Remote control system for data processing device

Abstract

The invention relates to the technical field of automation and control engineering, in particular to a remote control system for a data processing device, which comprises a data acquisition unit, an image acquisition unit, a data processing unit, a data storage unit, a communication unit, a control unit, a display unit, a data security unit, a hardware unit and a software unit. According to the system and the method, a user can monitor and control the controlled equipment in real time, manual intervention is reduced, working efficiency is improved, labor cost is reduced, meanwhile, through real-time monitoring and optimization control, the system can improve the utilization rate of the equipment and prolong the service life of the equipment, a high-performance processor and an optimization algorithm are adopted, and the system is high in practicability and easy to popularize. The data processing speed is remarkably improved, high reliability and stability are ensured, hardware and software expansion becomes easier due to the modular design of the system, a user is supported to flexibly configure and expand functions according to actual requirements, various safety measures are integrated in the system, and the safety of data and operation is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of automation and control engineering, and in particular to a remote control system for a data processing device. Background Art

[0002] In the modern information society, data processing and remote control technologies have been widely used in various fields. Traditional data processing devices usually rely on local computing resources to perform data storage, processing and analysis tasks. However, with the rapid development of the Internet of Things (IoT), cloud computing and big data technologies, the demand for remote control and data processing has become more urgent. At present, there are many remote control and data processing solutions on the market, mainly including the following categories: First, local data processing system: fast data processing speed and high security, but lack of remote control capabilities, need to be operated and maintained locally, and lack of flexibility; second, cloud data processing platform: has powerful computing resources and storage capabilities, supports remote access, but needs to rely on network connections, and there are data transmission delays and security risks; third, hybrid data processing system: combines the advantages of local and cloud, and provides a compromise solution, but the system is highly complex and difficult to configure and maintain.

[0003] For example, the Chinese patent is: CN114397828A, "A data processing and remote control system based on the Internet". The remote control system consists of a remote master PC, a network server, a local PC host computer, a wireless communication module, a remote execution terminal and a database server.

[0004] The various data processing device remote control systems currently on the market still have the following disadvantages: 1. Low system integration: Existing solutions usually separate data processing and remote control and lack an integrated system, resulting in complex management and low efficiency; 2. Security issues: With the continuous escalation of network attack methods, the existing remote control systems have major hidden dangers in data transmission and storage security, and are vulnerable to hacker attacks and data leaks; 3. Insufficient real-time performance and reliability: When processing real-time data, remote control systems are often affected by network delays and unstable transmission, making it difficult to ensure the timeliness of data processing and the reliability of the system; 4. Poor user experience: The user interfaces of many existing systems are complex, difficult to operate, lack intuitiveness and ease of use, and are not friendly to non-professional users; 5. Poor scalability: When faced with growing data volumes and complex application requirements, existing systems often exhibit insufficient scalability and are unable to meet diverse business needs.

[0005] In order to address the above-mentioned problems and deficiencies, the present application proposes a remote control system for a data processing device. Summary of the invention

[0006] The object of the present invention is to provide a remote control system for a data processing device to solve the above-mentioned problem.

[0007] To achieve the above object, the present invention provides the following technical solutions: A remote control system for a data processing device, comprising a data acquisition unit, an image acquisition unit, a data processing unit, a data storage unit, a communication unit, a control unit, a display unit, a data security unit, a hardware unit and a software unit: The data acquisition unit is used to collect data from various sensors and external devices in real time, receive data input from external devices through a data interface, and transmit the collected raw data to the data processing unit through a communication unit; The image acquisition unit uses a pan-tilt camera to acquire digital images of external devices, and transmits the digital images to the data security unit through the communication unit; The data processing unit includes a CPU, memory and cache hardware, and performs real-time analysis, processing and storage of the collected data through a data processing algorithm, and transmits the processed data to the data storage unit and the remote control platform through a communication unit; The data storage unit includes a local storage device and a cloud storage interface. The local storage device uses SSD or FLASH for fast storage and reading. The cloud storage interface backs up data to the cloud. The data storage unit is used to store processed data and supports fast reading and writing and large-capacity storage functions. The communication unit processes user instructions of the remote control platform by integrating wireless sensor networks, communication node distribution algorithms and normalization processing methods, supports multiple communication protocols, and realizes remote transmission of data and control signals between modules; The control unit includes a microprocessor, a control logic circuit and a remote signal receiver. The microprocessor receives user instructions from the remote control platform and transmits the user instructions to the remote signal receiver through the communication unit. The remote signal receiver is responsible for receiving the remote instructions, decoding and parsing the received signals using signal processing technology, and performing corresponding control operations to achieve remote control of the device. It also supports real-time control and timing control. Users can set control parameters and time through the interface. The display unit enables the remote control platform to display system status, data processing results, control options and user interface in real time through a liquid crystal display and a touch screen; The data security unit implements system data encryption, identity authentication, and access control functions, and can also identify abnormalities in devices; The hardware unit enables modules to be connected via a hardware bus and a standardized API interface, supports high-speed data transmission and control signal transmission, and supports expansion by adding or replacing hardware modules; The software unit supports adding new functions through software updates and plug-in installations, and provides an open API interface, so that third-party developers can develop and integrate new functional modules according to needs.

