Data processing method, data processing device, computer equipment and storage medium

Abstract

The invention discloses a data processing method, a data processing device, computer equipment and a storage medium. The method comprises the steps that an equipment data point table is obtained, the data format of data points of the equipment data point table is a preset data format, and the preset data format comprises a plurality of preset parameters; based on preset parameters, classifying the data points to determine data types of the data points; and based on the data points corresponding to the data types, respectively generating data sets corresponding to the data types. Therefore, the format of the equipment data point table is relatively standard, so that automatic classification and conversion of the data points can be performed subsequently. Meanwhile, compared with a method of manually converting the data points of the equipment data point table into the data of each data type of the scheduling system, the method can realize automatic classification and conversion of the data points and generate the data set corresponding to each data type, so that automatic data development can be realized, the data development efficiency is improved, and the data development cost is reduced. And the error rate is greatly reduced.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and more specifically, to a data processing method, a data processing device, a computer equipment, and a non-volatile computer-readable storage medium. Background Art

[0002] At present, a certain industry may develop subsystems based on various businesses or functions, and use an integrated dispatching system to uniformly monitor the equipment status of each subsystem. However, in the actual development of the integrated dispatching system, data development is a very arduous task. Due to the characteristics of the integrated dispatching system, the number of external subsystems and equipment connected is huge, and the number of data points that need to be developed is as high as hundreds of thousands or even millions. At the same time, the format of the equipment data point table provided by different customers may be different, making it difficult to use automated methods for data development. Therefore, at present, the equipment data point table provided by the external system is usually manually converted and manually entered according to the template provided by the configuration tool to complete the data development, which will result in low data development efficiency and accuracy. Summary of the invention

[0003] Embodiments of the present application provide a data processing method, a data processing apparatus, a computer device, and a non-volatile computer-readable storage medium.

[0004] The data processing method of the implementation mode of the present application includes obtaining a device data point table, wherein the data format of the data points in the device data point table is a preset data format, and the preset data format includes multiple preset parameters; based on the preset parameters, each of the data points is classified to determine the data type of each of the data points; based on the data points corresponding to each of the data types, a data set corresponding to each of the data types is generated respectively.

[0005] In some embodiments, the preset parameters include at least parameter type information, and the classification of each of the data points based on the preset parameters to determine the data type of each of the data points includes: obtaining a point type standard library corresponding to each of the data types, the point type standard library including each preset parameter type; matching the parameter type information and the preset parameter type of each of the point type standard libraries to determine the point type standard library to which the parameter type information belongs; and classifying each of the data points based on the point type standard library to which the parameter type information belongs to determine the data type of each of the data points.

[0006] In some embodiments, the point type standard library also includes associated parameters of each of the preset parameter types, and the method also includes: determining the target parameters of each of the data points based on the parameter type information of each of the data points, the target parameters being determined based on the associated parameters of the preset parameter type corresponding to the parameter type information; generating data sets corresponding to each of the data types based on the data points corresponding to each of the data types, including: generating data sets corresponding to each of the data types based on the parameter information matching the target parameters in the data points corresponding to each of the data types.

[0007] In some embodiments, the method further includes: performing data verification on the data points to determine the verification results of each of the data points, the data verification including at least one of a character type verification, a data legality verification, and a data validity verification, and the verification results including a verification pass and a verification fail; obtaining the data points corresponding to each data type preset by the scheduling system includes: based on the data points corresponding to each of the data types, the data points that have passed the verification, respectively generating data sets corresponding to each of the data types.

[0008] In some embodiments, the method further includes: performing data verification on the data points to determine the verification results of each of the data points, the data verification including at least one of a character type verification, a data legality verification, and a data validity verification, and the verification results including a verification pass and a verification fail; obtaining the data points corresponding to each data type preset by the scheduling system includes: based on the data points corresponding to each of the data types, the data points that have passed the verification, respectively generating data sets corresponding to each of the data types.

[0009] In some embodiments, the method further includes: performing standardization processing on each parameter information in the data point so that each parameter information is consistent with a standard parameter format of a corresponding parameter value of the preset parameter.

[0010] In some implementations, the method further includes: calling a data injection interface of a scheduling system to inject the data points corresponding to each of the data types into the scheduling system.

[0011] In certain embodiments, the method further includes: generating a media file corresponding to each data type based on the data points corresponding to each data type; calling the data injection interface of the scheduling system to inject the data points corresponding to each data type into the scheduling system, including: calling the data injection interface of the scheduling system to inject each media file into the scheduling system.

[0012] In some embodiments, the method further includes: performing a version difference comparison on the data set corresponding to the current device data point table and the data set corresponding to the historical device data point table to determine difference information, wherein the difference information includes parameter information of differences between the data points with the same parameter type.

[0013] In some embodiments, the data types include digital input points, digital output points, digital input and output points, analog input points, analog output points, analog input and output points, and field points.

