An industrial integrated central control system

Abstract

The present invention relates to an industrial integrated central control system, belonging to the field of automation control technology, the system comprises: a central control system, a plurality of execution devices and a human-computer interaction interface; the central control system is a server, the central control system creates a plurality of virtual device models, each virtual device model is associated with at least one execution device; the control interface of each execution device is integrated into the corresponding virtual device model interface; the human-computer interaction interface is connected to the central control system, the human-computer interaction interface is provided with a plurality of function blocks, each function block corresponds to an execution action; the human-computer interaction interface is used to generate a control flow chart corresponding to a target virtual device model in response to a user's dragging operation on a function block, triggering the target virtual device model to sequentially execute each function block in the control flow chart, so that at least one execution device corresponding to the target virtual device model executes the same control process. This method can improve efficiency and reduce costs.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to an industrial integrated central control system. Background Art

[0002] Central control technology (i.e. central control technology) refers to the technology that realizes centralized management and control of equipment, facilities or systems through integrated control units or systems. This technology is widely used in building automation, industrial control, smart home and other fields.

[0003] Traditional industrial automation control systems include logic controllers, human-machine interfaces, and motion execution devices. Logic controllers include PLCs (Programmable Logic Controllers), single-chip microcomputers, host computers, and other controllers, which mainly realize the logical operation, process control, data processing, and information recording of equipment; human-machine interfaces are mainly used for personnel to operate and control equipment and monitor data status, building an operating environment between people and equipment; motion execution systems are the moving parts of equipment and are the terminal actuators that realize automatic operation of equipment, such as motors, cylinders, grippers, etc.

[0004] For traditional industrial automation control systems, general industrial equipment uses PLC as a logic controller. Users use special programming software and languages ​​(ladder diagram, SFC, ST, etc.) to write logic control programs into the controller, and the controller installs the corresponding logic program to control the relevant action actuators to complete the automatic operation of the equipment; you can also use the host computer as a controller and use conventional computer programming methods (C\C#\C++\JAVA, etc.) to drive the relevant action actuators through industrial buses or other protocols to achieve motion control. Usually, each independent device has an independent control system. When multiple devices need to interact, they need to use relevant communication protocols to achieve interaction.

[0005] It can be seen that the traditional control solution requires professional technicians to implement the programming of the control system, which is difficult to operate. In addition, since each independent device has an independent control system, if multiple devices need to collaborate, users are also required to perform cumbersome operations and perform separate settings for each independent device. Summary of the invention

[0006] In order to solve the above-mentioned problems in the prior art, the present invention provides an industrial integrated central control system.

[0007] In the first aspect, an embodiment of the present application provides an industrial integrated central control system, comprising: a central control system, multiple execution devices and a human-computer interaction interface; the central control system is a server, and the central control system creates multiple virtual device models, each of which is associated with at least one execution device; the control interface of each execution device is integrated into the corresponding virtual device model interface; the human-computer interaction interface is connected to the central control system, and the human-computer interaction interface is provided with multiple function blocks, each of which corresponds to an execution action; the human-computer interaction interface is used to generate a control flow chart corresponding to the target virtual device model in response to the user's dragging operation on the function block, triggering the target virtual device model to execute each function block in the control flow chart in sequence, so that at least one execution device corresponding to the target virtual device model executes the same control process; wherein the target virtual device model is one of the multiple virtual device models.

[0008] Optionally, the human-computer interaction interface is also used to generate a web operation page in response to the user's access drag operation, so that the user can access through multiple terminal devices in the same local area network and realize control through the multiple terminal devices.

[0009] Optionally, the central control system is also used to obtain monitoring data from the multiple execution devices; wherein the monitoring data is data obtained by continuous monitoring of the wearing parts in each of the execution devices; the monitoring data is input into a pre-built long-term loss prediction model to obtain a long-term loss prediction value corresponding to the monitoring data; and the early warning action is determined in combination with the long-term loss prediction value and the monitoring data of the multiple execution devices.

[0010] Optionally, the monitoring data includes at least one of temperature data, vibration data, pressure data and current data; the central control system is also used to perform feature extraction on the monitoring data, obtain key features, input the key features into the long-term loss prediction model, and obtain the long-term loss prediction value corresponding to the monitoring data; wherein, when the monitoring data includes temperature data, the key features include average temperature, maximum temperature, minimum temperature, and temperature change rate; when the monitoring data includes vibration data, the key features include vibration spectrum features; when the monitoring data includes pressure data, the key features include autocorrelation coefficient and periodic features; when the monitoring data includes current data, the key features include current mean, current variance, current kurtosis, and current skewness.

