Vibration table vibration abnormity alarm method based on data processing

Abstract

The invention discloses a vibration table vibration abnormity alarm method based on data processing, and particularly relates to the technical field of vibration table vibration abnormity monitoring. The method comprises the steps of (1) collecting and preprocessing each monitoring data of the vibration table, (2) constructing a prediction model of each monitoring data of the vibration table, (3) obtaining each predicted monitoring data of the vibration table, (4) evaluating the vibration risk level of the vibration table in a prediction period, and (5) executing a response measure of abnormal vibration of the vibration table. According to the method, the prediction model of each monitoring data of the vibration table is constructed to predict each monitoring data of the vibration table in the prediction period, and risk judgment can be performed on each monitoring parameter of the vibration table in advance, so that response measures are taken in advance, and the risk of the vibration table in the working process is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vibration table vibration anomaly monitoring, and particularly relates to a vibration table vibration anomaly alarm method based on data processing. BACKGROUND

[0002] The vibration table is used for simulating the vibration caused by the earthquake, wind or other power source, and is widely used in the experiment and test of the building structure, mechanical equipment, automobile industry, aerospace and other fields. As a kind of precision equipment, the running state of the vibration table is crucial to the experimental results. Through real-time monitoring and alarm, measures can be taken in time when the equipment fails or abnormity occurs, so as to avoid more serious damage or safety accidents. Therefore, it is necessary to study the vibration table vibration anomaly alarm technology.

[0003] The existing technology for monitoring the vibration anomaly of the vibration table meets the basic needs, but also has some defects, specifically: on the one hand, the vibration table vibration anomaly alarm technology is the key to guarantee the safety of the experiment and the stability of the equipment, but the current technology still has some deficiencies in detection accuracy, real-time performance, intelligence and multi-dimensional analysis, etc., it is difficult to find the vibration anomaly of the vibration table in time, causing the abnormal risk of the vibration table in the working process, and it is difficult to meet the production needs. On the other hand, the existing technology directly judges the data collected by the vibration table, but in the actual process, due to the large amount of data to be collected, it is difficult to make accurate judgment on the vibration anomaly of the vibration table in real time, the depth processing of the historical monitoring data is ignored, the prediction of the vibration monitoring data of the vibration table is constituted, and it is difficult to realize the pre-discovery of the potential risk in the working process of the vibration table. SUMMARY

[0004] The purpose of the present application is to provide a vibration table vibration anomaly alarm method based on data processing, which solves the problems in the background art.

[0005] To solve the above technical problems, the present application adopts the following technical scheme: a vibration table vibration anomaly alarm method based on data processing, the method comprising: step one, collecting and preprocessing the monitoring data of the vibration table: collecting the monitoring data of each collection point of the vibration table in the target period, and normalizing the monitoring data of each collection point of the vibration table in the target period; Step two, constructing a prediction model of the monitoring data of the vibration table: based on the historical monitoring data of each collection point of the vibration table in each period extracted from the database, constructing a prediction model of the monitoring data of the vibration table; Step three, obtaining the predicted monitoring data of the vibration table: based on the normalized monitoring data of each collection point of the vibration table in the target period, inputting into the prediction model of the monitoring data of the vibration table, obtaining the predicted monitoring data of the vibration table in the prediction period; Step four, evaluating the vibration table vibration risk level in the prediction period: based on the predicted monitoring data of the vibration table in the prediction period, the vibration table vibration risk coefficient in the prediction period is calculated, and the vibration table vibration risk level in the prediction period is evaluated; Step five, executing the response measures of the vibration table vibration anomaly: implementing the response measures corresponding to the vibration table vibration risk level in the prediction period.

[0006] Compared with the prior art, the benefits of the present application are: first, the present application realizes the prediction of the monitoring data of the vibration table in the prediction period by constructing the prediction model of the monitoring data of the vibration table, which can make risk judgment on the monitoring parameters of the vibration table in advance, so as to make response measures in advance and reduce the risk of the vibration table in the working process.

