Coal power data processing method, industrial control equipment, storage medium and program product

Abstract

The invention discloses a coal power data processing method, industrial control equipment, a storage medium and a program product, and relates to the technical field of intelligent factories, and the method comprises the steps that the industrial control equipment receives coal power plant real-time operation data, stores the data to a preset storage space, and sends the data to a visual interface; calling a data monitoring service of the data processing center to carry out data monitoring on the real-time operation data of the coal power plant to obtain a data monitoring result; if the data monitoring result represents that the data is normal, calling a data modeling service of a data processing center to obtain coal-electricity factory storage operation data from a preset storage space, determining a factory data model based on the coal-electricity factory storage operation data through the data modeling service, and packaging the factory data model to a data monitoring service; and if the data monitoring result represents that the data is abnormal, calling a data analysis service of the data processing center to determine abnormal data, generating data warning information based on the abnormal data, and sending the data warning information to a visual interface. The operation efficiency of the coal power plant is improved.

Description

Technical Field

[0001] The present application relates to the field of smart factory technology, and in particular to a coal power data processing method, industrial control equipment, storage media and program products. Background Art

[0002] With the continuous advancement of industrialization and the rapid development of technology, coal-fired power plants are facing increasingly complex production management and data analysis challenges. Traditional production management models are usually based on manual experience and intuition, which are inadequate in the face of large-scale, highly complex production environments. In order to meet these challenges, many factories have begun to adopt digital and intelligent solutions to improve production efficiency and management levels. However, the existing smart factory management systems still have some shortcomings in practical applications. The user interfaces of these systems are usually not friendly enough, and it is difficult to provide clear and concise information views, making it difficult for production managers to quickly and accurately understand the production status and respond in a timely manner, affecting the operational efficiency of coal-fired power plants.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a coal power data processing method, industrial control equipment, storage medium and program product, aiming to solve the technical problem of low operating efficiency of coal power plants under the current intelligent factory management system.

[0005] To achieve the above purpose, the present application proposes a coal power data processing method, which is applied to industrial control equipment, in which a data processing center and a visualization interface based on a microservice architecture are deployed, and the coal power data processing method includes:

[0006] receiving real-time operation data of the coal-fired power plant collected by the sensor, storing the real-time operation data of the coal-fired power plant in a preset storage space, and sending the real-time operation data of the coal-fired power plant to the visualization interface for display;

[0007] Calling the data monitoring service of the data processing center to use the plant data model to monitor the real-time operation data of the coal-fired power plant to obtain data monitoring results;

[0008] If the data monitoring result indicates that the real-time operation data of the coal-fired power plant is normal, the data modeling service of the data processing center is called to obtain the storage operation data of the coal-fired power plant from the preset storage space, the plant data model is determined based on the storage operation data of the coal-fired power plant by the data modeling service, and the updated data model is encapsulated into the data monitoring service;

[0009] If the data monitoring result indicates that the real-time operating data of the coal-fired power plant is abnormal, the data analysis service of the data processing center is called to determine the abnormal data in the real-time operating data of the coal-fired power plant, and data warning information is generated based on the abnormal data and sent to the visualization interface for display.

[0010] In one embodiment, the plant data model is a regression model, and the step of calling the data monitoring service of the data processing center to use the plant data model to perform data monitoring on the real-time operation data of the coal power plant to obtain the data monitoring result includes:

[0011] Calling the data monitoring service of the data processing center to input the equipment operation data in the real-time operation data of the coal-fired power plant into the plant data model to obtain the model operation result;

[0012] If the model operation result matches the plant production data in the real-time operation data of the coal-fired power plant, then it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is normal;

[0013] If the model operation result does not match the factory production data, it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is abnormal.

[0014] In one embodiment, the step of calling the data analysis service of the data processing center to determine abnormal data in the real-time operation data of the coal-fired power plant includes:

[0015] Calling the data analysis service of the data processing center to determine the preset data value range corresponding to each data type;

[0016] For the data to be checked of any data type in the real-time operation data of the coal-fired power plant, if the data to be checked is not within the data value range corresponding to the data to be checked, the data to be checked is determined to be abnormal data.

[0017] In one embodiment, after the step of receiving the real-time operation data of the coal-fired power plant collected by the sensor, the method further includes:

[0018] Determining economic data from the real-time operation data of the coal-fired power plant, wherein the economic data is data generating economic benefits and data generating economic costs;

[0019] The data valuation service in the data processing is called to input the economic data and the preset data value corresponding to the economic data into a preset data valuation model to obtain a data valuation result, and the data valuation result is sent to the visualization interface for display.

[0020] In one embodiment, the preset storage space includes a plurality of storage nodes, and the step of storing the real-time operation data of the coal power plant into the preset storage space includes:

[0021] Slicing the real-time operation data of the coal-fired power plant according to the number of nodes of the storage node to obtain a plurality of slice data;

[0022] For any target data in each of the slice data, the target data is stored in a target node in each of the storage nodes, wherein the target node is any node in each of the storage nodes, and the storage nodes where each of the slice data is located are different;

[0023] The target data is replicated according to a preset replication factor to obtain a plurality of replicated data, and each of the replicated data is stored on a non-target node, wherein the non-target node is a node other than the target node in each of the storage nodes.

