Industrial control data request methods, devices, equipment and readable storage media

Abstract

This application discloses a method, apparatus, device, and readable storage medium for requesting industrial control data, relating to the field of industrial control software technology. This application generates a set of variables to be acquired based on the screen to be displayed, ensuring that all variables in the set are correlated with the screen to be displayed, thus avoiding the collection of redundant variable data. Furthermore, this embodiment generates different subsets of candidate variables based on each variable in the set of variables to be acquired, further expanding the variables to be collected based on the set of variables to be acquired, ensuring variable preloading capability, and improving the loading speed of new screens during screen switching. Simultaneously, this application optimizes the generated subsets of candidate variables, reducing the number of variables collected, avoiding over-collection of data, reducing data redundancy during communication, and improving communication efficiency.

Description

Technical Field

[0001] This application relates to the field of industrial control software technology, and in particular to an industrial control data request method, apparatus, device, and readable storage medium. Background Technology

[0002] HMI (Human Machine Interface) communication technology refers to the data interaction and communication between the HMI screen and other devices through communication protocols and technologies, that is, accessing variables between different devices. However, current traditional communication solutions are very inefficient when collecting variables. Each time, all variables with the same period in the system are collected, resulting in a high rate of invalid data collection, high collection costs, and low communication efficiency, which affects the overall performance of the human-machine interaction system. Summary of the Invention

[0003] The main purpose of this application is to provide an industrial control data request method, which aims to solve the technical problem of low communication efficiency in traditional communication schemes, which affects the performance of the entire human-computer interaction system.

[0004] To achieve the above objectives, this application provides an industrial control data request method, which includes:

[0005] Generate a set of variables to be acquired based on the display screen requirements of industrial control screens;

[0006] Different subsets of candidate variables are generated based on each variable in the set of variables to be obtained;

[0007] For any subset of candidate variables, the subset of candidate variables is optimized to obtain a target subset of variables based on the correlation influence between the candidate variables in the subset of candidate variables and the subset of candidate variables, and a variable data request is generated based on the target subset of variables.

[0008] Optionally, the step of generating a set of variables to be acquired based on the display screen requirements of the industrial control screen includes:

[0009] The display screen in the industrial control screen display requirement is used as the target screen, or the display screen and the screen associated with the display screen are used as the target screen;

[0010] The set of variables to be acquired is generated based on the variables associated with the controls in the target screen.

[0011] Optionally, the candidate variables include first candidate variables and second candidate variables, the variables in the set of variables to be obtained are the first candidate variables, and the step of generating different subsets of candidate variables based on each variable in the set of variables to be obtained includes:

[0012] For any first candidate variable in the set of variables to be obtained, the first candidate variable is used as the central variable of a subset of the candidate variables.

[0013] The second candidate variable in the industrial control system whose similarity to the central variable is greater than or equal to a preset similarity threshold is selected as an element in the subset of candidate variables.

[0014] Optionally, before the step of including a second candidate variable in the industrial control system with a similarity greater than or equal to a preset similarity threshold as an element in the subset of candidate variables, the method includes:

[0015] For any variable in the industrial control system, construct a vector of the variable based on its variable attributes;

[0016] The similarity between the variable and the central variable is calculated based on the vector of the variable and the vector of the central variable.

[0017] Optionally, the step of optimizing the candidate variable subset to obtain the target variable subset based on the correlation influence between the candidate variables in the candidate variable subset and the candidate variable subset includes:

[0018] The subset of candidate variables is used as the first candidate subset. For any second candidate variable in the subset of candidate variables, the second candidate variable is removed from the first candidate subset to generate the second candidate subset.

[0019] Calculate the association performance of the second candidate subset, and take the decrease in the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable;

[0020] If the correlation influence is less than the preset influence threshold, the second candidate subset is used as the new first candidate subset, and based on the new first candidate subset and the new second candidate variable, the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset is performed until all second candidate variables in the candidate variable subset are traversed.

[0021] The first candidate subset determined after traversing each second candidate variable in the candidate variable subset is taken as the target variable subset.

[0022] Optionally, after the step of using the performance decrease of the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable, the method includes:

[0023] If the correlation influence is greater than or equal to the preset influence threshold, the second candidate subset will be discarded, and the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset will be performed based on the new second candidate variable until all second candidate variables in the candidate variable subset are traversed.

[0024] The step of taking the first candidate subset determined after traversing each second candidate variable in the candidate variable subset as the target variable subset is performed.