[0011] Optionally, the long-term loss prediction model is constructed based on a long short-term memory network model; the long-term loss prediction model is stacked by multiple layers of LSTM, and an attention mechanism and a context vector are introduced; wherein the expression of the long-term loss prediction value output by the long-term loss prediction model is: ;in, Indicates The predicted loss value for each time step; represents the output layer weight matrix, Represents the output layer bias vector; represents the context vector, ; Represents the time step With time step The normalized attention weights of Represents the time step The hidden state of Indicates the total number of time steps.

[0012] Optionally, the central control system is also used to update the current warning threshold based on the long-term loss prediction value; determine the real-time loss value based on the monitoring data of the execution device; compare the real-time loss value with the updated warning threshold to determine the warning action.

[0013] Optionally, the central control system is also used to obtain original monitoring data of the multiple execution devices, perform data preprocessing on the original monitoring data, and generate the monitoring data; wherein the preprocessing includes at least one of noise removal by moving average and linear interpolation.

[0014] Optionally, multiple execution devices of the same model are associated with the same virtual device model; accordingly, the human-computer interaction interface is also used to generate a control flow chart corresponding to the target virtual device model in response to the user's dragging operation on the function block, triggering the target virtual device model to execute each function block in the control flow chart in sequence, so that multiple execution devices corresponding to the target virtual device model execute the same control flow.

[0015] Optionally, the central control system is further used to adopt a distributed control algorithm to collaboratively control heterogeneous devices; wherein the formula corresponding to the distributed control algorithm is: ;in, Indicates Execution device at time The state vector of Indicates Execution device at time The state vector of Indicates The neighbor set of a device, Indicates The execution device and The weights between the execution devices.

[0016] Optionally, the central control system is also specifically used to receive input device information related to the execution device, build a virtual device model based on the device information, and map the input signal and output signal of the execution device to the virtual device model; wherein the virtual device model is equipped with a physical engine for dynamically simulating the execution device; wherein the device information includes at least one of the physical parameters, functional description, and control interface of the execution device.

[0017] The beneficial effects of the present invention include: First, in this application, the central control system is a server, that is, a server is used to replace a traditional central logic controller, and multiple virtual device models are created in the central control system, each virtual device model is associated with at least one execution device, and then in a multi-device interaction scenario, the user can use the central control system to control a multi-device cluster without the need for separate programming control of each independent execution device, thereby improving efficiency and reducing costs, that is, the central control system can centrally manage and control multiple execution devices (controlled through associated virtual device models), which can improve equipment operation efficiency, reduce energy consumption, and reduce human resource investment, thereby reducing operating costs.

[0018] Second, this application provides a process-based programming method. Users can complete the process control of the equipment by simply dragging and dropping function blocks on the human-computer interaction interface. It uses a flowchart method, which is simple and easy to read, and has strong maintainability and scalability. Subsequent system maintenance also becomes simple, and no professional technicians are required to get started. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A module block diagram of an industrial integrated central control system provided by an embodiment of the present invention;

[0020] Figure 2 A system architecture diagram of an industrial integrated central control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0022] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0023] For traditional industrial automation control systems, general industrial equipment uses PLC as a logic controller. Users use special programming software and languages ​​(ladder diagram, SFC, ST, etc.) to write logic control programs into the controller, and the controller installs the corresponding logic program to control the relevant action actuators to complete the automatic operation of the equipment; you can also use the host computer as a controller and use conventional computer programming methods (C\C#\C++\JAVA, etc.) to drive the relevant action actuators through industrial buses or other protocols to achieve motion control. Usually, each independent device has an independent control system. When multiple devices need to interact, they need to use relevant communication protocols to achieve interaction.

[0024] It can be seen that the traditional control solution requires professional technicians to implement the programming of the control system, which is difficult to operate. In addition, since each independent device has an independent control system, if multiple devices need to collaborate, users are also required to perform cumbersome operations and perform separate settings for each independent device.

[0025] In view of the above problems, the present application proposes the following embodiments to solve the above technical problems.

[0026] See also Figure 1 The embodiment of the present application provides an industrial integrated central control system, including: a central control system, multiple execution devices and a human-computer interaction interface.

[0027] The central control system is a server, and a plurality of virtual device models are created in the central control system. Each virtual device model is associated with at least one execution device; and the control interface of each execution device is integrated into the corresponding virtual device model interface.

[0028] That is, the embodiment of the present application uses a server as a central logic controller, and can be connected to the execution device through an industrial bus or related protocols. Specifically, in the central control system, the execution device is modeled, and a virtual device model is created in the system to correspond to the real execution device, and the control interface of each execution device is integrated into the virtual device model interface. In actual process control, the system calls the corresponding virtual device model interface to complete the control of the real execution device.