[0024] In one embodiment, the industrial control device establishes a communication connection with a historical coal power data system, and the coal power data processing method further includes:

[0025] When it is detected that there is new data in the historical coal-fired power data system, obtaining historical operation data of the coal-fired power plant in the historical coal-fired power data system;

[0026] The historical operation data of the coal-fired power plant is converted into a data format to obtain standard operation data of the coal-fired power plant, and the standard operation data of the coal-fired power plant is stored in the preset storage space.

[0027] In one embodiment, the coal power data processing method further includes:

[0028] When receiving a data query request, parsing the data query request to obtain user identity information;

[0029] Determining the access permission corresponding to the data query request based on the user identity information;

[0030] If the data range queried by the data query request is consistent with the access permission, the preset storage space is accessed, and data is queried from the data range in the preset storage space according to the data query request.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes an industrial control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the coal power data processing method as described above.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the coal power data processing method described above are implemented.

[0033] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the coal power data processing method described above are implemented.

[0034] In the present application, the industrial control equipment receives the real-time operation data of the coal-fired power plant collected by the sensor, stores the real-time operation data of the coal-fired power plant in a preset storage space, and sends the real-time operation data of the coal-fired power plant to a visualization interface for display; calls the data monitoring service of the data processing center to use the factory data model to monitor the real-time operation data of the coal-fired power plant to obtain the data monitoring result; if the data monitoring result indicates that the real-time operation data of the coal-fired power plant is normal, calls the data modeling service of the data processing center to obtain the storage operation data of the coal-fired power plant from the preset storage space, determines the factory data model based on the storage operation data of the coal-fired power plant through the data modeling service, and encapsulates the updated data model into the data monitoring service; if the data monitoring result indicates that the real-time operation data of the coal-fired power plant is abnormal, calls the data analysis service of the data processing center to determine the abnormal data in the real-time operation data of the coal-fired power plant, generates data warning information based on the abnormal data and sends it to the visualization interface for display.

[0035] This application applies digital and intelligent technologies to the production management of coal-fired power plants, deploys a data processing center and a visual interface based on a microservice architecture, and can receive and store the operating data collected by sensors in real time, ensuring the immediacy and accuracy of the data. Secondly, by using the factory data model for data monitoring, it is possible to quickly identify whether the data is normal, thereby improving the efficiency of monitoring the production status. When the monitoring results show that the data is normal, the data model is updated through the data modeling service, and the monitoring algorithm is further optimized to achieve self-learning and adaptation, thereby improving the intelligence and predictive ability of the system. When the monitoring results show that the data is abnormal, the data analysis service can quickly locate the problem and generate warning information, which is displayed to the management personnel in a timely manner through the visual interface, so that the management personnel can respond quickly, reducing production delays or safety accidents caused by data problems. This application enhances the management personnel's understanding and response ability to the production status by improving the real-time, accuracy and visualization of the data, thereby improving the operational efficiency of the coal-fired power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flow chart of the first embodiment of the method for processing coal power data in this application;

[0039] Figure 2 A flow chart of the second embodiment of the method for processing coal power data in this application;

[0040] Figure 3 A schematic diagram of the architecture of a coal power data processing system provided in an embodiment of the present application;

[0041] Figure 4 This is a schematic diagram of the module structure of the coal power data processing device according to an embodiment of the present application;

[0042] Figure 5 Schematic diagram of the equipment structure of the hardware operating environment involved in the coal power data processing method in the embodiment of the present application.

[0043] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0045] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0046] The main solution of the embodiment of the present application is: the industrial control device receives the real-time operation data of the coal-fired power plant collected by the sensor, stores the real-time operation data of the coal-fired power plant in a preset storage space, and sends the real-time operation data of the coal-fired power plant to the visualization interface for display; calls the data monitoring service of the data processing center to use the factory data model to monitor the real-time operation data of the coal-fired power plant to obtain the data monitoring result; if the data monitoring result indicates that the real-time operation data of the coal-fired power plant is normal, calls the data modeling service of the data processing center to obtain the storage operation data of the coal-fired power plant from the preset storage space, determines the factory data model based on the storage operation data of the coal-fired power plant through the data modeling service, and encapsulates the updated data model into the data monitoring service; if the data monitoring result indicates that the real-time operation data of the coal-fired power plant is abnormal, calls the data analysis service of the data processing center to determine the abnormal data in the real-time operation data of the coal-fired power plant, generates data warning information based on the abnormal data and sends it to the visualization interface for display.

[0047] In this embodiment, for ease of description, the following description is made with industrial control equipment as the execution subject.

[0048] With the continuous advancement of industrialization and the rapid development of technology, coal-fired power plants are facing increasingly complex production management and data analysis challenges. Traditional production management models are usually based on manual experience and intuition, which are inadequate in the face of large-scale, highly complex production environments. In order to meet these challenges, many factories have begun to adopt digital and intelligent solutions to improve production efficiency and management levels. However, the existing smart factory management systems still have some shortcomings in practical applications. The user interfaces of these systems are usually not friendly enough, and it is difficult to provide clear and concise information views, making it difficult for production managers to quickly and accurately understand the production status and respond in a timely manner, affecting the operational efficiency of coal-fired power plants.