[0025] Optionally, the step of calculating the association performance of the second candidate subset includes:

[0026] The association performance of the second candidate subset is calculated based on the similarity between each second candidate variable in the second candidate subset and the central variable in the second candidate subset.

[0027] To achieve the above objectives, this application also provides an industrial control data request device, the industrial control data request device comprising:

[0028] The generation module is used to generate a set of variables to be acquired based on the display screen in the industrial control screen display requirements;

[0029] The partitioning module is used to generate different subsets of candidate variables based on each variable in the set of variables to be acquired.

[0030] The optimization module optimizes any candidate variable subset based on the correlation influence between the candidate variables in the candidate variable subset and the candidate variable subset itself to obtain a target variable subset, and generates a variable data request based on the target variable subset.

[0031] To achieve the above objectives, this application also provides an industrial control data request device, which includes: a memory, a processor, and an industrial control data request program stored in the memory and executable on the processor. When the industrial control data request program is executed by the processor, it implements the steps of the industrial control data request method described above.

[0032] To achieve the above objectives, this application also provides a readable storage medium, which is a computer-readable storage medium, and stores an industrial control data request program thereon. When the industrial control data request program is executed by a processor, it implements the steps of the industrial control data request method described above.

[0033] This application proposes an industrial control data request method, apparatus, device, and readable storage medium. In this embodiment, a set of variables to be acquired is generated based on the display screen requirements of the industrial control screen. Different subsets of candidate variables are generated based on each variable in the set of variables to be acquired. For any subset of candidate variables, the subset is optimized to obtain a target subset of variables based on the correlation influence between the candidate variables and the subset itself. A variable data request is then generated based on the target subset of variables. In other words, this application generates a set of variables to be acquired based on the screen to be displayed, ensuring that all variables in the set of variables to be acquired are correlated with the screen to be displayed, thus avoiding the collection of redundant variable data. Furthermore, this application generates different subsets of candidate variables based on each first candidate variable in the set of variables to be acquired, further expanding the variables to be collected based on the first candidate variables, ensuring variable preloading capability, and improving the loading speed of new screens during screen switching. Simultaneously, this application optimizes the generated subsets of candidate variables, reducing the number of variables collected, avoiding excessive data collection, reducing data redundancy during communication, and improving communication efficiency. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application;

[0035] Figure 2 This is a flowchart illustrating the first embodiment of the industrial control data request method in this application;

[0036] Figure 3 This is a flowchart illustrating the second embodiment of the industrial control data request method in this application;

[0037] Figure 4 This is a flowchart illustrating the third embodiment of the industrial control data request method in this application;

[0038] Figure 5 This is a schematic diagram of the industrial control data request method of this application;

[0039] Figure 6 This is a schematic diagram of the industrial control data request device in the industrial control data request method of this application.

[0040] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0041] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0042] like Figure 1 As shown, Figure 1This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.

[0043] The device in this application embodiment can be industrial control equipment, or it can be an electronic terminal device such as a PC, smartphone, tablet computer, or portable computer.

[0044] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0045] Optionally, the device may also include a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, a WiFi module, and so on. Sensors may include light sensors, motion sensors, and other sensors. Specifically, light sensors may include ambient light sensors and proximity sensors. The ambient light sensor can adjust the display brightness according to the ambient light level, while the proximity sensor can turn off the display and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, a gravity accelerometer can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity, and vibration recognition functions (such as pedometers, tapping, etc.). Of course, the mobile terminal may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, which will not be elaborated here.

[0046] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0047] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an industrial control data request program.

[0048] exist Figure 1In the device shown, network interface 1004 is mainly used to connect to the backend server and communicate with it; user interface 1003 is mainly used to connect to the client (user terminal) and communicate with it; while processor 1001 can be used to call the industrial control data request program stored in memory 1005 and perform the following operations:

[0049] Generate a set of variables to be acquired based on the display screen requirements of industrial control screens;

[0050] Different subsets of candidate variables are generated based on each variable in the set of variables to be obtained;

[0051] For any subset of candidate variables, the subset of candidate variables is optimized to obtain a target subset of variables based on the correlation influence between the candidate variables in the subset of candidate variables and the subset of candidate variables, and a variable data request is generated based on the target subset of variables.