[0029] Among them, the execution device can be but is not limited to a motor, a cylinder, a robot arm, a hand gripper, a hydraulic cylinder, etc., and this application does not limit it.

[0030] The human-machine interaction interface is connected to the central control system, and is provided with a plurality of function blocks, each of which corresponds to an execution action. The human-machine interaction interface is used to generate a control flow chart corresponding to the target virtual device model in response to the user's dragging operation on the function block, and trigger the target virtual device model to sequentially execute each function block in the control flow chart, so that at least one execution device corresponding to the target virtual device model executes the same control process; wherein the target virtual device model is one of the multiple virtual device models.

[0031] That is, the embodiment of the present application provides a process-based programming method, and the user can complete the process-based control of the device by dragging the function blocks on the human-computer interaction interface.

[0032] In summary, the industrial integrated central control system provided by the embodiment of the present application has the following effective effects, including:

[0033] First, in the embodiment of the present application, the central control system is a server, that is, a server is used instead of a traditional central logic controller, and multiple virtual device models are created in the central control system, each virtual device model is associated with at least one execution device, and then in a multi-device interaction scenario, the user can use the central control system to control a multi-device cluster without the need for separate programming control of each independent execution device, thereby improving efficiency and reducing costs, that is, the central control system can centrally manage and control multiple execution devices (controlled through associated virtual device models), which can improve equipment operation efficiency, reduce energy consumption, and reduce human resource investment, thereby reducing operating costs.

[0034] Second, the embodiment of the present application provides a process-based programming method. Users can complete the process-based control of the equipment by simply dragging the function blocks on the human-computer interaction interface. It adopts a flowchart method, which is simple and easy to read, and has strong maintainability and scalability. Subsequent system maintenance also becomes simple, and it can be operated without the need for professional technicians.

[0035] In addition, it should be noted that for execution devices directly controlled by traditional control systems, in scenarios with multiple execution devices, users need to write a large amount of code to achieve individual control of each execution device. However, the embodiments of the present application can be directly integrated and controlled by the central control system, and through the drag-and-drop process coding method, the control efficiency is improved, the operating cost is reduced, and the operability and scalability are strong.

[0036] Optionally, the human-computer interaction interface is also used to generate a web operation page in response to the user's access drag operation, so that the user can access through multiple terminal devices in the same local area network and realize control through multiple terminal devices.

[0037] That is, the embodiment of the present application adopts the drag-and-drop open technology, and a web page (WEB) operation page can be generated through the user's access drag operation to achieve access by multiple terminal devices in the local area network. The above access drag operation can be the dragging of the access button.

[0038] The above-mentioned terminal devices may be but are not limited to mobile phones, tablets, personal computers (PCs), etc.

[0039] That is, users can use any terminal device in the same local area network to access the central control system, thereby realizing remote centralized control of the execution equipment. When traditional PLC is used for human-computer interaction, it needs to be equipped with a touch screen and the interface design is done using dedicated software for the touch screen, and the page can only be operated on the touch screen. When the host computer is used for human-computer interaction, it is also necessary to draw the interface in the programming software, which requires high professional skills and cannot realize multi-terminal access control. In the traditional way, personnel can only control operations through the buttons or touch screen provided by the device.

[0040] Optionally, the central control system is also specifically used to receive input device information related to the execution device, build a virtual device model based on the device information, and map the input signal and output signal of the execution device to the virtual device model; wherein the virtual device model is equipped with a physical engine for dynamically simulating the execution device; wherein the device information includes at least one of the physical parameters, functional description, and control interface of the execution device.

[0041] Specifically, the purpose of building a virtual device model is to input device information and automatically generate and control virtual device models of different types of devices. This part may involve the following steps:

[0042] (1) Factory production line hardware equipment information input: Equipment information input is the basis for the construction of the entire virtual equipment model. Equipment information usually includes the physical parameters of the equipment (equipment quality, size, encoding information), functional description (working mode, motion execution, operation status), control interface, etc.

[0043] (2) Parametric modeling: Establish the geometric model and kinematic model based on the physical parameters of the device. Use the physics engine (Gazebo) to dynamically simulate the execution device and simulate the behavior of the execution device under different operating conditions.

[0044] (3) Control interface integration: combining the control interface of the execution device with the virtual device model to achieve automated control of the execution device.

[0045] Input and output signal mapping maps the input and output signals of the execution device to the virtual device model to achieve real-time control of the device.