[0049] This application applies digital and intelligent technologies to the production management of coal-fired power plants, deploys a data processing center and a visual interface based on a microservice architecture, and can receive and store the operating data collected by sensors in real time, ensuring the immediacy and accuracy of the data. Secondly, by using the factory data model for data monitoring, it is possible to quickly identify whether the data is normal, thereby improving the efficiency of monitoring the production status. When the monitoring results show that the data is normal, the data model is updated through the data modeling service, and the monitoring algorithm is further optimized to achieve self-learning and adaptation, thereby improving the intelligence and predictive ability of the system. When the monitoring results show that the data is abnormal, the data analysis service can quickly locate the problem and generate warning information, which is displayed to the management personnel in a timely manner through the visual interface, so that the management personnel can respond quickly, reducing production delays or safety accidents caused by data problems. This application enhances the management personnel's understanding and response ability to the production status by improving the real-time, accuracy and visualization of the data, thereby improving the operational efficiency of the coal-fired power plant.

[0050] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an industrial control device, etc. The following takes an industrial control device as an example to illustrate this embodiment and the following embodiments.

[0051] Based on this, the embodiment of the present application provides a method for processing coal power data, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the coal power data processing method of the present application.

[0052] In this embodiment, the coal power data processing method is applied to industrial control equipment, in which a data processing center and a visualization interface based on a microservice architecture are deployed. The coal power data processing method includes steps S10 to S40:

[0053] Step S10, receiving the real-time operation data of the coal-fired power plant collected by the sensor, storing the real-time operation data of the coal-fired power plant in a preset storage space, and sending the real-time operation data of the coal-fired power plant to a visualization interface for display;

[0054] It should be noted that industrial control system equipment is responsible for monitoring and controlling industrial processes and manufacturing equipment, including programmable logic controllers (PLC), distributed control systems (DCS), supervisory control and data acquisition systems (SCADA), and industrial Internet of Things (IIoT) platforms. A data processing center and visualization interface based on a microservice architecture are deployed in industrial control equipment. The data processing center splits data processing tasks into independent microservices, each of which focuses on specific tasks, such as data monitoring, data modeling, and data analysis. The microservice architecture can allocate an independent operating environment for each microservice, and users can view the real-time operating status and historical data of coal-fired power plants through this interface.

[0055] It should be noted that the preset storage space refers to a configured database or file system for storing the operating data of a coal-fired power plant. The preset storage space can be deployed on an industrial control device or on a device that is connected to the industrial control device for communication. There is no restriction here.

[0056] Industrial control equipment receives real-time operation data of coal-fired power plants collected by sensors, stores it in preset storage space and displays it through a visual interface, ensuring the timeliness and accessibility of the data, facilitating real-time monitoring and subsequent analysis.

[0057] Step S20, calling the data monitoring service of the data processing center to use the plant data model to monitor the real-time operation data of the coal power plant to obtain the data monitoring results;

[0058] The data processing center includes a data monitoring service, which is used to monitor whether the operating data of the coal-fired power plant is normal. The monitoring result of the data monitoring service is called a data monitoring result.

[0059] It is understandable that by monitoring real-time data using data monitoring services and factory data models, anomalies or deviations in the data can be discovered in a timely manner, providing a basis for subsequent decision-making.

[0060] Step S30: If the data monitoring result indicates that the real-time operation data of the coal-fired power plant is normal, the data modeling service of the data processing center is called to obtain the storage operation data of the coal-fired power plant from the preset storage space, and the plant data model is determined based on the storage operation data of the coal-fired power plant through the data modeling service, and the updated data model is encapsulated into the data monitoring service;

[0061] It should be noted that the data processing center includes a data modeling service, and the data modeling service is used to create and update the factory data model.

[0062] If the data monitoring result is normal, the industrial control device obtains the stored operation data of the coal-fired power plant from the storage space. The storage operation data of the coal-fired power plant may include real-time operation data of the coal-fired power plant collected and stored at different times. The storage operation data of the coal-fired power plant is input into the data modeling service to obtain the plant data model, and the new model is applied to the data monitoring service.

[0063] The method of constructing the plant data model is not limited here, and can be set according to the structure of the plant data model. For example, in one embodiment, the plant data model can be a neural network model, and training labels are added to each piece of coal-fired power plant storage and operation data. The training labels are abnormal results corresponding to the coal-fired power plant storage and operation data. The abnormal results are used to characterize whether the plant operation is normal. The coal-fired power plant storage and operation data carrying the training labels are input into the neural network model to be trained to obtain the plant data model; for example, in another embodiment, the plant data model can also be a statistical model, and the statistical relationship between the coal-fired power plant operation data is determined by a preset statistical algorithm to obtain the plant data model.

[0064] It is understandable that continuous optimization of the plant data model can improve the accuracy of data monitoring and better predict and adapt to changes in plant operations.

[0065] Step S40: If the data monitoring result indicates that the real-time operation data of the coal-fired power plant is abnormal, the data analysis service of the data processing center is called to determine the abnormal data in the real-time operation data of the coal-fired power plant, and data warning information is generated based on the abnormal data and sent to the visualization interface for display.

[0066] It should be noted that the data processing center also includes data analysis services, which are used to identify anomalies in the data and perform diagnosis.