[0014] The data processing device of the embodiment of the present application includes an acquisition module, a classification module and a first generation module. The acquisition module is used to acquire a device data point table, the data format of the data point in the device data point table is a preset data format, and the preset data format includes multiple preset parameters. The classification module is used to classify each of the data points based on the preset parameters to determine the data type of each of the data points. The first generation module is used to generate data sets corresponding to each of the data types based on the data points corresponding to each of the data types.

[0015] The computer device of an embodiment of the present application includes a processor, a memory and a computer program, wherein the computer program is stored in the memory and executed by the processor, and the computer program includes instructions for executing the data processing method described in any of the above embodiments.

[0016] The non-volatile computer-readable storage medium of the embodiment of the present application includes a computer program. When the computer program is executed by a processor, the processor executes the data processing method described in any of the above embodiments.

[0017] The data processing method, data processing device, computer equipment and computer-readable storage medium of the implementation mode of the present application normalize the equipment data point table so that the data format of the equipment data point table provided by the external system is a preset data format, which provides conditions for the subsequent automatic classification and conversion of data points. Since the preset data format contains multiple preset parameters, after obtaining the equipment data point table, by identifying the preset parameters (such as the corresponding data types of the preset parameters), each data point can be classified based on the preset parameters to determine the data type of each data point. Then, based on the data points corresponding to each data type, the data sets corresponding to each data type are generated respectively, thereby completing the data development, and the scheduling system can be developed based on the data sets in the future.

[0018] Thus, compared to the method that the format of the equipment data point table provided by the external system is diverse and cannot be automatically classified, the format of the equipment data point table of the present application is relatively standardized, so that the automatic classification and conversion of the data points can be performed later. At the same time, compared to the method that requires manual conversion of the data points of the equipment data point table into data of various data types preset by the scheduling system, the present application can realize the automatic classification and conversion of data points, and generate data sets corresponding to various data types, so that data automation development can be realized, thereby improving the efficiency of data development and greatly reducing the error rate.

[0019] Additional aspects and advantages of the embodiments of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0021] Figure 1 It is a scenario schematic diagram of a data processing method of certain embodiments of the present application;

[0022] Figure 2 is a flowchart of a data processing method of certain embodiments of the present application;

[0023] Figure 3 It is a scenario schematic diagram of a data processing method of certain embodiments of the present application;

[0024] Figure 4 is a flowchart of a data processing method of certain embodiments of the present application;

[0025] Figure 5 It is a scenario schematic diagram of a data processing method of certain embodiments of the present application;

[0026] Figure 6 is a flowchart of a data processing method of certain embodiments of the present application;

[0027] Figure 7 is a flowchart of a data processing method of certain embodiments of the present application;

[0028] Figure 8 is a flowchart of a data processing method of certain embodiments of the present application;

[0029] Fig. 9 is a flowchart of a data processing method of certain embodiments of the present application;

[0030] Fig.10is a flowchart of a data processing method of certain embodiments of the present application;

[0031] Fig.11 is a flowchart of a data processing method of certain embodiments of the present application;

[0032] Fig.12 is a schematic diagram of a module of a data processing device in some embodiments of the present application;

[0033] Fig.13 is a schematic diagram of the structure of a computer device of certain embodiments of the present application;

[0034] Fig.14 It is a schematic diagram of the connection status of a non-volatile computer-readable storage medium and a processor in certain embodiments of the present application. DETAILED DESCRIPTION

[0035] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of the present application, and cannot be understood as limiting the embodiments of the present application.

[0036] To facilitate understanding of this application, the terms appearing in this application are explained below:

[0037] Analog Input (AI): used to receive external continuously changing analog signals, such as temperature, pressure, flow, etc. These signals can be processed and analyzed by the control system after being converted into digital signals by the analog-to-digital converter.

[0038] Analog Output (AO): used to send analog control signals to adjust external devices or processes. For example, a continuously changing voltage or current signal can be output to control the position or speed of an actuator.

[0039] Analog Input and Output (AIO): A point with analog input and analog output functions.

[0040] Digital Input (DI): used to receive external digital signals, such as sensor output signals, button switch status, etc. These signals are usually binary, that is, there are only two states, such as on or off, 1 or 0. In industrial automation systems, DI points are often used to reflect the status of equipment or processes.

[0041] Digital Output (DO): Used to send digital control signals to control external devices or processes. These signals are also binary and can control the actions of switches, valves, motors, etc.

[0042] Digital Input and Output (DIO): A point that has both digital input and digital output functions.

[0043] FieldPoint (FP): In a control system, FieldPoint usually refers to a modular, distributed I / O (input / output) system.

[0044] At present, a certain industry may develop subsystems based on various businesses or functions, and use a comprehensive dispatching system to uniformly monitor the equipment status of each subsystem. For example, the current urban rail industry generally uses a dispatching system to uniformly monitor the equipment status of each subsystem and issue corresponding remote control, telemetry and other control instructions. The subsystems mainly include the Passenger Information System (PIS), Automatic Fare Collection (AFC), Environment and Equipment Monitoring System (BAS), Power Supervisory Control And Data Acquisition (PSCADA), Fire Alarm System (FAS), and the dispatching system can communicate data with each subsystem.

