Regulation and control operation report generation method, electronic equipment and storage medium

Abstract

The invention provides a regulation and control operation report generation method, electronic equipment and a storage medium. The method is applied to the technical field of data processing and comprises the steps that original operation data of a regulation and control object are obtained, and the original operation data comprise equipment operation parameters, load change data and regulation and control instruction execution records; extracting format characteristic parameters of the original operation data, wherein the format characteristic parameters comprise a data field type, a field association degree and a data redundancy rate; matching a preset report template according to the format characteristic parameters; determining a regulation and control demand threshold value of the regulation and control operation report; performing field screening, numerical calibration and format conversion on the original operation data according to the format characteristic parameters, a field arrangement rule of a preset report template and a regulation and control demand threshold value to form adjusted operation data; and filling the adjusted operation data according to a field arrangement rule of a preset report template to generate a regulation and control operation report. Therefore, the generation efficiency of the regulation and control operation report is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for generating control operation reports, an electronic device, and a storage medium. Background Technology

[0002] In fields requiring precise control, such as power generation and industrial processes, control operation reports are crucial for recording operational status, evaluating control effectiveness, and supporting decision-making analysis. Timely and accurate generation of reports reflecting the operational status of controlled entities (such as power grids and production lines) is essential for ensuring the safe, stable, and efficient operation of the system. Currently, generating control operation reports typically relies on semi-automated methods. This requires acquiring massive amounts of raw operational data from various data sources, including equipment operating parameters, load change data, and control command execution records. This raw data is often multi-source and heterogeneous, exhibiting inconsistent data formats, different storage standards, and varying field definitions. In existing technical practices, the report generation process faces the following main challenges: (1) Due to the inconsistent original data format and lack of intelligent parsing and recognition mechanisms, technicians need to manually analyze the data format, identify valid fields, and write specific data cleaning and conversion rules. This process is not only tedious and highly repetitive, but also prone to errors due to human negligence; (2) Existing report templates are usually fixed or have only a limited number of options. When the structure of the original data or the specific requirements of this report change, the fixed template is difficult to adapt flexibly. It often requires manual adjustment of the template structure or writing of new data mapping logic. The adaptation process is time-consuming and labor-intensive, resulting in low efficiency in generating control and operation reports. Summary of the Invention

[0003] To address the problems existing in the prior art, the present invention provides a method for generating control operation reports, an electronic device, and a storage medium.

[0004] According to a first aspect of the present invention, a method for generating control operation reports is provided. The method includes:

[0005] Acquire the raw operating data of the controlled object, including equipment operating parameters, load change data, and control command execution records;

[0006] The original running data is parsed to extract the format feature parameters of the original running data, including data field type, field correlation degree and data redundancy rate;

[0007] A preset report template is matched according to the format feature parameters. The preset report template contains several preset fields and field arrangement rules.

[0008] Determine the control requirement threshold for the target control operation report, wherein the control requirement threshold includes a data accuracy threshold and a report generation timeliness threshold;

[0009] Based on the format feature parameters, the field arrangement rules of the preset report template, and the control requirement threshold, the original running data is subjected to field filtering, numerical calibration, and format conversion to form adjusted running data;

[0010] The adjusted operating data is filled in according to the field arrangement rules of the preset report template to generate a control operation report.

[0011] Further, the step of parsing the original running data and extracting the format feature parameters of the original running data includes:

[0012] If the original running data is structured data, the field identifiers and corresponding data types of the original running data are directly read, the field correlation degree between each field is calculated, and the ratio of the number of null value fields to the total number of fields is calculated to determine the data redundancy rate.

[0013] If the original running data is unstructured data, then key information fragments in the original running data are identified by natural language processing algorithms, the key information fragments are classified and labeled to determine the data field type, and the recurrence rate of the same type of key information fragments is calculated to determine the data redundancy rate.

[0014] Furthermore:

[0015] After determining the control demand threshold, the equipment operating parameters and load change data in the original operating data are compared with the data accuracy threshold, and the data extraction priority is determined based on the comparison results.

[0016] If the deviation of the equipment operating parameters is less than the data accuracy threshold and the fluctuation of the load change data is less than the data accuracy threshold, the extraction priority of the corresponding data will be classified as the first-level extraction priority.

