Power plant intelligent control visualization system, method, device and storage medium

Abstract

The application belongs to the technical field of data visualization, and discloses a power plant intelligent control visualization system, method, device and storage medium. The original control data set read by the data processing module is resampled to obtain a target control data set. The working condition division module divides the target control data set into different state levels of the operation working condition of the power plant thermal power unit. The parameter classification module groups based on the divided operation working condition to obtain a multi-level parameter classification structure. The visualization generation module generates a time sequence space view and a parameter space view based on the operation working condition of the different state levels of the power plant thermal power unit and the multi-level parameter classification structure. The interaction module realizes the visualization interaction by using the constructed time sequence space view and parameter space view based on the time sequence space view and the parameter space view through the interactive operation. The data analysis demand of the large-scale power plant unit control data in the time sequence space and the parameter space can be met, and the data analysis efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, and in particular to a power plant intelligent control visualization system, method, device and storage medium. Background Technology

[0002] The historical control data of thermal power units in power plants has complex internal relationships and features a large-scale, high-density time sequence. Analysts find it difficult to directly establish an intuitive understanding of operating conditions and parameters, and need to use advanced visualization methods for observation and analysis.

[0003] In response to the above situation, existing data visualization methods such as lists, bar charts, and line charts are difficult to meet the needs of in-depth observation and comprehensive analysis. They cannot meet the data analysis requirements of large-scale power plant unit control data in both time-series and parameter-space domains, thus affecting data analysis efficiency. There is an urgent need for a visualization analysis method that can perform time-series and parameter-space visualization analysis on control data.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a power plant intelligent control visualization system, method, device, and storage medium, aiming to solve the technical problem of the urgent need for a means of visual analysis of control data in both time-series and parameter-space dimensions.

[0006] To achieve the above objectives, the present invention provides a power plant intelligent control visualization system, which includes: a data processing module, an operating condition division module, a parameter classification module, a visualization generation module, and an interaction module; The data processing module is used to read the original control dataset from the operation record of the power plant thermal power unit control system, and to resample the original control dataset to obtain the target control dataset. The operating condition division module is used to divide the target control dataset into operating conditions to obtain the operating conditions of different state levels of the power plant thermal power unit. The parameter classification module is used to classify mechanisms and group correlations based on the divided operating conditions to obtain a multi-level parameter classification structure. The visualization generation module is used to generate a time-series spatial view and a parameter spatial view based on the operating conditions of the thermal power unit at different state levels and the multi-level parameter classification structure of the power plant. The interaction module is used to achieve multi-view linkage between the temporal space and the parameter space through interactive operations based on the temporal space view and the parameter space view.

[0007] In some embodiments, the data processing module is configured to resample the original control dataset at different time intervals to obtain multiple control datasets with different time granularities; obtain the number of parameters in each control dataset with different time granularities; determine the target number of parameters based on the set time interval; and filter out the target control dataset from the multiple control datasets with different time granularities based on the target number.

[0008] In some embodiments, the operating condition segmentation module is used to segment the target control dataset according to the maximum continuous power generation of the thermal power unit to obtain operating condition segments corresponding to three state levels; a sliding window method is used to detect the fluctuation of key parameters of the unit in each operating condition segment; if the parameter value in the sliding window is in a stable state, the data at the next sampling time is continued to be added to the sliding window, and the fluctuation of key parameters of the unit is continued to be detected based on the expanded sliding window; if the parameter value in the sliding window is not in a stable state, the sliding window is moved forward by one sampling point until the operating conditions of the power plant thermal power unit under different state levels are obtained, and the different state levels include the original operating state level, the maximum power generation operating state level, and the stable operating state level.

[0009] In some embodiments, the visualization generation module is used to determine the core display parameters under each mechanism category based on the mechanism classification in the multi-level parameter classification structure; generate a time-series spatial view based on the core display parameters; optimize the parameter display density of the time-series spatial view according to the correlation grouping in the multi-level parameter classification structure; and encode the optimized time-series spatial view in terms of time dimension according to the operating conditions of different state levels of the power plant thermal power unit.

[0010] In some embodiments, the visualization generation module is used to determine a hierarchical framework based on the mechanism classification in the multi-level parameter classification structure; construct a parameter space view based on the hierarchical framework; optimize the association between parameters and interactions in the parameter space view according to the correlation grouping in the multi-level parameter classification structure; and adjust the display of parameters and corresponding time sequences in the optimized parameter space view according to the operating conditions of different state levels of the power plant thermal power unit.