[0067] It is understandable that if the data monitoring results show an abnormality, the coal power plant operation processing is input into the data analysis service, the data analysis service is used to identify the abnormal data, and warning information is generated based on the abnormal data, and then the warning information is displayed on the visual interface, which can quickly respond to abnormal situations and reduce potential risks and losses. Through visual warnings, operators can take quick action and improve processing efficiency. The method of generating warning information based on abnormal data is not limited here and can be set according to actual needs.

[0068] This application applies digital and intelligent technologies to the production management of coal-fired power plants, deploys a data processing center and a visual interface based on a microservice architecture, and can receive and store the operating data collected by sensors in real time, ensuring the immediacy and accuracy of the data. Secondly, by using the factory data model for data monitoring, it is possible to quickly identify whether the data is normal, thereby improving the efficiency of monitoring the production status. When the monitoring results show that the data is normal, the data model is updated through the data modeling service, and the monitoring algorithm is further optimized to achieve self-learning and adaptation, thereby improving the intelligence and predictive ability of the system. When the monitoring results show that the data is abnormal, the data analysis service can quickly locate the problem and generate warning information, which is displayed to the management personnel in a timely manner through the visual interface, so that the management personnel can respond quickly, reducing production delays or safety accidents caused by data problems. This application enhances the management personnel's understanding and response ability to the production status by improving the real-time, accuracy and visualization of the data, thereby improving the operational efficiency of the coal-fired power plant.

[0069] In one embodiment, the plant data model is a regression model. Step S20: calling the data monitoring service of the data processing center to use the plant data model to perform data monitoring on the real-time operation data of the coal power plant to obtain data monitoring results, including:

[0070] Step S201, calling the data monitoring service of the data processing center to input the equipment operation data in the real-time operation data of the coal-fired power plant into the plant data model to obtain the model operation result;

[0071] It should be noted that the operation data of coal-fired power plants include equipment operation data that characterizes the operation status of production equipment, such as equipment operation parameters, temperature, pressure, etc.; it also includes factory production data that characterizes the output of coal-fired power plants, such as voltage, power output, raw material consumption, etc.

[0072] The equipment operation data of the current coal-fired power plant is input into the plant data model, and the theoretical production data under the equipment operation data is obtained through simulation of the plant data model, which is hereinafter referred to as the model operation result.

[0073] Step S202, if the model operation result matches the plant production data in the real-time operation data of the coal-fired power plant, then it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is normal;

[0074] The model operation results are compared with the actual coal-fired power plant production data (ie, plant production data). If the two match, that is, the model prediction is consistent with the actual plant production data or is within an acceptable error range, it is determined that the data monitoring results are that the real-time operation data of the coal-fired power plant is normal.

[0075] Step S203: If the model operation result does not match the factory production data, it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is abnormal.

[0076] If the model operation results are inconsistent with the actual factory production data, the data monitoring results are determined to be abnormal.

[0077] It can be understood that by using regression models, the operating status of coal-fired power plants can be predicted more accurately, thereby improving production efficiency. By comparing the model output with the actual data, abnormal conditions in the production process can be quickly identified, equipment maintenance needs can be predicted, and abnormal conditions can be discovered and handled in a timely manner, reducing safety risks caused by equipment failure or operational errors.

[0078] In one embodiment, step S40: calling a data analysis service of a data processing center to determine abnormal data in the real-time operation data of a coal-fired power plant includes:

[0079] Step S401, calling the data analysis service of the data processing center to determine the preset data value range corresponding to each data type;

[0080] It is understandable that by calling the data analysis service to determine the preset value range for different types of data, a clear standard is provided for data monitoring, which makes it easier to identify whether the data is within the normal range and can reduce false positives and negatives.

[0081] It should be noted that the data value range can be set by engineering personnel according to actual needs, or it can be determined by data analysis services based on normal coal-fired power plant storage data in a preset storage space. There is no restriction here and it can be set according to actual needs.

[0082] Step S402: for any data type of the to-be-checked data in the real-time operation data of the coal-fired power plant, if the to-be-checked data is not within the data value range corresponding to the to-be-checked data, the to-be-checked data is determined to be abnormal data.

[0083] For each data type in the coal-fired power plant's operating data at the current moment, checking whether it exceeds the preset value range can quickly identify data points that do not meet expectations, provide a basis for further analysis and processing, and promptly discover and respond to potential problems.

[0084] It is understandable that by setting a reasonable data value range, the accuracy of the data can be ensured, decision-making errors caused by data errors can be avoided, and abnormal data can be quickly identified, and the efficiency and accuracy of anomaly detection can be improved. Timely detection of abnormal data can reduce safety risks caused by equipment failures, operational errors, etc., shorten problem response time, and optimize maintenance processes. In addition, clarifying the data value range and the identification criteria for abnormal data increases data transparency and facilitates problem tracking and analysis.

[0085] In one embodiment, after step S10: receiving the real-time operation data of the coal power plant collected by the sensor, the method further includes steps S50 to S60:

[0086] Step S50, determining economic data from the real-time operation data of the coal-fired power plant, wherein the economic data is data generating economic benefits and data generating economic costs;

[0087] It should be noted that economic data refers to the data on economic costs and economic benefits generated in the real-time operating data of coal-fired power plants. Economic data affects the economic value of the real-time operating data of coal-fired power plants.

