Railway equipment operation and maintenance data transmission management method and system

Abstract

The invention discloses a railway equipment operation and maintenance data transmission management method and system, and relates to the field of railway equipment data management. Historical equipment operation states, fault information, environment parameters and data of transmission logs are acquired, and a sample list is established; performing optimization processing on the railway equipment operation and maintenance data monitored in real time; judging the running state condition of the railway equipment in a period of time in the future; repeated and redundant information in the data is optimized, the storage space is reduced, and the transmission efficiency is improved; formulating data transmission safety management measures and schemes for ensuring the operation and maintenance data transmission safety of the railway equipment; and establishing a cloud database platform for storing, managing and analyzing the uploaded railway equipment operation information. The method has the advantages that the influence of the excitation peak value and the abnormal data is filtered through the Kalman filtering algorithm, the operation state condition of the railway equipment in a period of time in the future is judged through the BP neural network algorithm, and normal operation of the railway equipment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of railway equipment data management, and in particular to a railway equipment operation and maintenance data transmission management method and system. Background Art

[0002] Railway equipment operation and maintenance data transmission management is an important part of ensuring the safety and stability of railway operations. It is of great significance to improve the accuracy and stability of transmission data, strengthen the railway equipment operation and maintenance data processing and analysis capabilities, realize real-time monitoring, fault warning and rapid response of railway equipment, and provide strong support for railway operations.

[0003] However, existing technologies mainly focus on real-time monitoring of railway equipment and decision-making on problem handling. They lack the prediction and early warning capabilities for different railway equipment monitoring environments, have weak risk resistance, and are difficult to adapt to the complex and changeable railway operating environment, which is not conducive to the long-term development of railway operations. Summary of the invention

[0004] In order to solve the above-mentioned technical problems, a railway equipment operation and maintenance data transmission management method and system are provided. This technical solution solves the problems raised in the above-mentioned background technology, such as the lack of prediction and early warning capabilities for different railway equipment monitoring environments, weak risk resistance, difficulty in adapting to the complex and changeable railway operating environment, and being unfavorable to the long-term development of railway operations.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A railway equipment operation and maintenance data transmission management method, comprising: Obtain data on historical equipment operating status, fault information, environmental parameters, and transmission logs, and create a sample list; Based on the Kalman filter algorithm, the real-time monitored railway equipment operation and maintenance data is optimized and processed; Based on the BP neural network algorithm, the operating status of railway equipment in the future period is evaluated; Use data compression and transmission optimization technology to optimize repeated and redundant information in data, reduce storage space and improve transmission efficiency; Formulate data transmission security management measures and plans to ensure the security of data transmission in railway equipment operation and maintenance; Establish a cloud database platform to store, manage and analyze uploaded railway equipment operation information.

[0006] Preferably, the acquisition of historical equipment operation status, fault information, environmental parameters, and transmission log data, and the establishment of a sample list specifically includes: Based on the cloud database, obtain and analyze historical equipment operation status, fault information, environmental parameters, transmission log data and real-time collected railway equipment operation and maintenance data, and establish equipment operation and maintenance data sample data; Among them, the equipment operation status refers to the dynamic detection data of the mobile device, including: speed, vibration, temperature, and pressure. The fault information includes: the number of equipment failures, time, phenomenon, cause, treatment measures, treatment results, and treatment time. Environmental parameters include: temperature, humidity, degree of oxidation, degree of corrosion, and factors of sudden natural disasters. The transmission log includes: file and data packet information, basic data information, error and exception information, and security event information.

