Data processing method, device, equipment, storage medium and computer program product

Abstract

The present application discloses a data processing method, device, equipment, storage medium and computer program product, which relate to the field of power plant technology, including: collecting initial power plant data from different power plant areas; processing each initial power plant data to obtain processed data with consistent format; encoding each processed data to obtain multiple encoded data; comparing each encoded data through a preset comparison strategy, and determining the data relationship between each initial power plant data based on the comparison results. The present application collects initial power plant data, then processes and encodes the initial power plant data in sequence, and finally compares each encoded data through a preset comparison strategy to determine the data relationship between each initial power plant data, thereby realizing automatic processing and analysis of data relationships without manual intervention, effectively reducing the time required and improving the efficiency of data analysis.

Description

Technical Field

[0001] The present application relates to the field of power plant technology, and in particular to a data processing method, apparatus, device, storage medium, and computer program product. Background Art

[0002] Analyzing the data relationships between power plant data, such as association relationships and logical relationships, can provide a basic basis for scientific management and control, and is of great significance to the operation, management and decision-making of power plants.

[0003] Currently, the existing method for analyzing power plant data involves having plant personnel export data from the system, aggregate the data, and then conduct comparative analysis to determine the relationships between the power plant data. However, this method, which requires personnel to manually process the data and analyze the relationships between the power plant data, is time-consuming when dealing with large amounts of data, resulting in low data analysis efficiency.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art.

[0005] Application Contents

[0006] The main purpose of this application is to provide a data processing method, device, equipment, storage medium and computer program product, which aims to solve the technical problem that the existing technology processes data manually and then analyzes the data relationship between power plant data, which takes a lot of time and leads to low efficiency of data analysis.

[0007] To achieve the above objectives, the present application provides a data processing method, which includes:

[0008] Collect initial power plant data from different power plant areas;

[0009] Processing each of the initial power plant data to obtain processed data with a consistent format;

[0010] Encoding each of the processed data to obtain a plurality of encoded data;

[0011] The encoded data are compared using a preset comparison strategy, and the data relationship between the initial power plant data is determined based on the comparison results.

[0012] In one embodiment, the step of processing the initial power plant data to obtain processed data with a consistent format includes:

[0013] Filtering the initial power plant data to select data to be processed that is within a target time interval;

[0014] Processing each of the data to be processed to obtain processed data with consistent format;

[0015] Accordingly, before the step of screening out the data to be processed within the target time interval from the initial power plant data, the method further includes:

[0016] Obtain historical power plant data under different operating conditions;

[0017] Determining characteristic parameters of each of the historical power plant data at different time intervals;

[0018] Evaluate each time interval through each characteristic parameter to obtain an evaluation result;

[0019] A target time interval is determined from each time interval according to the evaluation result.

[0020] In one embodiment, the step of encoding each processed data to obtain a plurality of encoded data includes:

[0021] Obtaining a preset encoding rule library, wherein the preset encoding rule library stores preset encoding rules corresponding to different data types;

[0022] Matching each of the processed data with each of the preset coding rules in the preset coding rule library;

[0023] Determine the target encoding rule corresponding to each of the processed data according to the matching results;

[0024] Each processed data is encoded according to a corresponding target encoding rule to obtain a plurality of encoded data.

[0025] In one embodiment, the step of determining the target encoding rule corresponding to each processed data according to the matching result includes:

[0026] If there is first-category data that successfully matches the preset encoding rule in each of the processed data, the successfully matched preset encoding rule is used as the target encoding rule corresponding to the first-category data;

[0027] If there is second-category data in each of the processed data that fails to match each of the preset encoding rules, a target encoding rule is configured for the second-category data.

[0028] In one embodiment, if there is second-category data in each of the processed data that fails to match each of the preset encoding rules, the step of configuring a target encoding rule for the second-category data includes:

[0029] If there is second type data in each of the processed data that fails to match each of the preset coding rules, then an initial coding rule is randomly generated;

[0030] Encoding the second category of data using the initial encoding rule to obtain data to be evaluated;

[0031] Calculating the evaluation index corresponding to the data to be evaluated, and judging whether the accuracy of the initial encoding rule meets the preset accuracy requirement based on the evaluation index;

[0032] If not, adjusting the structure of the initial encoding rule and returning to the step of encoding the second type of data using the initial encoding rule to obtain the data to be evaluated, until the accuracy of the adjusted initial encoding rule meets the preset accuracy requirement;

[0033] When the accuracy of the adjusted initial encoding rule meets the preset accuracy requirement, the adjusted initial encoding rule is configured as the target encoding rule corresponding to the second type of data.