[0088] Step S60, calling the data valuation service in the data processing to input the economic data and the preset data value corresponding to the economic data into the preset data valuation model to obtain the data valuation result, and sending the data valuation result to the visualization interface for display.

[0089] It should be noted that the data valuation service encapsulates a data valuation model, and inputs economic data and its preset value into the model to obtain a valuation result. The preset data value corresponding to the economic data can be set according to actual needs and the economic market, and is not limited here. The data valuation model can be: Data valuation result = A*P1-B*P2, where A is the data that generates economic benefits, B is the data that generates economic costs, P1 is the data valuation corresponding to A, and P2 is the data valuation corresponding to B. It should be noted that A can include multiple data, and each A data corresponds to a different data valuation. The same is true for B. That is, the data valuation model can be: Among them, n is the label of A data, and m is the label of B data.

[0090] It is understandable that the valuation model can quantify the contribution or impact of economic data on the economic benefits of the factory, which helps the management understand the economic impact of the current operating conditions and provide a basis for decision-making.

[0091] By identifying and evaluating economic data, factories can have a more comprehensive understanding of their economic situation and know which factors have the greatest impact on economic benefits, which helps to allocate resources rationally, provides management with a quantitative economic benefit assessment, supports a more scientific decision-making process, visualizes economic data and valuation results, improves operational transparency, facilitates monitoring and auditing, and helps to promptly identify negative factors that may affect economic benefits, helping to take measures in advance and reduce risks.

[0092] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 The preset storage space includes multiple storage nodes. Step S10: storing the real-time operation data of the coal power plant into the preset storage space includes:

[0093] Step S101, slicing the real-time operation data of the coal power plant according to the number of storage nodes to obtain a plurality of slice data;

[0094] According to the number of storage nodes, the current coal-fired power plant operation data is divided into multiple data slices. Data slices allow parallel processing and storage, which improves the speed of data writing. Distributed storage can reduce the load of a single node and improve the stability and scalability of the system.

[0095] Step S102, for any target data in each slice data, the target data is stored in a target node in each storage node, wherein the target node is any node in each storage node, and each slice data is located in a different storage node;

[0096] The target node stores each slice data on a different storage node. By storing data slices on different nodes, the geographical distribution of data is achieved and the security of data is increased. The strategy of the target node helps to achieve load balancing and avoid overloading of certain nodes.

[0097] Step S103, the target data is replicated according to a preset replication factor to obtain a plurality of replicated data, and each replicated data is stored on a non-target node, wherein the non-target node is a node other than the target node in each storage node.

[0098] According to the preset replication factor, the data stored on the target node is replicated and the replicated data is stored on non-target nodes. Data replication enhances the redundancy of the data. Even if some nodes fail, the data will not be lost. By storing replicated data on multiple nodes, the data availability and fault tolerance are improved.

[0099] It can be understood that data slicing and replication reduce the risk of data loss due to single point failures. Even if some storage nodes are unavailable, replicated data can ensure that data access is not affected. Distributed storage and load balancing reduce the possibility of system crashes and improve overall stability. Parallel processing and storage of data slices can improve data writing and reading performance. Data replication can be performed on multiple nodes, reducing data access delays.

[0100] In a feasible implementation manner, the industrial control device establishes a communication connection with the historical coal power data system, and the coal power data processing method further includes:

[0101] Step A10, when it is detected that there is new data in the historical coal-fired power data system, obtaining historical operation data of the coal-fired power plant in the historical coal-fired power data system;

[0102] When new data is detected in the historical coal-fired power data system, the operating data of these historical coal-fired power plants is actively obtained to ensure the integration of new and old data, providing a more comprehensive data source for data analysis. By obtaining new data in a timely manner, the timeliness of the data can be maintained to support the latest decision-making and analysis.

[0103] Step A20, converting the data format of the historical operation data of the coal-fired power plant to obtain standard operation data of the coal-fired power plant, and storing the standard operation data of the coal-fired power plant in a preset storage space.

[0104] The acquired historical operation data of coal-fired power plants are converted into a standard format, and then these standardized data are stored in a preset storage space. The data in the historical coal-fired power data system is cleaned and converted into a new standard format to ensure data quality.

[0105] It is understandable that during the data migration process, either incremental migration or full migration strategy can be adopted, and there is no restriction here. The historical operation data of the coal-fired power plant is migrated to the industrial control equipment. During the data migration, a real-time or scheduled data synchronization mechanism is established to ensure the consistency and integrity of the data during the transition of the historical coal-fired power data system.

[0106] It should be noted that integrating historical data can provide analysis of longer time series, which helps to discover long-term trends and patterns. Combining historical and real-time data can provide more comprehensive data support and help management make more accurate decisions.

[0107] In one feasible implementation, the coal power data processing method further includes:

[0108] Step A30, when receiving a data query request, parsing the data query request to obtain user identity information;

[0109] When a data query request is received, the request is first parsed to obtain the user's identity information, ensuring that the source of the data query request is verified, providing a basis for subsequent access control.

[0110] Step A40, determining the access permission corresponding to the data query request based on the user identity information;

[0111] Based on the user's identity information, the corresponding data access rights of the user are determined, and role-based access control is implemented to ensure that users can only access the data they are authorized to access. This can enhance data security and prevent unauthorized access and data leakage.