[0007] Preferably, the optimization processing of the railway equipment operation and maintenance data monitored in real time based on the Kalman filter algorithm specifically includes: According to the category of monitoring data and historical experience, the relevant parameter matrices A, B, H, Q, and R of the Kalman filter are constructed, where A represents the state transfer matrix, B represents the control matrix, H represents the measurement matrix, R represents the noise covariance matrix, and Q represents the process excitation noise covariance matrix; Based on the Kalman filter algorithm, the predicted value of the next state and the error covariance of the state is calculated; Based on the Kalman filter algorithm, construct the update state and the error covariance expression of the state; Based on the setting of monitoring data and parameter matrix, the predicted value of railway equipment operation and maintenance data is output in real time, and the curve chart of real-time monitoring data value and predicted value is displayed through visual chart; The prediction value expression for calculating the next state and the error covariance of the state is: ; ; in, For the The state prediction value at the moment, For the The optimal state estimate at the moment is is the value of the control input, For the The noise covariance prediction value at the moment, For the The noise covariance value of the optimal state estimate at each moment; The error covariance expression of the updated state and state is: ; ; in, For the The optimal state estimate at the moment is is the Kalman gain, For the The monitoring value of the monitoring device at each moment, For the The noise covariance value of the optimal state estimate at the moment is, is a matrix whose elements are all 1.

[0008] Preferably, the BP neural network algorithm is used to judge the operation status of railway equipment in the future period of time, specifically including: According to the equipment operation and maintenance data samples, the data is standardized based on the Z-Score normalization algorithm; According to the equipment operation and maintenance data samples, the input vector matrix X is established. ,in, For the input data values, is the number of input categories, and the input categories are set according to the actual equipment operation and maintenance requirements and in combination with equipment operation and maintenance data samples; Determine the number of neurons in the hidden layer according to the input vector matrix; Based on historical testing experience for each input category, set The initial weight vector matrix F; Based on the Sigmoid function, set the activation function of the BP neural network; Based on the BP neural network algorithm, the weight vector matrix of the feedback is obtained ; Based on the model trained by the BP neural network algorithm, the input vector matrix is ​​set according to the actual situation, and the operation status of the railway equipment in the future is judged according to the iteration results of the model; The Z-Score normalization algorithm expression is: ; in, is the standardized equipment operation and maintenance data value, It is the original data of equipment operation and maintenance data. is the average value of the original data of equipment operation and maintenance data, is the standard deviation of the original data of equipment operation and maintenance data; The method for determining the number of neurons in the hidden layer is: ; in, is the number of neurons in the hidden layer, is the number of input categories, is the number of output categories, is a constant term from 1 to 10, The number of data for equipment operation and maintenance data samples; The initial weight vector matrix F is: ; Among them, each row represents the weight value of all neurons corresponding to a single input, and each column represents the weight value of all input categories corresponding to a single neuron; The Sigmoid function expression is: ; in, is a constant, is the value obtained by the hidden layer neurons; The weight vector matrix obtained by feedback The expression is: ; ; in, For the Line The weight values ​​of the feedback to the output layer are as follows, For the Line The columns are weight values ​​for feedback to the input layer. is the learning rate, For the error terms, For the predicted values, among which , is the target value; The weight vector matrix of the feedback for: ; Among them, each row represents the feedback weight value of all neurons corresponding to a single input, and each column represents the feedback weight value of a single neuron corresponding to all input categories.

[0009] Preferably, the use of data compression and transmission optimization technology to optimize repeated and redundant information in data, reduce storage space and improve transmission efficiency specifically includes: Using lossless compression technology, the repeated and redundant information in the collected data is optimized based on the LZW encoding method to ensure that no data is lost during the compression and decompression process, maintaining the integrity and original quality of the data.

[0010] Preferably, the formulation of data transmission security management measures and plans to ensure the security of railway equipment operation and maintenance data transmission specifically includes: Asymmetric encryption technology is used to encrypt the transmitted data to ensure that the data is not illegally intercepted or tampered with during the transmission process, thereby improving the security of data transmission and storage; According to the communication requirements of railway equipment information and the requirements of comprehensive environmental transmission characteristics, a secure data transmission process is constructed based on the TCP protocol and the SFTP protocol, wherein the comprehensive environmental transmission characteristics include: stability, reliability, real-time, bandwidth occupancy and security.

