Variable multiplexing method in PLC programming based on MVC model

Abstract

The invention discloses a method for variable multiplexing in PLC programming based on an MVC model, and relates to the technical field of PLC programming, in the variable processing process of PLC programming, position index data of variable data in the MVC model is deeply analyzed, the position relation of variables in a grouping structure can be accurately mastered, and the processing efficiency of the PLC programming is improved. Therefore, many remarkable advantages are brought to the whole programming process, for example, in the data organization and management process, the index position information of the variable groups where the variable data are located is obtained from the database, a specific calculation formula is applied, the layout situation of the variables in the grouping structure can be comprehensively and meticulously described, and the programming efficiency is improved. In the process of determining the dependency sequence of the variable data types, the dependency relationship between the data types is clarified according to the operation of the function on the variable data, the logic relationship between the data is strictly followed in the data processing flow, and errors caused by data dependency disorder are avoided.

Description

Technical Field

[0001] The invention relates to the technical field of logic controller PLC programming, and in particular to a method for reusing variables in PLC programming based on an MVC model. Background Art

[0002] The efficiency and quality of PLC programming are directly related to the operating efficiency and stability of the production system. In traditional PLC programming methods, there are many problems in variable management and reuse, such as data redundancy, high code complexity, and difficult maintenance.

[0003] Prior art, such as the invention patent application with announcement number CN113688136B, discloses a PLC variable table grouping method and system based on the MVC model, including: establishing the structure of the variable table, including: abstracting a common base class QMyDataType, deriving multiple basic data types from the base class, providing optional data types for the child nodes of the data items in the data type table, and storing the data of the variable table in an instance of the base class QMyUserType; deriving the grouping class from QMyUserType as a data type, the base class of QMyUserType has an isGroup attribute, which is set to 1 only when a group is added, and creating a QMyDataType object for each group added to the variable table, and inserting it into m_varRootType; creating a model associated with the grouped data, the variable table maintains a variable of a QMap container, and establishes a mapping between the group name and its model. When a user requests to view the grouped data, only the group name is displayed on the project tree, and the group model is obtained according to the group name and associated with the view class; traversing the rootType, storing it in a hierarchical xml format, and parsing the xml file to generate the rootType when reading.

[0004] There are at least the following technical problems with the above scheme: 1. The above scheme lacks in-depth analysis of the positional relationship of PLC variable data in the grouping structure, which will make it difficult to accurately locate and effectively manage variable data during variable processing and reuse. In a complex PLC programming environment, there is a lack of analysis of positional relationships, such as the calculation and consideration of relative column position influence factors, relative row position influence factors, and depth influence coefficients. When it comes to large-scale variable data processing, it is impossible to judge its importance and relevance based on its relative position in the grouping structure, which will cause confusion between key variables and non-key variables, thereby affecting the formulation of data serialization strategies.

[0005] 2. The above scheme lacks the evaluation of the dependency order of variable data types, which will lead to the inability to determine a reasonable data operation order in the variable data processing flow. Since the above scheme lacks the construction of the dependency matrix and the calculation of the dependency strength index, it is impossible to determine which variable data types should be processed first and which should be processed later, which will cause errors during data updating, calculation or transmission.

[0006] 3. The above scheme lacks detailed consideration and verification evaluation of the data serialization process, which will lead to the inability to guarantee the quality and reliability of data serialization. It also lacks the division of key data and non-key data according to the position relationship of variables and the setting of weight factors and calculation of data verification evaluation indexes, making it impossible to perform targeted processing according to the importance and characteristics of the data during data serialization, which will lead to waste of storage space or inefficient data transmission. Summary of the invention

[0007] The purpose of the present invention is to provide a method for reusing variables in PLC programming based on the MVC model, which solves the problems existing in the background technology.

[0008] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a method for variable reuse in PLC programming based on the MVC model, including: S1, analyzing the position index data of the variable data in the MVC model, and then evaluating the position relationship of each variable data in the grouping structure.

