Real-time monitoring, analysis and evaluation method and system for strong earthquakes in dams based on call-for-measurement instructions

Abstract

The present invention discloses a method and system for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-test instructions. The method is specifically as follows: step 1: a data acquisition instrument samples the signals of various monitoring sensors on the dam according to a set frequency and stores them in a conventional event monitoring data storage area; step 2: monitoring earthquake events, when judging that an earthquake has occurred at the current moment, a call-test instruction is sent, and the data acquisition instrument increases the sampling frequency and stores them in an earthquake event monitoring data storage area; if it is judged that no earthquake has occurred at the current moment, step 1 is continued; step 3: data cleaning is performed on conventional monitoring data and earthquake event monitoring data; step 4: dam safety is analyzed and evaluated based on the cleaned dam monitoring data. The method and system accurately evaluate dam safety by cleaning earthquake and conventional dam monitoring data, timely discover safety hazards existing in the dam, and perform early warning processing and call-test.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy project operation management, and in particular to a method and system for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-for-measurement instructions. Background Art

[0002] Dams mainly cover functions such as flood control, irrigation, water supply, power generation, and shipping. Dam risks are mainly related to the safety status of the dam structure, the level of dam operation and management, and external risk factors. Therefore, it is necessary to comprehensively rank dam risks based on dam safety risk management theory, conduct horizontal and vertical comparisons, and propose personalized solutions for different dam risks, highlight management priorities, and achieve accurate allocation of management resources.

[0003] At present, my country's dam safety concept focuses more on the structural safety of the project itself, and does not fully focus on dam risk factors, such as operation management issues and earthquake risk issues. In addition, structural safety evaluation often adopts a supervision and management model of periodic expert on-site inspection and evaluation. The factors considered and the evaluation criteria are greatly affected by the technical level and personal preferences of the participants, resulting in low objectivity and comparability of the evaluation results. Especially in the event of an earthquake, it will have a great impact on dam safety. The periodic supervision and management model is difficult to detect the safety hazards of the dam in a timely manner, which needs to be improved urgently. Summary of the invention

[0004] The problem to be solved by the present invention is to provide a method and system for real-time monitoring, analysis and evaluation of strong earthquakes of dams based on call for monitoring instructions, which combines conventional monitoring with high-frequency monitoring during earthquakes and can timely evaluate the safety of the dam.

[0005] A technical solution adopted by the present method to solve the above problem is: a real-time monitoring, analysis and evaluation method for dam strong earthquake based on call for measurement instructions, which specifically includes the following steps:

[0006] Step 1: The data acquisition instrument samples the signals of each monitoring sensor on the dam at a set frequency, and stores the conventional monitoring data sampled at the set frequency into the conventional event monitoring data storage area;

[0007] Step 2: Monitor earthquake events. If it is determined that an earthquake has occurred at the current moment, send a call for measurement. The data acquisition instrument increases the sampling frequency and stores the earthquake event monitoring data sampled at a high frequency in the earthquake event monitoring data storage area. If it is determined that no earthquake has occurred at the current moment, continue to execute step 1.

[0008] Step 3: Clean the conventional monitoring data in step 1 and the earthquake event monitoring data obtained in step 2 to obtain cleaned dam monitoring data;

[0009] Step 4: Analyze and evaluate dam safety based on the cleaned dam monitoring data;

[0010] The data cleaning in step 3 specifically includes:

[0011] Step 31: Use data cleaning tools to perform preliminary cleaning on the conventional monitoring data obtained in step 1 and the earthquake event monitoring data obtained in step 2, including deleting duplicate values ​​and identifying abnormal values;

[0012] Step 32: Use association rule learning algorithms to mine association rules between different monitoring indicators; fill in missing values ​​in the monitoring data after preliminary cleaning according to the mined association rules; and identify outliers more accurately;

[0013] Step 33: Use the machine learning model to predict and fill the remaining missing values, and further verify and process the outliers.

[0014] Compared with the existing technology, the advantage of this method is that it can accurately evaluate the safety of the dam and timely discover the safety hazards of the dam by cleaning the earthquake and conventional dam monitoring data.