[0007] Second, the present application analyzes and evaluates the vibration table vibration coefficient in the prediction period according to the predicted monitoring data of the vibration table in the prediction period, thereby automatically generating the response measures of the vibration table vibration risk in the prediction period, and provides a reliable technical solution support for avoiding the vibration table vibration risk. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0009] Figure 1 The method flowchart of the present application.

[0010] Figure 2 The system structure connection diagram of the present application.

[0011] Figure 3 The network model diagram of the present application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0013] In order to facilitate the understanding of the present application, some professional terms related to the present application are explained: Shaking table: Shaking table is used to simulate the vibration caused by earthquakes, wind or other dynamic sources, widely used in experimental and testing of building structures, mechanical equipment, automotive industry, aerospace and other fields.

[0014] Normalization: Normalization is to scale the data to a fixed range, and normalization is very important to ensure the contribution of different features to the model is fair, especially when using gradient descent method, which can speed up the training process.

[0015] LSTM: Long Short-Term Memory (LSTM) is a special type of Recurrent Neural Network (RNN) that is good at processing and predicting time-based sequence data.

[0016] Sliding window: The core idea of the sliding window method is to slide a fixed-size window over the time series data, extracting sub-sequences from each window. These sub-sequences will be used as input to the model. Each time the window slides, the data moves by a fixed step.

[0017] Gradient descent: By calculating the gradient of the objective function at the current parameter point (i.e. the derivative of the objective function), then updating the model parameters in the opposite direction of the gradient (i.e. the negative gradient direction), so as to gradually reduce the value of the objective function.

[0018] Example one: Referring to Figure 1 The present application provides a kind of vibration table vibration abnormality alarm method based on data processing, the method comprises: step one, the each monitoring data of vibration table is collected and preprocessed: the each monitoring data of each collection point of vibration table in target period is collected, the each monitoring data of each collection point of vibration table in target period is normalized.

[0019] In the specific embodiments of the present application, the each monitoring data of each collection point of vibration table in target period is collected, and the specific method is as follows: the each monitoring data of vibration table includes: vibration acceleration, vibration frequency, vibration displacement, working temperature and working voltage.

[0020] Through the acceleration sensor installed on the vibration table, the vibration acceleration of the vibration table is collected at a collection frequency of 100 Hz. The vibration acceleration of the vibration table is collected as a collection interval. represents the collection frequency extracted from the database.

[0021] Through the speed sensor installed on the vibration table, the vibration frequency of the vibration table is collected at a collection frequency of 100 Hz. The vibration frequency of the vibration table is collected as a collection interval.

[0022] Through the displacement sensor installed on the vibration table, the vibration displacement of the vibration table is collected at a collection frequency of 100 Hz. Collecting the vibration displacement of the vibration table as a collection interval.

[0023] Collecting the temperature of the vibration table by the temperature sensor installed on the vibration table at a collection interval. Collecting the working temperature of the vibration table as a collection interval.

[0024] Collecting the voltage of the vibration table by the voltage sensor installed on the vibration table at a collection interval. Collecting the working voltage of the vibration table as a collection interval.

[0025] It should be noted that in the specific embodiments of the present application, the collection frequency extraction method from the database is specifically set as follows: the vibration risk level of the vibration table in the previous period of the target period is extracted from the database, if the vibration risk level of the vibration table in the previous period of the target period is primary, the collection frequency in the target period is set to , if the vibration risk level of the vibration table in the previous period of the target period is intermediate, the collection frequency in the target period is set to , if the vibration risk level of the vibration table in the previous period of the target period is high, the collection frequency in the target period is set to , wherein is stored in the database, and satisfies , for example .

[0026] In the specific embodiments of the present application, the target period of the vibration table is normalized by the monitoring data of each collection point, and the specific analysis method is as follows: the monitoring data of each collection point of the vibration table in the target period is recorded as , wherein , represents the number of each collection point, represents the total number of collection points, , represents the number of each monitoring data, represents the total number of monitoring data.