[0045] However, in the actual development of the scheduling system, data development is a very arduous task. The data types of the internal data of the scheduling system include AI, AO, AIO, DI, DO, DIO and FP. However, the data types of the equipment data point table of the external system provided by the customer are obviously not these data types. Therefore, it is also necessary to convert the data types of the data points in the equipment data point table. Due to the characteristics of the scheduling system, the number of external subsystems and equipment connected is huge, and the number of data points that need to be developed is as high as hundreds of thousands or even millions. The formats of the equipment data point tables provided by different customers may be different, resulting in the inability to perform automated conversion. For example Figure 1In traditional technology, configuration tools are commonly used to develop data for integrated dispatching systems. The basic principle of this technology is to manually convert the equipment data point table provided by the external system and manually enter it according to the template provided by the configuration tool. This will lead to low data development efficiency. For large quantities of data, it is extremely labor-intensive and time-consuming, and the error rate is high, which is not conducive to the actual development of large projects. The solution of using configuration tools for data development in the integrated dispatching system has the following shortcomings:

[0046] 1. Low development efficiency. The use of configuration tools for data production is actually to manually convert the data point table provided by the external system and manually enter it according to the template of the configuration tool. It does not take advantage of the automation tool and the data development and production efficiency is low.

[0047] 2. High error rate and difficult maintenance. Since it mainly relies on manual conversion and manual input, the error rate will increase significantly when faced with massive data. Once an error occurs in data entry, it is very difficult to troubleshoot and maintain.

[0048] 3. High labor and time costs. Manual conversion and entry of batch data will undoubtedly increase labor costs. At the same time, in the face of repeated entry work, the error rate is high and the troubleshooting is difficult, which further wastes time. The high labor and time costs are not conducive to the research and development of large projects.

[0049] In order to solve the above technical problems, an embodiment of the present application provides a data processing method.

[0050] The data processing method of this application will be described in detail below:

[0051] See also Figure 2 The present application embodiment provides a data processing method, which includes:

[0052] Step 011: obtaining a device data point table, wherein the data format of the data points in the device data point table is a preset data format, and the preset data format includes a plurality of preset parameters;

[0053] Specifically, data development refers to the process of integrating, cleaning, converting, storing and analyzing data from different sources. It can be understood that the data development process of the dispatching system requires the integration, cleaning, conversion, storage and analysis of data from various external systems connected to the dispatching system. After the data development is completed, the function development of the dispatching system is carried out based on the developed data.

[0054] The equipment data point table usually refers to a table used to record and describe various data points of the equipment during the operation of the equipment. These data points represent the function, status or other key information of the equipment. The preset data template includes multiple preset parameters required for the subsequent development of the scheduling system, such as parameter type information and associated parameters of each preset parameter type. The parameter type information is the type of the data point, such as pressure or temperature. The associated parameters are the characteristics or properties of the data point, which describe various aspects of the data point. For example, the associated parameters include data type, format, range and accuracy. For example, when the parameter type information of the data point is the operating temperature, its associated parameters may include default values, upper and lower limits of the operating value, display accuracy and engineering units. Of course, the data point may also include the parameter name of each data point to facilitate the control of the subsequent scheduling system. For example, the parameter type information of data point A is the operating temperature, and the parameter name is T1. For example, the parameter type information of data point B is the set temperature, and the parameter name is T0. Then, when the function is developed later or the scheduling system is completed and officially put into operation, the scheduling system and the external system can interact according to the parameter name. For example, the external system can feedback the signal of T1=26℃ to the scheduling system to feedback the operating temperature of the external system to the scheduling system, or the scheduling system can send the signal of T0=24℃ to the external system to control the temperature of the external system.

[0055] At this time, the preset data format of the preset data template can be set according to the preset parameters, and the preset data template can be provided to the customer. The customer can determine the required data and fill it out according to the preset data template and the specific situation of the external system to generate the device data point table corresponding to the external system. It can be understood that at this time, the data format of the data point of the device data point table is the preset data format of the preset data template, so that the data point includes the parameter value corresponding to each preset parameter. In this way, the format of the device data point table sent by the external system can be standardized as much as possible to facilitate subsequent data conversion, creating conditions for the automation of subsequent data classification and conversion.

[0056] Therefore, when developing data for the dispatching system, each external system connected to the dispatching system will send a corresponding device data point table. At this time, the data format of the device data point table matches the preset data template, so that a device data point table with a relatively standardized format can be obtained to obtain data information of the external system that needs to be developed, so that the data points of each external system can be automatically classified and converted later. Of course, when obtaining the device data point table, the device data point table can also be scanned for file security to determine whether the device data point table is damaged, and subsequent operations can be performed after confirming that the device data point table is not damaged.