[0011] Preferably, the establishment of a cloud database platform for storing, managing and analyzing uploaded railway equipment operation information specifically includes: A cloud database platform is established to store, manage and analyze uploaded railway equipment operation information, and to display the real-time monitoring data values ​​and predicted value curves through visual charts. Based on the iterative results of the BP neural network algorithm model displayed on the observation interface, it is determined whether the railway equipment operation status is abnormal.

[0012] Furthermore, the present invention proposes a railway equipment operation and maintenance data transmission management system, which is used to implement the railway equipment operation and maintenance data transmission management method as described above, including: Data optimization and evaluation module, which is used to obtain data on historical equipment operation status, fault information, environmental parameters, and transmission logs, and to establish a sample list; based on the Kalman filter algorithm, optimize the real-time monitored railway equipment operation and maintenance data; based on the BP neural network algorithm, evaluate the operation status of railway equipment in the future; The data transmission and management module is used to utilize data compression and transmission optimization technology to optimize repeated and redundant information in the data, reduce storage space and improve transmission efficiency; formulate data transmission security management measures and plans to ensure the security of data transmission for railway equipment operation and maintenance; establish a cloud database platform for storing, managing and analyzing uploaded railway equipment operation information.

[0013] Preferably, the data optimization and evaluation module includes: Establishing a sample list unit, the sample list unit is used to obtain data on historical equipment operating status, fault information, environmental parameters, and transmission logs, and establish a sample list to provide railway equipment operation and maintenance data support for the data optimization unit and the operating status evaluation unit; A data optimization unit, wherein the data optimization unit is used to optimize the railway equipment operation and maintenance data monitored in real time based on a Kalman filter algorithm; The operating status evaluation unit is used to evaluate the operating status of the railway equipment in the future based on the BP neural network algorithm.

[0014] Preferably, the data transmission and management module includes: A compression and transmission optimization unit, which is used to optimize the repeated and redundant information in the data by using data compression and transmission optimization technology, reduce storage space and improve transmission efficiency; A data security management unit, which is used to formulate data transmission security management measures and plans to ensure the security of data transmission in railway equipment operation and maintenance; A cloud database unit is used to establish a cloud database platform for storing, managing and analyzing uploaded railway equipment operation information.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The Kalman filter algorithm can effectively filter out the influence of excitation peaks and abnormal data, so that the data remains smooth and stable, and the accuracy and stability of transmitted data are improved. Secondly, the BP neural network algorithm is used for sample data training to judge the operating status of railway equipment in the future, thereby improving the early warning capability and risk resistance in different railway equipment monitoring environments, ensuring the normal operation of railway equipment, and realizing real-time monitoring, fault warning and rapid response of railway equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a railway equipment operation and maintenance data transmission management method of the present invention; Figure 2 This is a flow chart of optimizing the processing of railway equipment operation and maintenance data monitored in real time based on the Kalman filter algorithm of the present invention; Figure 3 The present invention is a flow chart for judging the operating status of railway equipment in the future based on the BP neural network algorithm. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 As shown, a railway equipment operation and maintenance data transmission management method includes: Obtain data on historical equipment operating status, fault information, environmental parameters, and transmission logs, and create a sample list; Based on the Kalman filter algorithm, the real-time monitored railway equipment operation and maintenance data is optimized and processed; Based on the BP neural network algorithm, the operating status of railway equipment in the future period is evaluated; Use data compression and transmission optimization technology to optimize repeated and redundant information in data, reduce storage space and improve transmission efficiency; Formulate data transmission security management measures and plans to ensure the security of data transmission in railway equipment operation and maintenance; Establish a cloud database platform to store, manage and analyze uploaded railway equipment operation information.