[0009] S2. According to the positional relationship of the variable data in the grouping structure, locate the initial variable data type information corresponding to each variable data, and then calculate the dependency strength index corresponding to each variable data type, thereby evaluating the dependency order corresponding to each variable data type.

[0010] S3. Serialize the variable data according to the dependency order corresponding to each variable data type.

[0011] S4. After the variable data has completed data serialization, it is evaluated whether the variable data can be written to the clipboard.

[0012] The beneficial effects of the present invention are: 1. A method for reusing variables in PLC programming based on the MVC model provided by the present invention, in the variable processing process of PLC programming, through in-depth analysis of the position index data of the variable data in the MVC model, is conducive to accurately grasping the position relationship of the variables in the grouping structure, thereby bringing many significant advantages to the entire programming process. For example, in the process of data organization and management, by obtaining the index position information of the variable grouping where each variable data is located from the database, including the column position, row position, total number of columns, total number of rows and parent node index level, and using a specific calculation formula, it is conducive to comprehensively and meticulously depicting the layout of the variables in the grouping structure.

[0013] 2. In the process of determining the dependency order of variable data types, the embodiment of the present invention locates the initial variable data type information corresponding to each variable data, including the number of data type types, dependency matrix elements and complexity weights of the data type, and constructs a dependency matrix. The dependency relationship between data types is clarified based on the function's operation on the variable data, and the dependency strength index is calculated. This is beneficial for strictly following the logical relationship between data in the data processing flow and avoiding errors caused by confusion in data dependencies.

[0014] 3. In the data serialization process, the embodiment of the present invention divides the variable data into key data and non-key data according to the dependency order of the variable data type and the position relationship of the variable in the grouping structure, and sets the weight factor to calculate the number of key data blocks and the number of non-key data blocks. At the same time, the data verification evaluation index is calculated by the characteristic function and compared with the standard control data verification evaluation index to determine whether the serialization meets the requirements. This is beneficial for data storage and transmission, and can not only perform reasonable block division according to the importance of data to improve storage and transmission efficiency, but also ensure the accuracy of data serialization through the verification evaluation index. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic flow chart of the implementation steps of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, the present invention provides a method for variable reuse in PLC programming based on the MVC model, the method comprising: S1, analyzing the position index data of the variable data in the MVC model, and then evaluating the position relationship of each variable data in the grouping structure.

[0019] In a specific embodiment, the position index data of the analysis variables in the MVC model is specifically processed as follows: the position index data includes column index, row index and parent node index, and the index position information corresponding to the variable group where each variable data is located is obtained from the database. The index position information includes column position, row position, total number of columns, total number of rows, and parent node index level, and is recorded as , , , and , and then respectively through the calculation formula: , and , get the relative column position influence factor corresponding to each variable data , Relative row position influence factor and depth influence coefficient , and by the calculation formula: , and obtain the corresponding average correlation index between each variable data , is the number corresponding to each variable data, , is the total number corresponding to the variable data, Is a positive integer.

[0020] It should be noted that in similar structures such as the PLC variable grouping system under the MVC model, data is usually organized in a tree structure. The parent node index level is used to describe a node, which refers to the node corresponding to the variable data. The depth of the position in the tree structure is the number of layers from the root node.

[0021] In a specific embodiment, the position relationship of each variable data in the grouping structure is evaluated, and the specific evaluation process is as follows: according to the relative column position influence factor, relative row position influence factor, depth influence coefficient and average correlation index corresponding to each variable data, and then through the calculation formula: , get the position evaluation index of each variable data in the grouping structure ,in , , , They are respectively expressed as the weight factor corresponding to the relative column position influence factor, the weight factor corresponding to the relative row position influence factor, the weight factor corresponding to the depth influence coefficient, and the weight factor corresponding to the average correlation index between variable data.

[0022] The corresponding position area information in the grouping structure is obtained from the database. The position area information includes the core position, transition position and edge position. The core position, transition position and edge position respectively correspond to the standard position evaluation index threshold interval. The position evaluation index corresponding to each variable data in the grouping structure is compared with the standard position evaluation index threshold interval. If the position evaluation index corresponding to a variable data in the grouping structure belongs to the standard position evaluation index threshold interval corresponding to the core position, it indicates that the position of the variable data in the grouping structure is the core position, so as to obtain the position relationship of each variable data in the grouping structure.

