Recheck and verification method based on artificial intelligence electric power review data

Abstract

The invention provides an artificial intelligence-based electric power review data review and verification method, which comprises the following specific steps: S1, data preprocessing: data preprocessing comprises data cleaning, data standardization and feature extraction, input review data can be cleaned and converted into a uniform format and standard, and the data are extracted; then key features in the review data are extracted, and a basis is provided for subsequent review data processing; and S2, intelligent identification and classification: the intelligent identification and classification comprises natural language processing, image identification and data association, text and image processing can be carried out on the review data, and key information in the text and the image can be identified and classified. By constructing the efficient artificial intelligence algorithm model, rapid identification, classification and verification of the review data are realized, the data processing efficiency is greatly improved, and efficient and stable processing performance can be maintained even in the face of mass data.

Description

Technical Field

[0001] The present invention relates to the field of review and verification of power review data, and specifically to a method for review and verification of power review data based on artificial intelligence. Background Art

[0002] During the review process of power engineering projects, the accuracy, integrity, and consistency of data are crucial for project decision-making. However, traditional methods for reviewing and verifying power review data mainly rely on manual operations and have many limitations. First of all, manual review and verification are inefficient. Facing a large amount of review data, a large amount of human resources and time costs are often required. Secondly, human factors are prone to errors and omissions, affecting the accuracy and reliability of the review results. In addition, as the complexity and scale of power engineering projects continue to expand, traditional methods are difficult to meet the requirements of efficient and accurate data review and verification.

[0003] Currently, the following several technical means are mainly adopted for the review and verification of power review data:

[0004] 1. Excel or database query: By writing complex query statements or formulas, data is compared and verified. However, this method requires high skills from operators and is difficult to handle complex logical relationships and data associations.

[0005] 2. Professional software assistance: There are some professional software for reviewing and verifying power review data on the market, but such software often has a single function, cannot comprehensively cover all aspects of review data, and has poor customization, making it difficult to meet the needs of specific projects.

[0006] 3. Manual review and expert review: By organizing an expert team to review the data item by item, although the accuracy of review and verification can be improved to a certain extent, the efficiency is low and the cost is high.

[0007] The above technical means all have certain deficiencies in the review and verification of power review data, mainly manifested in the following aspects:

[0008] 1. Inefficiency problem: Traditional review and verification of power review data mainly rely on manual operations, which not only consume time and effort but also are difficult to cope with the challenge of large-scale data processing. In the case of a large amount of data, manual review is not only inefficient but also prone to errors and omissions caused by fatigue.

[0009] 2. Limited accuracy: Human factors are difficult to avoid in the data review process, such as negligence, misunderstanding, or intentional tampering, etc., which may seriously affect the accuracy of review data. In addition, complex review rules and data association relationships also increase the difficulty and error rate of manual review.

[0010] 3. High cost: To ensure the accuracy of review data, a large amount of human resources are often required for repeated checking and verification, which undoubtedly increases the cost burden of the project. At the same time, for large-scale power engineering projects, the high cost of data review may also become a bottleneck for project progress.

[0011] 4. Low level of intelligence: In the existing technology, although there are some software tools to assist in data review and verification, most of these tools have single functions and low levels of intelligence, making it difficult to comprehensively cover all aspects of review data and unable to flexibly adjust and optimize according to review rules and data characteristics.

[0012] In summary, there are many deficiencies and objective drawbacks in the existing technology for the review and verification of power review data. There is an urgent need for a more efficient, accurate, and intelligent review and verification method to solve these problems. The proposed invention is based on this background and aims to achieve automated and intelligent review and verification of power review data through artificial intelligence technology, improving data processing efficiency and accuracy. Summary of the Invention

[0013] (1) Technical problems to be solved

[0014] In view of the deficiencies of the existing technology, the present invention provides a method for review and verification of power review data based on artificial intelligence, which solves the problems of low efficiency, difficult accuracy guarantee, high cost, and low level of intelligence existing in the traditional method for review and verification of power review data.

[0015] (2) Technical solutions

[0016] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for review and verification of power review data based on artificial intelligence, including the following specific steps:

[0017] S1. Data preprocessing, which includes data cleaning, data standardization, and feature extraction. It can clean the input review data, convert it into a unified format and standard, and then extract the key features in the review data to provide a basis for subsequent review data processing;

[0018] S2. Intelligent identification and classification, which includes natural language processing, image recognition, and data association. It can separately process the text and images in the review data, identify and classify the key information in the text and images, and then establish an association model between the data according to the internal logical relationship of the review data to provide a basis for subsequent verification;

[0019] S3. Construction of the intelligent verification rule library. The construction of the intelligent verification rule library includes rule definition, rule coding, and rule library management. A series of verification rules can be defined according to the standards and specifications of power engineering reviews, and then transformed into algorithms and models executable by computers. These algorithms and models are uniformly managed, maintained, and updated to ensure the accuracy and timeliness of the verification rules.

[0020] S4. Intelligent review and verification execution. The intelligent review and verification execution includes data distribution, rule execution, and result output. The preprocessed review data is intelligently identified and classified according to the rules in the rule library, and then the results are assigned to the corresponding verification rules for processing. The verification results are output in the form of a report.