[0046] The input signal is mapped as: ;

[0047] in, Indicates the execution device at time The control input, represents the input signal and represents the mapping function.

[0048] The output signal is mapped as: ;

[0049] in, Indicates the execution device at time The output signal, Indicates the execution device at time status, Represents a mapping function.

[0050] Regarding the process-based coding method, in an embodiment of the present application, a control flow chart is constructed by dragging and dropping the function blocks corresponding to the target virtual device, and relevant information can be configured at the same time. The process-based control of the execution device can be completed without cumbersome codes.

[0051] Use flowchart tools to design flowcharts for device control. Each flowchart node represents a functional block, such as executing an action, driving, data collection (obtaining device operating parameters from sensors), data preprocessing (cleaning and processing data, including removing noise, handling abnormal values, etc.), control strategy selection (selecting PID controller, state feedback controller, etc.), communication protocol settings, etc., which are not limited in this application.

[0052] Each flowchart node corresponds to a function block, configure the parameters and input and output connections of these function blocks. For example, the PID controller in the example function block configuration adjusts the control response speed and stability: In this formula, represents the control input, represents the error signal, , , Represent the proportional, integral and differential coefficients of the controller respectively, Indicates time.

[0053] The state feedback controller adjusts the feedback gain matrix to optimize the control performance. The formula is: ; In this formula, represents the control input vector; represents the feedback gain matrix, Represents the state vector.

[0054] Optionally, the central control system is used to manage and coordinate the operations of multiple devices or systems to achieve overall performance optimization and collaborative work.

[0055] For example, MPC (Model Predictive Control) is used for central control to establish a collaborative relationship between execution devices and achieve synchronous control and coordinated work of multiple devices.

[0056] MPC control scheme: ;

[0057] in, Indicates The state vector of the step, Indicates The reference state vector of the step, Indicates The control input vector of the step, represents the state error weight matrix; represents the control input weight matrix; represents minimization, Indicates the total number of steps.

[0058] Optionally, the central control system is also used to adopt a distributed control algorithm to perform collaborative control on heterogeneous devices; wherein the formula corresponding to the distributed control algorithm is: ;in, Indicates Execution device at time The state vector of Indicates Execution device at time The state vector of Indicates The neighbor set of a device, Indicates The execution device and The weights between the execution devices.

[0059] It can be seen that the embodiment of the present application also provides a distributed control algorithm to achieve information sharing and collaborative control of heterogeneous execution devices.

[0060] Optionally, the central control system is also used to obtain monitoring data from multiple execution devices; wherein the monitoring data is data obtained by continuous monitoring of the wearing parts in each execution device; the monitoring data is input into a pre-built long-term loss prediction model to obtain a long-term loss prediction value corresponding to the monitoring data; and the early warning action is determined by combining the long-term loss prediction value and the monitoring data of multiple execution devices.

[0061] The above-mentioned wearing parts refer to components that are easily damaged due to frequent use, friction, wear or high stress, and can specifically be bearings, drivers, cylinders, sensors and other devices.

[0062] It should be noted that the failure of wearing parts often presents a diversified and complex pattern. Different from a simple complete failure, wearing parts experience gradual functional decline, abnormal behavior at different stages, or sudden failure events. Therefore, in the embodiment of the present application, not only a real-time detection function is established, but also a long-term loss prediction model is established to assist in the monitoring of wearing parts. In this way, accurate supervision of wearing parts can be achieved.

[0063] Optionally, the monitoring data includes at least one of temperature data, vibration data, pressure data and current data.

[0064] The above monitoring data can be obtained based on real-time sensor data. Sensor data refers to various parameters collected in real time during the operation of the equipment. These parameters can reflect the current state and working condition of the execution equipment. In the industrial production line, the present invention deploys sensors including temperature, vibration, pressure and current. Among them, the operating temperature of the execution equipment is monitored by a temperature sensor. Temperature data can help determine whether the execution equipment is operating within a safe temperature range to avoid damage caused by overheating. Monitor the vibration of the equipment through an accelerometer or vibration sensor. Abnormal vibration data indicates that the mechanical parts of the equipment are worn or faulty. Monitor the pressure changes inside the execution equipment through a pressure sensor. Abnormal pressure indicates signs of blockage or leakage inside the execution equipment. Monitor the current changes during the operation of the equipment through a current sensor to reflect the load condition and operating efficiency of the motor or other electrical components.

[0065] The central control system is also used to extract features from the monitoring data, obtain key features, input the key features into the long-term loss prediction model, and obtain the long-term loss prediction value corresponding to the monitoring data.