[0008] Furthermore, the various sensors in the data acquisition unit include temperature sensors, pressure sensors, humidity sensors, voltage sensors, and current sensors, and the temperature, pressure, humidity, voltage, and current data of the device are collected through the various sensors.

[0009] Furthermore, the data processing unit includes a data receiving module, a preprocessing module, a storage management module, an analysis and processing module and a result output module: The data receiving module is used to receive the original data collected by the data collection unit; The preprocessing module is used to filter, perform wavelet denoising and format conversion on the original data; The storage management module is used to store and manage the pre-processed data and transmit the storage management data to the analysis and processing module; The analysis and processing module uses parallel computing and optimization algorithms to analyze the storage management data and obtain processing results; The result output module stores the processing result locally or sends it to a cloud database via the network.

[0010] Furthermore, the preprocessing module performs wavelet denoising on the filtered data, including the following steps: A1. For a signal containing N data, select the maximum number of decomposition layers K, while satisfying the constraints in the following formula, namely: ; A2. Based on the noise preprocessing constraints in the above-determined equipment, the wavelet transform algorithm is used to implement noise preprocessing, and the following formula is obtained: Where: is the device noise signal data after noise reduction, is the initial frequency band value of the equipment noise signal, and u is the wavelet change coefficient; A3. Based on the above analysis, the wavelet coefficients of each layer are decomposed by selecting the number of decomposition layers based on the probability distribution of wavelet coefficients. Assuming that the kth decomposition results in and , based on this, a normal distribution test is conducted, and the wavelet decomposition coefficient process is used to determine whether the equipment obeys the normal distribution, that is, Gaussian white noise; A4. Save ,test Whether it satisfies normal distribution; A5. When Satisfying the normal distribution, calculate Threshold, threshold processing, at this time k=k+1, continue to decompose, when When the normal distribution is satisfied, stop decomposition, determine the decomposition level k-1, and perform signal reconstruction; A6. Based on the above analysis, the final number of decompositions is selected using the decomposition layer selection method; A7. The high-order contains a small amount of effective information. The decomposition coefficient obtained will not exceed the set maximum decomposition level k. The normal distribution test is realized through the KS test. The specific calculation method is as follows: Where: and For the degree of relevance, and The main features of the device; A8. Based on the above analysis, outliers are used to determine the thresholds of different wavelets. The corresponding thresholds are obtained through calculation, and each wavelet coefficient is preprocessed respectively. Finally, the signal is reconstructed to obtain the data curve after the device noise is eliminated.

[0011] Furthermore, the communication unit processes the user instructions of the remote control platform by integrating the wireless sensor network, the communication node distribution algorithm and the normalization processing method, including the following steps: B1. Use a ring topology structure to calculate the nodes in the coverage area of ​​the wireless sensor network and complete the collection and compilation of information node data. The calculation formula is: Where: represents the node expression function of the wireless sensor network coverage area, Indicates the transmission information parameters. represents the transmission information node parameter, t represents the information filtering threshold, i represents the number of valid information nodes, It represents the information transmission interference rate; B2. The distribution algorithm of communication nodes optimizes the distribution of nodes according to the characteristics and requirements of each sub-area and the performance of each communication node, selects nodes with better information transmission functions to undertake the task of data transmission, and realizes the accurate calculation of the distribution position of communication nodes in the device wireless sensor network; B3. Further iterative calculation of the network coverage scale can be performed. The iterative process can be expressed by the following formula: Where: represents the distribution structure of communication nodes, represents the average fitness during information transmission, Indicates the coverage scale of the wireless sensor network; B4. Normalize the user instructions, i.e., control signals, of the remote control platform.

[0012] Furthermore, in step B2, the communication node distribution algorithm optimizes the node distribution according to the characteristics and requirements of each sub-area and the performance of each communication node, including the following steps: B21,Communication Node Distribution Algorithm,First, a detailed analysis of each sub-area is performed,including the size of the sub-area, communication requirements and environmental interference factors; B22. Based on the above information and the performance parameters of the communication nodes, the nodes are screened and arranged; B23. The calculation formula of the communication node distribution algorithm is: Where: D represents the number of sub-regions, Represents the distribution structure of communication nodes.