[0017] If the deviation of the equipment operating parameters is greater than or equal to the data accuracy threshold or the fluctuation of the load change data is greater than or equal to the data accuracy threshold, the extraction priority of the corresponding data will be divided into a secondary extraction priority.

[0018] Furthermore:

[0019] For the data corresponding to the first-level extraction priority, a direct mapping method is used to match the data fields with the preset fields of the preset report template. If there are differences in field names, the matching field is determined based on the field correlation and the format is converted and adjusted.

[0020] For the data corresponding to the secondary extraction priority, the data is numerically calibrated, the deviation rate between the calibrated data and the data precision threshold is calculated, the number of digits to be retained in the data field is adjusted according to the deviation rate, and then the field filling is adjusted according to the field arrangement rules of the preset report template.

[0021] Furthermore:

[0022] After adjusting the original running data, the field matching deviation between the adjusted running data and the original running data is calculated. The field matching deviation is the ratio of the number of missing fields after adjustment to the total number of fields in the original data.

[0023] If the field matching deviation is less than the preset deviation threshold, then the adjusted running data is determined to meet the report generation requirements;

[0024] If the field matching deviation is greater than or equal to the preset deviation threshold, the data extraction priority is readjusted based on the field correlation, and the unmatched fields are extracted and converted in format.

[0025] Furthermore:

[0026] After the adjusted running data meets the report generation requirements, the matching degree between the adjusted running data and the preset fields of the preset report template is calculated. The matching degree is the ratio of the number of matching fields to the total number of preset fields.

[0027] If the matching degree is greater than or equal to the preset matching degree threshold, proceed directly to the report generation step;

[0028] If the matching degree is less than the preset matching degree threshold, the template optimization direction is determined based on the format feature parameters. The template optimization direction includes adding fields, adjusting the field order, and merging similar fields.

[0029] Furthermore:

[0030] If the template optimization direction is to add a field, then based on the field correlation in the format feature parameters, the unmatched field with the highest correlation is inserted as a new field into the corresponding position of the preset report template;

[0031] If the optimization direction of the template is to adjust the field order, then the field order of the preset report template is rearranged according to the time order of the execution record of the control instruction, so that the fields with a high degree of relevance to the control instruction are arranged first.

[0032] Furthermore:

[0033] After generating the control operation report, the report generation efficiency coefficient is calculated. The report generation efficiency coefficient is the ratio of the report generation time to the report generation time threshold.

[0034] If the report generation efficiency coefficient is less than or equal to 1, then the generation process of the control operation report is deemed qualified.

[0035] If the report generation efficiency coefficient is greater than 1, the generation process of the control operation report is deemed unqualified.

[0036] According to a second aspect of the present invention, a control operation report generation apparatus is provided. The apparatus includes:

[0037] The acquisition module is used to acquire the original operating data of the controlled object, including equipment operating parameters, load change data, and control command execution records.

[0038] The parsing module is used to parse the original running data and extract the format feature parameters of the original running data. The format feature parameters include data field type, field correlation degree and data redundancy rate.

[0039] A matching module is used to match a preset report template according to the format feature parameters. The preset report template includes several preset fields and field arrangement rules.

[0040] The determination module is used to determine the control requirement threshold of the target control operation report, wherein the control requirement threshold includes a data accuracy threshold and a report generation timeliness threshold;

[0041] The adjustment module is used to perform field filtering, numerical calibration, and format conversion on the original running data according to the format feature parameters, the field arrangement rules of the preset report template, and the control requirement threshold, so as to form adjusted running data.

[0042] The generation module is used to fill the adjusted running data according to the field arrangement rules of the preset report template to generate a control running report.

[0043] According to a third aspect of the present invention, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method.

[0044] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method.