[0034] In one embodiment, the step of comparing the encoded data using a preset comparison strategy and determining the data relationship between the initial power plant data according to the comparison result includes:

[0035] Divide the encoded data of the same data type into the same segment to obtain multiple data segments;

[0036] Obtaining a preset comparison strategy corresponding to the data type of each data segment;

[0037] Comparing the data in each of the data segments using a corresponding preset comparison strategy to obtain a comparison result for each data segment;

[0038] The data relationship between the initial power plant data is determined according to the comparison results.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a data processing device, which includes:

[0040] Data acquisition module, used to collect initial power plant data from different power plant areas;

[0041] A data processing module, configured to process the initial power plant data to obtain processed data in a consistent format;

[0042] A data encoding module, configured to encode each of the processed data to obtain a plurality of encoded data;

[0043] The data comparison module is used to compare the encoded data using a preset comparison strategy and determine the data relationship between the initial power plant data according to the comparison result.

[0044] In addition, to achieve the above-mentioned purpose, the present application also proposes a data processing 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 data processing method described above.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the data processing method described above are implemented.

[0046] 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, it implements the steps of the data processing method described above.

[0047] The present application proposes a data processing method, device, equipment, storage medium and computer program product, which collects initial power plant data from different power plant areas; then processes each initial power plant data to obtain processed data with a consistent format; then encodes each processed data to obtain multiple encoded data; finally, compares each encoded data through a preset comparison strategy, and determines the data relationship between each initial power plant data based on the comparison results. The present application collects initial power plant data, then processes and encodes the initial power plant data in turn, and compares each encoded data through a preset comparison strategy to determine the data relationship between each initial power plant data, thereby realizing automatic processing and analysis of data relationships. Compared with the prior art that manually analyzes the data relationship between power plant data, the present application does not require manual participation, effectively reduces the time required, and improves the efficiency of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the first embodiment of the data processing method of this application;

[0049] Figure 2 This is a flow chart of the second embodiment of the data processing method of this application;

[0050] Figure 3 This is a flow chart of the third embodiment of the data processing method of this application;

[0051] Figure 4 This is a schematic diagram of the module structure of the data processing device according to an embodiment of the present application;

[0052] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the data processing method in the embodiment of the present application.

[0053] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0055] 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.

[0056] The main solution of the embodiment of the present application is: collecting initial power plant data from different power plant areas; processing each of the initial power plant data to obtain processed data with a consistent format; encoding each of the processed data to obtain multiple encoded data; comparing each of the encoded data through a preset comparison strategy, and determining the data relationship between each of the initial power plant data based on the comparison results.

[0057] It should be noted that the execution entity of the method of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a mobile phone, tablet computer, personal computer, etc., or other electronic devices that perform the same or similar functions. For ease of description, the following description of this embodiment and the following embodiments uses the above-mentioned data processing device applied to the power plant intelligent data platform as the execution entity.

[0058] Among them, the above-mentioned power plant intelligent data platform can be a platform for storing data generated by equipment in various areas of the power plant, and relevant personnel can understand the operating status of various equipment in the power plant through the power plant intelligent data platform.

[0059] The current method for analyzing power plant data involves having plant personnel export data from the system, aggregate it, and then conduct comparative analysis to identify relationships between the data. This manual data processing and analysis is time-consuming when dealing with large amounts of data, resulting in low data analysis efficiency.

[0060] The present application provides a solution by collecting initial power plant data, then processing and encoding the initial power plant data in sequence, and comparing the encoded data through a preset comparison strategy to determine the data relationship between the initial power plant data. This realizes automatic processing and analysis of data relationships without the need for human intervention. Even if the amount of data is large, the time required can be reduced, effectively improving the efficiency of data analysis.

[0061] Based on this, the present application embodiment provides a data processing method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the data processing method of this application.

[0062] In this embodiment, the data processing method includes steps S10 to S40:

[0063] Step S10: collecting initial power plant data of different power plant areas.

[0064] It should be noted that the above-mentioned initial power plant data may be data generated by equipment in different areas of the power plant, and may include time series data, structured data, and unstructured data. The time series data may be data recorded and organized in chronological order. The time series data has a time dimension and can be used to analyze trends, periodicity, and correlations. The structured data may be data stored in a clear format and rules, which can be stored and managed through tables, databases, or other programmable data models. The structured data has clear fields and attributes, and each field has a specific data type and value range. The unstructured data may be data with an irregular or incomplete data structure, no predefined data model, and is not convenient to be represented by a two-dimensional logical table in a database.

[0065] In a specific implementation, the above-mentioned data processing equipment collects time series data, structured data and unstructured data generated by corresponding equipment from different areas of the power plant, such as a distributed control system or a traction control system, and uses the time series data, structured data and unstructured data as initial power plant data.

[0066] Step S20: Process the initial power plant data to obtain processed data with a consistent format.

