Data processing system and data processing method

Abstract

This disclosure provides a data processing system and a data processing method. It can be applied to the fields of industrial automation control and industrial data processing technology. The system includes: a data acquisition and communication layer, configured to acquire data from a programmable logic controller according to a sampling triggering strategy matching the tag parameters, to obtain the raw data of the tag parameters; a parameter parsing model layer, including: a parameter object encapsulation module, configured to encapsulate the raw data and processing strategy into parameter objects according to a standardized parameter structure, to obtain structured intermediate data; a parameter parsing module, configured to process the parameter fields based on the processing strategy in the intermediate data, to obtain the processing result; and a structured parameter output module, configured to determine the target child node of the parameter model tree based on the source information in the intermediate data, and update the node data of the target child node based on the processing result.

Description

Technical Field

[0001] This disclosure relates to the fields of industrial automation control and industrial data processing technology, and more specifically, to a data processing system and a data processing method. Background Technology

[0002] In existing industrial automation systems, programmable logic controllers (PLCs) serve as the core control unit, containing numerous tag parameters that describe equipment operating status, process variables, control signals, and equipment attributes. These PLCs are the core data source for industrial data acquisition and monitoring. Currently, mainstream PLC brands and communication protocols exhibit significant differences in the organization, data types, access methods, and attribute definitions of these tag parameters, leading to numerous technical bottlenecks in the acquisition and processing of parameters from various types of PLCs in industrial settings. Summary of the Invention

[0003] In view of this, the present disclosure provides a data processing system and a data processing method.

[0004] One aspect of this disclosure provides a data processing system, comprising:

[0005] The data acquisition and communication layer is configured to acquire data from the programmable logic controller according to a sampling triggering strategy that matches the tag parameters, and obtain the raw data of the tag parameters. The raw data includes the parameter fields of the tag parameters and source information. The source information indicates the source of the tag parameters. The tag parameters include at least one of the following: control signals sent by the programmable logic controller to the industrial equipment, equipment status information of the industrial equipment, equipment attribute information of the programmable logic controller, equipment attribute information of the industrial equipment, and attribute information of the tag parameters.

[0006] The parameter parsing model layer includes:

[0007] The parameter object encapsulation module is configured to encapsulate the raw data and processing strategy into parameter objects according to a standardized parameter structure, thereby obtaining structured intermediate data. The processing strategy is used to process the parameter fields of the tag parameters.

[0008] The parameter parsing module is configured to process parameter fields based on the processing strategy in the intermediate data to obtain the processing result;

[0009] The structured parameter output module is configured to determine the target child node of the parameter model tree based on the source information in the intermediate data, and update the node data of the target child node based on the processing result. The parameter model tree includes a root node and child nodes with a subordinate relationship. The root node represents the programmable logic controller, and the child nodes represent the label parameters of the programmable logic controller. The child nodes include the node data of the label parameters.

[0010] According to embodiments of this disclosure, the parameter parsing model layer further includes:

[0011] The attribute filtering module is configured to extract the initial parameter fields from the original data to obtain the parameter fields;

[0012] The parameter type determination module is configured to validate the parameter type of the parameter field based on the field attribute information of the parameter field and obtain the validation result.

[0013] The parameter object encapsulation module is configured to obtain the processing strategy when the verification result indicates that the original data is normal, and encapsulate the parameter fields, source information and processing strategy into structured intermediate data.

[0014] According to embodiments of this disclosure, the parameter parsing model layer further includes:

[0015] The sampling trigger strategy generation module is configured as follows:

[0016] The sampling trigger type is determined based on the parameter type of the tag parameter;

[0017] When the sampling trigger type includes the periodic trigger type, the sampling period of the tag parameter is determined based on the historical parameter field set of the tag parameter; and the sampling trigger strategy is determined based on the sampling period.

[0018] When the sampling trigger type includes a state trigger type, the sampling trigger strategy is determined based on the trigger state information of the tag parameter.

[0019] According to embodiments of this disclosure, the parameter field includes parameter values, and the sampling triggering strategy generation module determines the sampling period of the tag parameters based on the historical parameter field set of the tag parameters, including:

[0020] Determine the sequence of historical parameter values ​​for the target historical period adjacent to the current time period from the set of historical parameter values;

[0021] Based on the historical parameter value sequence, determine the parameter fluctuation amplitude value in the target historical period; and

[0022] The sampling period is obtained by adaptively adjusting the reference sampling period based on the parameter fluctuation amplitude and the maximum value in the historical parameter value set.

[0023] According to embodiments of this disclosure, the parameter parsing model layer further includes:

[0024] The modeling module is configured to determine the root node representing the programmable logic controller (PLC) based on the device layer of the data acquisition and communication layer. In the case that the PLC includes multiple tag parameters, it determines the first child node representing the data block and the second child node representing the tag parameter. The root node with a subordinate relationship is connected to the first child node, and the first child node with a subordinate relationship is connected to the second child node to obtain the parameter model tree.

[0025] According to embodiments of this disclosure, the system further includes:

[0026] The application service layer is configured to respond to a query request sent by the user layer, send the query request to the structured parameter output module, receive the query result sent by the structured parameter output module, and feed the query result back to the user layer. The query request includes the query source information of the queried tag parameter, and the query result is determined by the structured parameter output module based on the node data of the query child node corresponding to the query source information on the parameter model tree.

[0027] According to embodiments of this disclosure, the processing result includes the processed parameter field of the tag parameter;

[0028] The structured parameter output module updates the node data of the target child nodes based on the processing results, including:

[0029] The processed parameter fields and the sampling timestamps of the label parameters are added to the label parameter data column in the data record table of the target child node to obtain the updated node data;

[0030] The structured parameter output module is also configured to determine the query sub-nodes that match the query source information from the parameter model tree; based on the queried label parameters included in the query request, determine the target query column from the data record table of the node data included in the query sub-node; based on the queried sampling time in the query request, determine the target query field from the target query column to obtain the query result.

[0031] According to embodiments of this disclosure, the data acquisition and communication layer includes:

[0032] The acquisition module is configured to establish communication with the programmable logic controller (PLC) using a communication protocol that matches the PLC, and to send sampling instructions to the PLC according to the sampling triggering strategy, and receive raw data sent by the PLC in response to the sampling instructions.

[0033] The communication module is configured to send raw data to the parameter object encapsulation module.

[0034] According to embodiments of this disclosure, the system includes:

[0035] When the parameter type of the label parameter includes continuous type, the processing strategy includes at least one of the following: adding units, dimensional conversion, filtering, range conversion, and noise reduction;

[0036] When the parameter type of the label parameter includes discrete type, the processing strategy includes at least one of the following: adding units, dimensional conversion, parameter type description, format conversion, range conversion, and noise reduction.