[0011] Furthermore, to achieve the above objectives, this invention also proposes a power plant intelligent control visualization method, which is applied to the power plant intelligent control visualization system described above, and the method includes: The data processing module reads the original control dataset from the operation record of the power plant's thermal power unit control system, and performs resampling processing on the original control dataset to obtain the target control dataset; The operating condition division module divides the target control dataset into operating conditions to obtain the operating conditions of the power plant thermal power unit at different state levels. The parameter classification module performs mechanism classification and correlation grouping based on the divided operating conditions to obtain a multi-level parameter classification structure; The visualization generation module generates a time-series spatial view and a parameter spatial view based on the operating conditions of the thermal power units of the power plant at different state levels and the multi-level parameter classification structure. The interaction module enables multi-view linkage between the temporal space and the parameter space through interactive operations based on the temporal space view and the parameter space view.

[0012] In some embodiments, the data processing module reads the original control dataset from the operation record of the power plant's thermal power unit control system, performs resampling processing on the original control dataset, and obtains the target control dataset, including: The data processing module resamples the original control dataset at different time intervals to obtain multiple control datasets with different time granularities; obtains the number of parameters in each control dataset with different time granularities; determines the target number of parameters based on the set time interval; and selects the target control dataset from the multiple control datasets with different time granularities based on the target number.

[0013] In some embodiments, the operating condition division module divides the target control dataset into operating conditions to obtain operating conditions at different state levels of the power plant's thermal power units, including: The operating condition segmentation module divides the target control dataset according to the maximum continuous power generation of the thermal power unit, obtaining operating condition segments corresponding to three state levels. A sliding window method is used to detect fluctuations in key unit parameters within each operating condition segment. If the parameter value within the sliding window is stable, the data from the next sampling time is added to the sliding window, and fluctuations in key unit parameters are continued based on the expanded sliding window. If the parameter value within the sliding window is not stable, the sliding window is moved forward by one sampling point until the operating conditions of the power plant's thermal power unit at different state levels are obtained. These different state levels include the original operating state level, the maximum power generation operating state level, and the stable operating state level.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a power plant intelligent control visualization device, which includes: a memory, a processor, and a power plant intelligent control visualization program stored in the memory and executable on the processor. The power plant intelligent control visualization program is configured to implement the steps of the power plant intelligent control visualization system as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a power plant intelligent control visualization program, wherein when the power plant intelligent control visualization program is executed by a processor, it implements the steps of the power plant intelligent control visualization system as described above.

[0016] This invention uses a data processing module to resample the read raw control dataset to obtain a target control dataset. A condition partitioning module divides the target control dataset into different operating conditions at different state levels for power plant thermal power units. A parameter classification module groups the data based on these operating conditions to obtain a multi-level parameter classification structure. A visualization generation module generates a time-series spatial view and a parameter spatial view based on the different operating conditions at different state levels of the power plant thermal power units and the multi-level parameter classification structure. An interaction module enables visual interaction through interactive operations using the constructed time-series spatial view and parameter spatial view. This invention can meet the data analysis needs of large-scale power plant unit control data in both time-series and parameter spaces, improving data analysis efficiency. Attached Figure Description

[0017] Figure 1 This is a structural block diagram of the first embodiment of the intelligent control visualization system for power plants of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the intelligent control visualization method for power plants of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0020] This invention provides a power plant intelligent control visualization system, referring to... Figure 1 , Figure 1 This is a structural block diagram of a first embodiment of a power plant intelligent control visualization system according to the present invention.

[0021] It should be noted that the historical control data of thermal power units in power plants has complex internal relationships and is characterized by large-scale, high-density time-series arrangement. Analysts find it difficult to directly establish an intuitive understanding of operating conditions and parameters, requiring advanced visualization methods for observation and analysis. However, existing data visualization methods such as lists, bar charts, and line charts are insufficient for in-depth observation and comprehensive analysis. They cannot meet the data analysis needs of large-scale power plant unit control data in both time-series and parameter spaces, thus affecting data analysis efficiency. Therefore, there is an urgent need for a visualization analysis method capable of performing time-series and parameter-space visualization analysis on control data.