[0013] Furthermore, in step B4, the normalization process of the user command of the remote control platform, i.e., the control signal, includes the following steps: B41. Set a heterogeneous distance function between control signals. Through this function, analyze each control signal one by one. In this process, assuming that the two control functions are represented by X and Y, and the i-th attribute of X and Y is a continuous attribute, the distance between the two control signal points on a certain attribute i can be expressed as: Where: represents the distance between X and Y on a certain attribute i, represents the variance of attribute i; B42. When the i-th attribute of X and Y is a discrete attribute, the distance between the two signal points X and Y on the i-th attribute can be calculated according to the following formula: Where: Indicates that the value of the i-th attribute in M ​​is The number of control signals, Indicates that the value of the i-th attribute in M ​​is The number of control signals, Indicates that the value of the i-th attribute in M ​​is And the number of control signals that satisfy the output category c, Indicates that the value of the i-th attribute in M ​​is And the number of control signals that satisfy the output category c, C represents the total number of all output categories; B43. Collect all control signal data that need to be normalized; B44. Calculate the distance between each control signal and other signals according to the set heterogeneous distance function; B45. Based on these distance values, an appropriate normalization algorithm, such as linear transformation or nonlinear mapping, is used to convert the original control signal data into a uniform scale range. The normalization processing of all control signals is expressed as follows: Where: represents the control signal after normalization. represents the maximum signal value in the i-th attribute, represents the minimum signal value in the i-th attribute, represents the dual function, Represents the normalization function.

[0014] Furthermore, the data security unit includes a data encryption module, an identity authentication module, an access control module and a device abnormality identification module: The data encryption module uses the AES advanced encryption standard to encrypt, transmit and store data, and supports the SSL / TLS protocol; The authentication module adopts a multi-factor authentication mechanism, supports dynamic passwords and one-time passwords (OTP), authenticates users, and enables authorized users to access the system; The access control module is based on the role-based access control (RBAC), which enables different role users who log in to the platform system to have different operation permissions, supports fine-grained permission settings, and users can set different access permissions according to actual needs; The device abnormality recognition module receives the digital image acquired by the image acquisition unit, captures the device image in real time and performs preprocessing, including image conversion, image enhancement and image filtering. It uses the variational mode decomposition method to extract the device abnormal state features, and implements device abnormality recognition through the support vector machine method.

[0015] Beneficial effects of the present invention: In the present invention, through the remote control and data processing system of the present invention, the user can monitor and control the controlled equipment in real time, reduce manual intervention, improve work efficiency, and reduce labor costs. At the same time, through real-time monitoring and optimized control, the system can improve the utilization rate of the equipment and extend the service life of the equipment. The present invention adopts high-performance processors and optimization algorithms to significantly improve the data processing speed and ensure high reliability and stability. The modular design of the system makes it easier to expand hardware and software, and supports users to flexibly configure and expand functions according to actual needs.

[0016] In the present invention, when the data processing unit in the system pre-processes the collected data through the data processing algorithm, the pre-processing module performs wavelet denoising on the filtered data, effectively eliminating the noise and oscillation of the equipment, so that the system has better data pre-processing capabilities, and can also effectively reduce the system response time and obtain satisfactory remote control results.

[0017] In the present invention, in the system, the communication unit processes the user instructions of the remote control platform by integrating the wireless sensor network, the communication node distribution algorithm and the normalization processing method, so as to ensure the high efficiency and stability of data transmission. Through the reasonable node layout, not only the speed and accuracy of data transmission can be improved, but also the stability and reliability of the entire communication network can be ensured. At the same time, the network coverage scale is further iteratively calculated to ensure that under various operating conditions, the equipment can maintain stable and efficient communication, improve the reliability and stability of the communication unit, effectively improve the real-time and stability of the remote communication of the system equipment, and enhance the responsiveness and adaptability of the remote control system.

[0018] In the present invention, the data security unit in the system realizes system data encryption, identity authentication, and access control functions. At the same time, the image acquisition unit uses a pan-tilt camera to acquire digital images of external devices. The device abnormality recognition module intercepts the device image in real time and performs preprocessing, including image conversion, image enhancement, and image filtering. The variational mode decomposition method is used to extract the abnormal state characteristics of the device, and the support vector machine method is used to realize device abnormality recognition. The system integrates multiple security measures to ensure the security of data and operations, and effectively reduce the occurrence of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a system principle diagram of a remote control system for a data processing device of the present invention; Figure 2 The present invention is a flowchart of an operation of a preprocessing module of a remote control system of a data processing device performing wavelet denoising on filtered data.