[0021] S5. Anomaly detection and feedback optimization. The anomaly detection and feedback optimization includes anomaly detection, cause analysis, and feedback optimization. Machine learning algorithms are used to detect anomalies in the verification results, identify potential errors or abnormal patterns, then analyze the causes of the detected anomalies and find the root causes of the problems. Furthermore, the anomaly detection results and cause analysis are fed back to users or system administrators, and then the rule library is optimized and updated according to the feedback results to improve the accuracy and efficiency of verification.

[0022] Preferably, the data cleaning in S1 includes removing invalid, duplicate, incorrect, or inconsistent records from the review data, and performing preliminary purification of the data.

[0023] Preferably, the feature extraction in S1 includes, but is not limited to, project type, voltage level, and equipment parameters.

[0024] Preferably, the natural language processing in S2 can identify text-based review data, and the image recognition in S2 can identify image-based review data. Both the natural language processing and image recognition in S2 have deep learning algorithm functions. Using technologies such as natural language processing and image recognition, the review data is intelligently identified and classified, enabling the data to be quickly assigned to the corresponding processing processes, further accelerating the processing speed, shortening the project cycle, and enhancing competitiveness.

[0025] Preferably, the rule definition in S3 includes, but is not limited to, data range verification, logical relationship verification, and compliance verification.

[0026] Preferably, the verification results in S4 include the data items that pass the verification, the data items that fail the verification, and their reasons.

[0027] Preferably, the image-based review data includes, but is not limited to, charts and drawings.

[0028] Preferably, the data preprocessing, intelligent recognition and classification, construction of intelligent verification rule library, intelligent review and verification execution, and anomaly detection and feedback optimization are all carried out in an artificial intelligence environment. By introducing artificial intelligence technology, the intelligent processing and analysis of review data are realized, which provides strong technical support for the future development of power engineering review work. At the same time, it will provide useful reference and demonstration for the intelligent upgrading of other fields in the power industry. By promoting and applying similar technical solutions, the intelligent level of the entire power industry can be continuously improved.

[0029] (III) Beneficial Effects

[0030] The present invention provides a method for review and verification of power review data based on artificial intelligence. It has the following

[0031] Beneficial effects:

[0032] 1. Improve data processing efficiency: By constructing an efficient artificial intelligence algorithm model, rapid identification, classification, and verification of review data are achieved, significantly improving the efficiency of data processing. Even when faced with a large amount of data, high-efficiency and stable processing performance can be maintained.

[0033] 2. Ensure data accuracy: Utilize the precision and intelligence of artificial intelligence technology to achieve all-round and multi-angle verification of review data. Through preset verification rules and intelligent algorithms, errors and omissions in the data are automatically discovered and corrected to ensure the accuracy of the review data.

[0034] 3. By reducing the dependence on manual review, the labor cost input in the data review and verification link of the project is reduced. At the same time, improving the data processing efficiency also helps to shorten the project cycle and further reduce the overall cost of the project.

[0035] 4. The proposal of the present invention will promote the intelligent upgrading of power engineering review work. By introducing artificial intelligence technology, intelligent processing and analysis of review data are realized, providing more scientific and accurate data support for project decision-making. Specific Embodiments

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] The embodiments of the present invention provide a method for review and verification of power review data based on artificial intelligence, including the following specific steps:

[0038] S1. Data preprocessing. Data preprocessing includes data cleaning, data standardization, and feature extraction. It can clean the input review data, convert it into a unified format and standard, and then extract the key features in the review data to provide a basis for subsequent review data processing. Data cleaning includes removing invalid, duplicate, incorrect, or inconsistent records in the review data. Feature extraction includes, but is not limited to, project type, voltage level, and equipment parameters. By introducing artificial intelligence technology, the automated processing of review data is achieved, reducing the time and effort invested in manual operations. Compared with traditional manual review and verification methods, especially when faced with large-scale review data, the advantages of the present invention are more obvious;

[0039] S2. Intelligent recognition and classification. Intelligent recognition and classification include natural language processing, image recognition, and data association. It can separately process the text and images in the review data, identify and classify the key information in the text and images, and then establish an association model between the data according to the internal logical relationship of the review data to provide a basis for subsequent verification. Natural language processing can identify text-based review data, and image recognition can identify image-based review data. Image-based review data includes, but is not limited to, charts and drawings. Both natural language processing and image recognition have deep learning algorithm functions. Using technologies such as natural language processing and image recognition to perform intelligent recognition and classification on review data enables the data to be quickly assigned to the corresponding processing processes, further accelerating the processing speed, shortening the project cycle, and improving competitiveness;

[0040] S3. Construction of an intelligent verification rule library. The construction of an intelligent verification rule library includes rule definition, rule coding, and rule library management. A series of verification rules can be defined according to the standards and specifications of power engineering reviews, and converted into algorithms and models that can be executed by a computer, and these algorithms and models are uniformly managed, maintained, and updated to ensure the accuracy and timeliness of the verification rules. Rule definition includes, but is not limited to, data range verification, logical relationship verification, and compliance verification. The present invention constructs an intelligent verification rule library, encoding the standards and specifications of power engineering reviews into algorithms or models that can be executed by a computer. These rules can automatically review and verify the review data, reducing errors and omissions caused by human factors, thereby improving the accuracy of the data;