[0066] Among them, when the monitoring data includes temperature data, the key features include average temperature, maximum temperature, minimum temperature, and temperature change rate.

[0067] The average temperature can be calculated as: ;in, represents the average temperature, Indicates the total number of temperature data, Indicates time point The corresponding temperature data.

[0068] The maximum temperature is ;in, Indicates temperature data , , ..., The maximum value in .

[0069] Minimum temperature is ;in, Indicates temperature data , , ..., The minimum value in .

[0070] The temperature change rate can be calculated as: ;in, represents the rate of temperature change, Indicates time Temperature data, Indicates time Temperature data.

[0071] When the monitoring data includes vibration data, the key features include vibration spectrum features.

[0072] Specifically, Fourier transform can be performed on the vibration data to extract the vibration spectrum characteristics. The calculation formula can be:

[0073] In this formula, The vibration spectrum value representing the frequency, Indicates at a point in time Vibration data of Indicates the total number of vibration data, represents the imaginary unit, ; The main frequency is ; Represents the main frequency of the vibration data, argmax is a function.

[0074] When the monitoring data includes pressure data, the key features include autocorrelation coefficient and periodic characteristics.

[0075] When the monitoring data includes current data, the key features include current mean, current variance, current kurtosis, and current skewness.

[0076] The calculation formula of the average power value can be: In this formula, represents the mean value of the current data, Indicates the total number of current data, Indicates time point Current data.

[0077] The current variance can be calculated as: In this formula, represents the variance of the current data.

[0078] The calculation formula of current peak can be: In this formula, Represents the kurtosis of the current data.

[0079] The calculation formula of current deflection can be: In this formula, Represents the skewness of the current data.

[0080] It should be noted that through the above data collection and processing steps, high-quality feature data can be obtained, providing a solid foundation for subsequent model training and prediction.

[0081] Optionally, the long-term loss prediction model is constructed based on a long short-term memory network model; the long-term loss prediction model is stacked by multiple layers of LSTM (Long Shot-Term Memory, long short-term memory network), and an attention mechanism and a context vector are introduced.

[0082] That is, the embodiment of the present application adopts an improved LSTM model to predict the long-term loss of equipment.

[0083] By adding an attention mechanism and using multi-layer LSTM stacking, the model's prediction ability is improved. The improved LSTM and attention mechanism improves the modeling ability of long-term dependencies through multi-layer LSTM stacking and the introduction of context vectors. Finally, the prediction value is output through the fully connected layer, and the MSE (Mean Squared Error) loss function is used for training optimization. This method can not only capture complex nonlinear relationships, but also effectively deal with the heterogeneity of high-dimensional data streams, and improve the accuracy of long-term equipment loss prediction.

[0084] Among them, for its input layer, set the input feature matrix for time steps of input data, each time step consists of Features:

[0085] ;

[0086] in, Indicates The feature vector of each time step.

[0087] For each time step , and its forget gate can be:

[0088] ;

[0089] In this method, represents the forget gate vector, is the Sigmoid activation function, is the forget gate weight matrix, represents the hidden state of the previous time step, is the input of the current time step, is the bias vector of the forget gate.

[0090] The input gate can be:

[0091] ;

[0092] ;

[0093] In the above method, represents the input gate vector, represents the new candidate memory cell state, tanh is the hyperbolic tangent activation function, and are the weight matrices of the input gate and candidate memory cells, respectively. and are their bias vectors.

[0094] Next, the memory cell state is updated as follows:

[0095] ;

[0096] In this method, represents the memory cell state at the current time step, is the memory cell state at the previous time step.

[0097] The output gate is represented as:

[0098] ;

[0099] ;

[0100] In the above method, represents the output gate vector, is the hidden state at the current time step, is the output gate weight matrix; is the bias vector for the output gate.

[0101] In order to improve the expressiveness of the model, a multi-layer LSTM stacking structure is used. is the number of stacked LSTM layers, then The hidden state of the layer is:

[0102] ;

[0103] In this formula, Indicates Layer at time step The hidden state of .

[0104] In order to better capture long-term dependencies, the attention mechanism is introduced. The attention weight is calculated as follows:

[0105] ;

[0106] ;

[0107] In this formula, is the attention score, represents the weight matrix, represents the bias vector, Represents the time step With time step The normalized attention weights.

[0108] Context vector The calculation expression of is:

[0109] ; Represents the time step The hidden state of Indicates the total number of time steps;

[0110] Use the fully connected layer and activation function to map the LSTM output to the loss prediction value. That is, the expression of the long-term loss prediction value output by the long-term loss prediction model is: ;in, Indicates The predicted loss value for each time step; represents the output layer weight matrix, Represents the output layer bias vector; represents the context vector.