[0022] To address the aforementioned technical issues, this embodiment employs a data processing module to resample the read raw control dataset to obtain a target control dataset. A condition partitioning module divides the target control dataset into different operating conditions at various state levels for power plant thermal power units. A parameter classification module groups the data based on these operating conditions to obtain a multi-level parameter classification structure. A visualization generation module generates a time-series spatial view and a parameter spatial view based on the operating conditions at different state levels of the power plant thermal power units and the multi-level parameter classification structure. An interaction module, through interactive operations based on the time-series spatial view and the parameter spatial view, enables visual interaction. This approach meets the data analysis needs of large-scale power plant unit control data in both time-series and parameter spaces, improving data analysis efficiency. Specifically, it can be implemented as follows.

[0023] It should be noted that the power plant intelligent control visualization system in this embodiment includes a data processing module 10, an operating condition segmentation module 20, a parameter classification module 30, a visualization generation module 40, and an interaction module 50. The data processing module 10 effectively reduces the data size while retaining key operational characteristics. First, it resamples the original data at different intervals. Then, from the perspective of unit operation sequence, it segments and extracts operating condition periods according to different time granularities and operating states, making them comparable and analyzable. Finally, from the perspective of parameters, it designs a hierarchical classification organization method based on the unit's system composition and the statistical correlation between parameters, supporting parameter adjustments for unit operation processes.

[0024] The specific data processing procedure involves resampling the original control dataset at different time intervals to obtain multiple control datasets with different time granularities; obtaining the number of parameters within each time granularity control dataset; determining the target number of parameters based on the set time interval; and selecting the target control dataset from the multiple control datasets with different time granularities based on the target number. In this embodiment, the time interval can be set to 1 min, 5 min, or 10 min, and then the original control dataset is resampled according to these time intervals to obtain control datasets with 1 min, 5 min, or 10 min granularities. The number of parameters in control datasets with different granularities varies, and the corresponding standards also differ, i.e., the target number is different. The shorter the time interval, the larger the number of parameters obtained. In this embodiment, the target number can be determined based on the set time interval. After determining the target number, control datasets with fewer parameters than the corresponding target number are first eliminated. If only one control dataset remains after elimination, this control dataset is the target control dataset. If two or more control datasets remain after elimination, the difference between the number of parameters in each dataset and the target number is calculated, and the control dataset with the larger difference is selected as the target control dataset. The aforementioned time intervals and the correspondence between the time intervals and the target number of parameters can be adjusted according to the time situation, and this embodiment does not impose any restrictions on this.

[0025] Furthermore, the specific process of dividing the operating conditions through the operating condition division module 20 is as follows: the target control dataset is divided according to the maximum continuous power generation of the thermal power unit to obtain operating condition segments corresponding to three state levels; the sliding window method is used to detect the fluctuation of key parameters of the unit in each operating condition segment; if the parameter value in the sliding window is in a stable state, the data of the next sampling time is continued to be expanded into the sliding window, and the fluctuation of key parameters of the unit is continued to be detected based on the expanded sliding window; if the parameter value in the sliding window is not in a stable state, the sliding window is moved forward by one sampling point until the operating conditions of the power plant thermal power unit under different state levels are obtained.

[0026] It should be noted that the different state levels in this embodiment include the original operating state level, the maximum power generation operating state level, and the stable operating state level. Assuming the maximum continuous power generation is P, the operating condition segments of the three different state levels can be the operating condition segments of the original operating state level, the operating condition segments of the maximum power generation operating state level, and the operating condition segments of the stable operating state level. Specifically, the power corresponding to the operating condition segment of the original operating state level can be 90%~100%P, the power corresponding to the operating condition segment of the maximum power generation operating state level can be 80%~90%P, and the power corresponding to the operating condition segment of the stable operating state level can be 70%~80%P. The size of the sliding window can be set to the same as the aforementioned time interval. The specific judgment of whether the parameter value detected by this sliding window is stable can refer to the following formula: |(Amax-Amin) / (Amax+Amin)|≤δ Where Amax and Amin represent the maximum and minimum values ​​of the corresponding parameter within the time window, respectively, and δ is the maximum allowable fluctuation range of the parameter. If the value is within this range, it indicates that the parameter value is in a stable state, and the stable parameter value corresponds to the stable operating condition. By repeating the above process, the operating conditions of the power plant's thermal power unit under different state levels can be obtained.