[0037] Another aspect of this disclosure provides a data processing method, including:

[0038] This system is applied to data processing systems, which include a data acquisition and communication layer and a parameter parsing model layer. The parameter parsing model layer includes a parameter object encapsulation module, a parameter parsing module, and a structured parameter module. The methods include:

[0039] The data acquisition and communication layer acquires data from the programmable logic controller according to a sampling triggering strategy that matches the tag parameters, and obtains the raw data of the tag parameters. The raw data includes the parameter fields of the tag parameters and source information. The source information indicates the source of the tag parameters. The tag parameters include at least one of the following: control signals sent by the programmable logic controller to the industrial equipment, equipment status information of the industrial equipment, equipment attribute information of the programmable logic controller, equipment attribute information of the industrial equipment, and attribute information of the tag parameters.

[0040] The parameter object encapsulation module encapsulates the raw data and processing strategy into parameter objects according to the standardized parameter structure, resulting in structured intermediate data. The processing strategy is used to process the parameter fields of the tag parameters.

[0041] The parameter parsing module processes the parameter fields based on the processing strategy in the intermediate data to obtain the processing results;

[0042] The structured parameter output module determines the target child node of the parameter model tree based on the source information in the intermediate data; and

[0043] The node data of the target child node is updated based on the processing result. The parameter model tree includes a root node and child nodes with a subordinate relationship. The root node represents the programmable logic controller, and the child nodes represent the label parameters of the programmable logic controller. The child nodes include the node data of the label parameters.

[0044] The data processing system provided according to embodiments of this disclosure includes a data acquisition and communication layer and a parameter parsing model layer. The data acquisition and communication layer can acquire data from a programmable logic controller (PLC) according to a sampling triggering strategy matching the tag parameters, obtaining the raw data of the tag parameters. It implements differentiated sampling strategy control, solving the problem of invalid data redundancy caused by homogeneous sampling, reducing the proportion of invalid data; it enables the sampling strategy to dynamically adapt to application requirements, shortening the response time for sampling strategy adjustments. In the parameter parsing model layer, the parameter object encapsulation module encapsulates the raw data and processing strategy into parameter objects, obtaining structured intermediate data. It achieves standardized object-oriented modeling of tag parameters in multi-brand, multi-protocol PLCs, solving the problem of repetitive development of parsing logic caused by differences in tag parameter organization rules, and improving the development efficiency of upper-layer applications interfacing with PLCs. The parameter parsing module processes the parameter fields based on the processing strategy in the intermediate data to obtain the processing result. By formulating unified parsing and formatting rules for multiple types of tag parameters, it solves the problem of poor reusability of upper-layer applications caused by inconsistent output data formats, reducing the development workload of upper-layer application parsing logic. The final structured parameter output module determines the target child node of the parameter model tree based on the source information in the intermediate data. It then updates the node data of the target child node based on the processing results. This ensures that upper-layer applications can obtain the required parameter information in real time, accurately, and in a structured manner. Attached Figure Description

[0045] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0046] Figure 1 This illustration schematically shows an exemplary system architecture to which data processing systems and data processing methods can be applied according to embodiments of the present disclosure;

[0047] Figure 2 A schematic diagram of a data processing system according to an embodiment of the present disclosure is shown.

[0048] Figure 3 A flowchart illustrating the generation of a sampling trigger strategy for a data processing system according to an embodiment of the present disclosure is shown.

[0049] Figure 4 A schematic diagram illustrating the parameter model tree structure of a data processing system according to an embodiment of the present disclosure is shown.

[0050] Figure 5 This diagram schematically illustrates the parameter model tree update process of a data processing system according to an embodiment of the present disclosure.

[0051] Figure 6 This schematically illustrates an architecture diagram of a data processing system applicable according to another embodiment of the present disclosure;

[0052] Figure 7 A flowchart illustrating a data processing method according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0053] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0054] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0055] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0056] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0057] In the embodiments disclosed herein, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0058] In the embodiments disclosed herein, user authorization or consent is obtained before acquiring or collecting user personal information.

[0059] In related technologies, the same sampling triggering strategy is used for different types of tag parameters, and the sampling triggering strategy is mostly a fixed configuration that cannot be dynamically adjusted according to the subscription needs of upper-layer applications. This results in a high proportion of invalid data and an inability to match different application requirements. The inconsistent tag parameter format leads to poor reusability of upper-layer applications.

[0060] To address the aforementioned problems, embodiments of this disclosure provide a data processing system and a data processing method, comprising: a data acquisition and communication layer configured to acquire data from a programmable logic controller (PLC) according to a sampling triggering strategy matching tag parameters, thereby obtaining raw data of tag parameters, wherein the raw data includes parameter fields and source information of the tag parameters, the source information indicating the source of the tag parameters, and the tag parameters including at least one of the following: control signals issued by the PLC to the industrial equipment, equipment status information of the industrial equipment, equipment attribute information of the PLC, equipment attribute information of the industrial equipment, and attribute information of the tag parameters; and a parameter parsing model layer, comprising: a parameter object encapsulation module, configured to... The system is configured to encapsulate raw data and processing strategies into parameter objects according to a standardized parameter structure, resulting in structured intermediate data. The processing strategy is used to process the parameter fields of the label parameters. The parameter parsing module is configured to process the parameter fields based on the processing strategy in the intermediate data to obtain the processing results. The structured parameter output module is configured to determine the target child node of the parameter model tree based on the source information in the intermediate data, and update the node data of the target child node based on the processing results. The parameter model tree includes a root node and child nodes with a subordinate relationship. The root node represents the programmable logic controller, and the child nodes represent the label parameters of the programmable logic controller, including the node data of the label parameters.

[0061] Figure 1 The illustration schematically depicts an exemplary system architecture to which a data processing system and data processing method can be applied according to embodiments of the present disclosure. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0062] like Figure 1 As shown, the system architecture according to this embodiment may include a device layer and a data processing system.

[0063] The device layer includes multi-brand, multi-protocol programmable logic controllers (PLCs), such as Siemens S7-1500, Rockwell Micro800, and their connected industrial field devices (sensors, actuators, instruments, etc.), used to generate raw control signals, operating status parameters, and process variable data. It supports mainstream industrial communication protocols such as Modbus TCP, Profinet, and EtherNet / IP. The device layer establishes a communication connection with the data acquisition and communication layer through industrial Ethernet, adopting a bidirectional data transmission mode (downlink: sampling commands; uplink: raw tag data).

[0064] Data Acquisition and Communication Layer: The core components are a multi-protocol adapter module and a data transmission module, which are used to communicate with the programmable logic controller device, acquire raw tag data according to the sampling trigger command, and provide the raw sampling results to the upper layer.