[0057] Step 012: Classify each data point based on preset parameters to determine the data type of each data point;

[0058] Specifically, the data types preset by the dispatching system include digital input points (DI), digital output points (DO), digital input and output points (DIO), analog input points (AI), analog output points (AO), analog input and output points (AIO) and field points (FP). The subsequent dispatching system controls the system according to the data of these data types. Obviously, these data types will not be included in the preset data format, so at this time, each data point in the device data point table can be classified to determine the data type of each data point. For example, each data point in the device data point table has corresponding parameter type information, and the correspondence between the parameter type information and the data type in the data point can be preset. For example, the operating temperature is an analog input point, and the switch quantity is a digital input point. Then, the parameter type information in the device data point table is obtained, and the data type of the data point corresponding to the parameter type information is determined according to the preset correspondence. Of course, other associated parameters of the data point can also be used to determine the data type of the data point, which is not limited here.

[0059] Step 013: Based on the data points corresponding to each data type, generate data sets corresponding to each data type.

[0060] Specifically, after determining the data type of each data point, a data set corresponding to each data type can be generated based on the data points corresponding to each data type. For example, a corresponding summary table can be set for each data type, and according to the data type of each data point, the parameter information of the data point, such as the parameter type information and associated parameters of the data point, is filled into the corresponding summary table, thereby obtaining a summary table corresponding to each data type. In this way, the data automation development of each external system is completed, and the function development of the scheduling system can be carried out based on the data set to complete the development of the scheduling system, so that the scheduling system can uniformly monitor the equipment status of each external system and issue corresponding remote control, telemetry and other control instructions.

[0061] For example Figure 3 , this application can set a preset data template according to the needs of data development, and the external system fills in the data according to the preset data template and sends the device data point table. At this time, this application can automatically identify and convert the data points in the device data point table to determine the data type corresponding to each data point, and convert the data points in the device data point table into data of the data type required for development to generate a data set. After the data set is generated, other operations can be performed, such as error screening, that is, character type verification, or data verification, that is, data legitimacy verification and data validity verification, or version difference comparison. Then, batch underlying injection is performed based on the generated data set to inject the data set into the scheduling system, so that the scheduling system can perform subsequent development based on the data set.

[0062] In this way, compared with the existing method of manually classifying and converting the data points in the equipment data point table provided by the external system, or manually entering the data according to the template provided by the configuration tool, this application can automatically classify each data point and generate a data set corresponding to each data type to complete data development. Obviously, in the case of manual classification or conversion of data, if faced with massive equipment data, it is bound to increase labor costs; at the same time, in the face of repetitive and tedious conversion and entry work, as the amount of data doubles, the error rate will also increase significantly, and the difficulty of investigation will increase, which consumes manpower and is not conducive to the research and development of large-scale projects. However, this application can automatically identify and convert data points in the equipment data point table with a relatively standardized format, which can greatly improve the efficiency of data development, save a lot of manpower and time costs, and provide safe and reliable technical guarantees for the research and development of large-scale projects and the development of millions of data.

[0063] The data processing method of the embodiment of the present application standardizes the equipment data point table so that the data format of the equipment data point table provided by the external system is a preset data format, which provides conditions for the subsequent automatic classification and conversion of data points. Since the preset data format contains multiple preset parameters, after obtaining the equipment data point table, by identifying the preset parameters (such as the corresponding data types of the preset parameters), each data point can be classified based on the preset parameters to determine the data type of each data point. Then, based on the data points corresponding to each data type, the data sets corresponding to each data type are generated respectively, thereby completing the data development, and the scheduling system can be developed based on the data sets in the future.

[0064] Thus, compared to the method that the format of the equipment data point table provided by the external system is diverse and cannot be automatically classified, the format of the equipment data point table of the present application is relatively standardized, so that the automatic classification and conversion of the data points can be performed later. At the same time, compared to the method that requires manual conversion of the data points of the equipment data point table into data of various data types preset by the scheduling system, the present application can realize the automatic classification and conversion of data points, and generate data sets corresponding to various data types, so that data automation development can be realized, thereby improving the efficiency of data development and greatly reducing the error rate.

[0065] See also Figure 4 In some embodiments, the preset parameters include at least parameter type information. Step 012: Classifying each data point based on the preset parameters to determine the data type of each data point includes:

[0066] Step 0121: Obtain a point type standard library corresponding to each data type, the point type standard library including each preset parameter type;

[0067] Step 0122: matching the parameter type information with the preset parameter types of each point type standard library to determine the point type standard library to which the parameter type information belongs;

[0068] Step 0123: Classify each data point based on the point type standard library to which the parameter type information belongs to, so as to determine the data type of each data point.

[0069] Specifically, please combine Figure 5 , you can set the corresponding point type standard library for each data type, and the point type standard library includes various preset parameter types. For example, set the AI ​​point type standard library and the DI point type standard library, where the preset parameter types corresponding to the AI ​​point type standard library include temperature and pressure, and the preset parameter types corresponding to the DI point type standard library include switch quantity.

[0070] After receiving the device data point table, the point type standard library to which the parameter type information belongs is determined based on the parameter type information corresponding to the data point and the preset parameter types of each point type standard library. For example, the preset parameter type corresponding to each point type standard library is obtained, and the parameter type information corresponding to the data point is searched therein. The point type standard library whose preset parameter type includes the parameter type information corresponding to the data point is the point type standard library to which the parameter type information belongs. Then, the data type of each data point can be determined based on the data type of the point type standard library to which the parameter type information belongs. For example, the parameter type information of data point A is temperature, and the parameter type information of data point B is switch quantity. The preset parameter types corresponding to the AI ​​point type standard library include temperature, and the preset parameter types corresponding to the DI point type standard library include switch quantity. Then it can be determined that the data type of data point A is AI point, and the data type of data point B is DI point.