[0052] Furthermore, the processor 1001 can call the industrial control data request program stored in the memory 1005 and also perform the following operations:

[0053] The step of generating a set of variables to be acquired based on the display screen requirements of the industrial control screen includes:

[0054] The display screen in the industrial control screen display requirement is used as the target screen, or the display screen and the screen associated with the display screen are used as the target screen;

[0055] The set of variables to be acquired is generated based on the variables associated with the controls in the target screen.

[0056] Furthermore, the processor 1001 can call the industrial control data request program stored in the memory 1005 and also perform the following operations:

[0057] The candidate variables include first candidate variables and second candidate variables, and the variables in the set of variables to be obtained are the first candidate variables. The step of generating different subsets of candidate variables based on each variable in the set of variables to be obtained includes:

[0058] For any first candidate variable in the set of variables to be obtained, the first candidate variable is used as the central variable of a subset of the candidate variables.

[0059] The second candidate variable in the industrial control system whose similarity to the central variable is greater than or equal to a preset similarity threshold is selected as an element in the subset of candidate variables.

[0060] Furthermore, the processor 1001 can call the industrial control data request program stored in the memory 1005 and also perform the following operations:

[0061] Before the step of including a second candidate variable in the industrial control system with a similarity greater than or equal to a preset similarity threshold as an element in the subset of candidate variables, the method includes:

[0062] For any variable in the industrial control system, construct a vector of the variable based on its variable attributes;

[0063] The similarity between the variable and the central variable is calculated based on the vector of the variable and the vector of the central variable.

[0064] Furthermore, the processor 1001 can call the industrial control data request program stored in the memory 1005 and also perform the following operations:

[0065] The step of optimizing the candidate variable subset to obtain the target variable subset based on the correlation influence between the candidate variables in the candidate variable subset and the candidate variable subset includes:

[0066] The subset of candidate variables is used as the first candidate subset. For any second candidate variable in the subset of candidate variables, the second candidate variable is removed from the first candidate subset to generate the second candidate subset.

[0067] Calculate the association performance of the second candidate subset, and take the decrease in the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable;

[0068] If the correlation influence is less than the preset influence threshold, the second candidate subset is used as the new first candidate subset, and based on the new first candidate subset and the new second candidate variable, the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset is performed until all second candidate variables in the candidate variable subset are traversed.

[0069] The first candidate subset determined after traversing each second candidate variable in the candidate variable subset is taken as the target variable subset.

[0070] Furthermore, the processor 1001 can call the industrial control data request program stored in the memory 1005 and also perform the following operations:

[0071] After the step of taking the performance decrease of the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable, the method includes:

[0072] If the correlation influence is greater than or equal to the preset influence threshold, the second candidate subset will be discarded, and the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset will be performed based on the new second candidate variable until all second candidate variables in the candidate variable subset are traversed.

[0073] The step of taking the first candidate subset determined after traversing each second candidate variable in the candidate variable subset as the target variable subset is performed.

[0074] Furthermore, the processor 1001 can call the industrial control data request program stored in the memory 1005 and also perform the following operations:

[0075] The step of calculating the association performance of the second candidate subset includes:

[0076] The association performance of the second candidate subset is calculated based on the similarity between each second candidate variable in the second candidate subset and the central variable in the second candidate subset.

[0077] It should be noted that current traditional communication solutions mostly request data based on pre-defined classification results when collecting variables. For example, data is acquired periodically, and variables within the same period are collected after the acquisition period is completed. However, among all the variables collected for the same period, some variables will not play a role in the front end of the industrial control system. For example, if a variable does not need to be displayed on the current screen, then that variable is actually redundant data. In addition to classifying by period, classifying by acquisition mode, acquisition address, etc., can also lead to data redundancy, resulting in low communication efficiency in the human-machine interaction industrial control system and affecting overall performance. Therefore, this application provides an industrial control data request method that reduces communication data redundancy and improves communication efficiency while ensuring communication effectiveness.

[0078] Reference Figure 2 The first embodiment of the industrial control data request method of this application includes:

[0079] Step S10: Generate a set of variables to be acquired based on the display screen in the industrial control screen display requirements;

[0080] It should be noted that the above-mentioned industrial control screen display requirements refer to the current screen display requirements of the industrial control system, and the above-mentioned display screen is the screen that needs to be displayed at present.

[0081] For example, a set of variables to be acquired can be generated based on the display requirements of the industrial control screen. For instance, the variables associated with the controls in the display screen can be used as elements in the set of variables to be acquired.

[0082] Understandably, since the variables in the set of variables to be acquired are related to the display screen, the number of variables in the set of variables to be acquired is much smaller than the number of variables in the same period of traditional communication schemes, thus greatly reducing the data redundancy of the acquisition results.