[0067] In specific implementations, since the sources of the initial power plant data are different and the formats of the power plant data from different sources are different, compared with the method of manually analyzing power plant data to directly make subjective comparisons of different types of data, the above-mentioned data processing equipment needs to integrate and process the initial power plant data from different sources and types to obtain processed data with a consistent format, so as to facilitate subsequent comparisons at the same latitude or dimension.

[0068] In a feasible implementation, step S20 may include steps S201 to S202:

[0069] Step S201 : Filtering the initial power plant data to obtain data to be processed that is within a target time interval.

[0070] It should be noted that since a large amount of data is generated during the operation of a power plant, not all power plant data are involved in data analysis. Therefore, a target time interval can be set. The target time interval can be a time interval in which the data generated has an obvious trend of change and is relevant to the data analysis.

[0071] It is understandable that when the power plant is operating normally, the data generated in each time interval has the same daily change trend area. Therefore, according to the required data analysis needs, the technicians determine the time interval where the data with a more obvious change trend is generated from the previous data, and feed the time interval as the target time interval back to the data processing equipment.

[0072] In a specific implementation, the above-mentioned data processing equipment can determine the generation time of each initial power plant data, and then filter out the data to be processed whose generation time is within the target time interval, so as to reduce the amount of data, avoid the impact of data irrelevant to the data analysis requirements on the data analysis, improve the efficiency of data analysis, and improve the accuracy of data analysis.

[0073] In a feasible implementation manner, step S201 may include steps S2011 to S2014 before step S201:

[0074] Step S2011: Acquire historical power plant data under different operating conditions.

[0075] It should be noted that, in addition to manual configuration of the target time interval by a technician, in order to improve the accuracy of determining the target data interval, the data processing device can also automatically determine the target time interval.

[0076] In a specific implementation, in order to automatically determine the target time interval, the above-mentioned data processing equipment can collect historical power plant data generated by the power plant in different operating conditions, such as normal operation, startup, shutdown, load change, etc.

[0077] Step S2012: determining characteristic parameters of each of the historical power plant data in different time intervals.

[0078] In a specific implementation, the data processing device can divide the collected historical power plant data into time intervals and determine the time when each historical power plant data was generated. Then, for each time interval, characteristic parameters of the historical power plant data in that time interval are extracted. These characteristic parameters can be parameters that characterize the historical power plant data in a standard time interval, such as statistical quantities such as average, maximum, minimum, and standard deviation, or more complex indicators such as trend change rate and periodic analysis.

[0079] Step S2013: Evaluate each time interval using each characteristic parameter to obtain an evaluation result.

[0080] In a specific implementation, the above-mentioned data processing device can obtain pre-set preset evaluation conditions to determine the quality of each time interval, such as characteristic parameters of a specific category, change rate threshold, correlation coefficient threshold, clustering effect, etc. The specific preset evaluation conditions used can be set according to actual needs. After obtaining the preset evaluation conditions, each characteristic parameter can be compared with the preset evaluation conditions to evaluate each time interval. Specifically, when the characteristic parameter meets the preset evaluation conditions, it is determined that the time interval corresponding to the characteristic parameter is better, and the power plant data generated in the time interval meets the requirements. On the contrary, when the characteristic parameter does not meet the preset evaluation conditions, it is determined that the time interval corresponding to the characteristic parameter is worse, and the power plant data generated in the time interval does not meet the requirements.

[0081] Exemplarily, when the above-mentioned preset evaluation condition is a preset change rate threshold, the above-mentioned characteristic parameter may be the change rate of each historical power plant data within a time interval. During the evaluation, each change rate may be compared with the preset change rate threshold. When the change rate reaches the preset change rate threshold, it is determined that the time interval corresponding to the change rate is better. On the contrary, when the change rate is lower than the preset change rate threshold, it is determined that the time interval corresponding to the change rate is worse.

[0082] Step S2014: determining a target time interval from each time interval according to the evaluation result.

[0083] In a specific implementation, the above-mentioned data processing device can extract candidate time intervals whose characteristic parameters meet the preset evaluation conditions based on the evaluation results, and then calculate the correlation between the characteristic parameters of each candidate time interval and the preset evaluation conditions, sort the candidate time intervals according to the degree of correlation, and then extract the time interval ranked first as the target time interval.

[0084] Exemplarily, when the above-mentioned preset evaluation condition is a preset change rate threshold, when determining multiple candidate time intervals where the change rate threshold reaches the preset change rate threshold, the deviation between the change rate of each candidate time interval and the preset change rate threshold can be calculated. The larger the deviation, the higher the correlation between the change rate and the preset change rate threshold. On the contrary, the smaller the deviation, the smaller the correlation between the change rate and the preset change rate threshold. Based on the size of this deviation, the candidate time intervals can be sorted.