[0015] Preferably, the call instruction in step 2 is a defined callback function type and a callback function array, wherein the callback function type is used to accept the frequency of the data acquisition instrument, and the callback function array is used to register callbacks of various observers who need to subscribe to the sampling frequency change; when the sampling frequency increases, each registered callback function is called to notify all observers that the sampling frequency has changed; the observer is a set A of earthquake event monitoring data, A={E,L,Q,σ 1 ,τ}; where E is the environmental quantity, L is the deformation, Q is the seepage, σ 1 is stress, τ is strain;

[0016] In step 2, there are two situations for judging whether an earthquake occurs at the current moment:

[0017] , no earthquake occurred at this time; , an earthquake occurred at this time;

[0018] in, represents the probability of an earthquake occurring. ; Indicates that an earthquake is likely to have occurred or has already occurred. ; The type of dam.

[0019] Preferably, the method further includes step 21: when the data acquisition instrument increases the sampling frequency, the data acquisition instrument continues to track and monitor earthquake events, and when it is determined that no earthquake has occurred at the current moment during the monitoring of earthquake events, the data acquisition instrument operating frequency is modified to the set frequency. The sampling frequency is reduced after an earthquake occurs to avoid data redundancy.

[0020] As a preferred method, the method for identifying outliers is:

[0021] use To identify outliers, for monitoring indicators ,That Defined as ;in, is the monitoring value of water level, displacement or seepage pressure, is the mean, is the standard deviation, when When a certain threshold is exceeded, it is considered is an outlier.

[0022] Preferably, the association rule mining method includes:

[0023] a. Data preparation

[0024] Input: A collection of subset transactions, denoted as transaction database D. The number of subset transactions T appearing in transaction database D is counted and the statistical results are used Represents; if the item set X, Y ⊂ D, and Then define There is an association rule, where X and Y are called the predecessor and successor of the association rule respectively;

[0025] Minimum support threshold min_sup,

[0026]

[0027] Association Rules middle The probability of an item set combination appearing in the transaction database D is denoted as ,Each transaction in the transaction database D is an item set, which contains multiple sub-items, for The number of occurrences in the transaction database D. The closer the support is to 1, the higher the correlation between the predecessor X and the successor Y, and vice versa.

[0028] b. Generate frequent itemsets

[0029] Initialization: Find all frequent 1-item sets, that is, the item sets whose support is greater than or equal to min_sup;

[0030] Iteration process: Starting with k=2, until no more frequent itemsets can be generated, perform the following steps:

[0031] Generate candidate K item sets: Generate candidate K item sets Ck based on frequent k-1 item sets;

[0032] Calculate support: Calculate the support of each candidate item set in Ck;

[0033] Pruning: retain candidate item sets with support greater than or equal to min_sup as frequent K item sets Lk;

[0034] c. Generate association rules

[0035] Extract all possible rules from frequent itemsets;

[0036] Calculate the confidence of each rule, that is, the conditional probability , , used to indicate the proportion of item sets X and Y occurring simultaneously to item set X;

[0037] The rules whose confidence is greater than or equal to the minimum confidence threshold are retained as association rules.

[0038] As a preferred embodiment, the specific operations of step 4 dam safety analysis and evaluation are:

[0039] Step 41: Utilize the cleaned historical monitoring data to analyze the historical extreme values ​​and historical average values ​​of the three types of measuring points of dam deformation, seepage, and stress and strain, and automatically divide the warning thresholds;

[0040] Step 42: Based on the actual operation of the dam and historical monitoring data, a dynamically adjustable measurement point scoring rule library is established to automatically or manually adjust the scoring standards and warning thresholds of various measurement points;

[0041] Step 43: Analyze and evaluate the dam safety status within the specified time period using the scoring standard to obtain the dam safety analysis and evaluation results. The cleaned monitoring data can more accurately reflect the dam status.

[0042] As a preferred embodiment, the method further includes step 5: processing different types of warning information of the warning measuring points according to the results of the dam safety analysis and evaluation, and reminding the users to respond to work.