[0083] Step S20: Generate different subsets of candidate variables based on each variable in the set of variables to be acquired;

[0084] It should be noted that, in order to ensure communication effectiveness, that is, to ensure the loading speed of the new screen when switching screens, variables will be preloaded.

[0085] For example, a subset of candidate variables is generated based on each variable in the set of variables to be acquired (and these variables will also be considered as first candidate variables in subsequent steps). For any variable in the set of variables to be acquired, other variables associated with that variable in the industrial control system can be grouped together with that variable to generate a subset of candidate variables. The other variables associated with that variable can be variables with the same or similar attributes as that variable, or variables related to controls on the screen associated with the display screen, etc. The screen associated with the display screen usually refers to a screen with a jump relationship, for example, a jump interface on screen A that jumps to screen B. A subset of candidate variables can be obtained from one variable in the corresponding set of variables to be acquired. Therefore, in this embodiment, in addition to taking the first candidate variable of the currently displayed screen as the acquisition object, the variables associated with the first candidate variable will also be taken as acquisition objects, thereby improving the preloading capability of variables. It can be understood that since there are related screens, the variables on the screen will also be related. Therefore, while acquiring the variables on the display screen, the variables on the screen associated with the display screen can also be acquired, thereby ensuring that the screen switching can be completed quickly.

[0086] Step S30: For any candidate variable subset, optimize the candidate variable subset to obtain a target variable subset based on the correlation influence between the candidate variables in the candidate variable subset and the candidate variable subset, and generate a variable data request based on the target variable subset.

[0087] For example, for any subset of candidate variables, the subset is optimized to obtain a target subset of variables based on the correlation influence between the candidate variables in the subset and the subset itself. It should be noted that the variables in the subset are the candidate variables, and the correlation influence between the candidate variables and the subset is related to the similarity between the candidate variable and the central variable in the subset (i.e., the variable in the variable set to be obtained when generating the subset). For example, the higher the similarity, the higher the correlation influence. If the correlation influence of a candidate variable relative to a subset is less than a preset influence threshold, the candidate variable can be deleted from the subset; otherwise, it is retained. This optimizes the subset of candidate variables to obtain the target subset of variables. Then, a variable data request is generated based on the variables in the target subset. This variable data request can be used to request data. In practical applications, a variable data request can be a frame of variables in the target subset to request data. Accordingly, one variable data request can be generated for each target subset of variables. Understandably, the optimized target subset reduces the number of variables, thus further reducing data redundancy in communication and improving communication efficiency.

[0088] In this embodiment, a set of variables to be acquired is generated based on the display screen requirements of the industrial control screen. Different subsets of candidate variables are generated for each variable in the set of variables to be acquired. For any subset of candidate variables, the subset is optimized to obtain a target subset of variables based on the correlation influence between the candidate variables and the subset itself. A variable data request is then generated based on the target subset of variables. In other words, in this embodiment, the screen to be displayed is used to generate a set of variables to be acquired, ensuring that all variables in the set are correlated with the screen to be displayed, thus avoiding the collection of redundant variable data. Furthermore, this embodiment also generates different subsets of candidate variables based on each variable in the set of variables to be acquired, further expanding the variables to be collected and ensuring variable preloading capability, thereby improving the loading speed of new screens during screen switching. Simultaneously, this application also optimizes the generated subsets of candidate variables, reducing the number of variables collected, avoiding excessive data collection, reducing data redundancy during communication, and improving communication efficiency.

[0089] In one feasible implementation, the step of generating a set of variables to be acquired based on the display screen requirements of the industrial control screen includes:

[0090] Step S110: Use the display screen in the industrial control screen display requirement as the target screen, or use the display screen and the screen associated with the display screen as the target screen;

[0091] Step S120: Generate the set of variables to be acquired based on the variables associated with the controls in the target screen.

[0092] For example, in this embodiment, in addition to using the display screen to be displayed as the target screen, the display screen and the screen associated with it can also be used as the target screen, such as the screen that can be jumped from from the display screen. Then, the aforementioned set of variables to be obtained is generated based on the variables associated with the controls in the target screen (the variables associated with the controls can be obtained by compiling based on the control attributes in the target screen). In practical applications, variable data requests can also be generated based on each variable in the set of variables to be obtained. It is understood that, for the case where the display screen and the screen associated with it are used as the target screen, since the set of variables to be obtained includes the variables of the screen associated with the display screen, the ability of the human-computer interaction industrial control system to preload variables can be increased, thus improving the screen switching performance.