[0045] This invention, by analyzing data format characteristics and combining them with control thresholds, can intelligently filter out necessary fields, eliminate redundant data, and perform precise numerical calibration, reducing the amount of data to be processed and avoiding the inefficiency of full-scale processing. By automatically matching preset report templates based on data characteristics, it eliminates the tedious process of manually designing report formats and layouts each time, significantly shortening the time for defining report structures. By guiding the data processing flow through control thresholds, it can dynamically adjust the depth and scope of data processing according to timeliness requirements, prioritizing speed while meeting minimum requirements and avoiding unnecessary and time-consuming fine-tuning. By converting the format of the original data before filling the template, it ensures that the data format is completely consistent with the template requirements, eliminating the need for additional conversion or adjustment steps due to format mismatch during the filling stage or after generation, making the filling process smooth and efficient. The entire process is highly automated, greatly reducing the time cost and error probability of manual judgment, selection, and operation, thereby improving the generation efficiency of controlled reports.

[0046] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0047] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of the invention. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0048] Figure 1 A flowchart of a method for generating control operation reports according to an embodiment of the present invention is shown;

[0049] Figure 2 A block diagram of a control operation report generation device according to an embodiment of the present invention is shown;

[0050] Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present invention is shown. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Furthermore, the terms "and / or" in this article are merely a description of the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0053] Figure 1 A flowchart of a method for generating control operation reports according to an embodiment of the present invention is shown. The method includes:

[0054] S101, Obtain the original operating data of the controlled object, the original operating data including equipment operating parameters, load change data and control command execution records;

[0055] S102, parse the original running data and extract the format feature parameters of the original running data. The format feature parameters include data field type, field correlation degree and data redundancy rate.

[0056] S103, Match a preset report template according to the format feature parameters, wherein the preset report template includes several preset fields and field arrangement rules;

[0057] S104, determine the control requirement threshold of the target control operation report, wherein the control requirement threshold includes a data accuracy threshold and a report generation timeliness threshold;

[0058] S105, based on the format feature parameters, the field arrangement rules of the preset report template, and the control requirement threshold, the original running data is subjected to field filtering, numerical calibration, and format conversion to form adjusted running data;

[0059] S106, The adjusted operating data is filled in according to the field arrangement rules of the preset report template to generate a control operation report.

[0060] In some embodiments, parsing the original running data and extracting the format feature parameters of the original running data includes: if the original running data is structured data, directly reading the field identifiers and corresponding data types of the original running data, calculating the field correlation degree between each field, and calculating the ratio of the number of null value fields to the total number of fields to determine the data redundancy rate; if the original running data is unstructured data, identifying key information fragments in the original running data through natural language processing algorithms, classifying and labeling the key information fragments to determine the data field type, and calculating the recurrence rate of similar key information fragments to determine the data redundancy rate. According to embodiments of the present invention, the algorithm automatically identifies fields, types, relationships, and redundancy, significantly reducing manual preparation time; by distinguishing between structured and unstructured data and employing optimal methods for each, inefficient uniform processing methods are avoided; by pre-calculating and determining key metadata such as field relationships and data redundancy rates, a direct basis is provided for subsequent data cleaning, transformation, and report logic generation, reducing runtime computation; by using natural language processing algorithms to automatically identify, classify, and label key information in unstructured data, data that is difficult to use directly is transformed into structured or semi-structured features, greatly improving the processing efficiency of unstructured data, thereby improving the generation efficiency of regulatory operation reports.

[0061] For example, when the original operational data is structured data, assuming it comes from a database table of the power control system, this table contains 100 records. Each record has a Device ID, Status, Load, and Timestamp. The fields to be read are DeviceID, Status, Load, and Timestamp; the data types to be retrieved are string, integer, floating-point, and date / time, respectively. By analyzing the data records, the frequency of simultaneous occurrence of field values ​​is counted. When the segment value is 1 (indicating running), records with a Load field value greater than 100 appear 80 times; when the Status field value is 0 (indicating shutdown), records with a Load field value of 0 appear 15 times. Calculating the field correlation, the correlation between Status and Load is 95 / 100 = 0.95. Checking for null values, it was found that the Load field has 5 null values ​​and the Timestamp field has 2 null values, for a total of 7 null value fields (number of null value cells). The total number of fields is 400 (4 fields × 100 records), so the data redundancy rate is 7 / 400 = 0.0175.

