method can quickly and effectively cover all target objects.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0112] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. The cloud data-based dam monitoring and early warning system is characterized by: The monitoring and early warning system comprises: The data acquisition module is used to collect monitoring data of historical safety accidents and identify abnormal parameters in the safety parameters of the monitoring data in historical safety accidents, where the safety parameters specifically include water level, structural stress, seepage, temperature and rainfall; Analysis and identification module, used to identify the mapping relationship between abnormal parameters and safety accidents; The evaluation and definition module is used to evaluate the types of dam safety parameters and quantify the risk level of each safety parameter type based on the evaluation results combined with the mapping relationship between abnormal parameters and safety accidents, so as to set the corresponding safety parameter type priority; The threshold setting module is used to define the boundary between abnormal parameters and normal fluctuations to set the primary warning threshold, and to set the advanced warning threshold based on the mapping relationship between abnormal parameters and safety accidents; A data collection module, used to set a corresponding collection frequency level based on the priority of the safety parameter type, and collect real-time monitoring data of the dam according to the collection frequency level; The processing and judgment module is used to collect and pre-process the real-time monitoring data of the dam, and use the data analysis algorithm to compare and judge whether the value of the safety parameter in the pre-processed data exceeds the primary warning threshold or the advanced warning threshold; The early warning notification module is used to generate a primary early warning notification or an advanced early warning notification in response to the value of the security parameter exceeding the primary early warning threshold or the advanced early warning threshold.

2. The cloud data-based dam monitoring and early warning system according to claim 1 is characterized in that: The data acquisition module specifically includes: The cleaning processing module is used to collect historical monitoring data of the dam and clean the historical monitoring data to remove duplicate, erroneous or invalid data points. An extraction module is used to extract safety parameters from the cleaned historical monitoring data, including water level, structural stress, seepage, temperature and rainfall; The anomaly identification module is used to identify abnormal parameters of safety parameters in historical safety accidents using statistical methods.

3. The cloud data-based dam monitoring and early warning system according to claim 1 is characterized in that: The analysis and identification module specifically includes: Statistical analysis module, which is used to identify abnormal parameters that lead to safety accidents based on statistical techniques and domain knowledge; The mapping identification module is used to determine the specific mapping relationship between the abnormal parameters and the safety accident through data mining technology according to the abnormal parameters that cause the safety accident.

4. The cloud data-based dam monitoring and early warning system according to claim 1 is characterized in that: The evaluation and definition module specifically includes: An evaluation module is used to identify the safety parameter type of the dam and evaluate each safety parameter type to obtain an evaluation result; The quantification module is used to comprehensively quantify the risk level of each safety parameter type based on the mapping relationship between abnormal parameters and safety incidents and the evaluation results; The weight module is used to set a corresponding priority for each security parameter type based on the risk level of each security parameter type.

5. The cloud data-based dam monitoring and early warning system according to claim 1 is characterized in that: The threshold setting module specifically includes: The first setting module is used to count and calculate the boundary between abnormal parameters and normal fluctuations of monitoring data change characteristics, so as to set the primary warning threshold; The second setting module is used to set the advanced warning threshold according to the mapping relationship between abnormal parameters and safety accidents, and the fluctuation correlation between abnormal parameters.

6. The cloud data-based dam monitoring and early warning system according to claim 1 is characterized in that: The data acquisition module specifically includes: A collection parameter setting module, used to set the corresponding collection frequency level based on the priority of the security parameter type, wherein the collection frequency level specifically includes monthly collection, weekly collection and daily collection; The collection and transmission module is used to collect the safety parameter data of the dam in real time according to the collection frequency level corresponding to the safety parameter type and transmit it through the cloud platform.

7. The cloud data-based dam monitoring and early warning system according to claim 1 is characterized in that: The processing and judgment module specifically includes: The preprocessing module is used to clean, format and process missing values ​​of the received security parameter data; The judgment module is used to compare and judge whether the safety parameters in the pre-processed data exceed the primary warning threshold or the advanced warning threshold.

8. The cloud data-based dam monitoring and early warning system according to claim 1 is characterized in that: The early warning notification module specifically includes: A response module, used to respond to the value of the security parameter exceeding the primary warning threshold or the advanced warning threshold, and determine the corresponding warning level; The warning information generation module is used to generate corresponding warning information according to the warning level, including the warning type, time, location and impact range; The warning module is used to send warning notifications to relevant personnel or organizations.

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