[0093] Reference Figure 3 Based on the first embodiment of this application, a second embodiment of this application is proposed. In this embodiment, the parts that are the same as or similar to those in the above embodiment can be referred to the above content, and will not be repeated here. In this embodiment, the types of candidate variables include first candidate variables and second candidate variables, and the variables in the set of variables to be obtained are the first candidate variables. The step of generating different subsets of candidate variables based on each variable in the set of variables to be obtained includes:

[0094] Step S210: For any first candidate variable in the set of variables to be obtained, the first candidate variable is used as the central variable of the subset of candidate variables;

[0095] Step S220: The second candidate variable in the industrial control system with a similarity greater than or equal to the central variable is selected as an element in the subset of candidate variables.

[0096] For example, the candidate variables include first candidate variables and second candidate variables, and all variables in the set of variables to be acquired are first candidate variables. Each first candidate variable in the set of variables to be acquired can generate a subset of candidate variables. For any first candidate variable in the set of variables to be acquired, that first candidate variable can be used as the central variable of a subset of candidate variables. Then, second candidate variables in the industrial control system (i.e., human-machine control system) whose similarity to the central variable is greater than a preset similarity threshold can be used as elements in the subset of variables to be acquired. In this way, different subsets of candidate variables can be generated based on each first candidate variable in the set of variables to be acquired.

[0097] In one feasible embodiment, before the step of including a second candidate variable in the industrial control system with a similarity greater than or equal to a preset similarity threshold as an element in the subset of candidate variables, the method includes:

[0098] Step S201: For any variable in the industrial control system, construct a vector of the variable based on the variable attributes of the variable;

[0099] Step S202: Calculate the similarity between the variable and the central variable based on the vector of the variable and the vector of the central variable.

[0100] For example, the similarity between variables in an industrial control system is pre-calculated. For any variable in the industrial control system, a vector of the variable is constructed based on its variable attributes. These attributes may include variable name, type, screen ID (Identification), scope, value range, acquisition period, and address. The variable attributes are then encoded using a hash table to obtain the variable's vector. Based on the variable's vector and the vector of the central variable, the similarity between the variable and the central variable is calculated. The similarity can be calculated using the cosine similarity formula. For example, the cosine similarity formula is as follows:

[0101]

[0102] In the formula, m and n represent two different vectors, m i The i-th dimension of the representation vector m, n i The i-th dimension of vector n is represented by cosine_similarity(m, n), which represents the similarity between m and n.

[0103] Reference Figure 4 Based on the first and second embodiments of this application, a third embodiment of this application is proposed. In this embodiment, the parts that are the same as or similar to those in the above embodiments can be referred to the above content, and will not be repeated here. The step of optimizing the candidate variable subset to obtain the target variable subset based on the correlation influence between the candidate variables in the candidate variable subset and the candidate variable subset includes:

[0104] Step S310: The subset of candidate variables is used as the first candidate subset. For any second candidate variable in the subset of candidate variables, the second candidate variable is removed from the first candidate subset to generate the second candidate subset.

[0105] Step S320: Calculate the association performance of the second candidate subset, and use the decrease in the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable;

[0106] Step S331: If the correlation influence is less than the preset influence threshold, the second candidate subset is used as the new first candidate subset, and based on the new first candidate subset and the new second candidate variable, the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset is performed until all second candidate variables in the candidate variable subset are traversed.

[0107] Step S332: The first candidate subset determined after traversing each second candidate variable in the candidate variable subset is taken as the target variable subset.

[0108] For example, the optimization process for any subset of candidate variables is as follows: This subset of candidate variables is taken as the first candidate subset. Then, a second candidate variable is randomly selected from the first candidate subset, and this second candidate variable is removed from the first candidate subset to obtain the second candidate subset. The association performance of the second candidate subset is then evaluated. This association performance can be calculated based on the similarity between each second candidate variable in the second candidate subset and the central variable (i.e., the first candidate variable) in the second candidate subset. Generally, association performance is proportional to similarity; that is, the higher the similarity between the second candidate variable and the central variable, the higher the association performance of the candidate subset. The decrease in the association performance of the second candidate subset relative to the association performance of the first candidate subset is taken as the association influence of the second candidate variable. For example, if the first candidate subset has association performance A and the second candidate subset has association performance B, then (association performance A - association performance B) / association performance A = association influence. If the obtained correlation influence is less than the preset influence threshold, the second candidate subset can be used as the new first candidate subset, and the variables in the candidate subset can be simplified and optimized to reduce communication data redundancy. Based on the new first candidate subset and the new second candidate variables, the step of removing the second candidate variables from the first candidate subset to generate the second candidate subset is performed until all second candidate variables in the candidate variable subset are traversed. This indicates that the optimization of the candidate variable subset is complete. The first candidate subset finally determined after traversing all second candidate variables in the candidate variable subset is then used as the target variable subset.

