Intelligent management and self-adaptive updating method of power grid standard semantic knowledge base

Abstract

The invention discloses an intelligent management and self-adaptive updating method for a power grid standard semantic knowledge base, which comprises the following steps: modular design of the knowledge base, establishment of an intelligent auditing and updating mechanism, setting of import and export functions of the knowledge base and starting of intelligent recommendation and early warning. Through modular design, an intelligent auditing and self-adaptive updating mechanism, a knowledge base import and export function and an intelligent recommendation and early warning system, the problems existing in an existing power grid standard semantic knowledge base management mode are effectively solved. The management efficiency of the knowledge base is improved, the accuracy and real-time performance of knowledge are ensured, and reliable data support is provided for intelligent operation of a power grid. And meanwhile, the flexibility and the expandability of the knowledge base are enhanced, and the diversified requirements of intelligent management of the power grid are met.

Description

Technical Field

[0001] The present invention relates to the field of smart grids, and in particular to an intelligent management and adaptive updating method of a grid standard semantic knowledge base. Background Art

[0002] In the field of smart grid, the grid standard semantic knowledge base is the core component of intelligent grid management. However, there are many problems with the existing management methods of grid standard semantic knowledge base. First, the knowledge base lacks modular design, resulting in low management efficiency and difficulty in knowledge retrieval and updating. Secondly, knowledge updates are not timely and cannot keep up with the rapid development of grid technology and the continuous updating of standards. In addition, the existing knowledge base management methods lack intelligent review and adaptive update mechanisms, making it difficult to ensure the accuracy of knowledge. Finally, the knowledge base import and export functions are imperfect, which limits the convenience of non-knowledge base managers to conduct professional review and confirmation of the knowledge base. These problems seriously restrict the level of intelligent management of smart grids.

[0003] How to invent intelligent management and adaptive update methods for the power grid standard semantic knowledge base to improve these problems has become an urgent issue to be solved by technical personnel in this field. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides an intelligent management and adaptive update method of the power grid standard semantic knowledge base, aiming to improve the existing power grid standard semantic knowledge base management method, which has problems such as lack of modular design, untimely knowledge update, lack of intelligent review and adaptive update mechanism, and imperfect knowledge base import and export functions.

[0005] The present invention is implemented as follows: a method for intelligent management and adaptive updating of a power grid standard semantic knowledge base, the method comprising the following steps: S1. Divide the power grid standard semantic knowledge base into several modules, each module contains a specific knowledge domain; S2. Use machine learning algorithms to intelligently review the knowledge in the power grid standard semantic knowledge base, identify and mark the knowledge content that needs to be updated; S3. Establish an adaptive update mechanism based on the actual needs of power grid operation to dynamically adjust the update frequency and content of the knowledge base; S4. Provides the function of importing and exporting files in various formats of the entire knowledge base, facilitating professional review and confirmation by non-knowledge base managers; S5. Based on the user's usage habits and grid operation requirements, relevant knowledge items are intelligently recommended, and an early warning system is established to issue early warning signals in a timely manner.

[0006] In a preferred technical solution of the present invention, the several modules include an electric power equipment information module, an electric power technical standard module and a power grid dispatching rule module.

[0007] In a preferred technical solution of the present invention, the machine learning algorithm includes a support vector machine, a random forest or a deep learning algorithm.

[0008] In a preferred technical solution of the present invention, the adaptive update mechanism dynamically adjusts the update strategy of the knowledge base according to multi-dimensional information.

[0009] In a preferred technical solution of the present invention, the import function periodically saves the knowledge base content in stages to ensure that changes to knowledge items are traceable.

[0010] In a preferred technical solution of the present invention, the intelligent recommendation system adopts a collaborative filtering algorithm or a content-based recommendation algorithm to provide personalized knowledge recommendation services to users.