[0020] In the figure: 1. Data acquisition unit; 2. Image acquisition unit; 3. Data processing unit; 4. Data storage unit; 5. Communication unit; 6. Control unit; 7. Display unit; 8. Data security unit; 9. Hardware unit; 10. Software unit; 31. Data receiving module; 32. Preprocessing module; 33. Storage management module; 34. Analysis and processing module; 35. Result output module; 81. Data encryption module; 82. Identity authentication module; 83. Access control module; 84. Equipment abnormality identification module. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1: Please refer to Figure 1 and Figure 2 , this design proposes an implementation method, a data processing device remote control system, including a data acquisition unit 1, an image acquisition unit 2, a data processing unit 3, a data storage unit 4, a communication unit 5, a control unit 6, a display unit 7, a data security unit 8, a hardware unit 9 and a software unit 10: The data acquisition unit 1 is used to collect data from various sensors and external devices in real time, receive data input from external devices through a data interface (such as RS485, RS232, I2C, UART, 4-20MA, 0-10V), and transmit the collected raw data to the data processing unit 3 through the communication unit 5. The various sensors in the data acquisition unit 1 include temperature sensors, pressure sensors, humidity sensors, voltage sensors, and current sensors. The temperature, pressure, humidity, voltage, and current data of the device are collected through the various sensors; The image acquisition unit 2 uses a pan-tilt camera to acquire digital images of external devices, and transmits the digital images to the data security unit 8 through the communication unit 5; The data processing unit 3 includes a CPU, memory and cache hardware, and performs real-time analysis (such as neural network, decision tree, etc.), processing and storage of the collected data through data processing algorithms, and transmits the processed data to the data storage unit 4 and the remote control platform through the communication unit 5; The data storage unit 4 includes a local storage device and a cloud storage interface. The local storage device uses SSD or FLASH for fast storage and reading. The cloud storage interface backs up data to the cloud. The data storage unit 4 is used to store processed data and supports fast reading and writing and large-capacity storage functions; The communication unit 5 processes the user instructions of the remote control platform by integrating the wireless sensor network, the communication node distribution algorithm and the normalization processing method, supports multiple communication protocols (such as MQTT, HTTP / HTTPS), and realizes the remote transmission of data and control signals between modules; The control unit 6 includes a microprocessor, a control logic circuit and a remote signal receiver. The microprocessor receives user instructions from the remote control platform and transmits the user instructions to the remote signal receiver through the communication unit 5. The remote signal receiver is responsible for receiving the remote instructions, decoding and parsing the received signals using signal processing technology, and executing corresponding control operations to achieve remote control of the device. It also supports real-time control and timing control. Users can set control parameters and time through the interface. The display unit 7 enables the remote control platform to display system status, data processing results, control options and user interface in real time through a liquid crystal display and a touch screen, supports graphical display and simplified operation functions, provides a graphical user interface, supports multiple chart forms (such as line charts, bar charts, pie charts) to display data analysis results, provides preset operation procedures and user guidance, supports quick operation and one-key configuration, and users can operate through a touch screen or other input devices; The data security unit 8 implements system data encryption, identity authentication, and access control functions, and can also identify abnormalities of the device. The data security unit 8 includes a data encryption module 81, an identity authentication module 82, an access control module 83, and a device abnormality identification module 84: The data encryption module 81 uses the AES advanced encryption standard to encrypt data transmission and storage, and supports the SSL / TLS protocol; The authentication module 82 uses a multi-factor authentication mechanism, supports dynamic passwords and one-time passwords (OTPs), and authenticates users, allowing authorized users to access the system; The access control module 83 is based on the role-based access control (RBAC), which enables different role users who log in to the platform system to have different operation permissions and supports fine-grained permission settings. Users can set different access permissions according to actual needs. The device abnormality recognition module 84 receives the digital image acquired by the image acquisition unit 2, intercepts the device image in real time and performs preprocessing, including image conversion, image enhancement and image filtering, and uses the variational mode decomposition method to extract the abnormal state characteristics of the device, and realizes the device abnormality recognition through the support vector machine method; The hardware unit 9 connects the modules through the hardware bus and the standardized API interface, supports high-speed data transmission and control signal transmission, and supports the addition or replacement of hardware modules for expansion; The software unit 10 supports adding new functions through software updates and plug-in installation, and provides an open API interface, so that third-party developers can develop and integrate new functional modules according to their needs; The data processing device remote control system of this embodiment is actually used in the water quality emergency prevention and control monitoring system of a certain area to monitor the water quality environment. Through real-time data collection and analysis, the response speed of water pollution treatment is increased by 15%, the operation and maintenance cost of water quality emergency prevention and control monitoring is reduced by 16%, the data collection accuracy error rate is less than 1%, and the communication delay is reduced by 30%.