[0109] In one feasible implementation, the step of calculating the association performance of the second candidate subset includes:

[0110] Step S321: Calculate the association performance of the second candidate subset based on the similarity between each second candidate variable in the second candidate subset and the central variable in the second candidate subset.

[0111] For example, the association performance of the second candidate subset is calculated based on the similarity between each second candidate variable in the second candidate subset and the central variable (i.e., the first candidate variable) in the second candidate subset. For instance, the cumulative sum of the similarities (or the sum of squares, the specific calculation method can be set by the technician according to actual needs) can be used as the association performance. In addition, the association performance calculation for the initial first candidate subset can also refer to the above process, and will not be repeated here.

[0112] In one feasible implementation, after the step of taking the performance decrease of the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable, the method includes:

[0113] Step S341: If the correlation influence degree is greater than or equal to the preset influence threshold, the second candidate subset will be discarded, and the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset will be performed based on the new second candidate variable until all second candidate variables in the candidate variable subset are traversed.

[0114] Step S342: Perform the step of taking the first candidate subset determined after traversing each second candidate variable in the candidate variable subset as the target variable subset.

[0115] For example, if the correlation influence is greater than or equal to a preset influence threshold, it indicates that the second candidate variable has a significant impact on the corresponding first candidate subset. Removing the second candidate variable may significantly affect the performance of variable preloading, so it needs to be retained. Then, based on the new variable to be acquired, the step of removing the variable to be acquired from the first candidate subset to generate the second candidate subset is performed until all second candidate variables in the candidate variable subset are traversed. Finally, the step of using the first candidate subset determined after traversing all second candidate variables in the candidate variable subset as the target variable subset is performed.

[0116] It should also be noted that the optimization process for any subset of candidate variables can refer to the above process, and will not be repeated here.

[0117] Reference Figure 5The diagram illustrates the framework of the industrial control data request method in this application. In the diagram, compilation refers to compiling based on the properties of controls on the screen to obtain a set of variables to be acquired. Related variable retrieval refers to retrieving related variables based on each first candidate variable in the set of variables to be acquired to generate different subsets of candidate variables, such as variable set 1, variable set 2, and variable set 3. Optimization is then performed on variable set 1, variable set 2, and variable set 3 to obtain target combination 1, target set 2, and target set 3, respectively. Frame assembly refers to framing based on the variables in each subset of target variables (such as target combination 1, target set 2, and target set 3) to request data.

[0118] To clearly illustrate this solution, assume that the current industrial control system contains variables LW0, LW1, LW2, and LW3, with the following attributes:

[0119] LW 0: screen_id=1; address=1000; type=int; area=lw;

[0120] LW 1: screen_id=2; address=1001; type=int; area=rw;

[0121] LW 2: screen_id=1; address=500; type=int; area=lw;

[0122] LW 3: screen_id=2; address=200; type=int; area=rw;

[0123] Vectorizing the variables above yields:

[0124] LW 0[1,1,1,1];

[0125] LW 1[0,1,1,0];

[0126] LW 2[1,0,1,1];

[0127] LW 3[0,0,1,0];

[0128] The similarity is calculated by performing similarity calculations on the vectors of any two variables respectively.

[0129] cosine_similarity(LW 0,LW 1)=0.7071;

[0130] cosine_similarity(LW 0,LW 2)=0.8660;

[0131] cosine_similarity(LW 0,LW 3)=0.5;

[0132] cosine_similarity(LW 1,LW 2)=0.4082;

[0133] cosine_similarity(LW 1,LW 3)=0.7071;

[0134] cosine_similarity(LW 2,LW 3)=0.5774;

[0135] Taking the candidate variable subset generated by variable LW 0 as an example, if the preset similarity threshold is 0.5, the candidate variable subset generated based on variable LW 0 will include {LW 0, LW 1, LW 2, LW 3}. Then, the candidate variable subset is optimized to obtain the target variable subset {LW 0, LW 1, LW 2}. Then, the variable data request can be generated based on the target variable subset.