[0071] In this way, the mapping relationship between the data type and the parameter type information can be determined according to the point type standard library, so as to accurately classify each data point and obtain the accurate data type of each data point.

[0072] See also Figure 6 In some embodiments, the point type standard library further includes associated parameters of each preset parameter type, and the data processing method further includes:

[0073] Step 014: determining the target parameter of each data point based on the parameter type information of each data point, wherein the target parameter is determined based on the associated parameter of the preset parameter type corresponding to the parameter type information;

[0074] Step 013: Based on the data points corresponding to each data type, generate data sets corresponding to each data type, including:

[0075] Step 0131: Based on the parameter information matching the target parameters in the data points corresponding to each data type, respectively generate data sets corresponding to each data type.

[0076] Specifically, the point type standard library also includes associated parameters of each preset parameter type, and the associated parameters can be considered as information of data points corresponding to the preset parameter types required in the development process of the scheduling system.

[0077] After determining the point type standard library corresponding to each data point, the target parameter of the data point can be determined according to the associated parameters of the preset parameter type corresponding to the parameter type information corresponding to the data point in the point type standard library corresponding to the data point. In this way, the target parameter of each data point can be determined according to the parameter type information of each data point. Then, the parameter information matching the target parameter in each data point can be obtained. It can be understood that the parameter information matching the target parameter is the information of the data point required in the development process of the scheduling system. Then, the data set corresponding to each data type is generated according to the parameter information matching the corresponding target parameter in the data point corresponding to each data type. At this time, the data set includes the parameter type information and target parameter of the corresponding data point.

[0078] For example, the AI ​​point type standard library includes the associated parameters corresponding to temperature and the associated parameters corresponding to pressure. After determining that the data points corresponding to temperature and the data points corresponding to pressure are AI points, the target parameters of the data points corresponding to temperature and the target parameters of the data points corresponding to pressure can be determined according to the point type standard library. Then, based on the target parameters of the data points corresponding to temperature and the target parameters of the data points corresponding to pressure, a data set corresponding to the AI ​​point is generated. At this time, the data set corresponding to the AI ​​point includes the parameter type information and target parameters of the data points corresponding to temperature, and the parameter type information and target parameters of the data points corresponding to pressure.

[0079] In this way, the parameter information required to match the target parameters in the data point can be determined according to the associated parameters in the point type standard library, and a data set can be generated to facilitate the subsequent smooth completion of the development of the scheduling system according to the target parameters in the data set.

[0080] See also Figure 7 In some embodiments, the data processing method further comprises:

[0081] Step 015: Perform data verification on the data points to determine the verification results of each data point, the data verification includes at least one of character type verification, data legality verification and data validity verification, and the verification results include verification pass and verification fail;

[0082] Step 013: Based on the data points corresponding to each data type, generate data sets corresponding to each data type, including:

[0083] Step 0132: Based on the data points corresponding to each data type and the data points that have passed the verification, generate data sets corresponding to each data type respectively.

[0084] Specifically, the data points in the device data point table may be incorrect, such as incorrect character type, or the value of a parameter of the data point exceeds the normal value range of the parameter. These incorrect data points will obviously affect subsequent development. For example, the normal range of a parameter is 0-100, but the value in the device data point table is 111.

[0085] Therefore, after the data points are classified, data verification can be performed on the data points, such as at least one of character type verification, data legitimacy verification and data validity verification, to determine whether the data points are correct. The legitimacy of the data means that the data must meet the requirements of the application and meet given or implicit conditions. This legitimacy is usually based on the constraints of the data dictionary, including data type, value range, data unit, data precision, etc. Data validity verification includes multiple aspects such as data format verification, data range verification, data logic verification, data integrity verification and data consistency verification.

[0086] After the data verification is completed, the verification results can be obtained, including the verification passed and the verification failed. Then, the data sets corresponding to each data type can be generated based on the data points corresponding to each data type, which have passed the verification, to ensure the data accuracy of the data set, so as to ensure that the subsequent development of the scheduling system can be successfully completed based on the data set. At this time, the data points that failed the verification can also be displayed to facilitate developers to check the data points that failed the verification.

[0087] In another embodiment, after obtaining the parameter information matching the target parameter in the data points corresponding to each data type, data verification can be performed on the data points, and the verification results of each data point can be obtained. Then, based on the parameter information matching the target parameter in the data points corresponding to each data type, the data sets corresponding to each data type are generated respectively. That is, the data sets are generated according to the parameter information that has passed the verification. In this way, the accuracy of the parameter information can be ensured, so as to ensure that the development of the scheduling system can be successfully completed in the future based on the data sets with correct data.