[0112] Step A50: If the data range queried by the data query request is consistent with the access permission, access the preset storage space, and query data from the data range in the preset storage space according to the data query request.

[0113] If the data range of the data query request is consistent with the user's access rights, the preset storage space is accessed, and data of the corresponding range is retrieved from the storage space according to the data query request, ensuring the compliance of data access. Only authorized users can access specific data, improving the efficiency and accuracy of data retrieval, and users can quickly obtain the required information.

[0114] It is understandable that through strict access control, sensitive data is protected from unauthorized access, data usage can be managed and monitored more effectively, the possibility of data abuse or misuse is reduced, malicious attacks and system failures are prevented, and the stability and reliability of the system are improved.

[0115] For example, to help understand the implementation process of the coal power data processing method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A schematic diagram of the architecture of a coal power data processing system is provided, which specifically includes:

[0116] 1. Data collection module: The unified data platform includes a data collection module, which communicates with the sensors in the factory and is used to collect factory data. The unified data platform also provides a standardized data interface for data interaction with the group company data platform and the regional company data center. The collected data and the data received by the interface are integrated to obtain platform data.

[0117] 2. Data storage module: Distributed storage technology is used to deploy distributed storage clusters on the server. The number of nodes is planned according to storage requirements and budget to ensure that data can be redundantly stored across multiple nodes. When data is written, the system will automatically slice it and replicate it on different nodes according to the configuration strategy. Configure the load balancing mechanism so that data read and write requests can be evenly distributed to each storage node (that is, the real-time operation data of the coal-fired power plant is sliced ​​according to the number of storage nodes to obtain multiple slice data; for any target data in each slice data, the target data is stored on the target node in each storage node, where the target node is any node in each storage node, and the storage nodes where each slice data is located are different; the target data is replicated according to the preset replication factor to obtain multiple replicated data, and each replicated data is stored on a non-target node, where the non-target node is a node in each storage node except the target node). Configure data backup strategy and data recovery mechanism, set up periodic data backup to the disaster recovery server, and quickly switch to the disaster recovery server to provide backup data once the primary data is lost or unavailable.

[0118] 3. Data migration module: clean the data in the old system and convert it to the new standard format to ensure data quality. Use incremental or full migration strategies to migrate data to the new system. During the data migration, establish a real-time or scheduled data synchronization mechanism to ensure the consistency and integrity of the data during the transition of the old system (that is, the industrial control equipment establishes a communication connection with the historical coal-fired power data system, and when new data is detected in the historical coal-fired power data system, obtains the historical operation data of the coal-fired power plant in the historical coal-fired power data system; converts the data format of the historical operation data of the coal-fired power plant to obtain the standard coal-fired power plant operation data, and stores the standard coal-fired power plant operation data in the preset storage space).

[0119] 4. Data security module: configure security policies, such as using data federation technology to allow data access across data centers without directly moving data. For example, implement fine-grained access control to ensure that only authorized users and applications can access data. For example, use differential privacy, anonymization and other technologies to protect sensitive data. (That is, when receiving a data query request, parse the data query request to obtain user identity information; determine the access rights corresponding to the data query request based on the user identity information; if the data range queried by the data query request is consistent with the access rights, access the preset storage space, and query data from the data range in the preset storage space according to the data query request).

[0120] 5. Data service module: Build a data processing center with a microservice architecture, split the data processing tasks into independent microservices, each of which focuses on a specific task, such as data cleaning, format conversion, and real-time analysis, and allocate an independent operating environment for each microservice. Specifically, data services include:

[0121] Data monitoring service: Establish a real-time data analysis engine, use stream processing technology to process the real-time data generated by the equipment, and quickly respond to production changes (that is, call the data monitoring service of the data processing center to use the factory data model to monitor the real-time operation data of the coal-fired power plant to obtain data monitoring results).

[0122] Data modeling service: Deeply apply data modeling support (that is, call the data modeling service of the data processing center to obtain the storage and operation data of the coal-fired power plant from the preset storage space, determine the plant data model based on the storage and operation data of the coal-fired power plant through the data modeling service, and encapsulate the updated data model into the data monitoring service).

[0123] Data analysis services: data association, mining and analysis (that is, if the data monitoring results indicate that the real-time operating data of the coal-fired power plant is abnormal, the data analysis service of the data processing center is called to determine the abnormal data in the real-time operating data of the coal-fired power plant, and data warning information is generated based on the abnormal data and sent to the visualization interface for display).

[0124] Data valuation service: data valuation, data realization (i.e. determining economic data from the real-time operating data of coal-fired power plants, where economic data refers to data that generates economic benefits and data that generates economic costs; calling the data valuation service in data processing to input the economic data and the preset data value corresponding to the economic data into the preset data valuation model to obtain the data valuation result, and then sending the data valuation result to the visualization interface for display).