[0111] Finally, the mean squared error is used as the loss function to measure the error between the predicted value and the true value: ;in, Represents the true value.

[0112] Optionally, the central control system is also used to update the current warning threshold based on the long-term loss prediction value; determine the real-time loss value based on the monitoring data of the execution equipment; compare the real-time loss value with the updated warning threshold to determine the warning action.

[0113] That is, in the actual application process, the warning threshold is updated based on the long-term loss prediction value, and then combined with the real-time loss value to determine the warning action.

[0114] Specifically, the update formula of the warning threshold can refer to the following formula:

[0115] ;

[0116] In this formula, represents the dynamic threshold, represents the long-term loss prediction value, Indicates the real-time loss value, represents the mean of historical loss values, represents the standard deviation of historical loss values, Represents the weight coefficient, which is used to adjust the impact of historical data; represents the adjustment factor, which is used to adjust the impact of the standard deviation; Represents a correction factor that is used to reflect the difference between actual loss and predicted loss.

[0117] Here, we are mainly targeting the problem of long-term loss. The preset threshold cannot be a fixed value, because the loss characteristics of the executing equipment are different under different operating conditions. The normal loss values ​​of the equipment under high load and low load are very different. The predicted loss value can be adjusted (dynamically updated) according to the current working conditions of the equipment, thereby capturing the trend of equipment loss and improving the accuracy of early warning.

[0118] It should be noted that the current real-time loss value can be obtained by monitoring the real-time operation data of the execution equipment, including sensor data, working status, etc. .

[0119] The above-mentioned comparing the real-time loss value with the updated warning threshold value to determine the warning action may specifically include:

[0120] if ≥ , triggering a high-level alarm, indicating that there are serious problems with the wearing parts of the execution equipment or urgent maintenance is required.

[0121] if > ≥ , triggering a medium-level alarm, indicating that there is a potential problem with the wearing parts of the execution equipment and that monitoring and attention are required. For setting less than The threshold value.

[0122] if < , the device is considered to be operating normally and no alarm is triggered.

[0123] Optionally, the central control system is also used to obtain original monitoring data of multiple execution devices, perform data preprocessing on the original monitoring data, and generate monitoring data; wherein the preprocessing includes at least one of noise removal by moving average and linear interpolation.

[0124] For random errors and interference in sensor data, noise is removed by moving average and other methods.

[0125] Moving average formula: ;

[0126] In this formula, Indicates time The smoothed value of Represents the original data at time The value of Indicates the window size.

[0127] Furthermore, missing values ​​in the data are processed by interpolation, filling, deletion and other methods.

[0128] Linear interpolation formula:

[0129] ;

[0130] in, Indicates time The interpolation result of and Indicates the time points before and after the missing value; and Indicates time point and The original data value.

[0131] The standard deviation method is used to detect and process outliers.

[0132] In addition, in one embodiment, the industrial integrated central control system provided by the present application can provide more efficient decision support and prediction capabilities through improved algorithms combined with human-computer interaction capabilities. With the help of large model data, the present application edits the execution process through user scenario description, and simulates and diagnoses the operation process through AI (Artificial Intelligence) diagnosis, helping users to improve the system data system analysis program and analyze each execution module.

[0133] The intelligent decision support system significantly improves decision support and prediction capabilities through improved algorithms and powerful human-computer interaction capabilities. The core functions of the system include user scenario description editing and execution process, AI diagnosis and operation process simulation, and system data analysis program optimization and execution module analysis.

[0134] User scenario description editing and execution process: The user scenario description editing and execution process module is the core of the intelligent decision support system. Its function is to dynamically generate and optimize the execution process based on the scenario description and requirements provided by the user. During the operation, the user enters specific business scenario descriptions and requirements through the system's user interface or input port. For example, specific requirements such as equipment operation status monitoring and production plan optimization can be described.

[0135] AI diagnosis and operation process simulation: The system uses advanced AI technology to monitor and analyze the operation process. Through real-time data collection and processing, it monitors data trends, identifies abnormal situations, and makes improvement suggestions to optimize the data analysis process.

[0136] The following describes the requirements analysis and system design of the system implemented in this application:

[0137] Determine the functional requirements and performance indicators of industrial integrated central control systems, including data acquisition, remote monitoring, control algorithms and other aspects.

[0138] Analyze user needs and scenario characteristics, and determine the system's functional modules and interface design.

[0139] Design the system architecture based on the results of the demand analysis and determine the software module division and organizational structure.