[0027] The maximum and minimum values of each monitoring data in the target period are extracted from the monitoring data of each collection point of the vibration table in the target period, and are recorded as and .

[0028] The normalized monitoring data of each collection point of the vibration table in the target period is calculated by the formula: .

[0029] ​It should be noted that in the specific embodiments of the present application, the calculation formula of the normalized monitoring data of each collection point of the vibration table in the target period is based on the data normalization processing method, which realizes the scaling of the monitoring data of each collection point of the vibration table in the target period to a fixed range Normalization is very important to ensure the fair contribution of different features to the model.

[0030] Step two, building a prediction model of the monitoring data of the vibration table: based on the historical monitoring data of each collection point of the vibration table in each period extracted from the database, a prediction model of the monitoring data of the vibration table is built.

[0031] In the specific embodiments of the present application, the specific analysis method of the prediction model of the monitoring data of the vibration table is: the detailed method steps of building the prediction model of the monitoring data of the vibration table through the LSTM long short-term memory neural network are: A1: extracting window size and step from the database, creating a sliding window, and segmenting the normalized historical monitoring data of each collection point of the vibration table in each period extracted from the database to obtain the subsequence of the normalized historical monitoring data of each collection point of the vibration table in each period.

[0032] It should be noted that in the specific embodiments of the present application, the specific analysis method of the subsequence of the normalized historical monitoring data of each collection point of the vibration table in each period is: for example, the vibration frequency data of the vibration table of each collection point in the historical first period is recorded as , the window size is set to , the step is set to , and the subsequence is extracted through the sliding window, then the first window is obtained: , the second window is: , the third window is: , and the fourth window is: .

[0033] It should be noted that in the specific embodiments of the present application, the sliding window is a powerful method that can effectively process time series data. In building the prediction model of the monitoring data of the vibration table, the sliding window technology can help the model capture the local features of the time series data and provide effective support for subsequent machine learning tasks.

[0034] A2: The subsequence of the normalized historical monitoring data of each collection point of the vibration table in each period is used as the training input of the LSTM long short-term memory neural network.

[0035] It should be noted that in the specific embodiments of the present application, the LSTM long short-term memory neural network is specifically: the LSTM long short-term memory neural network mainly comprises an input layer, an LSTM layer, a full connection layer and an output layer.

[0036] The output layer is the first layer of the neural network, which receives data input from the outside, and in time series problems, the input layer receives feature data at each time. The input layer passes these data to the LSTM layer as initial information for the model.

[0037] The role of the LSTM layer is to capture the long and short-term dependencies in the time series. It can effectively remember and forget past information, thereby solving the gradient vanishing or explosion problem that standard RNNs may encounter in long sequences.

[0038] The full connection layer is responsible for mapping the features extracted by the LSTM layer to higher-level representations, which is usually used for classification or regression tasks. Through the full connection layer, the network can learn the mapping from the features extracted from the time series data to the predicted values of each monitoring data of the vibration table.

[0039] The role of the output layer is to generate the final prediction or classification result according to the processing result of the previous layer.

[0040] A3: by formula: , the loss function of the LSTM long short-term memory neural network is constructed , wherein represents the true value of the training input of the LSTM long short-term memory neural network, represents the predicted value of the output of the LSTM long short-term memory neural network.

[0041] A4: by formula: , the gradient descent method is used to update the weight of the LSTM long short-term memory neural network, wherein represents the weight of the LSTM long short-term memory neural network, represents the learning rate of the LSTM long short-term memory neural network extracted from the database.

[0042] A5: by formula: , the gradient descent method is used to update the bias parameter of the LSTM long short-term memory neural network, wherein represents the bias parameter of the LSTM long short-term memory neural network.