[0136] In addition, refer to Figure 6 This application also proposes an industrial control data request device 100, which includes:

[0137] Generation module 10 is used to generate a set of variables to be acquired based on the display screen in the industrial control screen display requirements;

[0138] The partitioning module 20 is used to generate different subsets of candidate variables based on each variable in the set of variables to be acquired.

[0139] The optimization module 30 optimizes any candidate variable subset based on the correlation influence between the candidate variables in the candidate variable subset and the candidate variable subset itself to obtain a target variable subset, and generates a variable data request based on the target variable subset.

[0140] Optionally, the generation module 10 is further configured to:

[0141] The display screen in the industrial control screen display requirement is used as the target screen, or the display screen and the screen associated with the display screen are used as the target screen;

[0142] The set of variables to be acquired is generated based on the variables associated with the controls in the target screen.

[0143] Optionally, the candidate variables include first candidate variables and second candidate variables, the variables in the set of variables to be obtained are the first candidate variables, and the partitioning module 20 is further used for:

[0144] For any first candidate variable in the set of variables to be obtained, the first candidate variable is used as the central variable of a subset of the candidate variables.

[0145] The second candidate variable in the industrial control system whose similarity to the central variable is greater than or equal to a preset similarity threshold is selected as an element in the subset of candidate variables.

[0146] Optionally, the partitioning module 20 is further configured to:

[0147] For any variable in the industrial control system, construct a vector of the variable based on its variable attributes;

[0148] The similarity between the variable and the central variable is calculated based on the vector of the variable and the vector of the central variable.

[0149] Optionally, the optimization module 30 is further configured to:

[0150] The subset of candidate variables is used as the first candidate subset. For any second candidate variable in the subset of candidate variables, the second candidate variable is removed from the first candidate subset to generate the second candidate subset.

[0151] Calculate the association performance of the second candidate subset, and take the decrease in the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable;

[0152] If the correlation influence is less than the preset influence threshold, the second candidate subset is used as the new first candidate subset, and based on the new first candidate subset and the new second candidate variable, the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset is performed until all second candidate variables in the candidate variable subset are traversed.

[0153] The first candidate subset determined after traversing each second candidate variable in the candidate variable subset is taken as the target variable subset.

[0154] Optionally, the optimization module 30 is further configured to:

[0155] If the correlation influence is greater than or equal to the preset influence threshold, the second candidate subset will be discarded, and the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset will be performed based on the new second candidate variable until all second candidate variables in the candidate variable subset are traversed.

[0156] The step of taking the first candidate subset determined after traversing each second candidate variable in the candidate variable subset as the target variable subset is performed.

[0157] Optionally, the optimization module 30 is further configured to:

[0158] The association performance of the second candidate subset is calculated based on the similarity between each second candidate variable in the second candidate subset and the central variable in the second candidate subset.

[0159] The industrial control data request device provided in this application adopts the industrial control data request method in the above embodiments, aiming to solve the technical problem of low communication efficiency in traditional communication schemes, which affects the performance of the entire human-computer interaction system. Compared with the prior art, the beneficial effects of the industrial control data request device provided in this application are the same as those of the industrial control data request method provided in Embodiment 1 above, and other technical features in this industrial control data request device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0160] Furthermore, this application embodiment also proposes an industrial control data request device, which includes: a memory, a processor, and an industrial control data request program stored in the memory and executable on the processor. When the industrial control data request program is executed by the processor, it implements the steps of the industrial control data request method as described above.

[0161] The specific implementation of the industrial control data request device in this application is basically the same as the embodiments of the above-mentioned industrial control data request method, and can achieve the same technical effect, so it will not be described again here.

[0162] Furthermore, this application also proposes a medium, which is a computer-readable storage medium, on which an industrial control data request program is stored. When the industrial control data request program is executed by a processor, it implements the steps of the industrial control data request method described above.

[0163] The specific implementation of the medium in this application is basically the same as the embodiments of the above-mentioned industrial control data request method, and can achieve the same technical effect, so it will not be described again here.