[0027] Furthermore, in this embodiment, the parameter classification module 30 classifies parameters based on mechanistic classification and correlation grouping. After classification, a multi-level parameter classification structure is obtained. Mechanism classification, for example, classifies parameters from the perspective of their mechanistic attributes according to the unit's operation and control principles. For instance, boiler-side parameters are first classified into one category, then various burner sway angles are classified into another, flue gas dampers into another, and main steam parameters into yet another. Correlation grouping, for example, calculates the Pearson correlation coefficient between parameters within each category based on mechanistic classification, forming a parameter correlation matrix. Further grouping is then performed based on this matrix, resulting in higher correlation between parameters within a group and lower correlation between parameters between groups. This provides a more effective parameter selection method for displaying and analyzing the operational patterns of specific control processes.

[0028] After completing the parameter classification, this embodiment further generates a time-series spatial view and a parameter spatial view through the visualization generation module 40.

[0029] Specifically, the process of generating the time-series spatial view involves determining the core display parameters for each mechanism category based on the mechanism classification in the multi-level parameter classification structure; generating the time-series spatial view based on the core display parameters; optimizing the parameter display density of the time-series spatial view by grouping the correlations in the multi-level parameter classification structure; and encoding the optimized time-series spatial view in terms of time dimension according to the operating conditions of different state levels of the power plant's thermal power units.

[0030] It should be noted that core display parameters, such as "main steam temperature" and "burner tilt angle" on the boiler side, which are strongly related to combustion control, are selected as core display parameters. On the turbine side, parameters strongly related to energy conversion, such as "power generation" and "circulating water temperature," are selected as core display parameters. Specifically, in the overview view (such as a calendar heatmap), only the "core parameters" of each strongly related group are displayed (e.g., the downward tilt angle is selected as the representative for the burner tilt angle group), avoiding visual overload caused by high-dimensional parameters. In the detailed view (such as a multi-resolution hierarchical chemical condition view), strongly related groups can be interactively expanded to display all parameters within the group (e.g., specific tilt angle parameters are displayed after expanding the burner tilt angle group), supporting fine-grained analysis. When encoding the time dimension, color grading can be used. For example, the original operating status layer (without dividing operating conditions) is filled with low transparency (50%) gray; the MCR operating condition layer (such as 100% MCR) is filled with medium transparency (80%) blue; and the stable operating condition layer (fluctuation ≤ threshold) is filled with high transparency (100%) dark blue, which intuitively reflects the hierarchical relationship of operating conditions. For key dates that users are concerned about (such as days with abnormal parameter fluctuations), the pixel block height of its time row is enlarged to 3 times the base height (to display 1-minute granular data of stable operating conditions in a fine-grained manner). Non-focus date rows are gradually reduced in size according to their distance from the focus (2 times for 1-3 days, and the base height for others), balancing the global overview with local details.

[0031] Furthermore, the generation process of the parameter space view specifically involves determining a hierarchical framework based on the mechanism classification in the multi-level parameter classification structure; constructing a parameter space view based on the hierarchical framework; optimizing the association between parameters and interactions in the parameter space view according to the correlation grouping in the multi-level parameter classification structure; and adjusting the display of parameters and corresponding time sequences in the optimized parameter space view according to the operating conditions of different state levels of the power plant thermal power unit.

[0032] It should be noted that the hierarchical framework includes the innermost ring of the radial ring diagram and the middle ring of the radial ring diagram. The innermost ring of the radial ring diagram includes the first-level categories of the mapping mechanism classification (such as "boiler side" and "turbine side"), with each category corresponding to a ring sector (e.g., boiler side occupies 180°, turbine side occupies 180°). The middle ring of the radial ring diagram includes the mechanism classification sectors, further mapping the correlation grouping results (e.g., "burner angle group" and "main steam temperature group" for the boiler side), with colors consistent with the inner mechanism classification (e.g., red for the boiler side). Transparency encodes the correlation of parameters within the group (higher transparency indicates stronger correlation of parameters within the group). For example, in the outermost sector ring of the radial ring diagram, each sector ring corresponds to a specific parameter, with colors consistent with the middle layer groupings. For chord graph correlation, a chord graph is superimposed on the outer edge of the radial ring diagram, using the thickness of the chords to represent the typical correlation magnitude between different parameter groups (e.g., the chord thickness between "burner angle group" and "reheat steam temperature group" is 0.8mm, reflecting a strong correlation), supporting users to quickly identify parameter coupling relationships across mechanism categories. Finally, based on the stable operating condition segment data (1-minute granularity) divided by operating conditions, the statistical characteristics and temporal patterns of the parameters are displayed in the parameter space view. The multi-parameter detail view (time series plot) shows the time series curves (line graphs) of strongly correlated parameter groups (such as the "burner tilt angle group") within the stable operating condition segment, and overlays the mean line (the mean value of parameters in the same stable operating condition segment) to intuitively show the consistency of parameter adjustment. The parallel coordinate view uses the parameters of strongly correlated parameter groups as coordinate axes (such as burner tilt angle, reheat steam temperature, and main steam pressure). Each stable operating condition segment corresponds to a line. Through the intersection and clustering characteristics of the lines (such as the synchronous increase of reheat steam temperature when the burner tilt angle increases), the linkage between parameters is revealed.