[0043] The present application realizes the prediction of the monitoring data of the vibration table in the prediction period by constructing the prediction model of each monitoring data of the vibration table, can pre-judge the risk of each monitoring parameter of the vibration table, and thus pre-responds to the measures, reduces the risk of the vibration table in the working process.

[0044] Step three, obtaining the predicted monitoring data of the vibration table: based on the normalized monitoring data of each collection point of the vibration table in the target period, inputting into the prediction model of the monitoring data of the vibration table, and obtaining the predicted monitoring data of the vibration table in the prediction period.

[0045] In specific embodiments of the present application, the specific analysis method for obtaining the predicted monitoring data of the vibration table in the prediction period is as follows: based on the normalized monitoring data of each collection point of the vibration table in the target period, performing sliding window processing to obtain the subsequence of the normalized monitoring data of each collection point of the vibration table in the target period, as the input of the prediction model of the monitoring data of the vibration table, obtaining the predicted monitoring data of the vibration table in the prediction period, denoted as .

[0046] Step four, evaluating the vibration risk level of the vibration table in the prediction period: based on the predicted monitoring data of the vibration table in the prediction period, calculating the vibration risk coefficient of the vibration table in the prediction period, and evaluating the vibration risk level of the vibration table in the prediction period.

[0047] In specific embodiments of the present application, the specific analysis method for calculating the vibration risk coefficient of the vibration table in the prediction period is as follows: based on the predicted monitoring data of the vibration table in the prediction period , the abnormal judgment value of the predicted monitoring data of the vibration table in the prediction period is calculated by the formula: , wherein represents the normal working interval of the monitoring data of the vibration table stored in the database.

[0048] It should be noted that in specific embodiments of the present application, the calculation method of the abnormal judgment value of the predicted monitoring data of the vibration table in the prediction period is as follows: for example, if the predicted vibration frequency of the vibration table in the prediction period is not in the normal working interval of the vibration frequency of the vibration table, the abnormal judgment value of the predicted vibration frequency of the vibration table in the prediction period is set to 1, and if the predicted vibration frequency of the vibration table in the prediction period is in the normal working interval of the vibration frequency of the vibration table, the abnormal judgment value of the predicted vibration frequency of the vibration table in the prediction period is set to 0.

[0049] It should be noted that in specific embodiments of the present application, the abnormal judgment value of the predicted monitoring data of the vibration table in the prediction period reflects whether the predicted monitoring data of the vibration table in the prediction period is in the normal working state interval.

[0050] ​It should be noted that in the specific embodiments of the present application, the normal working interval of each monitoring data of the vibration table is artificially set by the technician according to the actual working each monitoring data of the vibration table and the working state of the vibration table, and is stored in the database after comprehensive analysis.

[0051] The vibration table vibration risk coefficient in the prediction period is calculated by the formula: Wherein represents a natural constant.

[0052] It should be noted that in the specific embodiments of the present application, the calculation method of the vibration table vibration risk coefficient in the prediction period comprehensively considers the abnormal judgment value of each monitoring data of the vibration table predicted in the prediction period. The greater the sum of the abnormal judgment value of each monitoring data of the vibration table predicted in the prediction period, the greater the vibration risk of the vibration table in the prediction period is analyzed, and the vibration table vibration risk coefficient in the prediction period is used as a reference value reflecting the vibration risk of the vibration table in the prediction period.

[0053] In the specific embodiments of the present application, the specific analysis method of the vibration table vibration risk level in the prediction period is based on the vibration table vibration risk coefficient in the prediction period If is in the vibration risk first interval extracted from the database, it is determined that the vibration risk level of the vibration table in the prediction period is primary.

[0054] If is in the vibration risk second interval extracted from the database, it is determined that the vibration risk level of the vibration table in the prediction period is intermediate.

[0055] If is in the vibration risk third interval extracted from the database, it is determined that the vibration risk level of the vibration table in the prediction period is high.