[0043] A technical solution adopted by the system to solve the above problems is: a dam strong earthquake real-time monitoring, analysis and evaluation system based on call for measurement instructions, which is used to implement the above-mentioned dam strong earthquake real-time monitoring, analysis and evaluation method based on call for measurement instructions, including a dam strong earthquake real-time monitoring module, a dam safety analysis and evaluation module and a dam safety analysis and evaluation disposal call response module;

[0044] The dam strong earthquake real-time monitoring module is used to monitor earthquake events, and when it is determined that an earthquake has occurred at the current moment, it sends a call for measurement instruction to the data acquisition instrument, and when it is determined that the earthquake has ended at the current moment, it sends an instruction to modify the working frequency to the set frequency to the data acquisition instrument; and is used to clean the earthquake event monitoring data and conventional monitoring data;

[0045] The dam safety analysis and evaluation module is used to analyze and evaluate the dam safety based on the cleaned dam monitoring data;

[0046] The dam safety analysis and evaluation disposal and response module is used to process different types of warning information for warning measurement points according to the dam safety analysis and evaluation results, and to remind users of work responses.

[0047] Compared with the existing technology, the advantage of this system is that it can accurately evaluate the safety of the dam by cleaning the earthquake and conventional dam monitoring data, timely discover the safety hazards of the dam, and carry out early warning processing and response. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a structural diagram of the dam strong earthquake real-time monitoring, analysis and evaluation system of the present invention.

[0049] Figure 2 It is a schematic diagram of the flow of dam monitoring data in the present invention.

[0050] Figure 3 It is a flow chart of the dam safety analysis, evaluation and disposal response in the present invention. DETAILED DESCRIPTION

[0051] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0052] This preferred embodiment is for example Figure 1 The figure shows a dam strong earthquake real-time monitoring analysis and evaluation system based on the call command, including a dam strong earthquake real-time monitoring module, a dam safety analysis and evaluation module and a dam safety analysis and evaluation disposal call response module;

[0053] The dam strong earthquake real-time monitoring module is used to monitor earthquake events, and when it is determined that an earthquake has occurred at the current moment, it sends a call for measurement instruction to the data acquisition instrument, and when it is determined that the earthquake has ended at the current moment, it sends an instruction to modify the working frequency to the set frequency to the data acquisition instrument; and is used to clean the earthquake event monitoring data and conventional monitoring data;

[0054] The dam safety analysis and evaluation module is used to analyze and evaluate the dam safety based on the cleaned dam monitoring data;

[0055] The dam safety analysis and evaluation disposal and response module is used to process different types of warning information for warning measurement points according to the dam safety analysis and evaluation results, and to remind users of work responses.

[0056] Real-time monitoring, analysis and evaluation method of dam strong earthquake based on call-for-measurement instructions, such as Figure 2 As shown, the specific steps include:

[0057] Step 1: The data acquisition instrument samples the signals of each monitoring sensor on the dam at the set frequency, stores the routine monitoring data sampled at the set frequency into the routine event monitoring data storage area, and calculates the probability of an earthquake occurring at the current moment; the normal setting frequency of the data acquisition instrument is 400H. Z ,The monitoring sensors include environmental quantity, deformation, seepage, and stress-strain dynamic monitoring sensors;

[0058] Step 2: Monitor earthquake events. If it is determined that an earthquake has occurred at the current moment, send a call for measurement. The data acquisition instrument increases the sampling frequency and stores the earthquake event monitoring data sampled at a high frequency in the earthquake event monitoring data storage area. If it is determined that no earthquake has occurred at the current moment, continue to execute step 1. The high frequency of the data acquisition instrument is generally 2000H. Z -4000H Z , selected according to the sensor frequency;

[0059] Step 3: Clean the earthquake event monitoring data obtained in step 2 or the conventional monitoring data in step 1 to obtain cleaned dam monitoring data;

[0060] Step 4: Analyze and evaluate the dam safety based on the cleaned dam monitoring data.

[0061] The call instruction in step 2 is a defined callback function type and a callback function array. The callback function type is used to accept the frequency of the data acquisition instrument, and the callback function array is used to register callbacks of various observers who need to subscribe to the sampling frequency change. When the sampling frequency changes, each registered callback function is called to notify all observers that the sampling frequency has changed. The observer is a set A of earthquake event monitoring data, A={E,L,Q,σ 1 ,τ}, where E is the environmental quantity, L is the deformation, Q is the seepage, σ 1 is stress, τ is strain;

[0062] In step 2, there are two situations for judging whether an earthquake occurs at the current moment:

[0063] , no earthquake occurred at this time; , an earthquake occurred at this time;

[0064] in, represents the probability of an earthquake occurring. ; Indicates that an earthquake is likely to have occurred or has already occurred. ; The type of dam.