[0065] Parameter parsing model layer: Located in the central part of the system, it is used for unified modeling, parsing, and formatting of raw label data, and is the core technology module of the system. It is used for unified modeling, attribute filtering, type identification, sampling strategy generation, parsing, and formatting of raw label data, achieving decoupling between the acquisition logic and the application logic. This layer contains multiple functional modules, each with independent functions but working collaboratively through an internal data bus.

[0066] Figure 2 A schematic diagram of a data processing system according to an embodiment of the present disclosure is shown.

[0067] like Figure 2 As shown, the data processing system 200 includes a data acquisition and communication layer 210 and a parameter analysis model layer 220.

[0068] The data acquisition and communication layer 210 is configured to acquire data from the programmable logic controller according to a sampling triggering strategy that matches the tag parameters, thereby obtaining the raw data of the tag parameters.

[0069] Tag parameters can be data configured within the programmable logic controller (PLC), including at least one of the following: control signals issued by the PLC to the industrial equipment, equipment status information of the industrial equipment, equipment attribute information of the PLC, equipment attribute information of the industrial equipment, and attribute information of the tag parameters. Tag parameters have different parameter types, including but not limited to: analog parameters, digital / Boolean parameters, composite structure parameters, bit-level subscription parameters, etc.

[0070] The sampling triggering strategy can be a sampling strategy instruction for data acquisition from a programmable logic controller, including but not limited to the sampling period and the range of fields to be acquired.

[0071] Different parameter types correspond to different sampling triggering strategies. Data is collected from the programmable logic controller according to the sampling triggering strategy that matches the parameter type to obtain the raw data of the tag parameters. The raw data includes the parameter fields of the tag parameters and source information, which indicates the source of the tag parameters.

[0072] The parameter parsing model layer 220 includes a parameter object encapsulation module 221, a parameter parsing module 222, and a structured parameter output module 223.

[0073] The parameter object encapsulation module 221 is configured to encapsulate the raw data and processing strategy into parameter objects according to a standardized parameter structure, so as to obtain structured intermediate data.

[0074] The processing strategy is used to process the parameter fields of the tag parameters. Specifically, different parameter types of tag parameters correspond to different category identifiers and processing strategy templates.

[0075] A standardized parameter structure can be a unified format for parameters. Based on this format, raw data and processing strategies are encapsulated into parameter objects to obtain structured intermediate data. In this structured intermediate data, each parameter object includes at least: a unique identifier for the parameter object, a parameter name, a parameter path, a parameter data type, a parameter category identifier, and a parameter processing strategy.

[0076] The parameter parsing module 222 is configured to process the parameter fields based on the processing strategy in the intermediate data to obtain the processing result.

[0077] The parameter parsing module processes the parameter fields of the tag parameters according to the processing strategy in the structured intermediate data, and obtains structured parameter data with unified standards.

[0078] To illustrate, for analog parameters, engineering quantity conversion is performed based on their associated conversion factor K and offset B (the calculation formula is V = Vr × K + B), and precision adaptation adjustments and unit of measurement are added; for digital parameters, target bits are extracted through a pre-configured bit mask, and their original values ​​are converted into status identifiers with clear engineering meanings according to a mapping table; for Boolean parameters, their original binary values ​​(0 / 1) are converted into logical values ​​(false / true), and a status description is added; for composite structure parameters, their constituent sub-parameters are processed hierarchically, and each sub-parameter matches its corresponding processing strategy according to its own type.

[0079] The structured parameter output module 223 is configured to determine the target child node of the parameter model tree based on the source information in the intermediate data, and update the node data of the target child node based on the processing result.

[0080] A parameter model tree can refer to a parameter tree structure that represents hierarchical information, including a root node and child nodes with a subordinate relationship. The root node represents the programmable logic controller, and the child nodes represent the labeled parameters of the programmable logic controller. The child nodes include the node data of the labeled parameters.

[0081] The intermediate data contains source information, which indicates the source of the label parameters. The structured parameter output module determines the target child node corresponding to this data in the parameter model tree based on the source information, and then updates the node data of the target child node with the unified standard structured parameter data.

[0082] According to embodiments of this disclosure, the data acquisition and communication layer can acquire data from the programmable logic controller (PLC) according to a sampling triggering strategy matching the tag parameters, obtaining the raw data of the tag parameters. This achieves differentiated sampling strategy control, solving the problem of invalid data redundancy caused by homogeneous sampling and reducing the proportion of invalid data; it also enables the sampling strategy to dynamically adapt to application requirements, shortening the response time for sampling strategy adjustments. In the parameter parsing model layer, the parameter object encapsulation module encapsulates the raw data and processing strategy into parameter objects, obtaining structured intermediate data. This achieves standardized object-oriented modeling of tag parameters in multi-brand, multi-protocol PLCs, solving the problem of repetitive development of parsing logic caused by differences in tag parameter organization rules, and improving the development efficiency of upper-layer applications interfacing with PLCs. The parameter parsing module processes the parameter fields based on the processing strategy in the intermediate data to obtain the processing results. By formulating unified parsing and formatting rules for multiple types of tag parameters, it solves the problem of poor reusability of upper-layer applications caused by inconsistent output data formats, reducing the development workload of upper-layer application parsing logic. Finally, the structured parameter output module determines the target child node of the parameter model tree based on the source information in the intermediate data. Update the node data of the target child node based on the processing results. This ensures that upper-layer applications can obtain the required parameter information in real time, accurately, and in a structured manner.

[0083] According to embodiments of this disclosure, for example, Figure 2 The data acquisition and communication layer 210 shown may further include:

[0084] The acquisition module is configured to establish communication with the programmable logic controller (PLC) using a communication protocol that matches the PLC, and to send sampling instructions to the PLC according to the sampling triggering strategy, and receive raw data sent by the PLC in response to the sampling instructions.

[0085] The communication module is configured to send raw data to the parameter object encapsulation module.

[0086] The acquisition module is configured in the data acquisition and communication layer. It can establish communication with the programmable logic controller (PLC) using a communication protocol compatible with the PLC, a process that takes no more than 200ms. After establishing a communication connection, it acquires tag parameter information, sends sampling commands to the PLC according to the sampling trigger strategy corresponding to the tag parameter type, and receives the raw data sent back by the PLC in response to the sampling commands. If the communication establishment process fails, it repeats this process twice, with an interval of 1000ms each time. If it still fails, the data acquisition and communication layer returns a communication result including the offline status to the parameter parsing model layer.

[0087] By using the automatic matching method of the communication protocol in the acquisition module, the development cost of accessing multi-protocol devices is reduced, while the communication module ensures the reliability and accuracy of the transmission of raw data to subsequent processing stages.

[0088] According to embodiments of this disclosure, for example, Figure 2 The parameter object encapsulation module 221 shown is configured to encapsulate the raw data and processing strategy into parameter objects according to a standardized parameter structure, so as to obtain structured intermediate data.