[0062] When the original operational data is unstructured, assuming it's log text from the control center, the content might be: Device A is overloaded, control command reduces load; Device B operates normally, load stabilizes; Device A overloads again, control is repeated; segments like Device A, overload, control command, Device B, and normal operation are identified; Device A and Device B are labeled as device identifiers, overload and normal operation as operational status types, and control commands as command types. This determines the data field types; the recurrence rate of similar key information segments is calculated. Device A appears twice, overload appears twice, the total number of key information segments is 10, and the number of repeated segments is 4 (Device A repeats once, overload repeats once, count the number of recurrences). Therefore, the data redundancy rate is 4 / 10 = 0.4.

[0063] In some embodiments, after determining the control demand threshold, the equipment operating parameters and load change data in the original operating data are compared with the data accuracy threshold, and the data extraction priority is determined based on the comparison results. Specifically, if the deviation of the equipment operating parameters is less than the data accuracy threshold and the fluctuation of the load change data is less than the data accuracy threshold, the corresponding data is classified as a first-level extraction priority. If the deviation of the equipment operating parameters is greater than or equal to the data accuracy threshold or the fluctuation of the load change data is greater than or equal to the data accuracy threshold, the corresponding data is classified as a second-level extraction priority. According to the embodiments of the present invention, by skipping complex verification or correction steps and directly using data that meets the accuracy requirements, the overall computational complexity is significantly reduced, thereby improving the generation efficiency of control operation reports.

[0064] For example, when determining the control demand threshold, the data precision threshold is set to 0.05, and the original operating data includes the operating parameters and load change data of equipment A and equipment B;

[0065] For device A, the operating parameters are the current load value, the expected load value of 100, and the actual load value of 102. The deviation value is calculated as 102 - 100 / 100 = 0.02. The load change data is the load value sequence of the past 5 time points: 101, 102, 103, 101, 102. The average value is calculated to be approximately 101.8, the standard deviation is approximately 0.75, and the fluctuation value is calculated as standard deviation / average value ≈ 0.75 / 101.8 ≈ 0.0074. The deviation value of 0.02 and the fluctuation value of 0.0074 are compared with the data precision threshold of 0.05. The deviation value is less than the threshold (0.02 < 0.05), and the fluctuation value is less than the threshold (0.0074 < 0.05). Therefore, the data extraction priority of device A is classified as Level 1 extraction priority.

[0066] For device B, the operating parameters are the current load value, the expected load value is 100, the actual load value is 95, and the deviation value is 95-100 / 100=0.05. The load change data is the load value sequence of the past 5 time points: 90, 110, 85, 105, 95, with an average value of approximately 97 and a standard deviation of approximately 9.27. The fluctuation value is calculated as standard deviation / average value ≈ 9.27 / 97 ≈ 0.0956. Comparing the deviation value of 0.05 and the fluctuation value of 0.0956 with the data precision threshold of 0.05, the deviation value is equal to the threshold (0.05=0.05), and the fluctuation value is greater than the threshold (0.0956>0.05). Therefore, the data extraction priority for device B is divided into secondary extraction priority.

[0067] In some embodiments, for data corresponding to the first-level extraction priority, a direct mapping method is used to match the data fields with the preset fields of the preset report template. If there are differences in field names, the matching field is determined based on the field correlation and the format is converted and adjusted. For data corresponding to the second-level extraction priority, the data is numerically calibrated, the deviation rate between the calibrated data and the data precision threshold is calculated, and the number of digits retained in the data field is adjusted according to the deviation rate. Then, the field is filled and adjusted according to the field arrangement rules of the preset report template. According to the embodiments of the present invention, for data with matching field names, complex conversion or calculation steps are eliminated, and the data is filled directly. For fields with mismatched names, the corresponding relationship is quickly determined using predefined correlation, avoiding manual intervention or complex pattern matching calculations. By completing the necessary format conversion during matching, it is ensured that the data meets the template requirements before filling, avoiding subsequent processing, thereby improving the generation efficiency of the control operation report.