[0164] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0165] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0166] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0167] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for requesting industrial control data, characterized in that, The industrial control data request method includes: Generate a set of variables to be acquired based on the display screen requirements of industrial control screens; Different subsets of candidate variables are generated based on each variable in the set of variables to be obtained; For any subset of candidate variables, the subset of candidate variables is optimized to obtain a target subset of variables based on the correlation influence between the candidate variables in the subset of candidate variables and the subset of candidate variables. A variable data request is then generated based on the target subset of variables. The correlation influence between the candidate variables in the subset of candidate variables and the subset of candidate variables is related to the similarity between the candidate variable and the central variable in the subset of candidate variables. The higher the similarity, the higher the correlation influence. The central variable is the variable in the variable set to be obtained when generating the subset of candidate variables.

2. The industrial control data request method as described in claim 1, characterized in that, The step of generating a set of variables to be acquired based on the display screen requirements of the industrial control screen includes: The display screen in the industrial control screen display requirement is used as the target screen, or the display screen and the screen associated with the display screen are used as the target screen; The set of variables to be acquired is generated based on the variables associated with the controls in the target screen.

3. The industrial control data request method as described in claim 1, characterized in that, The candidate variables include first candidate variables and second candidate variables, and the variables in the set of variables to be obtained are the first candidate variables. The step of generating different subsets of candidate variables based on each variable in the set of variables to be obtained includes: For any first candidate variable in the set of variables to be obtained, the first candidate variable is used as the central variable of a subset of the candidate variables. The second candidate variable in the industrial control system whose similarity to the central variable is greater than or equal to a preset similarity threshold is selected as an element in the subset of candidate variables.

4. The industrial control data request method as described in claim 3, characterized in that, Before the step of including a second candidate variable in the industrial control system with a similarity greater than or equal to a preset similarity threshold as an element in the subset of candidate variables, the method includes: For any variable in the industrial control system, construct a vector of the variable based on its variable attributes; The similarity between the variable and the central variable is calculated based on the vector of the variable and the vector of the central variable.

5. The industrial control data request method as described in claim 1, characterized in that, The step of optimizing the candidate variable subset to obtain the target variable subset based on the correlation influence between the candidate variables in the candidate variable subset and the candidate variable subset includes: The subset of candidate variables is used as the first candidate subset. For any second candidate variable in the subset of candidate variables, the second candidate variable is removed from the first candidate subset to generate the second candidate subset. Calculate the association performance of the second candidate subset, and take the decrease in the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable; If the correlation influence is less than the preset influence threshold, the second candidate subset is used as the new first candidate subset, and based on the new first candidate subset and the new second candidate variable, the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset is performed until all second candidate variables in the candidate variable subset are traversed. The first candidate subset determined after traversing each second candidate variable in the candidate variable subset is taken as the target variable subset.

6. The industrial control data request method as described in claim 5, characterized in that, After the step of taking the performance decrease of the association performance of the second candidate subset relative to the association performance of the first candidate subset as the association influence of the second candidate variable, the method includes: If the correlation influence is greater than or equal to the preset influence threshold, the second candidate subset will be discarded, and the step of removing the second candidate variable from the first candidate subset to generate the second candidate subset will be performed based on the new second candidate variable until all second candidate variables in the candidate variable subset are traversed. The step of taking the first candidate subset determined after traversing each second candidate variable in the candidate variable subset as the target variable subset is performed.

7. The industrial control data request method as described in claim 5, characterized in that, The step of calculating the association performance of the second candidate subset includes: The association performance of the second candidate subset is calculated based on the similarity between each second candidate variable in the second candidate subset and the central variable in the second candidate subset.

8. An industrial control data request device, characterized in that, The industrial control data request device includes: The generation module is used to generate a set of variables to be acquired based on the display screen in the industrial control screen display requirements; The partitioning module is used to generate different subsets of candidate variables based on each variable in the set of variables to be acquired. The optimization module optimizes any candidate variable subset based on the correlation influence between the candidate variables in the subset and the subset itself to obtain a target variable subset. It then generates a variable data request based on the target variable subset. The correlation influence between the candidate variables in the subset and the subset itself is related to the similarity between the candidate variable and the central variable in the subset; higher similarity results in higher correlation influence. The central variable is the variable in the variable set to be obtained when generating the candidate variable subset.

9. An industrial control data request device, characterized in that, The industrial control data request device includes: a memory, a processor, and an industrial control data request program stored in the memory and executable on the processor. When the industrial control data request program is executed by the processor, it implements the steps of the industrial control data request method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium is a computer-readable storage medium, and the readable storage medium stores an industrial control data request program. When the industrial control data request program is executed by a processor, it implements the steps of the industrial control data request method as described in any one of claims 1 to 7.

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