[0089] Different tag parameters have different processing strategies.

[0090] When the parameter type of the label parameter includes continuous type, the processing strategy includes at least one of the following: adding units, dimensional conversion, filtering, range conversion, and noise reduction;

[0091] When the parameter type of the label parameter includes discrete type, the processing strategy includes at least one of the following: adding units, dimensional conversion, parameter type description, format conversion, range conversion, and noise reduction.

[0092] Schematic, when the parameter type of the label parameter is continuous, such as analog quantities like temperature, pressure, and flow rate, the processing strategy implemented by the parameter parsing module includes at least one of the following: adding physical units, such as ℃ or MPa; performing dimension conversion, such as converting the original voltage value to the corresponding temperature value; performing digital filtering to smooth data fluctuations; performing linear or nonlinear mapping on the original value according to the upper and lower limits of the range; and performing noise reduction processing to suppress random interference.

[0093] When the parameter type of the tag parameter is discrete, such as switch state, counter value, enumerated state, etc., the parameter parsing module implements the following processing strategies: adding units, such as times, state; performing dimensional conversion, such as converting the original count value to the actual output; adding parameter type descriptions to clarify their meaning, such as describing the value "1" as "running"; performing format conversion, such as converting binary bits to Boolean values; range conversion; and performing noise reduction processing to eliminate transient erroneous signals.

[0094] By configuring targeted processing strategies for continuous and discrete parameters respectively, the system can transform raw data into structured parameter data that conforms to engineering semantics and can be directly used for monitoring and analysis according to a unified standard.

[0095] In embodiments of this disclosure, the parameter parsing model layer further includes:

[0096] The attribute filtering module is configured to extract the initial parameter fields from the original data to obtain the parameter fields;

[0097] The parameter type determination module is configured to validate the parameter type of the parameter field based on the field attribute information of the parameter field and obtain the validation result.

[0098] The parameter object encapsulation module is configured to obtain the processing strategy when the verification result indicates that the original data is normal, and encapsulate the parameter fields, source information and processing strategy into structured intermediate data.

[0099] The device identifier and tag path are matched and validated using predefined regular expression patterns to ensure that their format conforms to the system's naming rules and hierarchical structure. For example, the device identifier must be a combination of numbers and letters of a specific length, and the tag path must conform to the hierarchical structure of "device name / data block / tag name". For the communication protocol type, its existence in the pre-configured list of supported protocols is checked to confirm whether the system has the ability to communicate with the programmable logic controller.

[0100] If the verification fails, an error code will be returned, such as 1001 - Device does not exist, 1002 - Invalid Tag path, 1003 - Protocol not supported and Chinese prompt information. If there is no response after timeout, it will automatically retry twice, with a retry interval of 1000ms.

[0101] If the verification is successful, the attribute filtering module receives the raw data and establishes a mapping table between the raw attributes and standardized attributes of the raw data. For example, the raw attribute "Value" is mapped to "current value", "Unit" is mapped to "unit", and "Time" is mapped to "update time". The mapping table can be updated through the configuration file.

[0102] Based on the classification identifier of the initial parameter field, the standardized attribute set associated with that type of parameter is determined from the predefined mapping relationship. Subsequently, the attribute filtering module traverses all attribute fields of the parameter object, and through a field matching algorithm, retains only fields that match the target attribute set and removes all irrelevant fields not in the set, thereby generating a parameter field that only contains the attributes required for subscription. Furthermore, the system supports users extending the filtering logic through custom rules. Users can write XML configuration files, such as defining new attribute matching rules in the file. The system automatically loads and immediately takes effect upon detecting an update to the configuration file.

[0103] For the generated parameter fields that only contain the attributes required for subscription, the parameter type determination module performs multi-level validation on the parameter type based on its field attribute information to obtain the validation result.

[0104] Specifically, the first step is to perform a level-one verification: read the classification identifier pre-assigned by the parameter object encapsulation module, and preliminarily determine the parameter type of this parameter field based on the classification identifier. For example, 01 represents analog quantity, 02 represents digital quantity, 03 represents Boolean quantity, and 04 represents composite structure quantity.

[0105] Then, a second-level discrimination is performed: based on the data type field, specific subtypes are further identified on the basis of the major category. For example, for parameters that have been identified as analog quantities (01), they can be distinguished into subtypes such as temperature, pressure, current or voltage according to the data type field; for digital quantities (02), they can be further subdivided into counter values, encoder values ​​or cumulative values, etc.

[0106] If an unknown type is encountered whose category identifier is not within the pre-assigned range, it will be uniformly marked as "unknown". Default processing and sampling strategies will be applied to such parameters, such as collecting data at a fixed 1000ms interval and retaining all attributes. Simultaneously, a detailed exception log will be generated, recording the occurrence time, involved device identifiers, and tag paths. This log will be saved for 30 days by default for backend query and analysis by operations and maintenance personnel.

[0107] If the verification result indicates that the original data is normal, the parameter object encapsulation module obtains the parameter field processing strategy for this tag parameter.

[0108] Specifically, based on the parameter type of the tag parameter, its classification identifier is first determined, for example, 01-analog quantity, 02-digital quantity, 03-Boolean quantity, 04-composite structure quantity, with a unique and non-repeating code; then, a processing strategy template is determined, which is associated with the processing strategy template in the database through the processing strategy identifier. The template can be modified through system configuration. In particular, for the processing strategy encapsulated in composite structure parameters, based on its internal structure, it is decomposed into multiple independent sub-parameter objects. Each sub-object is a complete standardized parameter object, and the processing strategy can be determined according to its parameter type. At the same time, the system generates a parent parameter object for the original composite structure tag parameter and assigns a unique parent parameter ID pointing to the parent parameter object to each sub-object, thereby constructing a "parent-child" hierarchical structure at the data level.

[0109] Finally, the parameter fields, source information, and processing strategies are encapsulated into structured intermediate data.

[0110] The attribute filtering module first precisely extracts the target parameter fields from the raw data, effectively removing irrelevant data and reducing the amount of data to be processed in subsequent steps. The parameter type determination module then performs type validation on the extracted fields, promptly identifying and isolating parameters with abnormal data types to prevent them from entering subsequent stages and causing system instability. This process improves the accuracy of data processing and the efficiency of resource utilization.

[0111] In this embodiment of the disclosure, the parameter parsing model layer further includes: a sampling triggering strategy generation module, configured as follows:

[0112] The sampling trigger type is determined based on the parameter type of the tag parameter;

[0113] When the sampling trigger type includes the periodic trigger type, the sampling period of the tag parameter is determined based on the historical parameter field set of the tag parameter; and the sampling trigger strategy is determined based on the sampling period.

[0114] When the sampling trigger type includes a state trigger type, the sampling trigger strategy is determined based on the trigger state information of the tag parameter.