[0088] See also Figure 8 In some embodiments, the data processing method further comprises:

[0089] Step 016: Standardize each parameter information in the data point so that each parameter information is consistent with the standard parameter format of the parameter value of the corresponding preset parameter.

[0090] Specifically, the standard parameter format is the parameter format of the parameter value required during system development. In the case where the parameter type information of the data point is inconsistent, the standard parameter format of the parameter value of the corresponding preset parameter may be different. For example, in the case where the preset parameters are both engineering units, the standard parameter format of the data point with parameter type information of temperature is Celsius, and the standard parameter format of the data point with parameter type information of pressure is Pascal.

[0091] The equipment data point table is provided by the customer. The parameter format of the parameter information in the equipment data point table may be different from the parameter format required during development. For example, for the default value of the operating temperature, the standard parameter format can be a numerical value in digital form, such as 26°C, while the default value in the equipment data point table is a textual value, such as twenty-six degrees. Therefore, it is necessary to standardize the various parameter information so that the various parameter information of the data point is consistent with the standard parameter format of the parameter value of the preset parameter to facilitate subsequent development. For example, the preset parameters of the equipment data point table include engineering units. When developing the scheduling system, Celsius is used as the engineering unit for development, while the engineering unit in the equipment data point table is Fahrenheit. At this time, it is necessary to convert the various parameter information of the data point according to the conversion relationship between Celsius and Fahrenheit.

[0092] Standardization can be performed before or after classification. In one embodiment, after obtaining the device data point table, each parameter information in the data point table is standardized to ensure smooth subsequent development. In another embodiment, the standard parameter format of the parameter value can also be stored in the point type standard library, that is, the point type standard library includes the associated parameters of each preset parameter type and the standard parameter format of each associated parameter. When determining the parameter information matching the target parameter in the data point according to the target parameter, the parameter information matching the target parameter is also standardized according to the standard parameter format of the associated parameter to ensure smooth subsequent development.

[0093] See also Fig. 9 In some embodiments, the data processing method further comprises:

[0094] Step 017: Call the data injection interface of the scheduling system to inject data points corresponding to each data type into the scheduling system.

[0095] Specifically, the dispatching system is provided with a data injection interface to receive data from an external system connected to the dispatching system, so as to facilitate the subsequent functional development of the dispatching system based on the data from the external system. Therefore, after generating a data set corresponding to the external system, the data injection interface of the dispatching system can be called to inject data points corresponding to each data type into the dispatching system, so that the development of the dispatching system can be completed based on the data points corresponding to each data type of the external system, and it is also convenient for the subsequent dispatching system to monitor and control the external system according to the data points.

[0096] See also Fig.10 In some embodiments, the data processing method further comprises:

[0097] Step 018: Generate a medium file corresponding to each data type based on the data points corresponding to each data type;

[0098] Step 017: Call the data injection interface of the scheduling system to inject data points corresponding to each data type into the scheduling system, including:

[0099] Step 0171: Call the data injection interface of the scheduling system to inject various media files into the scheduling system.

[0100] Specifically, data injection can be completed in the form of a media file, and the media file can be generated based on the information required for development and the data points corresponding to each data type. At this time, the media file corresponding to each data type can be generated based on the data points corresponding to each data type. In one embodiment, the media file may include a media file for the data points corresponding to each data type, so as to summarize the data points corresponding to a data type in one media file, such as generating an AI media file, which stores data points of each data type as AI, so as to facilitate the scheduling system to obtain the information of the data points of the external system corresponding to the media file, and then facilitate the scheduling system to control and monitor the external system according to the data type of the data point. Figure 5 The DBUnit_AIO.csv, DBUnit_DIO.csv and DBUnit_FP.csv in the figure can all be media files storing data points of corresponding data types. In another embodiment, a media file related to the control operation can be generated. For example, a media file corresponding to the control channel of each data point can be generated based on the data points corresponding to each data type, so that the subsequent scheduling system can control the external system based on the control channel in the media file. Figure 5 The DBUnit_Base.csv, DBUnit_Method, DBUnit_Script and DBUnit_FP.csv in the file can all be medium files corresponding to other information required during development except for the data points under each data type.

[0101] After the media files are generated, the data injection interface of the scheduling system can be called to inject each media file into the scheduling system. For example, the underlying database method can be used to batch inject each media file into the scheduling system. At this time, the scheduling system can automatically complete data import, registration or loading, so that the scheduling system functions can be developed and the external system can be monitored and controlled based on the injected media files.

[0102] In this way, media files can be generated according to the data points corresponding to each data type, and data injection can be completed according to the media files, so that batch injection of data can be realized by using media files, thereby improving data injection efficiency. After the data injection is completed, the scheduling system can obtain various information of the external system corresponding to the media file based on the media file, so as to facilitate the development of the scheduling system's functions and the monitoring and control of the external system.

[0103] See also Fig.11 In some embodiments, the data processing method further comprises:

[0104] Step 019: Perform version difference comparison on the data set corresponding to the current device data point table and the data set corresponding to the historical device data point table to determine difference information, where the difference information includes parameter information of differences between data points with the same parameter type.