[0019] It can be explained that during the operation of railway equipment, it will be affected by vibration, electromagnetism and the external environment, resulting in filtered excitation peaks and abnormal data in the received equipment monitoring data. These sudden abnormal peaks are not conducive to judging the equipment operation status. This solution uses the Kalman filter algorithm to filter the influence of excitation peaks and abnormal data, so that the data remains smooth and stable, to improve the accuracy and stability of the transmitted data. At the same time, the BP neural network algorithm is used to train historical data samples, and the prediction results of the model are used to judge the operating status of the railway equipment in the future, thereby improving the early warning capability and risk resistance in different railway equipment monitoring environments, ensuring the normal operation of railway equipment, and realizing real-time monitoring, fault warning and rapid response of railway equipment.

[0020] Reference Figure 2 As shown, the optimization processing of the railway equipment operation and maintenance data monitored in real time based on the Kalman filter algorithm specifically includes: According to the category of monitoring data and historical experience, the relevant parameter matrices A, B, H, Q, and R of the Kalman filter are constructed, where A represents the state transfer matrix, B represents the control matrix, H represents the measurement matrix, R represents the noise covariance matrix, and Q represents the process excitation noise covariance matrix; Based on the Kalman filter algorithm, the predicted value of the next state and the error covariance of the state is calculated; Based on the Kalman filter algorithm, construct the update state and the error covariance expression of the state; Based on the setting of monitoring data and parameter matrix, the predicted value of railway equipment operation and maintenance data is output in real time, and the curve chart of real-time monitoring data value and predicted value is displayed through visual chart; The prediction value expression for calculating the next state and the error covariance of the state is: ; ; in, For the The state prediction value at the moment, For the The optimal state estimate at the moment is is the value of the control input, For the The noise covariance prediction value at the moment, For the The noise covariance value of the optimal state estimate at each moment; The error covariance expression of the updated state and state is: ; ; in, For the The optimal state estimate at the moment is is the Kalman gain, For the The monitoring value of the monitoring device at each moment, For the The noise covariance value of the optimal state estimate at the moment is, is a matrix whose elements are all 1.

[0021] It can be explained that, depending on the type of monitoring equipment, when there is no external control input, the monitoring data conforms to the one-dimensional Kalman filter model, in which the A matrix is ​​equal to the transpose of the A matrix, B is a zero matrix, and H is a matrix whose elements are all 1. The Kalman filter algorithm can effectively filter out the influence of excitation peaks and abnormal data, so that the data remains smooth and stable.

[0022] Reference Figure 3 As shown, the BP neural network algorithm is used to judge the operation status of railway equipment in the future, including: According to the equipment operation and maintenance data samples, the data is standardized based on the Z-Score normalization algorithm; According to the equipment operation and maintenance data samples, the input vector matrix X is established. ,in, For the input data values, is the number of input categories, and the input categories are set according to the actual equipment operation and maintenance requirements and in combination with equipment operation and maintenance data samples; Determine the number of neurons in the hidden layer according to the input vector matrix; Based on historical testing experience for each input category, set The initial weight vector matrix F; Based on the Sigmoid function, set the activation function of the BP neural network; Based on the BP neural network algorithm, the weight vector matrix of the feedback is obtained ; Based on the model trained by the BP neural network algorithm, the input vector matrix is ​​set according to the actual situation, and the operation status of the railway equipment in the future is judged according to the iteration results of the model; The Z-Score normalization algorithm expression is: ; in, is the standardized equipment operation and maintenance data value, It is the original data of equipment operation and maintenance data. is the average value of the original data of equipment operation and maintenance data, is the standard deviation of the original data of equipment operation and maintenance data; The method for determining the number of neurons in the hidden layer is: ; in, is the number of neurons in the hidden layer, is the number of input categories, is the number of output categories, is a constant term from 1 to 10, The number of data for equipment operation and maintenance data samples; The initial weight vector matrix F is: ; Among them, each row represents the weight value of all neurons corresponding to a single input, and each column represents the weight value of all input categories corresponding to a single neuron; The Sigmoid function expression is: ; in, is a constant, is the value obtained by the hidden layer neurons; The weight vector matrix obtained by feedback The expression is: ; ; in, For the Line The weight values ​​of the feedback to the output layer are as follows, For the Line The columns are weight values ​​for feedback to the input layer. is the learning rate, For the error terms, For the predicted values, among which , is the target value; The weight vector matrix of the feedback for: ; Among them, each row represents the feedback weight value of all neurons corresponding to a single input, and each column represents the feedback weight value of a single neuron corresponding to all input categories.