[0011] The beneficial effect of the present invention is that the intelligent management and adaptive update method of the power grid standard semantic knowledge base obtained by the present invention through the above design, when used, effectively solves the problems existing in the existing power grid standard semantic knowledge base management method through modular design, intelligent review and adaptive update mechanism, knowledge base import and export function and intelligent recommendation and early warning system. It improves the management efficiency of the knowledge base, ensures the accuracy and real-time nature of the knowledge, and provides reliable data support for the intelligent operation of the power grid. At the same time, it also enhances the flexibility and scalability of the knowledge base, meeting the diverse needs of intelligent management of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 It is a schematic block diagram of the method steps provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] See also Figure 1 The present invention provides a technical solution: an intelligent management and adaptive updating method of a power grid standard semantic knowledge base, the method comprising: S1. Divide the power grid standard semantic knowledge base into several modules, each module contains a specific knowledge domain; S2. Use machine learning algorithms to intelligently review the knowledge in the power grid standard semantic knowledge base, identify and mark the knowledge content that needs to be updated; S3. Establish an adaptive update mechanism based on the actual needs of power grid operation to dynamically adjust the update frequency and content of the knowledge base; S4. Provides the function of importing and exporting files in various formats of the entire knowledge base, facilitating professional review and confirmation by non-knowledge base managers; S5. Based on the user's usage habits and grid operation requirements, relevant knowledge items are intelligently recommended, and an early warning system is established to issue early warning signals in a timely manner.

[0016] Furthermore, the several modules include an electric power equipment information module, an electric power technical standard module and a power grid dispatching rule module.

[0017] The specific implementation of modular design includes: Module division: According to the content characteristics and actual needs of the power grid standard semantic knowledge base, the knowledge base is divided into multiple modules, such as power equipment information module, power technology standard module, power grid dispatch rule module, etc. Each module contains a specific knowledge field, and the modules are relatively independent to reduce system complexity and improve maintainability.

[0018] Demand research and analysis: First, it is necessary to conduct in-depth research and analysis on the content characteristics and actual needs of the power grid standard semantic knowledge base. This includes understanding what types of information are contained in the knowledge base, how this information is organized, and how users access and use this information.

[0019] Knowledge domain identification: Based on the results of demand research, different knowledge domains in the knowledge base are identified. These domains may include power equipment information, power technology standards, power grid dispatching rules, etc.

[0020] Module division and definition: Based on the identified knowledge domain, the knowledge base is divided into multiple relatively independent modules. Each module should contain a specific knowledge domain and maintain a certain degree of independence from other modules to reduce system complexity.

[0021] Module boundary determination: Clarify the boundaries and scope of each module to ensure that the functions between modules do not overlap while maintaining necessary relevance.

[0022] Module interface design: Design clear interfaces for each module to ensure that modules can correctly exchange data and communicate with each other. Interface design should follow the principles of standardization, generalization, and simplicity to improve the scalability and compatibility of the system.

[0023] Interface requirement analysis: Analyze the data exchange and communication requirements between each module and other modules, and determine the type and number of interfaces that need to be designed.

[0024] Interface specification formulation: Develop detailed specifications for each interface, including interface name, input parameters, output parameters, communication protocol, etc.

[0025] Interface design and implementation: Design and implement the specific functions of the interface according to the interface specification. This includes writing interface code, performing unit testing, etc.

[0026] Interface documentation writing: Write detailed documentation for each interface, explaining its purpose, usage, precautions, etc., to facilitate subsequent development and maintenance.

[0027] Module testing and integration: Each module is tested independently to ensure its functional correctness and stability. Then, the modules are integrated together and tested as a whole to ensure that the system can operate normally.

[0028] Unit testing: Perform independent unit testing on each module to ensure its functional correctness and stability. This includes writing test cases, executing tests, and recording test results.

[0029] Integration testing: Integrate all modules together for overall testing, including verifying whether the interfaces between modules are correctly connected and whether data can be transmitted correctly.

[0030] System testing: Comprehensive testing of the entire system, including functional testing, performance testing, security testing, etc. This is to ensure that the system can operate normally and meet user needs.

[0031] Problem fixing and optimization: According to the test results, fix the problems found and make necessary optimizations. This includes modifying the code, adjusting the configuration, etc.

[0032] Furthermore, the machine learning algorithm includes a support vector machine, a random forest or a deep learning algorithm.