[0023] Example 2: Please refer to Figure 1 and Figure 2 , this design proposes an implementation method, a data processing device remote control system, in this embodiment, preferably, the data processing unit 3 includes a data receiving module 31, a preprocessing module 32, a storage management module 33, an analysis and processing module 34 and a result output module 35: The data receiving module 31 is used to receive the original data collected by the data collection unit 1; The preprocessing module 32 is used to filter, perform wavelet denoising and format conversion on the original data. The preprocessing module 32 performs wavelet denoising on the filtered data, including the following steps: A1. For a signal containing N data, select the maximum number of decomposition layers K, while satisfying the constraints in the following formula, namely: ; A2. Based on the noise preprocessing constraints in the above-determined equipment, the wavelet transform algorithm is used to implement noise preprocessing, and the following formula is obtained: Where: is the device noise signal data after noise reduction, is the initial frequency band value of the equipment noise signal, and u is the wavelet change coefficient; A3. Based on the above analysis, the wavelet coefficients of each layer are decomposed by selecting the number of decomposition layers based on the probability distribution of wavelet coefficients. Assuming that the kth decomposition results in and , based on this, a normal distribution test is conducted, and the wavelet decomposition coefficient process is used to determine whether the equipment obeys the normal distribution, that is, Gaussian white noise; A4. Save ,test Whether it satisfies normal distribution; A5. When Satisfying the normal distribution, calculate Threshold, threshold processing, at this time k=k+1, continue to decompose, when If the normal distribution is not satisfied, stop the decomposition, determine the decomposition level k-1, and perform signal reconstruction; A6. Based on the above analysis, the final number of decompositions is selected using the decomposition layer selection method; A7. The high-order contains a small amount of effective information. The decomposition coefficient obtained will not exceed the set maximum decomposition level k. The normal distribution test is realized through the KS test. The specific calculation method is as follows: Where: and For the degree of relevance, and The main features of the device; A8. Based on the above analysis, outliers are used to determine the thresholds of different wavelets. The corresponding thresholds are obtained through calculation, and each wavelet coefficient is preprocessed respectively. Finally, the signal is reconstructed to obtain the data curve after the device noise is eliminated. The storage management module 33 is used to store and manage the pre-processed data and transmit the storage management data to the analysis and processing module 34; The analysis and processing module 34 uses parallel computing and optimization algorithms to analyze the storage management data and obtain processing results; The result output module 35 stores the processing result locally or sends it to a cloud database via the network.

[0024] Example 3: Please refer to Figure 1 This design proposes an implementation method, a remote control system for a data processing device. In this embodiment, preferably, the communication unit 5 processes the user instructions of the remote control platform by integrating the wireless sensor network, the communication node distribution algorithm and the normalization processing method, including the following steps: B1. Use a ring topology structure to calculate the nodes in the coverage area of ​​the wireless sensor network and complete the collection and compilation of information node data. The calculation formula is: Where: represents the node expression function of the wireless sensor network coverage area, Indicates the transmission information parameters. represents the transmission information node parameter, t represents the information filtering threshold, i represents the number of valid information nodes, It represents the information transmission interference rate; B2. The distribution algorithm of communication nodes optimizes the distribution of nodes according to the characteristics and requirements of each sub-area and the performance of each communication node, selects nodes with better information transmission functions to undertake the task of data transmission, and realizes the accurate calculation of the distribution position of the communication nodes of the device wireless sensor network, including the following steps: B21,Communication Node Distribution Algorithm,First, a detailed analysis of each sub-area is performed,including the size of the sub-area, communication requirements and environmental interference factors; B22. Based on the above information and the performance parameters of the communication nodes, the nodes are screened and arranged; B23. The calculation formula of the communication node distribution algorithm is: Where: D represents the number of sub-regions, Represents the distribution structure of communication nodes; B3. Further iterative calculation of the network coverage scale can be performed. The iterative process can be expressed by the following formula: Where: represents the distribution structure of communication nodes, represents the average fitness during information transmission, Indicates the coverage scale of the wireless sensor network; B4. Normalizing the user command, i.e., the control signal, of the remote control platform, including the following steps: B41. Set a heterogeneous distance function between control signals. Through this function, analyze each control signal one by one. In this process, assuming that the two control functions are represented by X and Y, and the i-th attribute of X and Y is a continuous attribute, the distance between the two control signal points on a certain attribute i can be expressed as: Where: represents the distance between X and Y on a certain attribute i, represents the variance of attribute i; B42. When the i-th attribute of X and Y is a discrete attribute, the distance between the two signal points X and Y on the i-th attribute can be calculated according to the following formula: Where: Indicates that the value of the i-th attribute in M ​​is The number of control signals, Indicates that the value of the i-th attribute in M ​​is The number of control signals, Indicates that the value of the i-th attribute in M ​​is And the number of control signals that satisfy the output category c, Indicates that the value of the i-th attribute in M ​​is And the number of control signals that satisfy the output category c, C represents the total number of all output categories; B43. Collect all control signal data that need to be normalized; B44. Calculate the distance between each control signal and other signals according to the set heterogeneous distance function; B45. Based on these distance values, an appropriate normalization algorithm, such as linear transformation or nonlinear mapping, is used to convert the original control signal data into a uniform scale range. The normalization processing of all control signals is expressed as follows: Where: represents the control signal after normalization. represents the maximum signal value in the i-th attribute, represents the minimum signal value in the i-th attribute, represents the dual function, Represents the normalization function.