[0085] In this embodiment, historical power plant data under different operating conditions are obtained, characteristic parameters of each of the historical power plant data in different time intervals are determined, each time interval is evaluated by each characteristic parameter to obtain an evaluation result, and a target time interval is determined from each time interval based on the evaluation result, thereby automatically determining the target time interval. Moreover, by evaluating each time interval by each characteristic parameter, a more accurate target time interval can be determined, that is, the accuracy of determining the target time interval is improved.

[0086] Step S202: Process the data to be processed to obtain processed data with a consistent format.

[0087] In a specific implementation, after the above-mentioned data processing equipment filters out the data to be processed within the target time interval from the initial power plant data, it can parse the data to be processed to obtain parsed data in a format that is easy to process; after completing the data parsing, the parsed data can be parsed and cleaned, and during the cleaning process, errors, anomalies or missing values ​​in the parsed data can be identified and processed. For errors or anomalies, threshold methods, clustering and other methods can be used to identify and correct them; after completing the data cleaning, the integrity and accuracy of the cleaned data can be checked, and after completing the inspection of the cleaned data, standardization processing can be performed to convert the cleaned data into standardized data with a unified format and scale; after completing the standardization processing, specific mathematical or logical operations can be performed on the data to generate new features or improve existing features. After completing the specific mathematical or logical operations, processed data with a consistent format can be obtained.

[0088] Step S30: Encode each of the processed data to obtain a plurality of encoded data.

[0089] In a specific implementation, the above-mentioned data processing device can select different encoding rules according to different types of processed data. For example, for numerical type data, the encoding rule of binary conversion can be selected to convert the numerical type data into binary data; for image type data, the encoding rule of image compression can be selected to compress the image type data; for text data, the encoding rule of ASCII encoding can be selected to map the text data into a single byte.

[0090] Step S40 , comparing the encoded data using a preset comparison strategy, and determining the data relationship between the initial power plant data based on the comparison result.

[0091] It should be noted that the above-mentioned preset comparison strategy may be a strategy determined based on data analysis requirements.

[0092] In a specific implementation, after determining the preset comparison strategy, the preset comparison strategy can be used to compare the encoded data to determine the data relationship between any two encoded data, and further determine the data relationship between any two initial power plant data.

[0093] For example, when the data analysis requirement is to determine the correlation relationship between each initial power plant data, the preset comparison strategy is determined to be correlation comparison. The correlation between each encoded data can be calculated, and then the correlation is compared with a preset correlation threshold. When the correlation between the first encoded data and the second encoded data reaches the preset correlation threshold, it is determined that there is a correlation relationship between the first encoded data and the second encoded data, and then it is determined that there is a correlation relationship between the first initial power plant data corresponding to the first encoded data and the second initial power plant data corresponding to the second encoded data.

[0094] For example, when the data analysis requirement is to determine the correlation relationship between each initial power plant data, the preset comparison strategy is determined to be correlation comparison, and the correlation coefficient between each encoded data can be calculated, and then the correlation is compared with a preset correlation coefficient threshold. When the correlation coefficient between the first encoded data and the second encoded data reaches the preset correlation coefficient threshold, it is determined that there is a correlation relationship or logical relationship between the first encoded data and the second encoded data, and then it is determined that there is a correlation relationship or logical relationship between the first initial power plant data corresponding to the first encoded data and the second initial power plant data corresponding to the second encoded data. The above-mentioned correlation relationship or logical relationship can represent the data relationship between each initial power plant data.

[0095] This embodiment provides a data processing method, which collects initial power plant data from different power plant areas; then processes each initial power plant data to obtain processed data with a consistent format; then encodes each processed data to obtain multiple encoded data; finally, compares each encoded data using a preset comparison strategy, and determines the data relationship between each initial power plant data based on the comparison results. This embodiment collects initial power plant data, then processes and encodes the initial power plant data in sequence, and compares each encoded data using a preset comparison strategy to determine the data relationship between each initial power plant data, thereby achieving automatic processing and analysis of data relationships. Compared to the prior art that manually analyzes the data relationship between power plant data, the data processing method of this embodiment does not require manual participation, effectively reduces the time required, and improves the efficiency of data analysis.

[0096] 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 embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the data processing method of this application.

[0097] In this embodiment, step S30 may include steps S301 to S304:

[0098] Step S301: obtaining a preset encoding rule library, wherein the preset encoding rule library stores preset encoding rules corresponding to different data types.

[0099] In a specific implementation, the above-mentioned data processing device can obtain previous historical power plant data, and perform the above-mentioned processing on each historical power plant data to obtain historical processed data with a consistent format, and then determine the data type of each historical processed data, and then configure a preset coding rule corresponding to the data type for each historical processed data. The preset coding rule can clarify the coding method of each historical processed data. After determining the preset coding rule corresponding to each historical processed data, the preset coding rule is stored in the same database to obtain a preset coding rule library.