[0033] Finally, interaction is achieved through the interaction module 50. Specific interactive operations include filtering work condition segments, selecting parameter groups, and switching views. For example, filtering work condition segments involves clicking on pixel blocks in the multi-resolution hierarchical work condition view or clicking on the color area of ​​the boundary parameter clustering diagram to filter the target work condition segment. Selecting parameter groups involves clicking on the radial aurora node in the high-dimensional parameter hierarchical navigation view to select strongly correlated parameter groups. Switching views involves clicking on the date label in the multi-resolution hierarchical work condition view to switch between a 30-minute granularity pixel block mapping and a 1-minute granularity line chart mapping.

[0034] Furthermore, the interactive linkage also includes interactive transformations of the high-dimensional parameter hierarchical navigation view: Expand / Collapse: Clicking on a parameter group node in the high-dimensional parameter hierarchical navigation view (such as the underspray desuperheating fan ring) expands the view to display the next layer of related groups with that node as the innermost layer; clicking on the blank area in the center of the ring collapses the view back to the mechanism classification layer; Merge Groups: Selecting parameter groups with typical high correlation (such as burner sway angle and reheat steam temperature), clicking the "Merge" button generates a new group, recalculates the typical correlation coefficients between the new group and other groups, and supports the exploration of parameter associations across mechanism categories.

[0035] In this embodiment, the data processing module resamples the read raw control dataset to obtain the target control dataset. The operating condition partitioning module divides the target control dataset into different state levels of operating conditions for power plant thermal power units. The parameter classification module groups the data based on the partitioned operating conditions to obtain a multi-level parameter classification structure. The visualization generation module generates a time-series spatial view and a parameter spatial view based on the different state levels of operating conditions for power plant thermal power units and the multi-level parameter classification structure. The interaction module enables visualization interaction through interactive operations based on the time-series spatial view and the parameter spatial view. This approach can meet the data analysis needs of large-scale power plant unit control data in both time-series and parameter spaces, thus improving data analysis efficiency.

[0036] Furthermore, this embodiment of the invention also proposes a storage medium storing a power plant intelligent control visualization program, which, when executed by a processor, implements the steps of the power plant intelligent control visualization method described above.

[0037] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the intelligent control visualization method for power plants of the present invention.

[0038] like Figure 2 As shown in the embodiments of the present invention, the power plant intelligent control visualization method includes: Step S10: The data processing module reads the original control dataset from the operation record of the power plant thermal power unit control system, performs resampling processing on the original control dataset, and obtains the target control dataset.

[0039] Step S20: The operating condition division module divides the target control dataset into operating conditions to obtain the operating conditions of different state levels of the power plant thermal power units.

[0040] Step S30: The parameter classification module performs mechanism classification and correlation grouping based on the divided operating conditions to obtain a multi-level parameter classification structure.

[0041] Step S40: The visualization generation module generates a time-series spatial view and a parameter spatial view based on the operating conditions of the thermal power unit at different state levels and the multi-level parameter classification structure.

[0042] Step S50: The interaction module realizes multi-view linkage between the temporal space and the parameter space through interactive operations based on the temporal space view and the parameter space view.

[0043] In this embodiment, the data processing module resamples the read raw control dataset to obtain the target control dataset. The operating condition partitioning module divides the target control dataset into different state levels of operating conditions for power plant thermal power units. The parameter classification module groups the data based on the partitioned operating conditions to obtain a multi-level parameter classification structure. The visualization generation module generates a time-series spatial view and a parameter spatial view based on the different state levels of operating conditions for power plant thermal power units and the multi-level parameter classification structure. The interaction module enables visualization interaction through interactive operations based on the time-series spatial view and the parameter spatial view. This approach can meet the data analysis needs of large-scale power plant unit control data in both time-series and parameter spaces, thus improving data analysis efficiency.