[0033] The application of machine learning algorithms in the power grid standard semantic knowledge base is mainly used to intelligently review the knowledge content in the knowledge base and identify and mark the knowledge items that need to be updated. Specific implementation methods include: Data preprocessing: preprocess the data in the power grid standard semantic knowledge base, including data cleaning, data format conversion, feature extraction, etc. The preprocessed data will be used as the input of the machine learning algorithm. The specific implementation method includes the following steps: Data collection and integration First, relevant data is collected from the grid standard semantic knowledge base. This may include knowledge items in multiple aspects such as power equipment information, power technical standards, and grid dispatching rules. The collected data may come from different data sources, such as databases, files, and networks, so it needs to be integrated to ensure the integrity and consistency of the data.

[0034] Data cleaning Data cleaning is a key step in data preprocessing, which aims to identify and correct errors, anomalies or missing values ​​in the data. This may include operations such as deleting duplicate records, filling missing values, and correcting erroneous data. For data in the power grid standard semantic knowledge base, special attention may need to be paid to the cleaning of text data, such as removing irrelevant characters and standardizing text formats.

[0035] Data format conversion Since machine learning algorithms usually require input data to have a specific format (such as numerical, vector, etc.), the collected data needs to be converted. For data in the power grid standard semantic knowledge base, this may include converting text data into numerical features (such as word frequency, TF-IDF, etc.), or converting unstructured data into structured data (such as extracting text information into key-value pairs, etc.).

[0036] Feature extraction Feature extraction is the process of extracting useful information from raw data for machine learning algorithms. For data in the power grid standard semantic knowledge base, it may be necessary to extract features related to power equipment, technical standards, power grid dispatch, etc. These features may include keywords, phrases, concepts, etc. in the text, or they may be statistics based on the text content (such as word frequency, sentence length, etc.).

[0037] Data standardization and normalization In order to ensure that machine learning algorithms can treat all data points fairly, it is usually necessary to standardize or normalize the data. This helps to eliminate the dimensional differences between different features, allowing the algorithm to learn more effectively. For data in the power grid standard semantic knowledge base, it may be necessary to convert text features into numerical features and standardize or normalize these numerical features.

[0038] Data partitioning Before inputting data into a machine learning algorithm, it is usually necessary to divide the data into training sets, validation sets, and test sets. This helps evaluate the performance of the algorithm and ensures that the algorithm can perform well on unseen data. For data in the power grid standard semantic knowledge base, these data sets can be divided according to the characteristics and requirements of the data.

[0039] Data monitoring and updating Data preprocessing is not only a one-time process, but also requires continuous monitoring and updating. As the power grid standard semantic knowledge base continues to develop, new data may be continuously added, and old data may become outdated or invalid. Therefore, the preprocessing process needs to be regularly checked and updated to ensure the accuracy and timeliness of the data.

[0040] Algorithm selection and training: Select appropriate machine learning algorithms according to actual needs, such as support vector machines, random forests, deep learning, etc. Then, use the preprocessed data to train the algorithm so that it can accurately identify the knowledge items that need to be updated.

[0041] Among them, the support vector machine (SVM) is a binary classification model. Its basic idea is to separate samples of different categories by finding a hyperplane and maximize the distance (i.e., interval) between the two types of samples and the hyperplane. During the training process, the SVM will try to find an optimal hyperplane, which usually involves solving a convex quadratic programming problem.

[0042] Core principle / formula: Linearly separable case: For a linearly separable data set, the goal of SVM is to find a weight vector w and bias b such that for all positive samples yi=1, wxi+b≥1; for all negative samples yi=-1, wxi+b≤-1. The optimization goal is to maximize the interval 1 / ‖w‖, which is equivalent to minimizing ‖w‖^2 / 2 while satisfying the above constraints.

[0043] Kernel function: For nonlinearly separable data sets, SVM maps the input data into a high-dimensional space by introducing a kernel function, making it linearly separable. Commonly used kernel functions include linear kernel, polynomial kernel, radial basis function (RBF), etc.