[0100] Step S302: Match each of the processed data with each preset encoding rule in the preset encoding rule library.

[0101] Step S303: determining the target encoding rule corresponding to each processed data according to the matching result.

[0102] In a specific implementation, the above-mentioned data processing device can determine the data type of each processed data, and then match each processed data with each preset coding rule in the preset coding rule library based on the data type, and determine whether there is a preset coding rule that matches the data type of each processed data. When there is a preset coding rule that matches the data type brand of any processed data, the preset coding rule that successfully matches can be used as the target coding rule for any processed data.

[0103] In a feasible implementation, step S303 may include steps S3031 to S3032:

[0104] Step S3031: If there is first-category data that successfully matches the preset encoding rule in each of the processed data, the successfully matched preset encoding rule is used as the target encoding rule corresponding to the first-category data.

[0105] Step S3032: If there is second-category data in each of the processed data that fails to match each of the preset encoding rules, configure a target encoding rule for the second-category data.

[0106] In a specific implementation, when the above-mentioned data processing device detects that there is first-category data that successfully matches the preset coding rule in each processed data, the successfully matched preset coding rule will be used as the target coding rule corresponding to the first-category data. If there is second-category data that fails to match all the preset coding rules, it is determined that there is no preset coding rule applicable to the second-category data in the preset coding rule library. At this time, the statistical characteristics or pattern characteristics of the second-category data can be predicted by the preset rule model, and the target coding rule applicable to the second-category data can be generated based on the statistical characteristics or pattern characteristics.

[0107] It should be understood that the above-mentioned data processing equipment can use historical processed data to construct training samples, pre-learn the training samples through unsupervised learning algorithms (such as clustering) or supervised learning algorithms (such as classification), and construct a rule model for predicting the statistical characteristics or model characteristics of the data.

[0108] It should be noted that the statistical characteristics of the data described above describe its distribution, central tendency, and degree of dispersion. These characteristics help determine the nature and patterns of the data. Based on these characteristics, appropriate encoding rules can be developed to achieve effective data compression, storage, and transmission. For example, for data with a significantly skewed distribution, an asymmetric encoding method can be used to better preserve the original characteristics of the data.

[0109] It's understandable that the model features of the data described above can reflect the correlations and regularities between data. By identifying these model features, we can fully utilize the encoding rules of the data structure. For example, in text data, if certain words or phrases appear frequently, we can assign them shorter codes, thereby improving encoding efficiency.

[0110] In a feasible implementation, step S3032 includes steps S30321 to S30325:

[0111] Step S30321: If there is second-category data in each of the processed data that fails to match each of the preset encoding rules, an initial encoding rule is randomly generated.

[0112] In a specific implementation, the above encoding rules are composed of a series of characters or values. By defining the structure and constraints of the encoding rules (such as length, character set, etc.), encoding rules that meet specific requirements can be created. Based on this, when the above data processing device detects that there is a second type of data in each processed data that fails to match each preset encoding rule library, it can determine the data structure of the second type of data, such as the length of the code, whether it contains a specific character set (such as uppercase letters, lowercase letters, numbers, special characters, etc.), and whether it contains a fixed part or separator, etc., and then select a baseline encoding rule from the preset encoding rule library that is associated with the character or value of the data structure of the second type of data, and randomly generate a random number through a random number generator to fill the variable part of the baseline encoding rule to obtain an initial encoding rule.

[0113] Step S30322: Encode the second category of data using the initial encoding rule to obtain data to be evaluated.

[0114] Step S30323: Calculate the evaluation index corresponding to the data to be evaluated, and determine whether the accuracy of the initial encoding rule meets the preset accuracy requirement based on the evaluation index.

[0115] In a specific implementation, the above-mentioned data processing device can, after obtaining the initial encoding rules, encode the second category of data using the initial encoding rules to obtain the data to be evaluated, and then decode the data to be evaluated to obtain decoded data, calculate the difference between the decoded data and the second category of data, such as accuracy, recall rate, and F1 score, etc., use the difference as an evaluation indicator, and then configure the preset difference range as the preset accuracy requirement, and compare the evaluation indicator with the preset difference range. When the evaluation indicator is within the preset difference range, it is judged that the accuracy of the initial encoding rule meets the preset accuracy requirement. On the contrary, when the evaluation indicator is not within the preset difference range, it is judged that the accuracy of the initial encoding rule does not meet the preset accuracy requirement.

[0116] It should be understood that the above-mentioned preset difference range can be a pre-set range used to determine whether the difference between the decoded data and the second category of data is large, that is, when the difference between the decoded data and the second category of data is within the preset difference range, it is determined that the difference between the decoded data and the second category of data is large; when the difference between the decoded data and the second category of data is not within the preset difference range, it is determined that the difference between the decoded data and the second category of data is small.