[0056] It should be noted that in the specific embodiments of the present application, the vibration risk first interval, the vibration risk second interval and the vibration risk third interval are artificially set by the technician according to the vibration risk coefficient of the vibration table in each historical period and the actual working state of the subsequent vibration table, and are stored in the database after comprehensive analysis. For example, the vibration risk first interval is: The vibration risk first interval is: The vibration risk first interval is: .

[0057] Step five, execute the response measure of vibration table vibration anomaly: implement the response measure corresponding to the vibration risk level of the vibration table in the prediction period.

[0058] ​In the specific embodiment of the present application, the specific analysis method of implementing the response measure corresponding to the vibration table vibration risk level in the prediction period is: based on the abnormality judgment value of each monitoring data predicted by the vibration table in the prediction period, extracting the abnormal monitoring parameters of the vibration table in the prediction period, extracting the abnormal processing method of the abnormal monitoring parameters of the historical vibration table from the database, generating the vibration risk response scheme of the vibration table in the prediction period, and issuing the vibration risk response scheme of the vibration table in the prediction period to the vibration table to implement the vibration risk response measure of the vibration table in the prediction period.

[0059] It should be noted that in the specific embodiment of the present application, the specific method steps of implementing the vibration risk response measure of the vibration table in the prediction period include the following information: the abnormal monitoring parameters of the vibration table in the prediction period, the response measure of the abnormal monitoring parameters of the vibration table in the prediction period, and the vibration risk response emergency identifier of the vibration table in the prediction period. The vibration risk response emergency identifier of the vibration table in the prediction period is extracted from the vibration risk response measure of the vibration table in the prediction period. If the vibration risk response emergency identifier of the vibration table in the prediction period is , the vibration table work is immediately stopped, the response measure of the abnormal monitoring parameters of the vibration table in the prediction period is extracted, and the vibration risk response of the vibration table is performed.

[0060] If the vibration risk response emergency identifier of the vibration table in the prediction period is , the vibration table continues to work for a period of time, the response measure of the abnormal monitoring parameters of the vibration table in the prediction period is extracted, and the vibration risk response of the vibration table is performed.

[0061] If the vibration risk response emergency identifier of the vibration table in the prediction period is , the vibration table work is maintained, the response measure of the abnormal monitoring parameters of the vibration table in the prediction period is extracted, and the vibration risk response of the vibration table is performed.

[0062] It should be noted that in the specific embodiment of the present application, the vibration table work state in the vibration risk response process is controlled according to the vibration risk response emergency identifier of the vibration table in the prediction period. Since the vibration risk of the vibration table in the prediction period is predicted based on the monitoring parameters of the vibration table in the target period, the working efficiency of the vibration table can be maintained in the vibration risk response process.

[0063] In the specific embodiment of the present application, the specific analysis method of extracting the abnormal monitoring parameters of the vibration table in the prediction period is: based on the abnormality judgment value of each monitoring data predicted by the vibration table in the prediction period, if is 1, the first The monitoring data are recorded as abnormal monitoring parameters.

[0064] According to the prediction of the vibration table within the prediction period The analysis method of each monitoring data is used to analyze the abnormal judgment value of each monitoring data predicted by the vibration table within the prediction period, and the abnormal monitoring parameters of the vibration table within the prediction period are obtained.

[0065] In a specific embodiment of the present invention, the vibration risk response plan for the vibration table within the prediction period is generated, and its specific analysis method is: based on the abnormal monitoring parameters of the vibration table within the prediction period, the response measures of the abnormal monitoring parameters of the historical vibration table are extracted from the database to generate the vibration risk response plan for the vibration table within the prediction period.

[0066] Based on the vibration risk level of the shaking table within the prediction period, a shaking table vibration risk response emergency identifier within the prediction period is generated.