[0025] In the present invention, the system is composed of a data acquisition unit 1, an image acquisition unit 2, a data processing unit 3, a data storage unit 4, a communication unit 5, a control unit 6, a display unit 7, a data security unit 8, a hardware unit 9, a software unit 10 and a remote control platform; the data acquisition unit 1 in the system collects data in real time from a variety of sensors and external devices; the data processing unit 3 analyzes, processes and saves the collected data in real time through a data processing algorithm, and transmits the processed data to the data storage unit 4 and the remote control platform through the communication unit 5; the data storage unit 4 stores the processed data; the communication unit 5 supports a variety of communication protocols to achieve remote transmission of data and control signals between modules; the microprocessor in the control unit 6 receives user instructions from the remote control platform, and transmits the user instructions to the remote signal receiver through the communication unit 5, the remote signal receiver is responsible for receiving the remote instructions, decoding and parsing the received signals using signal processing technology, and performing corresponding control operations to achieve remote control of the device; Through the remote control and data processing system of the present invention, users can monitor and control the controlled equipment in real time, reduce human intervention and improve work efficiency; automated data processing and remote control effectively reduce human operational errors; the automation and efficient processing capabilities of the system reduce dependence on manual labor and reduce labor costs; through real-time monitoring and optimized control, the system can improve the utilization rate of equipment, extend the service life of equipment, reduce equipment idle time and maintenance costs; real-time data monitoring and predictive maintenance functions can detect potential problems in advance, reduce unplanned downtime of equipment, and further improve production continuity; the present invention adopts high-performance processors and optimization algorithms to significantly improve data processing speed; the system designs multi-level redundancy and fault recovery mechanisms to ensure high reliability and stability; the modular design of the system makes it easier to expand hardware and software, and supports users to flexibly configure and expand functions according to actual needs; the system ensures the accuracy of data collection through high-precision sensors and advanced data processing algorithms; the system adopts efficient communication protocols and data compression technologies, and the efficiency of the system in remote data transmission is significantly improved; through the optimized design of the user interface and the simplification of the operation process, the user experience is significantly improved; In the system, when the data processing unit 3 pre-processes the collected data through the data processing algorithm, the pre-processing module 32 performs wavelet denoising on the filtered data, and adopts the wavelet transform algorithm to realize noise pre-processing. At the same time, the detail coefficients in the device are retained through the decomposition layer number selection method based on the probability distribution of wavelet coefficients, and there is no need to select valid decomposition data. The final decomposition number of the decomposition layer number selection method is used. On the basis of the above analysis, the thresholds of different wavelets are determined by outliers, and the corresponding thresholds are obtained through calculation. Each wavelet coefficient is pre-processed respectively, and finally the signal is reconstructed to obtain the data curve after the device is denoised; the data in the device is processed by the adaptive wavelet denoising method, and the noise and oscillation phenomenon of the device are effectively eliminated, so that the system has a good data pre-processing capability, and can also effectively reduce the system response time and obtain satisfactory remote control results; In the system, the communication unit 5 processes the user instructions of the remote control platform by integrating the wireless sensor network, the communication node distribution algorithm and the normalization processing method; the microprocessor in the control unit 6 receives the user instructions of the remote control platform, and transmits the user instructions to the remote signal receiver through the communication unit 5. The remote signal receiver is responsible for receiving the remote instructions, decoding and parsing the received signals by using signal processing technology, and performing corresponding control operations to realize remote control of the equipment; the nodes in the coverage area of ​​the wireless sensor network are calculated by adopting a ring topology structure, the information node data is collected and compiled, and then the nodes with better information transmission function are selected through the distribution algorithm of the communication nodes to undertake the task of data transmission, thereby ensuring the efficiency and stability of data transmission. Reasonable node layout can not only improve the speed and accuracy of data transmission, but also ensure the stability and reliability of the entire communication network, effectively prevent the problem of uneven distribution of communication nodes, and further iteratively calculate the network coverage scale to accurately evaluate the network coverage and performance to ensure that the equipment can maintain stable and efficient communication under various operating conditions. Through this iterative calculation process, it can better adapt to the variability of the equipment operating environment and improve the reliability and stability of the communication unit 5. After the distribution of equipment communication nodes is clarified, the user instructions of the remote control platform, i.e., the control signals, are normalized to ensure the uniformity and comparability of the remote control signals, effectively improve the real-time and stability of the remote communication of the system equipment, and enhance the responsiveness and adaptability of the remote control system. The data security unit 8 in the system realizes system data encryption, identity authentication, and access control functions. The system integrates multiple security measures to ensure the security of data and operations, which is particularly important for industries involving sensitive data or high-risk operations, and effectively reduces the occurrence of safety accidents. At the same time, the image acquisition unit 2 uses a pan-tilt camera to collect digital images of external devices, and transmits the digital images to the data security unit 8 through the communication unit 5. The device abnormality recognition module 84 in the data security unit 8 receives the digital images collected by the image acquisition unit 2, captures the device image in real time and performs preprocessing, including image conversion, image enhancement, and image filtering. The variational mode decomposition method is used to extract the abnormal state characteristics of the device, and the support vector machine method is used to realize device abnormality recognition.