[0068] For example, the preset fields of the pre-defined report template are: [Device ID, Status, Load Value, Time]. The original operating data is for device A (first-level extraction priority), and the data fields are: [DeviceID, Status, Load, Timestamp]. The data precision threshold is set to 0.05, and the extracted format feature parameters include a correlation of 0.95 between the data fields Status and Load. Comparing the data field names with the template field names, DeviceID differs from the device ID name, but based on the predefined field correlation, it matches as the device ID. Status differs from the status name, but based on the field correlation (Status and Load have a correlation of 0.95, while the status and load values ​​in the template have a high correlation), it matches as the status. The Load field has a different name than the load value field, but it is matched as the load value based on the field relevance. The Timestamp field has a similar name to the time field, so it is directly matched as the time field. For the Status field, the data value is an integer, but the template requires a string format, so the integer 1 is converted to the string "Running" and the integer 0 is converted to "Out of Service". For the Timestamp field, the data value is a date and time format (20235-10-01 10:00:00), but the template requires a simplified time format, so it is converted to 2025-10-01 10:00. The converted data is then filled according to the template field arrangement rules: Device number is filled as DeviceA, Status is filled as "Running", Load value is filled as 102, and Time is filled as 2025-10-01 10:00.

[0069] In some embodiments, after adjusting the original running data, the field matching deviation between the adjusted running data and the original running data is calculated. The field matching deviation is the ratio of the number of missing fields after adjustment to the total number of fields in the original data. If the field matching deviation is less than a preset deviation threshold, the adjusted running data is determined to meet the report generation requirements. If the field matching deviation is greater than or equal to the preset deviation threshold, the data extraction priority is readjusted based on the field correlation, and the unmatched fields are extracted and converted in a second time. According to the embodiments of the present invention, by calculating and comparing the field matching deviation with the preset threshold, it is possible to quickly determine whether the adjusted data is complete enough for report generation, avoiding wasting time on trying to generate reports with obviously incomplete data. By triggering secondary processing only when the deviation is too large, secondary extraction and conversion are performed only on the missing fields, rather than reprocessing all the original data, the amount of data that needs to be processed is greatly reduced. By supplementing only the missing parts and using threshold judgment to determine whether supplementary processing is needed, it is avoided to perform complete but potentially unnecessary secondary operations on all data each time, thereby improving the generation efficiency of the control operation report.

[0070] For example, suppose the original operating data is structured data containing 100 records. Each record has fields including DeviceID, Status, Load, and Timestamp. Therefore, the original total number of fields is 4. After adjusting the original operating data, adjusted operating data is generated. During the adjustment process, due to field matching issues, the adjusted operating data only contains 3 fields: Device ID, Status, and Load. The Timestamp field is missing. Therefore, the number of missing fields after adjustment is 1. Field matching deviation = number of missing fields after adjustment / total number of original fields = 1 / 4 = 0.25. The preset deviation threshold is 0.1. Since the field matching deviation of 0.25 is greater than the preset deviation threshold of 0.1, it is determined that the adjusted operating data does not meet the report generation requirements.

[0071] Based on the format characteristics of the original running data, the correlation between the Status and Load fields is 0.95, while the correlation between the Timestamp field and other fields is low. Therefore, the data extraction priority for the unmatched Timestamp field is adjusted to a higher priority. The Timestamp field value is extracted again from the original running data, and a format conversion is performed, changing the original date and time format of the Timestamp field ("2025-10-01 10:00:00") to the simplified time format required by the preset report template ("2025-10-01 10:00"). After the second extraction and format conversion, the adjusted running data includes the time field. At this point, all fields match, and the field matching deviation drops to 0, which is less than the preset deviation threshold of 0.1. Therefore, the adjusted running data is determined to meet the report generation requirements.

[0072] In some embodiments, after the adjusted running data meets the report generation requirements, the matching degree between the adjusted running data and the preset fields of the preset report template is calculated. The matching degree is the ratio of the number of matching fields to the total number of preset fields. If the matching degree is greater than or equal to the preset matching degree threshold, the report generation step is directly initiated. If the matching degree is less than the preset matching degree threshold, the template optimization direction is determined based on the format feature parameters. The template optimization direction includes adding fields, adjusting the field order, and merging similar fields. According to the embodiments of the present invention, when the matching degree between the data and the template is sufficiently high, the time-consuming template optimization is skipped directly, avoiding the redundant work of checking or modifying the template every time a report is generated. By using the preset matching degree threshold as a judgment standard, it can automatically decide whether to generate a report directly or to optimize the template without manual intervention, thus accelerating the process. When the matching degree is insufficient, the system directly determines the specific optimization direction based on the format feature parameters, avoiding blind and comprehensive template checks, making the optimization process more focused, and thereby improving the generation efficiency of the controlled running report.