[0105] Specifically, for the same external system, the device data point table may be submitted multiple times. In different device data point tables, data points with the same parameter type may be different, that is, the specific parameter information of the same data point may change. For example, the default value of temperature is a different value in the device data point table submitted twice. Parameter information is crucial to subsequent development. For example, the parameters of a function need to be determined based on parameter information. If the parameter information changes, the parameters of the function need to be modified accordingly.

[0106] Therefore, after the data set is generated, the data set corresponding to the current device data point table and the data set corresponding to the historical device data point table can be compared for version differentiation. It can be understood that the data set corresponding to the historical device data point table is the data that has been injected into the scheduling system. The data that has not been injected into the scheduling system does not affect the development of the scheduling system, so the version differentiation comparison can be omitted to reduce the amount of calculation for the version differentiation comparison. After the differentiation comparison, the parameter information that differs between the data points with the same parameter type can be determined to determine the difference information. At this time, the difference information can be displayed to facilitate developers to determine the difference between the data set corresponding to the current device data point table and the data set corresponding to the historical device data point table, and perform certain modification operations according to the difference information during the development process, such as modifying the parameters of the function corresponding to the data point with differences. If there is no difference between the data set corresponding to the current device data point table and the data set corresponding to the historical device data point table, the difference information can be empty or no difference, and the conventional system development operation can be performed according to the current device data point table.

[0107] by Figure 5For example, in some embodiments, the implementation process of the present application can be executed by the development system corresponding to the present application, and the development system includes a parsing factory, a point type standard library, a verification component and a data injection module. After the external system outputs the device data point table, the parsing factory can obtain the device data point table, and determine the data type of each data point based on the point type standard library corresponding to each data type, and then generate data sets corresponding to each data type based on the data points corresponding to each data type, that is, the parsing factory can execute the above steps 011-014 at this time. When generating a data set, the parsing factory can also perform standardization processing to ensure that the parameter information in the generated data set meets the requirements of subsequent system development, that is, the parsing factory can also execute the above step 016. After generating the data set, the verification component can perform data verification and version difference comparison on the data set, that is, the verification component can execute the above steps 015 and 019 to ensure that the subsequent parsing factory can generate a data set with accurate data, and display the difference information between the data set of the current device data point table and the data set of the historical device data point table, so as to facilitate subsequent development. Next, the parsing factory can generate a data set with correct data according to the verification result of the verification component, and then generate a media file based on the data set, that is, the parsing factory can execute the above step 018 at this time. Finally, the data injection module can inject the media file into the scheduling system to realize batch injection of data and facilitate the scheduling system to complete development according to the media file, that is, the data injection module can execute the above step 017. In this way, according to the parsing factory, the point type standard library, the verification component and the data injection module, the above data processing method can be executed, so that the media file corresponding to the external system can be automatically injected into the scheduling system without manual participation, so as to realize the automation of data development, thereby avoiding a series of problems caused by manual data development, making the error rate of data development lower and the efficiency higher.

[0108] See also Fig.12 In order to better implement the data processing method of the embodiment of the present application, the embodiment of the present application also provides a data processing device 10. The data processing device 10 may include an acquisition module 11, a classification module 12 and a first generation module 13. The acquisition module 11 is used to acquire a device data point table, the data format of the data point in the device data point table is a preset data format, and the preset data format includes multiple preset parameters; the classification module 12 is used to classify each data point based on the preset parameters to determine the data type of each data point; the first generation module 13 is used to generate data sets corresponding to each data type based on the data points corresponding to each data type.

[0109] The classification module 12 is specifically used to obtain a point type standard library corresponding to each data type, the point type standard library including each preset parameter type; match the parameter type information with the preset parameter type of each point type standard library to determine the point type standard library to which the parameter type information belongs; based on the point type standard library to which the parameter type information belongs, classify each data point to determine the data type of each data point.

[0110] The data processing device 10 further includes a determination module 14, which is used to determine the target parameters of each data point based on the parameter type information of each data point, and the target parameters are determined based on the associated parameters of the preset parameter type corresponding to the parameter type information. The first generation module 13 is specifically used to generate data sets corresponding to each data type based on the parameter information matching the target parameter in the data points corresponding to each data type.

[0111] The data processing device 10 further includes a verification module 15, which is used to perform data verification on the data points to determine the verification results of each data point, the data verification includes at least one of character type verification, data legitimacy verification and data validity verification, and the verification results include verification passed and verification failed. The first generation module 13 is specifically used to generate data sets corresponding to each data type based on the data points corresponding to each data type that have passed the verification.

[0112] The data processing device 10 further includes a standardization processing module 16, which is used to perform standardization processing on each parameter information in the data point so that each parameter information is consistent with the standard parameter format of the parameter value of the corresponding preset parameter.

[0113] The data processing device 10 further includes a scheduling module 17, which is used to call a data injection interface of the scheduling system to inject data points corresponding to each data type into the scheduling system.

[0114] The data processing device 10 also includes a second generation module 18, which is used to generate a media file corresponding to each data type based on the data points corresponding to each data type. The scheduling module 17 is specifically used to call the data injection interface of the scheduling system to inject each media file into the scheduling system.