[0125] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the coal power data processing method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0126] This application also provides a coal power data processing device, please refer to Figure 4 The coal power data processing device is deployed in an industrial control device, in which a data processing center and a visualization interface based on a microservice architecture are deployed. The coal power data processing device includes:

[0127] The receiving module 10 is used to receive the real-time operation data of the coal power plant collected by the sensor, store the real-time operation data of the coal power plant in a preset storage space, and send the real-time operation data of the coal power plant to the visualization interface for display;

[0128] A first calling module 20 is used to call the data monitoring service of the data processing center to use the plant data model to perform data monitoring on the real-time operation data of the coal power plant to obtain a data monitoring result;

[0129] The second calling module 30 is used for calling the data modeling service of the data processing center to obtain the storage and operation data of the coal power plant from the preset storage space if the data monitoring result indicates that the real-time operation data of the coal power plant is normal, determining the plant data model based on the storage and operation data of the coal power plant through the data modeling service, and encapsulating the updated data model into the data monitoring service;

[0130] The third calling module 40 is used to call the data analysis service of the data processing center to determine the abnormal data in the real-time operation data of the coal-fired power plant if the data monitoring results indicate that the real-time operation data of the coal-fired power plant is abnormal, generate data warning information based on the abnormal data and send it to the visualization interface for display.

[0131] Optionally, the plant data model is a regression model, and the first calling module 20 is used to:

[0132] Calling the data monitoring service of the data processing center to input the equipment operation data in the real-time operation data of the coal-fired power plant into the plant data model to obtain the model operation result;

[0133] If the model operation result matches the plant production data in the real-time operation data of the coal-fired power plant, then it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is normal;

[0134] If the model operation result does not match the factory production data, it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is abnormal.

[0135] Optionally, the second calling module 30 is used to:

[0136] Calling the data analysis service of the data processing center to determine the preset data value range corresponding to each data type;

[0137] For the data to be checked of any data type in the real-time operation data of the coal-fired power plant, if the data to be checked is not within the data value range corresponding to the data to be checked, the data to be checked is determined to be abnormal data.

[0138] Optionally, the device further comprises a valuation module, configured to:

[0139] Determining economic data from the real-time operation data of the coal-fired power plant, wherein the economic data is data generating economic benefits and data generating economic costs;

[0140] The data valuation service in the data processing is called to input the economic data and the preset data value corresponding to the economic data into a preset data valuation model to obtain a data valuation result, and the data valuation result is sent to the visualization interface for display.

[0141] Optionally, the preset storage space includes a plurality of storage nodes, and the receiving module 10 is used for:

[0142] Slicing the real-time operation data of the coal-fired power plant according to the number of nodes of the storage node to obtain a plurality of slice data;

[0143] For any target data in each of the slice data, the target data is stored in a target node in each of the storage nodes, wherein the target node is any node in each of the storage nodes, and the storage nodes where each of the slice data is located are different;

[0144] The target data is replicated according to a preset replication factor to obtain a plurality of replicated data, and each of the replicated data is stored on a non-target node, wherein the non-target node is a node other than the target node in each of the storage nodes.

[0145] Optionally, the industrial control device establishes a communication connection with a historical coal power data system, and the apparatus further includes a migration module for:

[0146] When it is detected that there is new data in the historical coal-fired power data system, obtaining historical operation data of the coal-fired power plant in the historical coal-fired power data system;

[0147] The historical operation data of the coal-fired power plant is converted into a data format to obtain standard operation data of the coal-fired power plant, and the standard operation data of the coal-fired power plant is stored in the preset storage space.

[0148] Optionally, the device further includes a permission module, configured to:

[0149] When receiving a data query request, parsing the data query request to obtain user identity information;

[0150] Determining the access permission corresponding to the data query request based on the user identity information;

[0151] If the data range queried by the data query request is consistent with the access permission, the preset storage space is accessed, and data is queried from the data range in the preset storage space according to the data query request.

[0152] The coal power data processing device provided by the present application adopts the coal power data processing method in the above embodiment, which can solve the technical problem of low operating efficiency of coal power plants under the current intelligent factory management system. Compared with the prior art, the beneficial effects of the coal power data processing device provided by the present application are the same as the beneficial effects of the coal power data processing method provided by the above embodiment, and other technical features in the coal power data processing device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0153] The present application provides an industrial control device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the coal power data processing method in the above-mentioned embodiment one.

[0154] Reference below Figure 5 , which shows a schematic diagram of the structure of an industrial control device suitable for implementing the embodiment of the present application. The industrial control device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The industrial control device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0155] like Figure 5As shown, the industrial control device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the industrial control device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the industrial control device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an industrial control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0156] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0157] The industrial control device provided by the present application adopts the coal power data processing method in the above embodiment, which can solve the technical problem of low operating efficiency of coal power plants under the current intelligent factory management system. Compared with the prior art, the beneficial effects of the industrial control device provided by the present application are the same as the beneficial effects of the coal power data processing method provided by the above embodiment, and the other technical features in the industrial control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0158] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0159] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0160] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the coal power data processing method in the above-mentioned embodiment.

[0161] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0162] The computer-readable storage medium may be included in the industrial control device; or may exist independently without being installed in the industrial control device.

[0163] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the industrial control device, the industrial control device: receives the real-time operation data of the coal-fired power plant collected by the sensor, stores the real-time operation data of the coal-fired power plant in a preset storage space, and sends the real-time operation data of the coal-fired power plant to the visualization interface for display; calls the data monitoring service of the data processing center to use the plant data model to perform data monitoring on the real-time operation data of the coal-fired power plant to obtain a data monitoring result; if the data monitoring result indicates that the real-time operation data of the coal-fired power plant is normal, calls the data modeling service of the data processing center to obtain the storage operation data of the coal-fired power plant from the preset storage space, determines the plant data model based on the storage operation data of the coal-fired power plant through the data modeling service, and encapsulates the updated data model into the data monitoring service; if the data monitoring result indicates that the real-time operation data of the coal-fired power plant is abnormal, calls the data analysis service of the data processing center to determine the abnormal data in the real-time operation data of the coal-fired power plant, generates data warning information based on the abnormal data and sends it to the visualization interface for display.