[0117] Step S30324: If it does not meet the requirements, the structure of the initial coding rule is adjusted, and the process returns to the step of encoding the second type of data using the initial coding rule to obtain the data to be evaluated, until the accuracy of the adjusted initial coding rule meets the preset accuracy requirements.

[0118] In a specific implementation, when the above-mentioned data processing device detects that the accuracy of the initial encoding rule does not meet the preset accuracy requirements, it can randomly regenerate random numbers through a random number generator to fill the variable part of the benchmark encoding rule to adjust the structure of the initial encoding rule, and return to the step of encoding the second type of data through the initial encoding rule to obtain the data to be evaluated, and repeat the above process until the accuracy of the adjusted initial encoding rule meets the preset accuracy requirements.

[0119] Step S30325: When the accuracy of the adjusted initial encoding rule meets the preset accuracy requirement, the adjusted initial encoding rule is configured as the target encoding rule corresponding to the second type of data.

[0120] In a specific implementation, after the above-mentioned data processing device continuously adjusts the initial encoding rules in the above-mentioned manner, when it detects that the accuracy of the adjusted initial encoding rules meets the above-mentioned preset accuracy requirements, it can determine that the adjustment of the initial encoding rules is completed, and configure the adjusted initial encoding rules at this time as the target encoding rules corresponding to the second category of data.

[0121] Step S304 , encoding each of the processed data using a corresponding target encoding rule to obtain a plurality of encoded data.

[0122] In a specific implementation, after determining the target encoding rules for the first category of data and the target encoding rules for the second category of data, the above-mentioned data processing device can encode the first category of data and the second category of data using the corresponding target encoding rules to obtain multiple encoded data.

[0123] This embodiment matches each processed data with each preset coding rule in the preset coding rule library. If there is first-category data that successfully matches the preset coding rule in each processed data, the successfully matched preset coding rule is used as the target coding rule corresponding to the first-category data. If there is second-category data that fails to match the preset coding rule in each processed data, the target coding rule is configured for the second-category data. Therefore, regardless of whether there is a successfully matched preset coding rule in the preset coding rule library, the corresponding target coding rule can be configured for the processed data, thereby avoiding the situation where the target coding rule of the second-category data cannot be determined when the preset rule matching library fails to match, effectively improving the determination accuracy of the target coding rule, and thereby improving the encoding accuracy of the processed data.

[0124] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and second embodiments can be referred to above and will not be described in detail. Figure 3 , Figure 3This is a flow chart of the third embodiment of the data processing method of this application.

[0125] In this embodiment, step S40 may include steps S401 to S404:

[0126] Step S401 : Divide the encoded data of the same data type into the same segment to obtain multiple data segments.

[0127] In a specific implementation, the above-mentioned data processing device can segment the encoded data according to the data type to obtain multiple data segments. The data in any data segment has the same data type. For example, there is a data segment containing all numerical type data, and there is another data segment containing all string type data.

[0128] Step S402: Obtain a preset comparison strategy corresponding to the data type of each data segment.

[0129] Step S403 : comparing the data in each data segment using a corresponding preset comparison strategy to obtain a comparison result for each data segment.

[0130] In a specific implementation, different preset comparison strategies can be obtained for different data types, and these preset comparison strategies can effectively reflect the relationships or differences between data within the same segment. For example, for numerical data, a preset comparison strategy using differences or ratios can be configured. Under this preset comparison strategy, the data relationship between the data in the same data segment is determined by calculating the differences or ratios between the data within the same data segment and then comparing the differences or ratios. For text data, a preset comparison strategy using similarity comparison can be configured. Under this preset comparison strategy, the data relationship between the data in the same data segment is determined by calculating the similarities between the data within the same data segment and then comparing the similarities.

[0131] Step S404: determining the data relationship between the initial power plant data according to the comparison results.

[0132] In a specific implementation, since the data in each data segment belongs to the same type of data, the data in the same data segment corresponds to the same type of initial power plant data. Based on this, after determining the data relationship between each data segment, the above-mentioned data processing device can determine the data relationship between the same type of initial power plant data.

[0133] This embodiment determines the data relationship between data of the same type by dividing data of the same type into the same section for comparison, thereby avoiding the situation where data of different types are difficult to compare, effectively improving the efficiency of data comparison, and further improving the efficiency of determining data relationships.

[0134] This application also provides a data processing device, referring to Figure 4 , Figure 4 This is a schematic diagram of the module structure of the data processing device according to an embodiment of the present application, wherein the data processing device includes:

[0135] The data acquisition module 10 is used to collect initial power plant data from different power plant areas.