[0065] Here, step 21 is also included: the data acquisition instrument continues to track and monitor the earthquake event when the sampling frequency is increased. When the earthquake ends, that is, when it is determined again during the monitoring of the earthquake event that no earthquake has occurred at the current moment, the operating frequency of the data acquisition instrument is modified to the set frequency.

[0066] The data cleaning in step 3 specifically includes:

[0067] Step 31: Use data cleaning tools such as Pandas to perform preliminary cleaning on the conventional monitoring data obtained in step 1 and the earthquake event monitoring data obtained in step 2, including deleting duplicate values ​​and identifying outliers.

[0068] Delete duplicate values: define a set D, assuming that each sample in set D is a vector (including water level, displacement, and osmotic pressure)

[0069] (That Indicates the samples, Not unique)

[0070] (That Indicates the samples, only)

[0071] Where D represents the set of original monitoring data, A collection of monitoring data to delete duplicates.

[0072] The method for identifying outliers is:

[0073] use To identify outliers, for monitoring indicators ,That Defined as ;in, is the monitoring value of water level, displacement or seepage pressure, is the mean, is the standard deviation, when When a certain threshold is exceeded, it is considered is an outlier.

[0074] Step 32: Use association rule learning algorithms to mine association rules between different monitoring indicators; reasonably fill in missing values ​​in the monitoring data after preliminary cleaning based on the mined association rules; and more accurately identify outliers.

[0075] Step 33: Use the machine learning model to predict and fill the remaining missing values, and further verify and process the outliers. The outliers can be processed by using a robust model such as ridge regression and support vector machine.

[0076] In this embodiment, the method for mining association rules includes:

[0077] a. Data preparation

[0078] Input: A collection of subset transactions, denoted as transaction database D. The number of subset transactions T appearing in transaction database D is counted and the statistical results are used Represents; if the item set X, Y ⊂ D, and Then define There is an association rule, where X and Y are called the predecessor and successor of the association rule respectively;

[0079] Minimum support threshold min_sup,

[0080]

[0081] Association Rules middle The probability of an item set combination appearing in the transaction database D is denoted as ,Each transaction in the transaction database D is an item set, which contains multiple sub-items, for The number of occurrences in the transaction database D. The closer the support is to 1, the higher the correlation between the predecessor X and the successor Y, and vice versa.

[0082] b. Generate frequent itemsets

[0083] Initialization: Find all frequent 1-item sets, that is, the item sets whose support is greater than or equal to min_sup;

[0084] Iteration process: Starting with k=2, until no more frequent itemsets can be generated, perform the following steps:

[0085] Generate candidate K item sets: Generate candidate K item sets Ck based on frequent k-1 item sets;

[0086] Calculate support: Calculate the support of each candidate item set in Ck;

[0087] Pruning: retain candidate item sets with support greater than or equal to min_sup as frequent K item sets Lk;

[0088] c. Generate association rules

[0089] Extract all possible rules (such as X ≥ Y) from frequent itemsets, where X and Y are subsets of the itemsets, and ;

[0090] Calculate the confidence of each rule, that is, the conditional probability , which indicates the proportion of item sets X and Y occurring simultaneously to item set X;

[0091]

[0092] The rules whose confidence is greater than or equal to the minimum confidence threshold are retained as association rules, also known as strong association rules.

[0093] Among them, the specific operations of step 4 dam safety analysis and evaluation are:

[0094] Step 41: Utilize the cleaned historical monitoring data to analyze the historical extreme values ​​and historical average values ​​of the three types of measuring points of dam deformation, seepage, and stress and strain, and automatically divide the warning thresholds;

[0095] Step 42: Based on the actual operation of the dam and historical monitoring data, a dynamically adjustable measurement point scoring rule library is established to automatically or manually adjust the scoring standards and warning thresholds of various measurement points;

[0096] Step 43: Analyze and evaluate the dam safety status within a specified time period using the scoring criteria to obtain dam safety analysis and evaluation results.