[0023] It should be noted that , , , The values ​​of are all greater than and less than For example, for a PLC system that monitors the temperature and pressure data of a production process, if the core business logic pays more attention to the processing order of column data, such as arranging data by different sensor columns, the weight factor corresponding to the relative column position influence factor is set to 0.4, and the relative row position influence factor weight is set to 0.2. Because the impact of row data is relatively small, the depth influence coefficient weight is set to 0.3. If the core data is at a shallow level, the depth weight is high, and the average correlation index weight is set to 0.1. If the data correlation has little impact on the overall logic, such as a system where the data storage structure is stored in rows and focuses on deep hierarchical logical relationships, the depth influence coefficient weight is increased to 0.4, and the relative column position influence factor weight is reduced to 0.2.

[0024] It should also be noted that, for example, in a certain PLC variable grouping system, there are variables A and B, the relative row position ratio of variable A is 0.3, the relative column position ratio is 0.6, and the depth index is 2, and the relative row position ratio of variable B is 0.4, the relative column position ratio is 0.6, and the depth index is 2. This means that A and B are in similar positions in the column direction in the grouping at the same depth level. Assuming that the relative column position ratio is calculated from left to right, they are both in a relatively right position, and A is above B in the vertical direction.

[0025] S2. According to the positional relationship of the variable data in the grouping structure, locate the initial variable data type information corresponding to each variable data, and then calculate the dependency strength index corresponding to each variable data type, thereby evaluating the dependency order corresponding to each variable data type.

[0026] In a specific embodiment, the initial variable data type information corresponding to each variable data is located, and the specific process is as follows: the initial variable data type information includes the number of data type types, the dependent matrix elements and the complexity weight of the data type, and the number of data type types corresponding to each variable data is obtained from the database. , and record each data type as , and then create The dependency matrix , the dependency matrix The rows and columns correspond to data type, will depend on the matrix All elements corresponding to the initial state Set to numeric value , obtain the functions, methods and operation statements in the PLC program code from the database, and then obtain the data type of the input variable data corresponding to each function. When the data type of the variable data corresponding to the input of a function is , if during the execution of this function, the data type used is The variable data completes calculation, assignment and comparison operations, indicating the data type Dependent data type ,Right now , otherwise, if the data type Not dependent on data type ,but ,in Indicates the row index and the corresponding data type , Indicates the row index and the corresponding data type , .

[0027] It should be noted that the data type used is The variable data to complete the calculation, assignment and comparison operations refers to the use of data types for different data types when the function is executed. Variable data is used to perform calculations, assignments, and comparisons. Variable data is determined by mathematical operations. Variable data is a computational operation based on The data is Assigning a variable a specific value is an assignment operation, which is done with Data and Comparison and judgment of data to determine subsequent behavior is a comparison operation.

[0028] In a specific embodiment, the calculation of the dependency strength index corresponding to each variable data type is performed as follows: , get the dependency intensity index corresponding to each variable data type , Represented as a dependency matrix Complexity correction factor corresponding to the column index.

[0029] It should be noted that The value of is a positive number. For example, in the PLC-controlled elevator system, there are "car load data type" and "floor stop data type". When setting the complexity correction factor, the complexity of data processing is considered. The "car load data type" is mainly a simple weighing comparison, and the correction factor is set to 1. The "floor stop data type" considers complex factors such as car load and call sequence, and the correction factor is set to 3 for a more accurate assessment of dependency intensity.

[0030] In a specific embodiment, the specific process of evaluating the dependency order corresponding to each variable data type is as follows: according to the dependency strength index corresponding to each variable data type, the dependency strength index corresponding to each variable data type is sorted in order from large to small, and the variable data type corresponding to the first-ranked dependency strength index is recorded as the first variable data type, and the variable data type corresponding to the second-ranked dependency strength index is recorded as the second variable data type, so as to obtain the dependency order corresponding to each variable data type.