[0136] The data processing module 20 is used to process the initial power plant data to obtain processed data with a consistent format.

[0137] The data encoding module 30 is used to encode each of the processed data to obtain a plurality of encoded data.

[0138] The data comparison module 40 is configured to compare the encoded data using a preset comparison strategy, and determine the data relationship between the initial power plant data based on the comparison result.

[0139] The data processing device provided in this application, which utilizes the data processing method of the aforementioned embodiment, can resolve the technical problem of the prior art that manually processing data to analyze the data relationships between power plant data requires a significant amount of time, resulting in low data analysis efficiency. Compared to the prior art, the beneficial effects of the data processing device provided in this application are the same as those of the data processing method provided in the aforementioned embodiment, and the other technical features of the data processing device are the same as those disclosed in the aforementioned embodiment method, and are not further described here.

[0140] The present application provides a data processing device, which includes: at least one processor; and a memory 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 to enable the at least one processor to execute the data processing method in the above-mentioned embodiment 1.

[0141] Reference below Figure 5 , Figure 5The following is a schematic diagram of the device structure of the hardware operating environment involved in the data processing method in the embodiments of the present application, which shows a schematic diagram of the structure of a data processing device suitable for implementing the embodiments of the present application. The data processing device in the embodiments 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), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The data processing device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application.

[0142] like Figure 5 As shown, the data processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the data processing device. Processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. Communication device 1009 can allow the data processing device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a data processing 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.

[0143] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising 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 via 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.

[0144] The data processing device provided in this application, using the data processing method of the above-described embodiment, can solve the technical problem of the prior art that manually processes data and then analyzes the data relationships between power plant data, which requires a lot of time and leads to low data analysis efficiency. Compared with the prior art, the beneficial effects of the data processing device provided in this application are the same as those of the data processing method provided in the above-described embodiment, and the other technical features of the data processing device are the same as those disclosed in the method of the previous embodiment, and are not further described here.

[0145] It should be understood that the various parts disclosed in this application can be implemented using 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.

[0146] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0147] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, wherein the computer-readable program instructions are used to execute the data processing method in the above-mentioned embodiment.

[0148] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. 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), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, 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), etc., or any suitable combination thereof.

[0149] The computer-readable storage medium may be included in a data processing device, or may exist independently without being incorporated into a data processing device.

[0150] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the data processing device, the data processing device is enabled to: collect initial power plant data from different power plant areas; process each of the initial power plant data to obtain processed data with a consistent format; encode each of the processed data to obtain multiple encoded data; compare each of the encoded data through a preset comparison strategy, and determine the data relationship between each of the initial power plant data based on the comparison results.

[0151] 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 stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving 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., through the Internet using an Internet service provider).

[0152] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of 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 box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0153] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0154] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., a computer program) for executing the above-mentioned data processing method. This can solve the technical problem that the prior art manually processes data and then analyzes the data relationships between power plant data, which requires a lot of time and leads to low data analysis efficiency. 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 data processing method provided in the above-mentioned embodiment, and will not be repeated here.

[0155] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned data processing method when executed by a processor.

[0156] The computer program product provided in this application can solve the technical problem of the prior art of manually processing data to analyze the data relationships between power plant data, which requires a lot of time and leads to low data analysis efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the data processing method provided in the above embodiment, and will not be elaborated here.

[0157] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data processing method, characterized in that: The method comprises: Collect initial power plant data from different power plant areas; Processing each of the initial power plant data to obtain processed data with a consistent format; Encoding each of the processed data to obtain a plurality of encoded data; Comparing the encoded data using a preset comparison strategy, and determining the data relationship between the initial power plant data based on the comparison results; The step of processing the initial power plant data to obtain processed data with a consistent format includes: Obtain historical power plant data under different operating conditions; Determining characteristic parameters of each of the historical power plant data in different time intervals, wherein the characteristic parameters are parameters characteristic of the historical power plant data in a standard time interval; Comparing each characteristic parameter with a preset evaluation condition to evaluate each time interval and obtain an evaluation result, wherein the preset evaluation condition is an evaluation condition used to judge the quality of each time interval; Extracting candidate time intervals whose characteristic parameters meet the preset evaluation conditions according to the evaluation results; Determining a target time interval from each time interval according to the degree of correlation between the characteristic parameters of each candidate time interval and the preset evaluation condition; Filtering the initial power plant data to select data to be processed that is within the target time interval; Processing each of the data to be processed to obtain processed data with consistent format; The step of encoding each of the processed data to obtain a plurality of encoded data includes: Obtaining a preset encoding rule library, wherein the preset encoding rule library stores preset encoding rules corresponding to different data types; Matching each of the processed data with each of the preset coding rules in the preset coding rule library; Determine the target encoding rule corresponding to each of the processed data according to the matching results; Encoding each of the processed data using a corresponding target encoding rule to obtain a plurality of encoded data; The step of determining the target encoding rule corresponding to each processed data according to the matching result includes: If there is second-category data in each of the processed data that fails to match each of the preset encoding rules, determining a data structure of the second-category data; Selecting a reference encoding rule of characters or values ​​associated with the data structure from the preset encoding rule library; Generate a random number using a random number generator, and fill the random number into the variable part of the reference coding rule to obtain an initial coding rule; Encoding the second category of data using the initial encoding rule to obtain data to be evaluated; Decoding the data to be evaluated to obtain decoded data; Calculating the difference between the decoded data and the second type of data, and using the difference as an evaluation index corresponding to the data to be evaluated; Determining whether the accuracy of the initial encoding rule meets the preset accuracy requirement based on the evaluation index; If not, adjusting the structure of the initial encoding rule and returning to the step of encoding the second type of data using the initial encoding rule to obtain the data to be evaluated, until the accuracy of the adjusted initial encoding rule meets the preset accuracy requirement; When the accuracy of the adjusted initial encoding rule meets the preset accuracy requirement, the adjusted initial encoding rule is configured as the target encoding rule corresponding to the second type of data.