[0073] For example, the preset fields of the preset report template include 5 fields [Device ID, Status, Load Value, Time, Remarks]. The total number of preset fields is 5. The adjusted running data comes from the original running data. The original running data is structured data and contains the fields DeviceID, Status, Load, and Timestamp. After field filtering and format conversion, the adjusted running data contains the fields Device ID (corresponding to DeviceID), Status (corresponding to Status), Load Value (corresponding to Load), and Time (corresponding to Timestamp), but the Remarks field is missing. Therefore, the number of matching fields is 4, and the matching degree is calculated as 4 / 5 = 0.8. The preset matching degree threshold is 0.9. Since the matching degree of 0.8 is less than the threshold of 0.9, the template optimization direction is determined based on the format feature parameters (field correlation) of the original running data: Field correlation analysis shows that the correlation between the Remarks field and the Status field is 0.6, and the data redundancy rate is low. Therefore, the optimization direction is to add a Remarks field and adjust the Remarks field to the end of the field order.

[0074] In some embodiments, if the template optimization direction is to add a new field, then according to the field correlation in the format feature parameters, the unmatched field with the highest correlation is inserted as a new field into the corresponding position of the preset report template; if the template optimization direction is to adjust the field order, then according to the time order of the control instruction execution record, the field order of the preset report template is rearranged so that the fields with high correlation to the control instruction are arranged first.

[0075] In some embodiments, after generating the control operation report, a report generation efficiency coefficient is calculated. The report generation efficiency coefficient is the ratio of the report generation time to the report generation timeliness threshold. If the report generation efficiency coefficient is less than or equal to 1, the generation process of the control operation report is deemed qualified. If the report generation efficiency coefficient is greater than 1, the generation process of the control operation report is deemed unqualified.

[0076] Figure 2 A block diagram of a control operation report generation apparatus according to an embodiment of the present invention is shown. The apparatus includes:

[0077] The acquisition module 201 is used to acquire the original operating data of the controlled object, the original operating data including equipment operating parameters, load change data and control command execution records;

[0078] The parsing module 202 is used to parse the original running data and extract the format feature parameters of the original running data. The format feature parameters include data field type, field correlation degree and data redundancy rate.

[0079] Matching module 203 is used to match a preset report template according to the format feature parameters. The preset report template includes several preset fields and field arrangement rules.

[0080] The determination module 204 is used to determine the control requirement threshold of the target control operation report, wherein the control requirement threshold includes a data accuracy threshold and a report generation timeliness threshold;

[0081] The adjustment module 205 is used to perform field filtering, numerical calibration and format conversion on the original running data according to the format feature parameters, the field arrangement rules of the preset report template and the control requirement threshold, so as to form the adjusted running data.

[0082] The generation module 206 is used to fill the adjusted running data according to the field arrangement rules of the preset report template to generate a control running report.

[0083] According to embodiments of the present invention, the present invention also provides an electronic device and a readable storage medium.

[0084] Figure 3A schematic block diagram of an electronic device that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0085] The electronic device includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM 302 or a computer program loaded into RAM 303 from storage unit 308. RAM 303 can also store various programs and data required for the operation of the electronic device. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0086] Multiple components in the electronic device are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, optical disk, etc.; and a communication unit 309, such as a network card, modem, wireless transceiver, etc. The communication unit 309 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0087] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as regulating the operation report generation method. For example, in some embodiments, the regulating operation report generation method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the regulating operation report generation method described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the regulating operation report generation method by any other suitable means (e.g., by means of firmware).

[0088] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0090] In the context of this invention, a readable storage medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. A readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0091] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0092] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0093] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0094] It should be understood that the various processes described above can be used to rearrange, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0095] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for regulating the generation of a run report, characterized by, The method comprises the following steps: obtaining original operation data of a control object, wherein the original operation data comprises device operation parameters, load change data and control instruction execution records; analyzing the original operation data to extract format characteristic parameters of the original operation data, wherein the format characteristic parameters comprise data field types, field correlation degrees and data redundancy rates; matching a preset report template according to the format characteristic parameters, wherein the preset report template comprises a plurality of preset fields and field arrangement rules; determining control requirement thresholds of a target control operation report, wherein the control requirement thresholds comprise data precision thresholds and report generation timeliness thresholds; performing field screening, value calibration and format conversion on the original operation data according to the format characteristic parameters, the field arrangement rules of the preset report template and the control requirement thresholds to form adjusted operation data; filling the adjusted operation data according to the field arrangement rules of the preset report template to generate the control operation report.