[0115] Different tag parameter types correspond to different sampling trigger types.

[0116] For parameters whose values ​​change continuously, there is a corresponding periodic triggering type. In this case, the sampling triggering strategy generation module can analyze the historical parameter field set of the label parameter, calculate a reasonable sampling period, and determine the sampling triggering strategy based on this sampling period.

[0117] For state-type parameters, their corresponding state trigger types are as follows: In this case, the sampling trigger strategy generation module continuously monitors the current value of the parameter and compares it with the previous sampled value. A sampling trigger strategy is generated only when the parameter state undergoes a specific change, such as changing from 0 to 1, and the duration of the change is ≥50ms. When there is no change, the module remains silent for a maximum of 30s.

[0118] Figure 3 A flowchart illustrating the generation of a sampling trigger strategy for a data processing system according to an embodiment of the present disclosure is shown.

[0119] First, the parameter type of the tag parameter is obtained, and a judgment is made based on the parameter type: if it is a periodic trigger type, an adaptive period strategy is used to calculate the sampling period; if it is a state trigger type, a state trigger mechanism is used with 50ms anti-jitter processing to ensure signal stability. Then, a specific sampling strategy instruction is generated according to the selected strategy and sent to the data acquisition and communication layer to execute the corresponding data acquisition task.

[0120] By matching different sampling trigger strategies to different label parameter types, redundant collection is minimized while ensuring data validity. Furthermore, for state-change parameters, collection is only initiated when the parameter value actually changes, avoiding idle time consumption when there is no change, thus improving the overall system efficiency and economy.

[0121] According to embodiments of this disclosure, the parameter field includes parameter values. The sampling triggering strategy generation module determines the sampling period of the label parameters based on the historical parameter field set of the label parameters, including: determining the historical parameter value sequence of the target historical period adjacent to the current time period from the historical parameter value set; determining the parameter fluctuation amplitude value in the target historical period based on the historical parameter value sequence; and adaptively adjusting the reference sampling period based on the parameter fluctuation amplitude value and the maximum value in the historical parameter value set to obtain the sampling period.

[0122] The sampling trigger strategy generation module selects a historical period adjacent to the current time from the historical data of the label parameter, i.e., the target historical period, and extracts all parameter values ​​arranged in chronological order within that period to form a sequence of historical parameter values.

[0123] By analyzing the historical parameter value sequence, the fluctuation range of the parameter within the historical period is calculated, which is used to quantify the drastic change of the parameter.

[0124] Finally, the reference sampling period is adjusted based on the calculated parameter fluctuation amplitude, the maximum value in historical data, and formula (1).

[0125] For example, for analog parameters, the core formula of the sampling triggering strategy is shown in equation (1) below:

[0126] (1)

[0127] in The sampling period is The reference sampling period is 1000ms (default). The parameter fluctuation range value. This represents the maximum value of the parameter, i.e., the historical maximum value of the parameter.

[0128] This method optimizes acquisition efficiency, reduces data redundancy and system processing load caused by fixed-frequency sampling while ensuring data validity and real-time performance, and improves overall resource utilization efficiency.

[0129] In this embodiment of the disclosure, the parameter parsing model layer further includes: a modeling module configured to determine a root node representing a programmable logic controller (PLC) based on a device layer including a data acquisition and communication layer, and, in the case that the PLC includes multiple tag parameters, determine a first child node representing a data block and a second child node representing a tag parameter, and connect the root node with the first child node and the first child node with the subordinate relationship using a connection, and connect the first child node and the second child node with the subordinate relationship to obtain a parameter model tree.

[0130] The modeling module is used to construct a hierarchical parameter model tree, which intuitively and structurally represents the hierarchical and organizational relationships of multiple programmable logic controllers and their internal tag parameters.

[0131] Specifically, the modeling module first creates a corresponding root node for each programmable logic controller (PLC) in the device layer connected to the data acquisition and communication layer. This root node can uniquely identify and represent the PLC.

[0132] When a programmable logic controller (PLC) contains multiple tag parameters, these parameters are typically logically grouped by data blocks. In this case, the modeling module creates a first child node for each data block and associates it with the corresponding PLC root node to indicate that the data block belongs to that PLC.

[0133] Furthermore, for each specific label parameter within each data block, the modeling module creates a second child node and associates it with the first child node of the data block to indicate that the label parameter belongs to that data block.

[0134] For example, consider a Siemens programmable logic controller (PLC) with model number "S7-1500" and device ID "PLC_001". The modeling module first creates a unique root node for this PLC, identified as "PLC_001". Internally, the controller is divided into two logical data blocks: "Data Block DB10" and "Data Block DB20". The modeling module creates a first child node for "Data Block DB10" and associates it with the root node "PLC_001"; simultaneously, it creates another first child node for "Data Block DB20", also associated with the same root node. Further, "Data Block DB10" contains two specific tag parameters: "Motor Speed" (Tag1) and "Equipment Temperature" (Tag2). The modeling module creates a second child node for "Motor Speed" and associates it with the first child node of "Data Block DB10"; similarly, it creates another second child node for "Equipment Temperature", also associated with the same data block node. In this way, a clear tree structure is finally formed: "PLC_001" (root node) → "Data block DB10" (first child node) → "Motor speed" (second child node) and "Equipment temperature" (second child node), which intuitively and structurally expresses the hierarchical relationship between equipment, data blocks and parameters.

[0135] To clearly illustrate the hierarchical relationships described above, the modeling module connects nodes with subordinate relationships using lines. For example, the first child node is connected to its root node, and the second child node is connected to its first child node. Through this process, a complete parametric model tree is ultimately formed, with the tree structure presenting a hierarchy from top to bottom as "device → data block → label parameter".

[0136] The parameter model tree supports node expansion / collapse, fuzzy search by tag name or device ID, quick subscription to tag parameters, and attribute filtering of tag parameters. Users can also adjust the node order by dragging and drop, and select tag parameters in batches for subscription. Each node is labeled with a corresponding status: normally if subscribed, offline if not subscribed, and abnormal if not.

[0137] Figure 4 A schematic diagram illustrating the parameter model tree structure of a data processing system according to an embodiment of the present disclosure is shown.

[0138] The parametric model tree structure in the diagram consists of three levels, arranged in the order of top, middle, and bottom. Each level corresponds to different types of nodes, and there are multiple nodes in each level.

[0139] The top layer is the root node, where each circle represents a specific programmable logic controller (PLC). It is the starting point of the parameter model tree and corresponds to a PLC device in the physical field. The middle layer consists of the first child nodes, where each square represents a data block within the PLC. The bottom layer consists of the second child nodes, where each triangle represents a specific labeled parameter within the data block, containing the parameter's specific value, attributes, and other data.