[0140] Development environment construction:

[0141] Configure the development environment, select the appropriate integrated development environment (IDE), programming language, and development tools. Build a database system (such as MySQL, MongoDB, etc.) and the necessary server environment for data storage and back-end services.

[0142] Software Development:

[0143] Develop a data acquisition module to interact with sensors and actuators, collect real-time data and send it to the central control system.

[0144] Develop control algorithm modules, analyze and process the collected data, and generate corresponding control instructions.

[0145] Develop user interface modules and design friendly human-computer interaction interfaces, including graphical interfaces, web interfaces, etc., to facilitate user monitoring and operating the system.

[0146] Develop a remote monitoring and management module to enable users to remotely access the system through the Internet, monitor the operating status of equipment and perform control and management.

[0147] System Integration and Testing:

[0148] Integrate each module into a unified system to ensure that the modules work together properly.

[0149] Conduct system function tests and performance tests to verify whether the normal operation and performance indicators of each functional module of the system meet the requirements.

[0150] Conduct system integration testing to simulate real usage scenarios and verify the stability and reliability of the system in actual operation.

[0151] Deployment and debugging:

[0152] Deploy the software system to the actual hardware platform to ensure that the software can run normally and communicate with the hardware devices.

[0153] Debug and optimize the system to resolve bugs and performance bottlenecks in software operation and ensure that the system meets the design requirements.

[0154] Training and maintenance:

[0155] Provide training to system administrators and operators to familiarize them with the system's operating procedures and usage.

[0156] Establish a system operation and maintenance mechanism, regularly check and maintain the software system, and ensure that the system runs stably and reliably.

[0157] Continuously optimize the software system, expand functions and optimize performance based on user feedback and changes in demand.

[0158] See also Figure 2 ,The system structure diagram can be divided into front-end UI, presentation layer, business layer, data layer, database and operating environment.

[0159] The front-end UI can include management and interaction design.

[0160] The presentation layer can include low-code rendering and Ajax interactions (including POST requests and GET requests).

[0161] The business layer can include system management, process management, model management, task management, equipment management, and process parameters.

[0162] The data layer can include Modbus, Serial port, Socket, database read and write, custom functions and custom scripts. Logging belongs to the business layer and data layer. Permission control belongs to the display layer, business layer and data layer.

[0163] Databases can include My SQL, H2 Dd, Oracle.

[0164] The operating environment can include Windows and Linux.

[0165] In summary, the industrial integrated central control system provided by the embodiment of the present application has the following beneficial effects:

[0166] Improve efficiency and reduce costs: The central control system can centrally manage and control multiple subsystems. Through automation and optimization of control algorithms, it can improve equipment operation efficiency, reduce energy consumption, and reduce human resource investment, thereby reducing operating costs.

[0167] Improve productivity and quality: By real-time monitoring and controlling key parameters in the production process, the central control system can promptly detect and adjust abnormal conditions in the production process, improve the stability and controllability of the production process, and thus improve product quality and production efficiency.

[0168] Improve environment and safety: The central control system can monitor and control environmental parameters such as temperature, humidity, air quality, etc., so as to ensure the comfort and safety of the production environment, reduce the risk of accidents and improve the working environment.

[0169] Enhanced data analysis and decision support: The central control system can collect and store large amounts of data in real time. Through data analysis technology, it can discover hidden patterns and trends, and provide support and reference for management decisions.

[0170] Improve user experience and satisfaction: Through a friendly human-computer interaction interface and remote monitoring function, the central control system can improve user experience and satisfaction with the system, making it convenient for users to monitor and manage equipment operation status anytime and anywhere.

[0171] Strengthen system security and stability: The central control system takes various security measures to protect the system from unauthorized access and attacks. It also has fault self-diagnosis and fault tolerance capabilities to ensure stable and reliable operation of the system.