[0023] It can be explained that, by training the railway equipment operation and maintenance historical data samples based on the BP neural network algorithm, the back-propagation error signal is used to adjust the weights and bias characteristics in the network, and the forward propagation and back-propagation processes are repeated until the preset training rounds are reached or the error is less than a certain threshold, thereby reducing the output error and optimizing the network performance, thereby making the predicted values ​​of railway equipment operation in different environments more accurate, and solving the problem of difficulty in accurately setting weight values. Secondly, the weight update strategy can be further improved through the small batch gradient descent method to accelerate the convergence speed and improve the training efficiency. Among them, the small batch gradient descent method is an optimization algorithm that achieves a compromise between training speed and training accuracy.

[0024] Further, based on the same inventive concept as the above-mentioned railway equipment operation and maintenance data transmission management method, this solution proposes a railway equipment operation and maintenance data transmission management system, including: Data optimization and evaluation module, which is used to obtain data on historical equipment operation status, fault information, environmental parameters, and transmission logs, and to establish a sample list; based on the Kalman filter algorithm, optimize the real-time monitored railway equipment operation and maintenance data; based on the BP neural network algorithm, evaluate the operation status of railway equipment in the future; The data transmission and management module is used to utilize data compression and transmission optimization technology to optimize repeated and redundant information in the data, reduce storage space and improve transmission efficiency; formulate data transmission security management measures and plans to ensure the security of data transmission for railway equipment operation and maintenance; establish a cloud database platform for storing, managing and analyzing uploaded railway equipment operation information.

[0025] The data optimization and evaluation module includes: Establishing a sample list unit, the sample list unit is used to obtain data on historical equipment operating status, fault information, environmental parameters, and transmission logs, and establish a sample list to provide railway equipment operation and maintenance data support for the data optimization unit and the operating status evaluation unit; A data optimization unit, wherein the data optimization unit is used to optimize the railway equipment operation and maintenance data monitored in real time based on a Kalman filter algorithm; The operating status evaluation unit is used to evaluate the operating status of the railway equipment in the future based on the BP neural network algorithm.

[0026] The data transmission and management module includes: A compression and transmission optimization unit, which is used to optimize the repeated and redundant information in the data by using data compression and transmission optimization technology, reduce storage space and improve transmission efficiency; A data security management unit, which is used to formulate data transmission security management measures and plans to ensure the security of data transmission in railway equipment operation and maintenance; A cloud database unit is used to establish a cloud database platform for storing, managing and analyzing uploaded railway equipment operation information.

[0027] To sum up, the advantages of the present invention are: the Kalman filtering algorithm can effectively filter out the influence of excitation peaks and abnormal data, so that the data remains smooth and stable, and the accuracy and stability of the transmitted data are improved; secondly, the BP neural network algorithm is used for sample data training to judge the operating status of railway equipment in the future, thereby improving the early warning capability and risk resistance in different railway equipment monitoring environments, ensuring the normal operation of railway equipment, and realizing real-time monitoring, fault warning and rapid response of railway equipment.

[0028] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A railway equipment operation and maintenance data transmission management method, characterized in that: include: Obtain data on historical equipment operating status, fault information, environmental parameters, and transmission logs, and create a sample list; Based on the Kalman filter algorithm, the real-time monitored railway equipment operation and maintenance data is optimized and processed; Based on the BP neural network algorithm, the operating status of railway equipment in the future period is evaluated; Use data compression and transmission optimization technology to optimize repeated and redundant information in data, reduce storage space and improve transmission efficiency; Formulate data transmission security management measures and plans to ensure the security of data transmission in railway equipment operation and maintenance; Establish a cloud database platform to store, manage and analyze uploaded railway equipment operation information.