[0140] The connections between the nodes in the diagram represent the hierarchical relationship of "Programmable Logic Controller (root node) → Data Block (first child node) → Tag Parameter (second child node)". For example, the root node A is connected to the first child nodes B and C, and the first child node B is connected to the second child nodes D and E. This connection indicates that data block B and data block C belong to the programmable logic controller A; and tag parameter D and tag parameter E belong to data block C.

[0141] By constructing a hierarchical parameter model tree, the programmable logic controller and its internal parameters are organized in a unified and visual structure, which improves the efficiency of parameter lookup, batch operation and status monitoring, and reduces the system's compatibility complexity with different brands and models of programmable logic controllers.

[0142] In this embodiment of the disclosure, the system further includes: an application service layer configured to, in response to receiving a query request sent by the user layer, send the query request to the structured parameter output module, receive the query result sent by the structured parameter output module, and feed back the query result to the user layer, wherein the query request includes query source information of the queried tag parameter, and the query result is determined by the structured parameter output module based on the node data of the query sub-nodes corresponding to the query source information on the parameter model tree.

[0143] In this embodiment, the system further includes an application service layer. This application service layer acts as a bridge connecting the user layer and the parameter parsing model layer, and is responsible for processing parameter query requests from users and returning results.

[0144] Specifically, when a user initiates a query request for one or more tag parameters through user-layer applications such as monitoring interfaces and control terminals, the request is first sent to the application service layer. The query request contains query source information used to locate the target parameters, such as the full path of the target tag, the tag name, or the identifier of the device to which it belongs.

[0145] Upon receiving this query request, the application service layer does not process the data directly but forwards it to the structured parameter output module of the parameter parsing model layer. This module performs rapid matching and navigation on the parameter model tree based on the query source information in the query request. For example, if the query source information is the tag path "PLC_A / DB10 / Temperature", the module will first locate the root node representing the "PLC_A" device, then find the first child node representing the "DB10" data block, and finally find the second child node representing the "Temperature" tag under that data block node. This node is the query child node. This query child node is associated with the latest standardized data after processing for that tag, which may include the current value, unit, timestamp, quality status, etc., constituting the node data.

[0146] The structured parameter output module will organize and generate a uniformly formatted query result based on the node data of this query sub-node and return it to the application service layer. Finally, the application service layer will feed this query result back to the user application that initially initiated the request, completing a full query interaction.

[0147] By coordinating and forwarding through the application service layer, the user's flexible query needs are decoupled from the system's underlying standardized data processing flow. The parameter model tree is used to achieve rapid and accurate parameter positioning, ensuring the efficiency of query response and the consistency of result data.

[0148] According to an embodiment of this disclosure, in the system, the processing result includes the processed parameter field of the label parameter; the structured parameter output module updates the node data of the target child node based on the processing result, including: adding the processed parameter field and the sampling timestamp of the label parameter to the data column of the label parameter in the data record table of the target child node to obtain the updated node data.

[0149] The structured parameter output module is also configured to determine the query sub-nodes that match the query source information from the parameter model tree; based on the queried label parameters included in the query request, determine the target query column from the data record table of the node data included in the query sub-node; based on the queried sampling time in the query request, determine the target query field from the target query column to obtain the query result.

[0150] A sampling timestamp can be the point in time when the data is generated. It is a time stamp that is automatically generated and recorded by the system when the data acquisition and communication layer actually reads the raw data of a certain tag from the programmable logic controller.

[0151] As the output unit of the parameter parsing model, the structured parameter output module will, on the one hand, record the standardized parameter fields obtained after processing, along with the sampling timestamps corresponding to the label parameters, into the data record table of the target child node in the parameter model tree, accurately filling the data column corresponding to the label parameter, forming updated node data with complete structure and information.

[0152] On the other hand, when a query request is received from an upper-layer application, the module first filters out the query sub-nodes that match the query source information from the parameter model tree. Then, based on the query label parameter specified in the query request, it locates the corresponding target query column in the data record table of the query sub-node. Finally, combined with the query sampling time specified in the request, it extracts the target query field that meets the conditions from the target query column and ultimately generates a query result that accurately matches the query requirements.

[0153] Figure 5 A schematic diagram illustrating the parameter model tree update process of a data processing system according to an embodiment of the present disclosure is provided.

[0154] When the label parameter corresponds to the second child node D in the parameter model tree, i.e., the target child node is the second child node D, only the second child node D is updated. The structured parameter output module enters the standardized parameter fields obtained after processing by the parameter parsing module, along with the sampling timestamp corresponding to the label parameter, into the data record table of the second child node D, filling the data column corresponding to the label parameter in the data record table, thus changing the node data of the second child node D, forming the updated second node F with different node data.

[0155] Figure 6 An exemplary system architecture to which data processing methods can be applied according to embodiments of this disclosure is illustrated;

[0156] A data processing system may include: a data acquisition and communication layer, a parameter parsing model layer, an application service layer, and a user layer.

[0157] The device layer includes multi-brand, multi-protocol programmable logic controllers (PLCs), such as Siemens S7-1500, Rockwell Micro800, and their connected industrial field devices (sensors, actuators, instruments, etc.), used to generate raw control signals, operating status parameters, and process variable data. It supports mainstream industrial communication protocols such as Modbus TCP, Profinet, and EtherNet / IP. The device layer establishes a communication connection with the data acquisition and communication layer through industrial Ethernet, adopting a bidirectional data transmission mode (downlink: sampling commands; uplink: raw tag data).

[0158] Data Acquisition and Communication Layer: The core components are a multi-protocol adapter module and a data transmission module, which are used to communicate with the programmable logic controller device, acquire raw tag data according to the sampling trigger command, and provide the raw sampling results to the upper layer.

[0159] Parameter parsing model layer: Located in the central part of the system, it is used for unified modeling, parsing, and formatting of raw label data, and is the core technology module of the system. It is used for unified modeling, attribute filtering, type discrimination, sampling strategy generation, parsing, and formatting of raw label data, achieving decoupling between the acquisition logic and application logic. This layer contains seven core functional modules: attribute filtering module, parameter type discrimination module, parameter object encapsulation module, sampling trigger strategy generation module, parameter parsing module, structured parameter output module, and modeling module. Each module is functionally independent but works collaboratively through an internal data bus.

[0160] Application Service Layer: This layer manages parameter subscriptions, organizes structured parameters, and controls data push. It includes a tag subscription management module, a structured parameter organization module, and a data push module. These modules receive subscription requests from the user layer, organize structured parameter data, and push the formatted parameter data to the user layer. It supports subscription permission management, subscription content modification, and batch / incremental data push. This layer connects to the parameter parsing model layer via a RESTful API interface and to the user layer via the WebSocket / MQTT protocol.