[0041] S4. Execution of intelligent review and verification. The execution of intelligent review and verification includes data distribution, rule execution, and result output. The preprocessed review data is intelligently recognized and classified according to the rules in the rule library, and then the results are assigned to the corresponding verification rules for processing, and the verification results are output in the form of a report. The verification results include the data items that pass the verification, the data items that fail the verification, and their reasons;

[0042] S5. Anomaly Detection and Feedback Optimization. Anomaly detection and feedback optimization include anomaly detection, root cause analysis, and feedback optimization. Machine learning algorithms are used to detect anomalies in the verification results, identify potential errors or abnormal patterns, then analyze the root causes of the detected anomalies, and further feedback the anomaly detection results and root cause analysis to users or system administrators. Then, the rule base is optimized and updated according to the feedback results to improve the accuracy and efficiency of verification. By using machine learning algorithms to detect anomalies in the verification results, potential errors or abnormal patterns can be discovered in a timely manner and root cause analysis can be carried out. This mechanism helps to further improve the accuracy of data and ensure the reliability of review results.

[0043] Data preprocessing, intelligent recognition and classification, construction of an intelligent verification rule base, intelligent review and verification execution, and anomaly detection and feedback optimization are all carried out in an artificial intelligence environment. By introducing artificial intelligence technology, the intelligent processing and analysis of review data are realized, providing strong technical support for the future development of power engineering review work. At the same time, it will provide useful reference and demonstration for the intelligent upgrading of other fields in the power industry. By promoting and applying similar technical solutions, the intelligent level of the entire power industry can be continuously improved.

[0044] Working Principle: Through core links such as data preprocessing, intelligent recognition and classification, construction of an intelligent verification rule base, intelligent review and verification execution, and anomaly detection and feedback optimization, the automated and intelligent processing of review data is realized, providing efficient and accurate technical support for power engineering review work. The popularization and application of this technology will help to improve the quality and efficiency of power engineering review work and promote the intelligent development of the power industry.

[0045] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for reviewing and verifying artificial intelligence-based electric power review data, characterized in that It includes the following specific steps: S1. Data preprocessing, which includes data cleaning, data standardization, and feature extraction. It can clean the input review data, convert it into a unified format and standard, and then extract the key features in the review data to provide a basis for subsequent review data processing; S2. Intelligent identification and classification, which includes natural language processing, image recognition, and data association. It can separately process the text and images in the review data, identify and classify the key information in the text and images, and then establish an association model between the data according to the internal logical relationship of the review data to provide a basis for subsequent verification; S3. Construction of an intelligent verification rule library, which includes rule definition, rule coding, and rule library management. It can define a series of verification rules according to the standards and specifications of power engineering reviews, convert them into algorithms and models executable by a computer, and uniformly manage, maintain, and update these algorithms and models to ensure the accuracy and timeliness of the verification rules; S4. Intelligent review and verification execution, which includes data distribution, rule execution, and result output. It intelligently identifies and classifies the preprocessed review data according to the rules in the rule library, then distributes the results to the corresponding verification rules for processing, and outputs the verification results in the form of a report; S5. Anomaly detection and feedback optimization, which includes anomaly detection, cause analysis, and feedback optimization. It uses machine learning algorithms to detect anomalies in the verification results, identify potential error or abnormal patterns, then analyze the causes of the detected anomalies and find the root causes of the problems, and then feedback the anomaly detection results and cause analysis to users or system administrators, and then optimize and update the rule library according to the feedback results to improve the accuracy and efficiency of verification.

2. The method for review and verification of artificial intelligence-based power review data according to claim 1, wherein: The data cleaning in S1 includes removing invalid, duplicate, incorrect, or inconsistent records from the review data.

3. The review and verification method based on artificial intelligence power review data according to claim 1, characterized in that: The feature extraction in S1 includes, but is not limited to, project type, voltage level, and equipment parameters.

4. The method for review and verification of artificial-intelligence-based power review data according to claim 1, wherein: The natural language processing in S2 can identify text-based review data, the image recognition in S2 can identify image-based review data, and both the natural language processing and image recognition in S2 have deep learning algorithm functions.

5. The review and verification method based on artificial intelligence power review data according to claim 4, characterized in that: The image-based review data includes, but is not limited to, charts and drawings.

6. The review and verification method based on artificial intelligence power review data according to claim 1, wherein: The rule definition in S3 includes, but is not limited to, data range verification, logical relationship verification, and compliance verification.

7. The review and verification method based on artificial intelligence power review data according to claim 1, characterized in that: The verification results in S4 include the data items that pass the verification, the data items that fail the verification, and their reasons.

8. The method for review and verification of artificial intelligence-based power review data according to claim 1, wherein: The data preprocessing, intelligent identification and classification, construction of an intelligent verification rule library, intelligent review and verification execution, and anomaly detection and feedback optimization are all carried out in an artificial intelligence environment.