[0164] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0165] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0166] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0167] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned coal power data processing method, and can solve the technical problem of low operating efficiency of coal power plants under the current intelligent factory management system. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the coal power data processing method provided in the above-mentioned embodiment, and will not be repeated here.

[0168] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned coal power data processing method when executed by a processor.

[0169] The computer program product provided by this application can solve the technical problem of low operating efficiency of coal-fired power plants under the current intelligent factory management system. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the coal-fired power data processing method provided by the above embodiment, and will not be repeated here.

[0170] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for processing coal power data, characterized in that: The coal power data processing method is applied to industrial control equipment, in which a data processing center and a visualization interface based on a microservice architecture are deployed. The coal power data processing method includes: receiving real-time operation data of the coal-fired power plant collected by the sensor, storing the real-time operation data of the coal-fired power plant in a preset storage space, and sending the real-time operation data of the coal-fired power plant to the visualization interface for display; Calling the data monitoring service of the data processing center to use the plant data model to monitor the real-time operation data of the coal-fired power plant to obtain data monitoring results; If the data monitoring result indicates that the real-time operation data of the coal-fired power plant is normal, the data modeling service of the data processing center is called to obtain the storage operation data of the coal-fired power plant from the preset storage space, the plant data model is determined based on the storage operation data of the coal-fired power plant by the data modeling service, and the updated data model is encapsulated into the data monitoring service; If the data monitoring result indicates that the real-time operating data of the coal-fired power plant is abnormal, the data analysis service of the data processing center is called to determine the abnormal data in the real-time operating data of the coal-fired power plant, and data warning information is generated based on the abnormal data and sent to the visualization interface for display.

2. The method for processing coal power data according to claim 1, characterized in that: The plant data model is a regression model, and the step of calling the data monitoring service of the data processing center to use the plant data model to perform data monitoring on the real-time operation data of the coal-fired power plant to obtain the data monitoring result includes: Calling the data monitoring service of the data processing center to input the equipment operation data in the real-time operation data of the coal-fired power plant into the plant data model to obtain the model operation result; If the model operation result matches the plant production data in the real-time operation data of the coal-fired power plant, then it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is normal; If the model operation result does not match the factory production data, it is determined that the data monitoring result is that the real-time operation data of the coal-fired power plant is abnormal.

3. The coal power data processing method according to claim 1, characterized in that: The step of calling the data analysis service of the data processing center to determine abnormal data in the real-time operation data of the coal-fired power plant includes: Calling the data analysis service of the data processing center to determine the preset data value range corresponding to each data type; For the data to be checked of any data type in the real-time operation data of the coal-fired power plant, if the data to be checked is not within the data value range corresponding to the data to be checked, the data to be checked is determined to be abnormal data.

4. The coal power data processing method according to claim 1, characterized in that: After the step of receiving the real-time operation data of the coal-fired power plant collected by the sensor, the method further includes: Determining economic data from the real-time operation data of the coal-fired power plant, wherein the economic data is data generating economic benefits and data generating economic costs; The data valuation service in the data processing is called to input the economic data and the preset data value corresponding to the economic data into a preset data valuation model to obtain a data valuation result, and the data valuation result is sent to the visualization interface for display.

5. The method for processing coal power data according to claim 1, characterized in that: The preset storage space includes a plurality of storage nodes, and the step of storing the real-time operation data of the coal power plant into the preset storage space includes: Slicing the real-time operation data of the coal-fired power plant according to the number of nodes of the storage node to obtain a plurality of slice data; For any target data in each of the slice data, the target data is stored in a target node in each of the storage nodes, wherein the target node is any node in each of the storage nodes, and the storage nodes where each of the slice data is located are different; The target data is replicated according to a preset replication factor to obtain a plurality of replicated data, and each of the replicated data is stored on a non-target node, wherein the non-target node is a node other than the target node in each of the storage nodes.

6. The coal power data processing method according to claim 1, characterized in that: The industrial control device establishes a communication connection with the historical coal power data system, and the coal power data processing method further includes: When it is detected that there is new data in the historical coal-fired power data system, obtaining historical operation data of the coal-fired power plant in the historical coal-fired power data system; The historical operation data of the coal-fired power plant is converted into a data format to obtain standard operation data of the coal-fired power plant, and the standard operation data of the coal-fired power plant is stored in the preset storage space.

7. The method for processing coal power data according to any one of claims 1 to 6, characterized in that: The coal power data processing method further includes: When receiving a data query request, parsing the data query request to obtain user identity information; Determining the access permission corresponding to the data query request based on the user identity information; If the data range queried by the data query request is consistent with the access permission, the preset storage space is accessed, and data is queried from the data range in the preset storage space according to the data query request.

8. An industrial control device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the coal power data processing method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the coal power data processing method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the coal power data processing method according to any one of claims 1 to 7 are implemented.

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