[0067] It should be noted that, in a specific embodiment of the present invention, the specific analysis method for generating the vibration risk response emergency identifier of the vibration table within the prediction period is: for example, if the vibration risk level of the vibration table within the prediction period is primary, then the vibration risk response emergency identifier of the vibration table within the prediction period is set to If the vibration risk level of the shaking table in the prediction period is medium, the vibration risk response emergency identifier of the shaking table in the prediction period is set to If the vibration risk level of the shaking table in the prediction period is high, the vibration risk response emergency identifier of the shaking table in the prediction period is set to .

[0068] The vibration risk plan for the shaking table within the prediction period includes: abnormal monitoring parameters of the shaking table within the prediction period, response measures for abnormal monitoring parameters of the shaking table within the prediction period, and an emergency identifier for response to vibration risks of the shaking table within the prediction period.

[0069] The present invention analyzes and evaluates the vibration coefficient of the vibration table within the prediction period based on the monitoring data predicted by the vibration table within the prediction period, thereby automatically generating response measures for the vibration risk of the vibration table within the prediction period, providing reliable technical solution support for avoiding the vibration risk of the vibration table.

[0070] Example 2: It should be noted that, in the specific embodiments of the present invention, referring to Figure 2 As shown, the present invention provides a system for executing a vibration abnormality alarm method for a vibration table based on data processing, which specifically includes: a monitoring data acquisition and preprocessing module, a prediction model construction module, a predicted monitoring data acquisition module, a vibration risk level assessment module, a vibration abnormality response module and a database.

[0071] The monitoring data acquisition and preprocessing module is configured to acquire each monitoring data of each collection point of the vibration table in a target period and perform normalization processing.

[0072] The prediction model construction module is configured to construct a prediction model of each monitoring data of the vibration table by using an LSTM long short-term memory neural network.

[0073] The predicted monitoring data acquisition module is configured to acquire each predicted monitoring data of the vibration table in a prediction period according to each monitoring data of each collection point of the vibration table in the target period after normalization processing.

[0074] The vibration risk level evaluation module is configured to calculate a vibration risk coefficient of the vibration table in the prediction period according to each predicted monitoring data of the vibration table in the prediction period, and evaluate a vibration risk level of the vibration table in the prediction period.

[0075] The vibration anomaly response module is configured to implement a response measure corresponding to the vibration risk level of the vibration table in the prediction period.

[0076] The database is configured to store a collection frequency of each monitoring data of the vibration table, store a window size and a step of a sliding window, store a normal working interval of each monitoring data of the vibration table, store each monitoring data of each collection point of the vibration table in each historical period after normalization processing, and store a vibration risk first interval, a vibration risk second interval and a vibration risk third interval.

[0077] Embodiment three: It should be noted that, in the specific embodiments of the present application, with reference to Figure 3 The network model of the vibration table vibration anomaly alarm method based on data processing is specifically implemented as follows: a vibration table, a monitoring data uploading end, a cloud platform and a response measure issuing end.

[0078] The vibration table collects monitoring data of the vibration table by various sensors distributed on the vibration table, receives a vibration risk response scheme of the vibration table in a prediction period generated by the cloud platform, and performs alarm and response processing.

[0079] The monitoring data uploading end collects monitoring data collected by various sensors distributed on the vibration table through a network transmission module, and sends the monitoring data to the cloud platform.

[0080] The cloud platform is configured to receive monitoring data of the vibration table, generate each predicted monitoring data of the vibration table in a prediction period by using a prediction model of each monitoring data of the vibration table, perform analysis and evaluation, and generate a vibration risk response scheme of the vibration table in the prediction period if the vibration table has a vibration risk.

[0081] The response measure issuing end issues the generated vibration table vibration risk response scheme in a prediction period to the vibration table through a network transmission module.

[0082] The above is only an example and explanation of the concept of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace, as long as the modifications or supplements do not deviate from the concept of the present application or exceed the scope defined by the present application, and should belong to the protection scope of the present application.