[0097] Users can conduct a dam safety check according to the selected time period with one click. The system will automatically check the status of each measuring point of the dam in the selected time period, and grade and score each measuring point according to the built-in rules, and finally get the safety check result. In practice, firstly, the three types of measuring points are scored separately (for example, the full score is 100 points, deformation is 40 points, seepage is 30 points, and stress and strain is 30 points). Then, the score of a single measuring point under a certain type of measuring point is calculated. If it does not exceed the warning threshold in the specified time period, no points will be deducted. If the warning threshold is exceeded for 1 / 5 or less of the time in the time period, half of the points will be deducted. If the warning threshold is exceeded for 1 / 5 or more of the time in the time period, all points will be deducted. The score of each measuring point is the total score of the category in which the measuring point belongs / the total number of such measuring points. Finally, the dam safety check result of the specified time period is obtained. If the dam safety evaluation score is 100 points, it is excellent; if it is greater than or equal to 80 points and less than 100 points, it is good; if it is less than 80 points, it is medium; it helps users quickly grasp the safety status of the dam.

[0098] It also includes step 5: according to the results of dam safety analysis and evaluation, different warning types of information processing are carried out on the warning measuring points, and users are reminded of work response. Figure 3 shown.

[0099] Among them, warning type information processing includes: occasional numerical abnormalities, warning threshold abnormalities, temporary equipment failure (requires maintenance), permanent equipment failure (long-term disuse), and supports uploading of warning processing reports or related files in multiple formats such as .rar, .zip, .doc, .docx, .pdf, etc., to facilitate the comprehensive recording and archiving of warning processing information.

[0100] Occasional numerical anomalies: Automatically identify and record occasional abnormal values ​​in the measurement point data, and provide a review and confirmation mechanism;

[0101] Warning threshold abnormality: When the monitoring data exceeds the preset safety threshold, the system automatically triggers a warning and records detailed information;

[0102] Temporary equipment failure: Identify and record temporary monitoring interruptions or data anomalies caused by equipment problems, and support the generation of maintenance requests;

[0103] Permanent equipment failure: For equipment failures that cannot be repaired for a long time, the system marks them as permanently disabled and updates the monitoring plan.

[0104] According to the dam safety warning situation and actual needs of users, the system responds to work through SMS, Zhejiang Government Ding, etc. It provides dam safety work response settings, and can customize the name of the person notified, contact information, notification time, notification content and other information settings under different warning levels (warning, sub-warning, offline warning).

[0105] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-for-measurement instructions, characterized in that: The specific steps include: Step 1: The data acquisition instrument samples the signals of each monitoring sensor on the dam at a set frequency, and stores the conventional monitoring data sampled at the set frequency into the conventional event monitoring data storage area; Step 2: Monitor earthquake events. If it is determined that an earthquake has occurred at the current moment, send a call for measurement. The data acquisition instrument increases the sampling frequency and stores the earthquake event monitoring data sampled at a high frequency in the earthquake event monitoring data storage area. If it is determined that no earthquake has occurred at the current moment, continue to execute step 1. Step 3: Clean the conventional monitoring data in step 1 and the earthquake event monitoring data obtained in step 2 to obtain cleaned dam monitoring data; Step 4: Analyze and evaluate dam safety based on the cleaned dam monitoring data; The data cleaning in step 3 specifically includes: Step 31: Use data cleaning tools to perform preliminary cleaning on the conventional monitoring data obtained in step 1 and the earthquake event monitoring data obtained in step 2, including deleting duplicate values ​​and identifying abnormal values; Step 32: Use association rule learning algorithms to mine association rules between different monitoring indicators; fill in missing values ​​in the monitoring data after preliminary cleaning according to the mined association rules; and identify outliers more accurately; Step 33: Use the machine learning model to predict and fill the remaining missing values, and further verify and process the outliers; The call instruction in step 2 is a defined callback function type and a callback function array, wherein the callback function type is used to receive the frequency of the data acquisition instrument, and the callback function array is used to register callbacks of various observers who need to subscribe to the sampling frequency change; when the sampling frequency increases, each registered callback function is called to notify all observers that the sampling frequency has changed; the observer is a set A of earthquake event monitoring data, A={E,L,Q,σ1,τ}; wherein E is the environmental quantity, L is the deformation, Q is the seepage, σ1 is the stress, and τ is the strain; In step 2, there are two situations for judging whether an earthquake occurs at the current moment: , no earthquake occurred at this time; , an earthquake occurred at this time; in, represents the probability of an earthquake occurring. ; It means that there is a high probability or an earthquake has already occurred. ; is the type of dam; The methods for mining association rules include: a. Data preparation Input: A collection of subset transactions, denoted as transaction database D. The number of subset transactions T appearing in transaction database D is counted and the statistical results are used Represents; if the item set X, Y ⊂ D, and Then define There is an association rule, where X and Y are called the predecessor and successor of the association rule respectively; Minimum support threshold min_sup, Association Rules middle The probability of an item set combination appearing in the transaction database D is denoted as ,Each transaction in the transaction database D is an item set, which contains multiple sub-items, for The number of occurrences in the transaction database D. The closer the support is to 1, the higher the correlation between the predecessor X and the successor Y, and vice versa. b. Generate frequent itemsets Initialization: Find all frequent 1-item sets, that is, the item sets whose support is greater than or equal to min_sup; Iteration process: Starting with k=2, until no more frequent itemsets can be generated, perform the following steps: Generate candidate K item sets: Generate candidate K item sets Ck based on frequent k-1 item sets; Calculate support: Calculate the support of each candidate item set in Ck; Pruning: retain candidate item sets with support greater than or equal to min_sup as frequent K item sets Lk; c. Generate association rules Extract all possible rules from frequent itemsets; Calculate the confidence of each rule, that is, the conditional probability , , used to indicate the proportion of item sets X and Y occurring simultaneously to item set X; The rules whose confidence is greater than or equal to the minimum confidence threshold are retained as association rules.