2. The data processing method according to claim 1, wherein: The step of determining the target encoding rule corresponding to each processed data according to the matching result includes: If there is first-category data that successfully matches the preset encoding rule in each of the processed data, the successfully matched preset encoding rule is used as the target encoding rule corresponding to the first-category data.

3. The data processing method according to any one of claims 1 to 2, characterized in that: The steps of comparing the encoded data using a preset comparison strategy and determining the data relationship between the initial power plant data according to the comparison results include: Divide the encoded data of the same data type into the same segment to obtain multiple data segments; Obtaining a preset comparison strategy corresponding to the data type of each data segment; Comparing the data in each of the data segments using a corresponding preset comparison strategy to obtain a comparison result for each data segment; The data relationship between the initial power plant data is determined according to the comparison results.

4. A data processing device, characterized in that: The device comprises: Data acquisition module, used to collect initial power plant data from different power plant areas; A data processing module, configured to process the initial power plant data to obtain processed data in a consistent format; A data encoding module, configured to encode each of the processed data to obtain a plurality of encoded data; A data comparison module, configured to compare the encoded data using a preset comparison strategy, and determine a data relationship between the initial power plant data based on the comparison results; The data processing module is further used to obtain historical power plant data under different operating conditions; determine characteristic parameters of each of the historical power plant data in different time intervals, wherein the characteristic parameters are parameters of the characteristics of the historical power plant data in a standard time interval; compare each characteristic parameter with a preset evaluation condition to evaluate each time interval and obtain an evaluation result, wherein the preset evaluation condition is an evaluation condition for judging the quality of each time interval; extract candidate time intervals whose characteristic parameters meet the preset evaluation condition based on the evaluation result; determine a target time interval from each time interval based on the degree of correlation between the characteristic parameters of each candidate time interval and the preset evaluation condition; filter out the data to be processed that is within the target time interval from the initial power plant data; and process each of the data to be processed to obtain processed data with a consistent format; The data encoding module is further configured to obtain a preset encoding rule library, wherein the preset encoding rule library stores preset encoding rules corresponding to different data types; match each processed data with each preset encoding rule in the preset encoding rule library; determine a target encoding rule corresponding to each processed data based on the matching result; and encode each processed data using the corresponding target encoding rule to obtain a plurality of encoded data; The data encoding module is further configured to, if there is second-category data in each processed data that fails to match each preset encoding rule, determine the data structure of the second-category data; select a base encoding rule whose characters or values ​​are associated with the data structure from the preset encoding rule library; generate a random number using a random number generator, and fill the random number into a variable part of the base encoding rule to obtain an initial encoding rule; encode the second-category data using the initial encoding rule to obtain data to be evaluated; decode the data to be evaluated to obtain decoded data; calculate the difference between the decoded data and the second-category data, and use the difference as an evaluation index corresponding to the data to be evaluated; determine whether the accuracy of the initial encoding rule meets the preset accuracy requirement based on the evaluation index; if not, adjust the structure of the initial encoding rule and return to the step of encoding the second-category data using the initial encoding rule to obtain the data to be evaluated, until the accuracy of the adjusted initial encoding rule meets the preset accuracy requirement; when the accuracy of the adjusted initial encoding rule meets the preset accuracy requirement, configure the adjusted initial encoding rule as the target encoding rule corresponding to the second-category data.

5. A data processing 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 data processing method according to any one of claims 1 to 3.

6. 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 data processing method according to any one of claims 1 to 3 are implemented.

7. 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 data processing method according to any one of claims 1 to 3 are implemented.

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