[0026] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote control system for a data processing device, comprising a data acquisition unit (1), an image acquisition unit (2), a data storage unit (4), a display unit (7) and a hardware unit (9), It is characterized in that It also includes a data processing unit (3), a communication unit (5), a control unit (6), a data security unit (8) and a software unit (10); The data processing unit (3) includes a CPU, a memory and a cache hardware, and performs filtering, wavelet denoising and format conversion on the data collected by the data collection unit (1), and then performs real-time analysis and storage of the data using a data processing algorithm, and transmits the processed data to the data storage unit (4) and the remote control platform via a communication unit (5); The control unit (6) comprises a microprocessor, a control logic circuit and a remote signal receiver. The microprocessor receives user instructions from the remote control platform and transmits the user instructions to the remote signal receiver through the communication unit (5). The remote signal receiver is responsible for receiving the remote instructions, decoding and parsing the received signals using signal processing technology, and executing corresponding control operations to achieve remote control of the device. It also supports real-time control and timing control. The user can set control parameters and time through the interface. The data security unit (8) implements system data encryption, identity authentication, and access control functions, and simultaneously uses a variational mode decomposition method to extract device abnormal state features, and implements device abnormality identification through a support vector machine method.

2. The data processing device remote control system according to claim 1, It is characterized in that The data acquisition unit (1) is used to acquire data from a variety of sensors and external devices in real time, receive data input from external devices through a data interface, and transmit the acquired raw data to a data processing unit (3) through a communication unit (5). The various sensors in the data acquisition unit (1) include a temperature sensor, a pressure sensor, a humidity sensor, a voltage sensor, and a current sensor. The temperature, pressure, humidity, voltage, and current data of the device are acquired through the various sensors.

3. The data processing device remote control system according to claim 1, It is characterized in that The image acquisition unit (2) uses a pan-tilt camera to acquire digital images of an external device, and transmits the digital images to a data security unit (8) through a communication unit (5); the data storage unit (4) includes a local storage device and a cloud storage interface; the local storage device uses an SSD or FLASH for fast storage and reading; the cloud storage interface backs up data to the cloud; the data storage unit (4) is used to store processed data and supports fast reading and writing and large-capacity storage functions; the communication unit (5) processes user instructions of a remote control platform by integrating a wireless sensor network, a communication node distribution algorithm, and a normalized processing method, supports multiple communication protocols, and realizes remote transmission of data and control signals between modules.

4. The data processing device remote control system according to claim 1, It is characterized in that The display unit (7) enables the remote control platform to display system status, data processing results, control options and user interface in real time through a liquid crystal display and a touch screen. The hardware unit (9) enables modules to be connected through a hardware bus and a standardized API interface, supports high-speed data transmission and control signal transmission, and supports expansion by adding or replacing hardware modules. The software unit (10) supports adding new functions through software updates and plug-in installations, and provides an open API interface so that third-party developers can develop and integrate new functional modules according to needs.

5. The data processing device remote control system according to claim 1, It is characterized in that The data processing unit (3) comprises a data receiving module (31), a preprocessing module (32), a storage management module (33), an analysis and processing module (34) and a result output module (35): The data receiving module (31) is used to receive the original data collected by the data collection unit (1); The pre-processing module (32) is used to filter, perform wavelet denoising and format conversion on the original data; The storage management module (33) is used to perform storage management on the pre-processed data and transmit the storage management data to the analysis and processing module (34); The analysis and processing module (34) uses parallel computing and optimization algorithms to analyze the storage management data and obtain processing results; The result output module (35) stores the processing result locally or sends it to a cloud database via a network.

6. The data processing device remote control system according to claim 5, It is characterized in that The pre-processing module (32) performs wavelet denoising on the filtered data, comprising the following steps: A1. For a signal containing N data, select the maximum number of decomposition layers K, while satisfying the constraints in the following formula, namely: ; A2. Based on the noise preprocessing constraints in the above-determined equipment, the wavelet transform algorithm is used to implement noise preprocessing, and the following formula is obtained: Where: is the device noise signal data after noise reduction, is the initial frequency band value of the equipment noise signal, and u is the wavelet change coefficient; A3. Based on the above analysis, the wavelet coefficients of each layer are decomposed by selecting the number of decomposition layers based on the probability distribution of wavelet coefficients. Assuming that the kth decomposition results in and , based on this, a normal distribution test is conducted, and the wavelet decomposition coefficient process is used to determine whether the equipment obeys the normal distribution, that is, Gaussian white noise; A4. Save ,test Whether it satisfies normal distribution; A5. When Satisfying the normal distribution, calculate Threshold, threshold processing, at this time k=k+1, continue to decompose, when If the normal distribution is not satisfied, stop the decomposition, determine the decomposition level k-1, and perform signal reconstruction; A6. Based on the above analysis, the final number of decompositions is selected using the decomposition layer selection method; A7. The high-order contains a small amount of effective information. The decomposition coefficient obtained will not exceed the set maximum decomposition level k. The normal distribution test is realized through the KS test. The specific calculation method is as follows: Where: and For the degree of relevance, and The main features of the device; A8. Based on the above analysis, outliers are used to determine the thresholds of different wavelets. The corresponding thresholds are obtained through calculation, and each wavelet coefficient is preprocessed respectively. Finally, the signal is reconstructed to obtain the data curve after the device noise is eliminated.