2. A railway equipment operation and maintenance data transmission management method according to claim 1, characterized in that: The acquisition of historical equipment operation status, fault information, environmental parameters, transmission log data, and the establishment of a sample list specifically include: Based on the cloud database, obtain and analyze historical equipment operation status, fault information, environmental parameters, transmission log data and real-time collected railway equipment operation and maintenance data, and establish equipment operation and maintenance data sample data; Among them, the equipment operation status refers to the dynamic detection data of the mobile device, including: speed, vibration, temperature, and pressure. The fault information includes: the number of equipment failures, time, phenomenon, cause, treatment measures, treatment results, and treatment time. Environmental parameters include: temperature, humidity, degree of oxidation, degree of corrosion, and factors of sudden natural disasters. The transmission log includes: file and data packet information, basic data information, error and exception information, and security event information.

3. A railway equipment operation and maintenance data transmission management method according to claim 2, characterized in that: The optimization processing of the railway equipment operation and maintenance data monitored in real time based on the Kalman filter algorithm specifically includes: According to the category of monitoring data and historical experience, the relevant parameter matrices A, B, H, Q, and R of the Kalman filter are constructed, where A represents the state transfer matrix, B represents the control matrix, H represents the measurement matrix, R represents the noise covariance matrix, and Q represents the process excitation noise covariance matrix; Based on the Kalman filter algorithm, the predicted value of the next state and the error covariance of the state is calculated; Based on the Kalman filter algorithm, construct the update state and the error covariance expression of the state; Based on the setting of monitoring data and parameter matrix, the predicted value of railway equipment operation and maintenance data is output in real time, and the curve chart of real-time monitoring data value and predicted value is displayed through visual chart; The predicted value expression for calculating the next state and the error covariance of the state is: ； ； in, For the The state prediction value at the moment, For the The optimal state estimate at the moment is is the value of the control input, For the The noise covariance prediction value at the moment, For the The noise covariance value of the optimal state estimate at each moment; The error covariance expression of the updated state and state is: ； ； in, For the The optimal state estimate at the moment is is the Kalman gain, For the The monitoring value of the monitoring device at each moment, For the The noise covariance value of the optimal state estimate at the moment is, is a matrix whose elements are all 1.

4. A railway equipment operation and maintenance data transmission management method according to claim 3, characterized in that: The BP neural network algorithm is used to judge the operation status of railway equipment in the future, including: According to the equipment operation and maintenance data samples, the data is standardized based on the Z-Score normalization algorithm; According to the equipment operation and maintenance data samples, the input vector matrix X is established. ,in, For the input data values, is the number of input categories, and the input categories are set according to the actual equipment operation and maintenance requirements and in combination with equipment operation and maintenance data samples; Determine the number of neurons in the hidden layer according to the input vector matrix; Based on historical testing experience for each input category, set The initial weight vector matrix F; Based on the Sigmoid function, set the activation function of the BP neural network; Based on the BP neural network algorithm, the weight vector matrix of the feedback is obtained ; Based on the model trained by the BP neural network algorithm, the input vector matrix is ​​set according to the actual situation, and the operation status of the railway equipment in the future is judged according to the iteration results of the model; The Z-Score normalization algorithm expression is: ； in, is the standardized equipment operation and maintenance data value, It is the original data of equipment operation and maintenance data. is the average value of the original data of equipment operation and maintenance data, is the standard deviation of the original data of equipment operation and maintenance data; The method for determining the number of neurons in the hidden layer is: ； in, is the number of neurons in the hidden layer, is the number of input categories, is the number of output categories, is a constant term from 1 to 10, The number of data for equipment operation and maintenance data samples; The initial weight vector matrix F is: ； Among them, each row represents the weight value of all neurons corresponding to a single input, and each column represents the weight value of all input categories corresponding to a single neuron; The Sigmoid function expression is: ； in, is a constant, is the value obtained by the hidden layer neurons; The weight vector matrix obtained by feedback The expression is: ； ； in, For the Line The weight values ​​of the feedback to the output layer are as follows, For the Line The columns are weight values ​​for feedback to the input layer. is the learning rate, For the error terms, For the predicted values, among which , is the target value; The weight vector matrix of the feedback for: ； Among them, each row represents the feedback weight value of all neurons corresponding to a single input, and each column represents the feedback weight value of a single neuron corresponding to all input categories.