Claims

1. A vibration abnormality alarm method for a vibration table based on data processing, characterized in that: The method comprises: Step 1: Collect and pre-process the monitoring data of the vibration table: collect the monitoring data of each collection point of the vibration table within the target period, and normalize the monitoring data of each collection point of the vibration table within the target period; Step 2: Constructing a prediction model for each monitoring data of the vibration table: Based on each monitoring data of each collection point of the vibration table in each historical period extracted from the database, constructing a prediction model for each monitoring data of the vibration table; Step 3: Obtaining the predicted monitoring data of the vibration table: Based on the normalized monitoring data of each collection point of the vibration table within the target period, the monitoring data are input into the prediction model of the monitoring data of the vibration table to obtain the predicted monitoring data of the vibration table within the prediction period; Step 4: Evaluate the vibration risk level of the vibration table within the prediction period: Based on the monitoring data predicted by the vibration table within the prediction period, calculate the vibration risk coefficient of the vibration table within the prediction period, and evaluate the vibration risk level of the vibration table within the prediction period; Step 5. Implement response measures for abnormal vibration of the vibration table: Implement response measures corresponding to the vibration risk level of the vibration table within the prediction period.

2. The vibration abnormality alarm method of a vibration table based on data processing according to claim 1 is characterized in that: The specific method for collecting the monitoring data of each collection point of the vibration table within the target period is as follows: The monitoring data of the vibration table include: vibration acceleration, vibration frequency, vibration displacement, operating temperature and operating voltage; The acceleration sensor is installed on the vibration table to collect the frequency The vibration acceleration of the vibration table is collected as the collection interval, where represents the acquisition frequency extracted from the database; The velocity sensor is installed on the vibration table to collect the frequency The vibration frequency of the vibration table is collected as a collection interval; The displacement sensor is installed on the vibration table to collect the frequency The vibration displacement of the vibration table is collected as a collection interval; The temperature sensor installed on the vibration table is used to collect the frequency Collect the working temperature of the vibration table as the collection interval; The frequency is collected by installing a voltage sensor on the vibration table The working voltage of the vibration table is collected as the collection interval.

3. The vibration abnormality alarm method of a vibration table based on data processing according to claim 2 is characterized in that: The monitoring data of each collection point of the vibration table within the target period are normalized, and the specific analysis method is as follows: The monitoring data of each collection point of the vibration table within the target period is recorded as ,in , Indicates the number of each collection point, Indicates the total number of collection points, , Indicates the number of each monitoring data, Indicates the total number of monitoring data; Extract the maximum and minimum values ​​of each monitoring data within the target period from each monitoring data of each collection point of the vibration table within the target period and record them as and ; By formula: , calculate the monitoring data of each collection point of the vibration table within the target period after normalization .

4. The vibration abnormality alarm method of a vibration table based on data processing according to claim 3 is characterized in that: The specific analysis method for constructing the prediction model of each monitoring data of the vibration table is as follows: The detailed steps of constructing a prediction model for each monitoring data of the vibration table through the LSTM long short-term memory neural network are as follows: A1: Extract the window size from the database and step length , create a sliding window, extract the normalized monitoring data of each collection point of the vibration table in each historical period from the database, perform segmentation processing, and obtain a subsequence of the normalized monitoring data of each collection point of the vibration table in each historical period; A2: Use the normalized subsequences of monitoring data from each collection point on the vibration platform in each historical period as training input for the LSTM long short-term memory neural network; A3: By formula: , construct the loss function of LSTM long short-term memory neural network ,in Represents the true value of the training input of the LSTM long short-term memory neural network, Represents the predicted value of the output of the LSTM long short-term memory neural network; A4: By the formula: , implement the gradient descent method to update the weights of the LSTM long short-term memory neural network, where Represents the weight of the LSTM long short-term memory neural network, represents the learning rate of the LSTM long short-term memory neural network extracted from the database, Indicates the identifier for partial derivative calculation; A5: By formula: , implement the gradient descent method to update the bias parameters of the LSTM long short-term memory neural network, where Represents the bias parameters of the LSTM long short-term memory neural network.