[0115] The data processing device 10 also includes a comparison module 19, which is used to perform version difference comparison on the data set corresponding to the current device data point table and the data set corresponding to the historical device data point table to determine difference information, including parameter information of differences between data points with the same parameter type.

[0116] In the above text, the data processing device 10 is described from the perspective of the functional module in conjunction with the accompanying drawings. The functional module can be implemented in the form of hardware, can be implemented by instructions in the form of software, or can be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit in the processor and / or the instructions in the form of software. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware coding processor to execute, or can be executed by a combination of hardware and software modules in the coding processor. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps in the above method embodiment in conjunction with its hardware.

[0117] See also Fig.13 The computer device 100 of the embodiment of the present application includes a processor 20, a memory 30 and a computer program, wherein the computer program is stored in the memory 30 and executed by the processor 20, and the computer program includes instructions for executing the data processing method of any of the above embodiments.

[0118] See also Fig.14 The embodiment of the present application also provides a computer-readable storage medium 300 on which a computer program 310 is stored. When the computer program 310 is executed by a processor 320, the steps of the data processing method of any of the above-mentioned embodiments are implemented. For the sake of brevity, they are not repeated here.

[0119] In the description of this specification, the descriptions with reference to the terms "certain embodiments", "in an example", "exemplarily", etc., mean that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0120] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0121] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire a device data point table, wherein the data format of the data points in the device data point table is a preset data format, and the preset data format includes a plurality of preset parameters; Based on the preset parameters, classify each of the data points to determine the data type of each of the data points; Based on the data points corresponding to the data types, data sets corresponding to the data types are generated respectively.

2. The data processing method according to claim 1, characterized in that: The preset parameters at least include parameter type information, and the classifying each of the data points based on the preset parameters to determine the data type of each of the data points includes: Acquire a point type standard library corresponding to each of the data types, wherein the point type standard library includes each preset parameter type; Matching the parameter type information with the preset parameter types of each of the point type standard libraries to determine the point type standard library to which the parameter type information belongs; Based on the point type standard library to which the parameter type information belongs, each of the data points is classified to determine the data type of each of the data points.

3. The data processing method according to claim 2, characterized in that: The point type standard library also includes associated parameters of each of the preset parameter types, and the method further includes: Determine the target parameter of each of the data points based on the parameter type information of each of the data points, the target parameter being determined based on an associated parameter of the preset parameter type corresponding to the parameter type information; Generating data sets corresponding to the data types based on the data points corresponding to the data types respectively includes: Based on the parameter information matching the target parameter in the data points corresponding to the data types, data sets corresponding to the data types are generated respectively.

4. The data processing method according to claim 1 or 3, characterized in that: Also includes: Performing data verification on the data points to determine verification results of the data points, wherein the data verification includes at least one of a character type verification, a data legality verification, and a data validity verification, and the verification results include a verification pass and a verification fail; Generating data sets corresponding to the data types based on the data points corresponding to the data types respectively includes: Based on the data points that have passed verification among the data points corresponding to the data types, data sets corresponding to the data types are generated respectively.

5. The data processing method according to any one of claims 1 to 4, characterized in that: Before classifying each of the data points based on the preset parameters, the method further includes: Each parameter information in the data point is standardized so that each parameter information is consistent with the standard parameter format of the corresponding parameter value of the preset parameter.

6. The data processing method according to claim 1, characterized in that: The method further comprises: The data injection interface of the scheduling system is called to inject the data points corresponding to each of the data types into the scheduling system.

7. The data processing method according to claim 6, characterized in that: The method further comprises: Generate a media file corresponding to each data type based on the data points corresponding to each data type; The calling of the data injection interface of the scheduling system to inject the data points corresponding to each of the data types into the scheduling system includes: The data injection interface of the scheduling system is called to inject each of the media files into the scheduling system.

8. The data processing method according to claim 6 or 7, characterized in that: The method further comprises: A version difference comparison is performed on the data set corresponding to the current device data point table and the data set corresponding to the historical device data point table to determine difference information, wherein the difference information includes parameter information of differences between the data points with the same parameter type.

9. The data processing method according to claim 1, characterized in that: The data types include digital input points, digital output points, digital input and output points, analog input points, analog output points, analog input and output points and field points.

10. A data processing device, characterized in that: The device comprises: An acquisition module, used for acquiring a device data point table, wherein the data format of the data points in the device data point table is a preset data format, and the preset data format includes a plurality of preset parameters; A classification module, used for classifying each of the data points based on the preset parameters to determine the data type of each of the data points; The first generating module is used to generate data sets corresponding to each of the data types based on the data points corresponding to each of the data types.

11. A computer device, characterized in that: include: Processor, memory; and A computer program, wherein the computer program is stored in the memory and executed by the processor, and the computer program includes instructions for executing the data processing method according to any one of claims 1 to 9.

12. A non-volatile computer-readable storage medium containing a computer program, wherein when the computer program is executed by a processor, the processor executes the data processing method according to any one of claims 1 to 9.

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