5. A railway equipment operation and maintenance data transmission management method according to claim 4, characterized in that: The use of data compression and transmission optimization technology to optimize repeated and redundant information in data, reduce storage space and improve transmission efficiency specifically includes: Using lossless compression technology, the repeated and redundant information in the collected data is optimized based on the LZW encoding method to ensure that no data is lost during the compression and decompression process, maintaining the integrity and original quality of the data.

6. A railway equipment operation and maintenance data transmission management method according to claim 5, characterized in that: The formulation of data transmission security management measures and plans to ensure the security of railway equipment operation and maintenance data transmission specifically includes: Asymmetric encryption technology is used to encrypt the transmitted data to ensure that the data is not illegally intercepted or tampered with during the transmission process, thereby improving the security of data transmission and storage; According to the communication requirements of railway equipment information and the requirements of comprehensive environmental transmission characteristics, a secure data transmission process is constructed based on the TCP protocol and the SFTP protocol, wherein the comprehensive environmental transmission characteristics include: stability, reliability, real-time, bandwidth occupancy and security.

7. A railway equipment operation and maintenance data transmission management method according to claim 6, characterized in that: The cloud database platform is established to store, manage and analyze uploaded railway equipment operation information, including: A cloud database platform is established to store, manage and analyze uploaded railway equipment operation information, and to display the real-time monitoring data values ​​and predicted value curves through visual charts. Based on the iterative results of the BP neural network algorithm model displayed on the observation interface, it is determined whether the railway equipment operation status is abnormal.

8. A railway equipment operation and maintenance data transmission management system, characterized in that: A method for managing railway equipment operation and maintenance data transmission according to any one of claims 1 to 7, comprising: Data optimization and evaluation module, which is used to obtain data on historical equipment operation status, fault information, environmental parameters, and transmission logs, and to establish a sample list; based on the Kalman filter algorithm, optimize the real-time monitored railway equipment operation and maintenance data; based on the BP neural network algorithm, evaluate the operation status of railway equipment in the future; The data transmission and management module is used to utilize data compression and transmission optimization technology to optimize repeated and redundant information in the data, reduce storage space and improve transmission efficiency; formulate data transmission security management measures and plans to ensure the security of data transmission for railway equipment operation and maintenance; establish a cloud database platform for storing, managing and analyzing uploaded railway equipment operation information.

9. A railway equipment operation and maintenance data transmission and management system according to claim 8, characterized in that: The data optimization and evaluation module includes: Establishing a sample list unit, the sample list unit is used to obtain data on historical equipment operating status, fault information, environmental parameters, and transmission logs, and establish a sample list to provide railway equipment operation and maintenance data support for the data optimization unit and the operating status evaluation unit; A data optimization unit, wherein the data optimization unit is used to optimize the railway equipment operation and maintenance data monitored in real time based on a Kalman filter algorithm; The operating status evaluation unit is used to evaluate the operating status of the railway equipment in the future based on the BP neural network algorithm.

10. A railway equipment operation and maintenance data transmission and management system according to claim 9, characterized in that: The data transmission and management module includes: A compression and transmission optimization unit, which is used to optimize the repeated and redundant information in the data by using data compression and transmission optimization technology, reduce storage space and improve transmission efficiency; A data security management unit, which is used to formulate data transmission security management measures and plans to ensure the security of data transmission in railway equipment operation and maintenance; A cloud database unit is used to establish a cloud database platform for storing, managing and analyzing uploaded railway equipment operation information.

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