2. The method for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-for-measurement instructions according to claim 1 is characterized in that: The method also includes step 21: when the data acquisition instrument increases the sampling frequency, the data acquisition instrument continues to track and monitor earthquake events, and when it is determined that no earthquake has occurred at the current moment during the monitoring of earthquake events, the operating frequency of the data acquisition instrument is modified to the set frequency.

3. The method for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-for-measurement instructions according to claim 1 is characterized in that: The method for identifying outliers is: use To identify outliers, for monitoring indicators ,That Defined as ;in, is the monitoring value of water level, displacement or seepage pressure, is the mean, is the standard deviation, when When a certain threshold is exceeded, it is considered is an outlier.

4. The method for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-for-measurement instructions according to claim 1 is characterized in that: Step 4: The specific operations of dam safety analysis and evaluation are as follows: Step 41: Utilize the cleaned historical monitoring data to analyze the historical extreme values ​​and historical average values ​​of the three types of measuring points of dam deformation, seepage, and stress and strain, and automatically divide the warning thresholds; Step 42: Based on the actual operation of the dam and historical monitoring data, a dynamically adjustable measurement point scoring rule library is established to automatically or manually adjust the scoring standards and warning thresholds of various measurement points; Step 43: Analyze and evaluate the dam safety status within a specified time period using the scoring criteria to obtain dam safety analysis and evaluation results.

5. The method for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-for-measurement instructions according to any one of claims 1 to 4, characterized in that: It also includes step 5: according to the results of dam safety analysis and evaluation, different warning types of information are processed for the warning measurement points, and users are reminded of work response.

6. A dam strong earthquake real-time monitoring, analysis and evaluation system based on call-up instructions, characterized in that: The method for real-time monitoring, analysis and evaluation of strong earthquakes in dams based on call-test instructions as claimed in any one of claims 1 to 5 comprises a real-time monitoring module for strong earthquakes in dams, a dam safety analysis and evaluation module and a dam safety analysis and evaluation disposal call-test module; The dam strong earthquake real-time monitoring module is used to monitor earthquake events, and when it is determined that an earthquake has occurred at the current moment, it sends a call for measurement instruction to the data acquisition instrument, and when it is determined that the earthquake has ended at the current moment, it sends an instruction to modify the working frequency to the set frequency to the data acquisition instrument; and is used to clean the earthquake event monitoring data and conventional monitoring data; The dam safety analysis and evaluation module is used to analyze and evaluate the dam safety based on the cleaned dam monitoring data; The dam safety analysis and evaluation disposal and response module is used to process different types of warning information for warning measurement points according to the dam safety analysis and evaluation results, and to remind users of work responses.

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