2. The method of claim 1, wherein, The step of analyzing the original operation data to extract format characteristic parameters of the original operation data comprises the following steps: if the original operation data is structured data, directly reading field identifiers and field corresponding data types of the original operation data, calculating field correlation degrees between fields, and calculating a ratio of the number of null fields to the total number of fields to determine the data redundancy rate; if the original operation data is unstructured data, identifying key information segments in the original operation data through a natural language processing algorithm, classifying and labeling the key information segments to determine the data field types, and calculating a repetition rate of the same type of key information segments to determine the data redundancy rate.

3. The method of claim 2, wherein, The method further comprises the following steps: after the control requirement thresholds are determined, comparing device operation parameters and load change data in the original operation data with the data precision thresholds, and determining data extraction priorities according to comparison results; if a deviation value of the device operation parameters is less than the data precision threshold and a fluctuation value of the load change data is less than the data precision threshold, the extraction priority of the corresponding data is classified as a first extraction priority; if the deviation value of the device operation parameters is greater than or equal to the data precision threshold or the fluctuation value of the load change data is greater than or equal to the data precision threshold, the extraction priority of the corresponding data is classified as a second extraction priority.

4. The control operation report generation method according to claim 3, wherein: for data corresponding to the first extraction priority, a direct mapping method is used to match data fields with preset fields of the preset report template, if there is a difference in field names, matching fields are determined based on the field correlation degrees and format conversion adjustment is performed; for data corresponding to the second extraction priority, value calibration is performed on the data, a deviation rate of the calibrated data from the data precision threshold is calculated, the number of reserved bits of the data field is adjusted according to the deviation rate, and then field filling adjustment is performed according to the field arrangement rules of the preset report template.

5. The control operation report generation method according to claim 4, wherein: After the adjustment on the original operation data, a field matching deviation of the adjusted operation data and the original operation data is calculated, the field matching deviation being a ratio of an adjusted missing field quantity to an original total field quantity; If the field matching deviation is less than a preset deviation threshold, it is determined that the adjusted operation data meets the report generation requirement; If the field matching deviation is greater than or equal to the preset deviation threshold, the data extraction priority is adjusted again based on the field correlation degree, and the unmatched field is extracted and converted again.

6. The method according to claim 5, wherein: After the adjusted operation data meets the report generation requirement, a matching degree of the adjusted operation data and preset fields of the preset report template is calculated, the matching degree being a ratio of a matching field quantity to a preset total field quantity; If the matching degree is greater than or equal to a preset matching degree threshold, a report generation step is directly entered; If the matching degree is less than the preset matching degree threshold, a template optimization direction is determined based on the format characteristic parameter, the template optimization direction including adding a field, adjusting a field order, and merging similar fields.

7. The method according to claim 6, wherein: If the template optimization direction is to add a field, an unmatched field with the highest correlation degree is inserted into a corresponding position of the preset report template as an added field according to the field correlation degree in the format characteristic parameter; If the template optimization direction is to adjust the field order, the field order of the preset report template is rearranged according to a time order of the regulation and control instruction execution record, so that fields with high correlation degrees to the regulation and control instruction are arranged in front.

8. The method of claim 7, wherein, Further comprising: After the regulation and control operation report is generated, a report generation efficiency coefficient is calculated, the report generation efficiency coefficient being a ratio of a report generation time length to the report generation time threshold; If the report generation efficiency coefficient is less than or equal to 1, it is determined that the generation process of the regulation and control operation report is qualified; If the report generation efficiency coefficient is greater than 1, it is determined that the generation process of the regulation and control operation report is unqualified.

9. An electronic device, comprising: Comprise: At least one processor; A memory in communication connection with the at least one processor; The memory stores instructions executable 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 method of any one of claims 1-8.

10. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to execute the method of any one of claims 1-8.

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