7. The data processing device remote control system according to claim 3, It is characterized in that The communication unit (5) processes user instructions of the remote control platform by integrating a wireless sensor network, a communication node distribution algorithm and a normalized processing method, including the following steps: B1. Use a ring topology structure to calculate the nodes in the coverage area of ​​the wireless sensor network and complete the collection and compilation of information node data. The calculation formula is: Where: represents the node expression function of the wireless sensor network coverage area, Indicates the transmission information parameters. represents the transmission information node parameter, t represents the information filtering threshold, i represents the number of valid information nodes, It represents the information transmission interference rate; B2. The distribution algorithm of communication nodes optimizes the distribution of nodes according to the characteristics and requirements of each sub-area and the performance of each communication node, selects nodes with better information transmission functions to undertake the task of data transmission, and realizes the accurate calculation of the distribution position of communication nodes in the device wireless sensor network; B3. Further iterative calculation of the network coverage scale can be performed. The iterative process can be expressed by the following formula: Where: represents the distribution structure of communication nodes, represents the average fitness during information transmission, Indicates the coverage scale of the wireless sensor network; B4. Normalize the user instructions, i.e., control signals, of the remote control platform.

8. The data processing device remote control system according to claim 7, It is characterized in that In step B2, the communication node distribution algorithm optimizes the node distribution according to the characteristics and requirements of each sub-area and the performance of each communication node, including the following steps: B21,Communication Node Distribution Algorithm,First, a detailed analysis of each sub-area is performed,including the size of the sub-area, communication requirements and environmental interference factors; B22. Based on the above information and the performance parameters of the communication nodes, the nodes are screened and arranged; B23. The calculation formula of the communication node distribution algorithm is: Where: D represents the number of sub-regions, Represents the distribution structure of communication nodes.

9. The data processing device remote control system according to claim 7, It is characterized in that In step B4, the normalization process of the user command of the remote control platform, i.e., the control signal, includes the following steps: B41. Set a heterogeneous distance function between control signals. Through this function, analyze each control signal one by one. In this process, assuming that the two control functions are represented by X and Y, and the i-th attribute of X and Y is a continuous attribute, the distance between the two control signal points on a certain attribute i can be expressed as: Where: represents the distance between X and Y on a certain attribute i, represents the variance of attribute i; B42. When the i-th attribute of X and Y is a discrete attribute, the distance between the two signal points X and Y on the i-th attribute can be calculated according to the following formula: Where: Indicates that the value of the i-th attribute in M ​​is The number of control signals, Indicates that the value of the i-th attribute in M ​​is The number of control signals, Indicates that the value of the i-th attribute in M ​​is And the number of control signals that satisfy the output category c, Indicates that the value of the i-th attribute in M ​​is And the number of control signals that satisfy the output category c, C represents the total number of all output categories; B43. Collect all control signal data that need to be normalized; B44. Calculate the distance between each control signal and other signals according to the set heterogeneous distance function; B45. Based on these distance values, an appropriate normalization algorithm, such as linear transformation or nonlinear mapping, is used to convert the original control signal data into a uniform scale range. The normalization processing of all control signals is expressed as follows: Where: represents the control signal after normalization. represents the maximum signal value in the i-th attribute, represents the minimum signal value in the i-th attribute, represents the dual function, Represents the normalization function.

10. The data processing device remote control system according to claim 4, It is characterized in that The data security unit (8) comprises a data encryption module (81), an identity authentication module (82), an access control module (83) and a device abnormality identification module (84): The data encryption module (81) uses the AES advanced encryption standard to encrypt, transmit and store data, and supports the SSL / TLS protocol; The identity authentication module (82) adopts a multi-factor identity authentication mechanism, supports dynamic passwords and one-time passwords (OTP), authenticates users, and enables authorized users to access the system; The access control module (83) is based on the role-based access control (RBAC), so that users with different roles who log in to the platform system have different operation permissions, and supports fine-grained permission settings, so that users can set different access permissions according to actual needs; The device abnormality recognition module (84) receives the digital image captured by the image acquisition unit (2), captures the device image in real time and performs preprocessing, including three parts: image conversion, image enhancement and image filtering, and uses a variational mode decomposition method to extract device abnormal state features, and implements device abnormality recognition through a support vector machine method.

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