[0031] In the process of determining the dependency order of variable data types, the embodiment of the present invention locates the initial variable data type information corresponding to each variable data, including the number of data type types, dependency matrix elements and complexity weights of the data type, and constructs a dependency matrix. The dependency relationship between data types is clarified based on the function's operation on the variable data, and the dependency strength index is calculated. This is beneficial for strictly following the logical relationship between data in the data processing flow and avoiding errors caused by confusion in data dependencies.

[0032] S3. Serialize the variable data according to the dependency order corresponding to each variable data type.

[0033] In a specific embodiment, the variable data corresponding to each variable data type is serialized, and the specific process is as follows: when the variable data corresponding to each variable data type is serialized, the data is serialized according to the dependency order corresponding to each variable data type, the total amount of variable data to be serialized is obtained from the database, and according to the position relationship of each variable data in the grouping structure, the variable data to be serialized is divided into key data and non-key data, the key data is the variable data at the core position and the transition position, and the non-key data is the variable data at the edge position, and the key data weight factor value is set and non-critical data weight factor values , thereby calculating the number of key data blocks and the number of non-key data blocks, and then judging whether the data serialization of the variable data meets the requirements.

[0034] It should be noted that , The values ​​of are all greater than and less than , , The setting process and , , , The setting process is the same as , so I will not go into details here.

[0035] In a specific embodiment, the specific process of calculating the number of key data blocks and the number of non-key data blocks is as follows: , , get the number of key data blocks , Number of non-critical data blocks ,in and The storage space capacity of the data blocks corresponding to the key data and the storage space capacity of the data blocks corresponding to the non-key data are respectively Indicates rounding up.

[0036] By calculating the formula , get the data verification evaluation index of the variable data corresponding to the data serialization , and Represented as key data blocks and non-key data blocks respectively. is the number corresponding to each key data block, , is the number corresponding to each non-critical data block, , and Key data blocks and non-critical data blocks The corresponding characteristic function.

[0037] It should be noted that the storage space capacity occupied by the data blocks corresponding to the key data and the storage space capacity occupied by the data blocks corresponding to the non-key data are obtained from the database.

[0038] It should also be noted that, for example, in a PLC-controlled factory production monitoring system, product quality inspection data is used as the key data block, and equipment operation time data is used as the non-key data block. For the key data block, set indivual, , characteristic function of key data blocks Defined by whether the product quality is qualified. If the quality test value is within the standard range, then If unqualified , Indicates the relevant value of product quality inspection. For non-critical data blocks, it is set to indivual, , characteristic function Determine whether the equipment operation time exceeds the regular maintenance period. If not, , if it exceeds , is the value of the equipment operation time, assuming that the weight factor of the key data block is 0.7 and the weight factor of the non-key data block is 0.3. If there are 4 key data blocks, and the quality inspection results are qualified, qualified, unqualified, and qualified respectively, and 3 non-key data blocks, and the operation time is within the maintenance period, exceeded the maintenance period, and did not exceed the maintenance period respectively, then: .

[0039] In a specific embodiment, the process of determining whether the data serialization performed on the variable data meets the requirements is as follows: obtaining the standard control data verification evaluation index corresponding to the data serialization process from the database, and comparing the data verification evaluation index of the data serialization corresponding to the variable data with the standard control data verification evaluation index; if the data verification evaluation index is greater than the standard control data verification evaluation index, it indicates that the data serialization performed on the variable data meets the requirements; otherwise, it indicates that the data serialization performed on the variable data does not meet the requirements.

[0040] In the data serialization process, the embodiment of the present invention divides the variable data into key data and non-key data according to the dependency order of the variable data type and the position relationship of the variable in the grouping structure, and sets the weight factor to calculate the number of key data blocks and the number of non-key data blocks. At the same time, the data verification evaluation index is calculated by the characteristic function and compared with the standard control data verification evaluation index to determine whether the serialization meets the requirements. This is beneficial for data storage and transmission, and can not only perform reasonable block division according to the importance of data to improve storage and transmission efficiency, but also ensure the accuracy of data serialization through the verification evaluation index.