5. The vibration abnormality alarm method of a vibration table based on data processing according to claim 4 is characterized in that: The specific analysis method for the monitoring data predicted by the vibration table within the prediction period is as follows: Based on the normalized monitoring data of each collection point of the vibration table within the target period, a sliding window process is performed to obtain a subsequence of the normalized monitoring data of each collection point of the vibration table within the target period. The subsequence is used as the input of the prediction model of each monitoring data of the vibration table to obtain the monitoring data predicted by the vibration table within the prediction period, which is recorded as .

6. The vibration abnormality alarm method of a vibration table based on data processing according to claim 5 is characterized in that: The specific analysis method for calculating the vibration risk coefficient of the vibration table within the prediction period is as follows: Based on the monitoring data predicted by the vibration table within the prediction period , through the formula: , calculate the abnormal judgment value of each monitoring data predicted by the vibration table within the prediction period ,in Indicates the normal working range of each monitoring data of the vibration table stored in the database; By formula: , calculate the vibration risk coefficient of the vibration table within the prediction period ,in Represents a natural constant.

7. The vibration abnormality alarm method of a vibration table based on data processing according to claim 6, characterized in that: The specific analysis method for the vibration risk level of the vibration table within the assessment prediction period is as follows: Based on the vibration risk factor of the shaking table within the prediction period ,like If the vibration risk level of the vibration table within the prediction period is in the first vibration risk interval extracted from the database, it is determined that the vibration risk level of the vibration table within the prediction period is primary; like If the vibration risk level of the vibration table within the prediction period is in the second vibration risk interval extracted from the database, it is determined that the vibration risk level of the vibration table within the prediction period is medium; like If the vibration risk level of the vibration table within the prediction period is in the third vibration risk interval extracted from the database, it is determined that the vibration risk level of the vibration table within the prediction period is high.

8. The vibration abnormality alarm method for a vibration table based on data processing according to claim 7 is characterized in that: The specific analysis method for the response measures corresponding to the vibration risk level of the vibration table within the implementation prediction period is as follows: Based on the abnormal judgment values ​​of each monitoring data predicted by the vibration table within the prediction period, the abnormal monitoring parameters of the vibration table within the prediction period are extracted, and the abnormal handling methods of the abnormal monitoring parameters of the historical vibration table are extracted from the database. The vibration risk response plan of the vibration table within the prediction period is generated, the vibration risk response plan of the vibration table within the prediction period is sent to the vibration table, and the vibration risk response measures of the vibration table within the prediction period are implemented.

9. The vibration abnormality alarm method for a vibration table based on data processing according to claim 8, characterized in that: The specific analysis method for extracting the abnormal monitoring parameters of the vibration table within the prediction period is as follows: Based on the abnormal judgment value of each monitoring data predicted by the vibration table within the prediction period, if If it is 1, the predicted first The monitoring data are recorded as abnormal monitoring parameters; According to the prediction of the vibration table within the prediction period The analysis method of each monitoring data is used to analyze the abnormal judgment value of each monitoring data predicted by the vibration table within the prediction period, and the abnormal monitoring parameters of the vibration table within the prediction period are obtained.

10. The vibration abnormality alarm method of a vibration table based on data processing according to claim 8, characterized in that: The specific analysis method for generating the vibration risk response plan of the shaking table within the prediction period is as follows: Based on the abnormal monitoring parameters of the vibration table within the prediction period, the response measures of the abnormal monitoring parameters of the historical vibration table are extracted from the database to generate the vibration risk response plan of the vibration table within the prediction period; Based on the vibration risk level of the shaking table within the prediction period, a vibration risk response emergency identifier of the shaking table within the prediction period is generated; The vibration risk plan for the shaking table within the prediction period includes: abnormal monitoring parameters of the shaking table within the prediction period, response measures for abnormal monitoring parameters of the shaking table within the prediction period, and an emergency identifier for response to vibration risks of the shaking table within the prediction period.

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