[0161] User layer: Includes upper-layer applications such as web monitoring interface, industrial control terminal, data analysis platform, and MES system, used to display parameter data in the form of parameter model tree, dashboard, etc., and supports user-initiated operations such as tag subscription, attribute filtering, and sampling strategy configuration; the user layer establishes a connection with the application service layer through the network and supports multiple terminals to access at the same time.

[0162] By storing standardized parameter fields and label parameter sampling timestamps in the target child node data table of the parameter model tree, the problems of fragmented data and difficulty in tracing are solved, ensuring data relevance and format uniformity. The structured parameter output module avoids the waste of full traversal through precise query logic, improving query efficiency and accuracy.

[0163] According to embodiments of this disclosure, a data processing method is also provided, applied to a data processing system. The data processing system includes a data acquisition and communication layer and a parameter parsing model layer. The parameter parsing model layer includes a parameter object encapsulation module, a parameter parsing module, and a structured parameter module.

[0164] Figure 7 A flowchart illustrating a data processing method according to an embodiment of the present disclosure is shown schematically.

[0165] like Figure 7As shown, the method includes operations S710 to S750.

[0166] When operating the S710, the data acquisition and communication layer acquires data from the programmable logic controller according to a sampling triggering strategy that matches the tag parameters, and obtains the raw data of the tag parameters. The raw data includes the parameter fields of the tag parameters and source information. The source information indicates the source of the tag parameters. The tag parameters include at least one of the following: control signals sent by the programmable logic controller to the industrial equipment, equipment status information of the industrial equipment, equipment attribute information of the programmable logic controller, equipment attribute information of the industrial equipment, and attribute information of the tag parameters.

[0167] When operating the S720, the parameter object encapsulation module encapsulates the raw data and processing strategy into parameter objects according to the standardized parameter structure, resulting in structured intermediate data. The processing strategy is used to process the parameter fields of the tag parameters.

[0168] When operating the S730, the parameter parsing module processes the parameter fields based on the processing strategy in the intermediate data to obtain the processing result.

[0169] When operating the S740, the structured parameter output module determines the target child node of the parameter model tree based on the source information in the intermediate data.

[0170] When operating the S750, the node data of the target child node is updated based on the processing result. The parameter model tree includes a root node and child nodes with a subordinate relationship. The root node represents the programmable logic controller, and the child nodes represent the label parameters of the programmable logic controller. The child nodes include the node data of the label parameters.

[0171] According to embodiments of this disclosure, the parameter parsing model layer includes an attribute filtering module and a parameter type discrimination module.

[0172] In such Figure 7 Before operation S720 is executed, the data processing method further includes:

[0173] The attribute filtering module extracts the initial parameter fields from the original data to obtain the parameter fields;

[0174] The parameter type determination module validates the parameter type of the parameter field based on the field attribute information of the parameter field and obtains the validation result.

[0175] like Figure 7 The operation S720 shown may specifically include: if the verification result indicates that the original data is normal, obtaining the processing strategy and encapsulating the parameter fields, source information and processing strategy into structured intermediate data.

[0176] According to embodiments of this disclosure, the parameter parsing model layer includes a sampling triggering strategy generation module.

[0177] In such Figure 7 Before the operation S710 shown is executed, the data processing method further includes:

[0178] The sampling trigger strategy generation module determines the sampling trigger type based on the parameter type of the tag parameters;

[0179] When the sampling trigger type includes the periodic trigger type, the sampling trigger strategy generation module determines the sampling period of the tag parameters based on the historical parameter field set of the tag parameters; and determines the sampling trigger strategy based on the sampling period.

[0180] When the sampling trigger type includes a state trigger type, the sampling trigger strategy generation module determines the sampling trigger strategy based on the trigger state information of the tag parameters.

[0181] According to embodiments of this disclosure, the parameter field includes parameter values.

[0182] According to embodiments of this disclosure, the sampling triggering strategy generation module determines the sampling period of the tag parameters based on the historical parameter field set of the tag parameters, including:

[0183] The sampling trigger strategy generation module determines the sequence of historical parameter values ​​for the target historical period adjacent to the current time period from the set of historical parameter values.

[0184] The sampling trigger strategy generation module determines the parameter fluctuation amplitude in the target historical period based on the historical parameter value sequence; and

[0185] The sampling trigger strategy generation module adaptively adjusts the reference sampling period based on the parameter fluctuation amplitude value and the maximum value in the historical parameter value set to obtain the sampling period.

[0186] According to embodiments of this disclosure, in such Figure 7 Before operation S740 is executed, the data processing method further includes:

[0187] The modeling module is based on the multiple programmable logic controllers (PLCs) included in the device layer of the data acquisition and communication layer. It determines the root node representing the PLC. When the PLC includes multiple tag parameters, it determines the root node representing the data block and the second child node representing the tag parameter. It connects the root node with the first child node with the subordinate relationship and the first child node with the subordinate relationship with the second child node to obtain the parameter model tree.

[0188] According to embodiments of this disclosure, the data processing system further includes an application service layer.

[0189] According to embodiments of this disclosure, the data processing method further includes:

[0190] When the application service layer receives a query request from the user layer, it sends the query request to the structured parameter output module, receives the query result sent by the structured parameter output module, and feeds back the query result to the user layer. The query request includes the query source information of the queried tag parameter, and the query result is determined by the structured parameter output module based on the node data of the query child node corresponding to the query source information on the parameter model tree.

[0191] According to embodiments of this disclosure, the processing result includes the processed parameter field of the tag parameter.

[0192] According to embodiments of this disclosure, in such Figure 7 The operation shown involves the S750 structured parameter output module updating the node data of the target child node based on the processing result, including:

[0193] The structured parameter output module adds the sample timestamps of the processed parameter fields and label parameters to the data column of the label parameter in the data record table of the target child node, thus obtaining the updated node data;

[0194] Specifically, the structured parameter output module determines the query sub-nodes that match the query source information from the parameter model tree; based on the query tag parameters included in the query request, the structured parameter output module determines the target query column from the data record table of the node data included in the query sub-nodes, and based on the query sampling time in the query request, determines the target query field from the target query column to obtain the query result.

[0195] According to embodiments of this disclosure, the data acquisition and communication layer includes an acquisition module and a communication module.

[0196] According to embodiments of this disclosure, in such Figure 7 The operation shown illustrates how the S710 data acquisition and communication layer acquires data from the programmable logic controller according to a sampling triggering strategy matched with the tag parameters, obtaining the raw data of the tag parameters, including:

[0197] The acquisition module establishes communication with the programmable logic controller (PLC) using a communication protocol that matches the PLC, and sends sampling instructions to the PLC according to the sampling triggering strategy, and receives the raw data sent by the PLC in response to the sampling instructions;

[0198] The communication module sends the raw data to the parameter object encapsulation module.