[0044] In some embodiments, the data processing module reads the original control dataset from the operation record of the power plant's thermal power unit control system, performs resampling processing on the original control dataset, and obtains the target control dataset, including: The data processing module resamples the original control dataset at different time intervals to obtain multiple control datasets with different time granularities; obtains the number of parameters in each control dataset with different time granularities; determines the target number of parameters based on the set time interval; and selects the target control dataset from the multiple control datasets with different time granularities based on the target number.

[0045] In some embodiments, the operating condition division module divides the target control dataset into operating conditions to obtain operating conditions at different state levels of the power plant's thermal power units, including: The operating condition segmentation module divides the target control dataset according to the maximum continuous power generation of the thermal power unit, obtaining operating condition segments corresponding to three state levels. A sliding window method is used to detect fluctuations in key unit parameters within each operating condition segment. If the parameter value within the sliding window is stable, the data from the next sampling time is added to the sliding window, and fluctuations in key unit parameters are continued based on the expanded sliding window. If the parameter value within the sliding window is not stable, the sliding window is moved forward by one sampling point until the operating conditions of the power plant's thermal power unit at different state levels are obtained. These different state levels include the original operating state level, the maximum power generation operating state level, and the stable operating state level.

[0046] In some embodiments, the method further includes: The visualization generation module determines the core display parameters under each mechanism category based on the mechanism classification in the multi-level parameter classification structure; generates a time-series spatial view based on the core display parameters; optimizes the parameter display density of the time-series spatial view according to the correlation grouping in the multi-level parameter classification structure; and performs time dimension encoding on the optimized time-series spatial view according to the operating conditions of different state levels of the power plant thermal power unit.

[0047] In some embodiments, the method further includes: The visualization generation module determines the hierarchical framework based on the mechanism classification in the multi-level parameter classification structure; constructs a parameter space view based on the hierarchical framework; optimizes the association between parameters and interactions in the parameter space view according to the correlation grouping in the multi-level parameter classification structure; and adjusts the display of parameters and corresponding time sequences in the optimized parameter space view according to the operating conditions of different state levels of the power plant thermal power unit.

[0048] This application embodiment also provides a power plant intelligent control visualization device, including a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other through the communication bus. The memory is used to store the power plant intelligent control visualization program. When the processor executes the program stored in the memory, it implements the above-mentioned power plant intelligent control visualization method.

[0049] The communication bus mentioned in the aforementioned intelligent control visualization equipment for power plants can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0050] The communication interface is used for communication between the aforementioned power plant intelligent control visualization equipment and other equipment.

[0051] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0052] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0053] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0054] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0055] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0056] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0057] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0058] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0059] In addition, for technical details not described in detail in this embodiment, please refer to the power plant intelligent control visualization method provided in any embodiment of the present invention, which will not be repeated here.

[0060] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0061] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0062] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0063] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0064] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

Claims

1. A power plant intelligent control visualization system, characterized in that, The power plant intelligent control visualization system includes: a data processing module, an operating condition division module, a parameter classification module, a visualization generation module, and an interaction module. The data processing module is used to read the original control dataset from the operation record of the power plant thermal power unit control system, and to resample the original control dataset to obtain the target control dataset. The operating condition division module is used to divide the target control dataset into operating conditions to obtain the operating conditions of different state levels of the power plant thermal power unit. The parameter classification module is used to classify mechanisms and group correlations based on the divided operating conditions to obtain a multi-level parameter classification structure. The visualization generation module is used to generate a time-series spatial view and a parameter spatial view based on the operating conditions of the thermal power unit at different state levels and the multi-level parameter classification structure of the power plant. The interaction module is used to achieve multi-view linkage between the temporal space and the parameter space through interactive operations based on the temporal space view and the parameter space view.

2. The power plant intelligent control visualization system as described in claim 1, characterized in that, The data processing module is used to resample the original control dataset at different time intervals to obtain multiple control datasets with different time granularities; obtain the number of parameters in each control dataset with different time granularities; determine the target number of parameters based on the set time interval; and filter out the target control dataset from the multiple control datasets with different time granularities based on the target number.