[0172] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0173] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0174] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0176] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0178] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An industrial integrated central control system, characterized in that: include: Central control system, multiple execution devices and human-computer interaction interface; The central control system is a server, and the central control system creates multiple virtual device models, each of which is associated with at least one execution device; the control interface of each execution device is integrated into the corresponding virtual device model interface; The human-computer interaction interface is connected to the central control system, and is provided with a plurality of function blocks, each of which corresponds to an execution action; the human-computer interaction interface is used to generate a control flow chart corresponding to a target virtual device model in response to a user's dragging operation on the function block, and trigger the target virtual device model to sequentially execute each function block in the control flow chart, so that at least one execution device corresponding to the target virtual device model executes the same control process; wherein the target virtual device model is one of the plurality of virtual device models; The central control system is also used to obtain monitoring data from the multiple execution devices; wherein the monitoring data is data obtained by continuously monitoring the wearing parts in each of the execution devices; the monitoring data is input into a pre-built long-term loss prediction model to obtain a long-term loss prediction value corresponding to the monitoring data; and a warning action is determined in combination with the long-term loss prediction value and the monitoring data of the multiple execution devices; The long-term loss prediction model includes: an input layer, setting an input feature matrix for time steps of input data, each time step consists of Features: ;in, Indicates feature vector of time steps; for each time step , and its forget gate can be: ; In this formula, represents the forget gate vector, is the Sigmoid activation function, is the forget gate weight matrix, represents the hidden state of the previous time step, is the input of the current time step, is the bias vector of the forget gate; the input gate is: ; ; In this formula, represents the input gate vector, represents the new candidate memory cell state, tanh is the hyperbolic tangent activation function, and are the weight matrices of the input gate and candidate memory cells, respectively. and are their bias vectors; the memory cell state is updated as: ; In this formula, represents the memory cell state at the current time step, is the state of the memory cell at the previous time step; the output gate is represented by: ; ; In this formula, represents the output gate vector, is the hidden state at the current time step, is the output gate weight matrix; is the bias vector of the output gate; the long-term loss prediction model uses a multi-layer LSTM stacking structure; suppose is the number of stacked LSTM layers, then The hidden state of the layer is: In this formula, Indicates Layer at time step The hidden state of ; The attention weight is calculated as follows: ; In this formula, is the attention score, represents the weight matrix, represents the bias vector, Represents the time step With time step The normalized attention weight of ; the context vector The calculation expression of is: ; Represents the time step The hidden state of Represents the total number of time steps; the LSTM output is mapped to the loss prediction value using a fully connected layer and activation function. The expression of the long-term loss prediction value output by the long-term loss prediction model is: ;in, Indicates The predicted loss value for each time step; represents the output layer weight matrix, Represents the output layer bias vector; represents the context vector.

2. The industrial integrated central control system according to claim 1, characterized in that: The human-computer interaction interface is also used to generate a web operation page in response to the user's access drag operation, so that the user can access through multiple terminal devices in the same local area network and realize control through the multiple terminal devices.

3. The industrial integrated central control system according to claim 1, characterized in that: The monitoring data includes at least one of temperature data, vibration data, pressure data and current data; The central control system is also used to extract features from the monitoring data to obtain key features, input the key features into the long-term loss prediction model, and obtain the long-term loss prediction value corresponding to the monitoring data; Among them, when the monitoring data includes temperature data, the key features include average temperature, maximum temperature, minimum temperature, and temperature change rate; when the monitoring data includes vibration data, the key features include vibration spectrum features; when the monitoring data includes pressure data, the key features include autocorrelation coefficient and periodic characteristics; when the monitoring data includes current data, the key features include current mean, current variance, current kurtosis, and current skewness.

4. The industrial integrated central control system according to claim 1, characterized in that: The central control system is also used to update the current warning threshold based on the long-term loss prediction value; Based on the monitoring data of the execution device, a real-time loss value is determined; the real-time loss value is compared with an updated warning threshold value to determine a warning action.

5. The industrial integrated central control system according to claim 1, characterized in that: The central control system is also used to obtain the original monitoring data of the multiple execution devices, perform data preprocessing on the original monitoring data, and generate the monitoring data; The preprocessing includes at least one of removing noise by moving average and linear interpolation.

6. The industrial integrated central control system according to claim 1, characterized in that: Multiple execution devices of the same model are associated with the same virtual device model; Correspondingly, the human-computer interaction interface is also used to generate a control flow chart corresponding to the target virtual device model in response to the user's dragging operation on the function block, triggering the target virtual device model to execute each function block in the control flow chart in sequence, so that multiple execution devices corresponding to the target virtual device model execute the same control process.

7. The industrial integrated central control system according to claim 1, characterized in that: The central control system is also used to adopt a distributed control algorithm to collaboratively control heterogeneous devices; wherein the formula corresponding to the distributed control algorithm is: ； in, Indicates Execution device at time The state vector of Indicates Execution device at time The state vector of Indicates The neighbor set of a device, Indicates The execution device and The weights between the execution devices.

8. The industrial integrated central control system according to claim 1, characterized in that: The central control system is also specifically used to receive input device information related to the execution device, build a virtual device model based on the device information, and map the input signal and output signal of the execution device to the virtual device model; wherein the virtual device model is equipped with a physical engine for dynamically simulating the execution device; The device information includes at least one of the physical parameters, functional description, and control interface of the execution device.

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