[0041] S4. After the variable data has completed data serialization, it is evaluated whether the variable data can be written to the clipboard.

[0042] In a specific embodiment, the specific process of evaluating whether the variable data can be written to the clipboard is as follows: the storage space capacity occupied by the data block corresponding to the critical data and the storage space capacity occupied by the data block corresponding to the non-critical data are added to obtain the storage space capacity occupied by the variable data, the effective capacity of the clipboard is obtained from the database, and the storage space capacity occupied by the variable data is compared with the effective capacity of the clipboard. If the storage space capacity occupied by the variable data is less than the effective capacity of the clipboard, the variable data can be written to the clipboard; otherwise, it indicates that the variable data cannot be written to the clipboard.

[0043] The present invention provides a method for reusing variables in PLC programming based on the MVC model. In the variable processing process of PLC programming, in-depth analysis of the position index data of variable data in the MVC model is performed, which is conducive to accurately grasping the position relationship of the variables in the grouping structure, thereby bringing many significant advantages to the entire programming process. For example, in the data organization and management process, by obtaining the index position information of the variable grouping where each variable data is located from the database, including the column position, row position, total number of columns, total number of rows and parent node index level, and using a specific calculation formula, it is conducive to comprehensively and meticulously depicting the layout of the variables in the grouping structure.

[0044] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A method for reusing variables in PLC programming based on the MVC model, characterized in that: include: S1, by analyzing the position index data of the variable data in the MVC model, and then evaluating the position relationship of each variable data in the grouping structure; S2. According to the position relationship of the variable data in the grouping structure, locate the initial variable data type information corresponding to each variable data, and then calculate the dependency strength index corresponding to each variable data type, thereby evaluating the dependency order corresponding to each variable data type; S3, serializing the variable data according to the dependency order corresponding to each variable data type; S4. After the variable data has completed data serialization, it is evaluated whether the variable data can be written to the clipboard.

2. The method for reusing variables in PLC programming based on the MVC model according to claim 1, characterized in that: The position index data of the analysis variable in the MVC model is as follows: The position index data includes column index, row index and parent node index. The corresponding index position information in the variable group where each variable data is located is obtained from the database. The index position information includes column position, row position, total number of columns, total number of rows, and parent node index level, and is recorded as , , , and , and then respectively through the calculation formula: , and , get the relative column position influence factor corresponding to each variable data , Relative row position influence factor and depth influence coefficient , and by the calculation formula: , and obtain the corresponding average correlation index between each variable data , is the number corresponding to each variable data, , is the total number corresponding to the variable data, Is a positive integer.

3. The method for reusing variables in PLC programming based on the MVC model according to claim 2 is characterized in that: The specific evaluation process of evaluating the position relationship of each variable data in the grouping structure is as follows: According to the relative column position influence factor, relative row position influence factor, depth influence coefficient and average correlation index corresponding to each variable data, the calculation formula is as follows: , get the position evaluation index corresponding to each variable data in the grouping structure ,in , , , They are respectively expressed as the weight factor corresponding to the relative column position influence factor, the weight factor corresponding to the relative row position influence factor, the weight factor corresponding to the depth influence coefficient, and the weight factor corresponding to the average correlation index between variable data; The corresponding position area information in the grouping structure is obtained from the database. The position area information includes the core position, transition position and edge position. The core position, transition position and edge position respectively correspond to the standard position evaluation index threshold interval. The position evaluation index corresponding to each variable data in the grouping structure is compared with the standard position evaluation index threshold interval. If the position evaluation index corresponding to a variable data in the grouping structure belongs to the standard position evaluation index threshold interval corresponding to the core position, it indicates that the position of the variable data in the grouping structure is the core position, so as to obtain the position relationship of each variable data in the grouping structure.