3. The power plant intelligent control visualization system as described in claim 1, characterized in that, The operating condition segmentation module is used to segment the target control dataset based on the maximum continuous power generation of the thermal power unit to obtain operating condition segments corresponding to three state levels. A sliding window method is used to detect fluctuations in key unit parameters within each operating condition segment. If the parameter value within the sliding window is stable, the data from the next sampling time is added to the sliding window, and fluctuations in key unit parameters are continued based on the expanded sliding window. If the parameter value within the sliding window is not stable, the sliding window is moved forward by one sampling point until the operating conditions of the power plant's thermal power unit at different state levels are obtained. These different state levels include the original operating state level, the maximum power generation operating state level, and the stable operating state level.

4. The power plant intelligent control visualization system as described in claim 1, characterized in that, The visualization generation module is used to determine the core display parameters under each mechanism category based on the mechanism classification in the multi-level parameter classification structure; and to generate a temporal spatial view based on the core display parameters. The parameter display density of the temporal spatial view is optimized based on the correlation grouping in the multi-level parameter classification structure. The optimized temporal spatial view is encoded in the time dimension according to the operating conditions of the thermal power units of the power plant at different state levels.

5. The power plant intelligent control visualization system as described in claim 4, characterized in that, The visualization generation module is used to determine the hierarchical framework based on the mechanism classification in the multi-level parameter classification structure; and to construct a parameter space view based on the hierarchical framework. Optimize the association between parameters and interactions in the parameter space view based on the correlation grouping in the multi-level parameter classification structure; The parameter space view displays the parameters and corresponding timing sequences after being adjusted and optimized according to the operating conditions of the thermal power units of the power plant at different state levels.

6. A visualization method for intelligent control of power plants, characterized in that, The power plant intelligent control visualization method is applied to the power plant intelligent control visualization system as described in any one of claims 1 to 5, and the method includes: The data processing module reads the original control dataset from the operation record of the power plant's thermal power unit control system, and performs resampling processing on the original control dataset to obtain the target control dataset; The operating condition division module divides the target control dataset into operating conditions to obtain the operating conditions of the power plant thermal power unit at different state levels. The parameter classification module performs mechanism classification and correlation grouping based on the divided operating conditions to obtain a multi-level parameter classification structure; The visualization generation module generates a time-series spatial view and a parameter spatial view based on the operating conditions of the thermal power units of the power plant at different state levels and the multi-level parameter classification structure. The interaction module enables multi-view linkage between the temporal space and the parameter space through interactive operations based on the temporal space view and the parameter space view.

7. The power plant intelligent control visualization method as described in claim 6, characterized in that, The data processing module reads the original control dataset from the operation records of the power plant's thermal power unit control system, resamples the original control dataset, and obtains the target control dataset, including: The data processing module resamples the original control dataset at different time intervals to obtain multiple control datasets with different time granularities; obtains the number of parameters in each control dataset with different time granularities; determines the target number of parameters based on the set time interval; and selects the target control dataset from the multiple control datasets with different time granularities based on the target number.

8. The power plant intelligent control visualization method as described in claim 6, characterized in that, The operating condition segmentation module segments the target control dataset into operating conditions to obtain operating conditions at different state levels for the power plant's thermal power units, including: The operating condition segmentation module divides the target control dataset according to the maximum continuous power generation of the thermal power unit, obtaining operating condition segments corresponding to three state levels. A sliding window method is used to detect fluctuations in key unit parameters within each operating condition segment. If the parameter value within the sliding window is stable, the data from the next sampling time is added to the sliding window, and fluctuations in key unit parameters are continued based on the expanded sliding window. If the parameter value within the sliding window is not stable, the sliding window is moved forward by one sampling point until the operating conditions of the power plant's thermal power unit at different state levels are obtained. These different state levels include the original operating state level, the maximum power generation operating state level, and the stable operating state level.

9. A power plant intelligent control visualization device, characterized in that, The power plant intelligent control visualization device includes: a memory, a processor, and a power plant intelligent control visualization program stored in the memory and executable on the processor, wherein the power plant intelligent control visualization program is configured to implement the steps of the power plant intelligent control visualization system as described in any one of claims 1 to 5.

10. A storage medium, characterized in that, The storage medium stores a power plant intelligent control visualization program, which, when executed by a processor, implements the steps of the power plant intelligent control visualization system as described in any one of claims 1 to 5.

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