[0199] According to embodiments of this disclosure, when the parameter type of the label parameter includes a continuous type, the processing strategy includes at least one of the following: adding units, dimensional conversion, filtering, range conversion, and noise reduction;

[0200] When the parameter type of the label parameter includes discrete type, the processing strategy includes at least one of the following: adding units, dimensional conversion, parameter type description, format conversion, range conversion, and noise reduction.

[0201] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented in systems and methods according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure may be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0202] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A data processing system, characterized in that, The system includes: The data acquisition and communication layer is configured to acquire data from the programmable logic controller (PLC) according to a sampling triggering strategy matching the tag parameters, thereby obtaining the raw data of the tag parameters. The raw data includes parameter fields and source information of the tag parameters, wherein the source information indicates the source of the tag parameters. The tag parameters include at least one of the following: control signals issued by the PLC to the industrial equipment, equipment status information of the industrial equipment, equipment attribute information of the PLC, equipment attribute information of the industrial equipment, and attribute information of the tag parameters. The parameter parsing model layer includes: The parameter object encapsulation module is configured to encapsulate the original data and processing strategy into parameter objects according to a standardized parameter structure to obtain structured intermediate data, wherein the processing strategy is used to process the parameter fields of the tag parameters; The parameter parsing module is configured to process the parameter fields based on the processing strategy in the intermediate data to obtain the processing result; The structured parameter output module is configured to determine the target child node of the parameter model tree based on the source information in the intermediate data, and update the node data of the target child node based on the processing result. The parameter model tree includes a root node and child nodes with a subordinate relationship. The root node represents a programmable logic controller, and the child nodes represent the label parameters of the programmable logic controller. The child nodes include the node data of the label parameters.

2. The system according to claim 1, characterized in that, The parameter parsing model layer also includes: The attribute filtering module is configured to extract the initial parameter field from the original data to obtain the parameter field; The parameter type determination module is configured to validate the parameter type of the parameter field based on the field attribute information of the parameter field, and obtain the validation result; The parameter object encapsulation module is configured to, when the verification result indicates that the original data is normal, obtain the processing strategy and encapsulate the parameter fields, the source information and the processing strategy into structured intermediate data.

3. The system according to claim 1, characterized in that, The parameter parsing model layer also includes a sampling triggering strategy generation module, configured as follows: Based on the parameter type of the label parameters, determine the sampling trigger type; When the sampling trigger type includes a periodic trigger type, the sampling period of the tag parameter is determined based on the historical parameter field set of the tag parameter; and the sampling trigger strategy is determined based on the sampling period. When the sampling trigger type includes a state trigger type, the sampling trigger strategy is determined based on the trigger state information of the tag parameters.

4. The system according to claim 3, characterized in that, The parameter field includes parameter values. The sampling triggering strategy generation module determines the sampling period of the label parameters based on the historical parameter field set of the label parameters, including: Determine the sequence of historical parameter values ​​for the target historical period adjacent to the current time period from the set of historical parameter values; Based on the historical parameter value sequence, determine the parameter fluctuation amplitude value in the target historical period; and Based on the fluctuation amplitude of the parameter and the maximum value in the set of historical parameter values, the reference sampling period is adaptively adjusted to obtain the sampling period.

5. The system according to claim 1, characterized in that, The parameter parsing model layer also includes: The modeling module is configured to determine the root node representing the programmable logic controller based on the multiple programmable logic controllers included in the device layer of the data acquisition and communication layer. In the case that the programmable logic controller includes multiple tag parameters, it determines the root node representing the data block and the second child node representing the tag parameter. It connects the root node with the first child node with the subordinate relationship using lines, and connects the first child node with the second child node with the subordinate relationship to obtain the parameter model tree.

6. The system according to claim 1, characterized in that, The system also includes: The application service layer is configured to respond to a query request received from the user layer by sending the query request to the structured parameter output module, receiving the query result sent by the structured parameter output module, and feeding back the query result to the user layer. The query request includes query source information of the queried tag parameter, and the query result is determined by the structured parameter output module based on the node data of the query sub-nodes corresponding to the query source information on the parameter model tree.

7. The system according to claim 6, characterized in that, The processing result includes the processed parameter field of the tag parameter; The structured parameter output module updates the node data of the target child node based on the processing result, including: The processed parameter field and the sampling timestamp of the label parameter are added to the data column of the label parameter in the data record table of the target child node to obtain the updated node data; The structured parameter output module is further configured to determine the query sub-node that matches the query source information from the parameter model tree; determine the target query column from the data record table of the node data included in the query sub-node based on the query tag parameters included in the query request; determine the target query field from the target query column based on the query sampling time in the query request; and obtain the query result.

8. The system according to claim 1, characterized in that, The data acquisition and communication layer includes: The acquisition module is configured to establish communication with the programmable logic controller (PLC) using a communication protocol compatible with the PLC, and to send sampling instructions to the PLC according to the sampling triggering strategy, and to receive the raw data sent by the PLC in response to the sampling instructions; The communication module is configured to send the raw data to the parameter object encapsulation module.

9. The system according to claim 1, characterized in that, When the parameter type of the label parameter includes a continuous type, the processing strategy includes at least one of the following: adding units, dimensional conversion, filtering, range conversion, and noise reduction; When the parameter type of the label parameter includes a discrete type, the processing strategy includes at least one of the following: adding units, dimensional conversion, parameter type description, format conversion, range conversion, and noise reduction.

10. A data processing method, characterized in that, The method is applied to the data processing system, which includes a data acquisition and communication layer and a parameter parsing model layer. The parameter parsing model layer includes a parameter object encapsulation module, a parameter parsing module, and a structured parameter module. The data acquisition and communication layer acquires data from the programmable logic controller according to a sampling triggering strategy that matches the tag parameters, thereby obtaining the raw data of the tag parameters. The raw data includes the parameter fields and source information of the tag parameters. The source information indicates the source of the tag parameters. The tag parameters include at least one of the following: control signals sent by the programmable logic controller to the industrial equipment, equipment status information of the industrial equipment, equipment attribute information of the programmable logic controller, equipment attribute information of the industrial equipment, and attribute information of the tag parameters. The parameter object encapsulation module encapsulates the original data and processing strategy into parameter objects according to a standardized parameter structure to obtain structured intermediate data. The processing strategy is used to process the parameter fields of the tag parameters. The parameter parsing module processes the parameter fields based on the processing strategy in the intermediate data to obtain the processing result; The structured parameter output module determines the target child node of the parameter model tree based on the source information in the intermediate data; and The node data of the target child node is updated based on the processing result. The parameter model tree includes a root node and child nodes with a subordinate relationship. The root node represents a programmable logic controller, and the child nodes represent the label parameters of the programmable logic controller. The child nodes include the node data of the label parameters.

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