4. The method for reusing variables in PLC programming based on the MVC model according to claim 3 is characterized in that: The specific process of locating the initial variable data type information corresponding to each variable data is as follows: The initial variable data type information includes the number of data types, dependent matrix elements, and the complexity weight of the data type. The number of data types corresponding to each variable data is obtained from the database. , and record each data type as , and then create The dependency matrix , the dependency matrix The rows and columns correspond to data type, will depend on the matrix All elements corresponding to the initial state Set to numeric value , obtain the functions, methods and operation statements in the PLC program code from the database, and then obtain the data type of the input variable data corresponding to each function. When the data type of the variable data corresponding to the input of a function is , if during the execution of this function, the data type used is The variable data completes calculation, assignment and comparison operations, indicating the data type Dependent data type ,Right now , otherwise, if the data type Not dependent on data type ,but ,in Indicates the row index and the corresponding data type , Indicates the row index and the corresponding data type , .

5. The method for reusing variables in PLC programming based on the MVC model according to claim 4 is characterized in that: The specific process of calculating the dependency strength index corresponding to each variable data type is as follows: By calculating the formula , get the dependency strength index corresponding to each variable data type , Represented as a dependency matrix Complexity weight factor corresponding to the column index.

6. The method for reusing variables in PLC programming based on the MVC model according to claim 5, characterized in that: The specific process of evaluating the dependency order corresponding to each variable data type is as follows: According to the dependency strength index corresponding to each variable data type, the dependency strength index corresponding to each variable data type is sorted in order from large to small, and the variable data type corresponding to the first-ranked dependency strength index is recorded as the first variable data type, and the variable data type corresponding to the second-ranked dependency strength index is recorded as the second variable data type, so as to obtain the dependency order corresponding to each variable data type.

7. The method for reusing variables in PLC programming based on the MVC model according to claim 6, characterized in that: The variable data corresponding to each variable data type is serialized, and the specific process is as follows: When serializing the variable data corresponding to each variable data type, the data is serialized according to the dependency order corresponding to each variable data type, and the total amount of variable data for data serialization is obtained from the database. According to the position relationship of each variable data in the grouping structure, the variable data for serialization is divided into key data and non-key data. The key data is the variable data in the core position and the transition position, and the non-key data is the variable data in the edge position, and the key data weight factor value is set. and non-critical data weight factor values , thereby calculating the number of key data blocks and the number of non-key data blocks, and then judging whether the data serialization of the variable data meets the requirements.

8. The method for reusing variables in PLC programming based on the MVC model according to claim 7, characterized in that: The specific process of calculating the number of key data blocks and the number of non-key data blocks is as follows: By calculating the formula , , get the number of key data blocks , Number of non-critical data blocks ,in and The storage space capacity of the data blocks corresponding to the key data and the storage space capacity of the data blocks corresponding to the non-key data are respectively It is rounded up; By calculating the formula , get the data verification evaluation index of the variable data corresponding to the data serialization , and Respectively expressed as The key data block and Non-critical data blocks, , , and Key data blocks and non-critical data blocks The corresponding characteristic function.

9. The method for reusing variables in PLC programming based on the MVC model according to claim 8, characterized in that: The specific process of judging whether the data serialization of the variable data meets the requirements is as follows: Obtain the corresponding standard control data verification evaluation index in the data serialization process from the database, and compare the data verification evaluation index of the data serialization corresponding to the variable data with the standard control data verification evaluation index. If the data verification evaluation index is greater than the standard control data verification evaluation index, it indicates that the data serialization of the variable data meets the requirements; otherwise, it indicates that the data serialization of the variable data does not meet the requirements.

10. The method for reusing variables in PLC programming based on the MVC model according to claim 9, characterized in that: The evaluation variable data can be written into the clipboard. The specific process is as follows: Add the storage space capacity occupied by the data block corresponding to the key data and the storage space capacity occupied by the data block corresponding to the non-key data to obtain the storage space capacity occupied by the variable data, obtain the effective capacity of the clipboard from the database, and compare the storage space capacity occupied by the variable data with the effective capacity of the clipboard. If the storage space capacity occupied by the variable data is less than the effective capacity of the clipboard, the variable data can be written to the clipboard. Otherwise, it indicates that the variable data cannot be written to the clipboard.

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