[0044] Random Forest (RF) is an ensemble learning method that builds multiple decision trees and combines their outputs by voting or averaging to improve the generalization and robustness of the model.

[0045] Core principle / formula: Decision Tree: Each decision tree is a classifier or regressor that recursively selects the best features to split until a stopping condition is met (such as the number of samples in a node is less than a certain threshold or the purity reaches a certain level).

[0046] Ensemble: Random forest generates multiple training subsets through bootstrap sampling (with replacement sampling) and trains a decision tree on each subset. Finally, for classification tasks, random forest outputs the mode of all decision tree prediction results; for regression tasks, it outputs the average of all decision tree prediction results.

[0047] Deep learning (DL) is a branch of machine learning that uses deep neural networks to simulate the learning process of the human brain. Deep learning models usually contain multiple hidden layers, each of which consists of multiple neurons connected by weights and biases.

[0048] Core principle / formula: Forward propagation: The input data is passed through the neural network layer by layer, and the output of each layer is used as the input of the next layer. In each layer, the input data is linearly transformed by the weight matrix and bias vector, and then nonlinearly transformed by the activation function (such as ReLU, sigmoid, tanh, etc.).

[0049] Backpropagation: During training, deep learning models update weights and biases by calculating the gradient of a loss function (e.g., cross entropy loss, mean squared error, etc.). This usually involves the chain rule (i.e., backpropagation algorithm) by calculating gradients layer by layer and propagating them back to the previous layer of the network.

[0050] Optimization: Use optimization algorithms (such as stochastic gradient descent, Adam, etc.) to update weights and biases to minimize the loss function.

[0051] Intelligent review and marking: Apply the trained machine learning algorithm to the power grid standard semantic knowledge base to conduct intelligent review of the knowledge in the base. The algorithm will automatically mark the knowledge items that need to be updated according to the preset rules and thresholds, and feed them back to the management personnel for further processing. The specific implementation method includes the following steps: Algorithm selection and training First, it is necessary to select a machine learning algorithm that is suitable for the characteristics of the power grid standard semantic knowledge base. This may include classification algorithms, clustering algorithms, regression algorithms, etc. The specific choice depends on the content of the knowledge base and the audit requirements.

[0052] The selected algorithm is trained using historical data and expert annotations. During the training process, the algorithm parameters need to be continuously adjusted to improve its accuracy and generalization ability.

[0053] Rules and threshold settings According to the characteristics of the power grid standard semantic knowledge base and the audit requirements, a series of rules and thresholds are set. These rules and thresholds will be used to guide the decision-making of the algorithm in the intelligent audit process.

[0054] Work with experts in the power grid field to jointly develop review rules and thresholds. These rules and thresholds may include requirements for the timeliness, accuracy, and completeness of knowledge items.

[0055] Smart Audit Execution Apply the trained machine learning algorithm to the grid standard semantic knowledge base to perform intelligent review. The algorithm automatically analyzes knowledge items based on preset rules and thresholds and marks those that need to be updated.

[0056] Use programming languages ​​and machine learning frameworks (such as Python, TensorFlow, etc.) to write an intelligent audit program. The program will read the data in the knowledge base, analyze it through algorithms, and output the labeled results.

[0057] Result feedback and processing The results of the intelligent audit are fed back to the management staff, who will conduct further verification and processing based on the knowledge items marked by the algorithm.

[0058] Develop a user interface or backend system to display the results of intelligent auditing. Managers can view the marked knowledge items through the system and perform corresponding processing (such as update, delete, modify, etc.).

[0059] Algorithm optimization and iteration Continuously optimize and adjust machine learning algorithms based on feedback from managers and actual audit results. This may include adjusting parameters, improving model structure, and increasing training data.

[0060] Regularly collect feedback and review data from managers to iterate and optimize the algorithm. You can use methods such as A / B testing to compare the performance of different algorithm versions and select the best algorithm for deployment.

[0061] Furthermore, the adaptive update mechanism dynamically adjusts the update strategy of the knowledge base according to multi-dimensional information.

[0062] The adaptive update mechanism is a mechanism that dynamically adjusts the update frequency and content of the knowledge base according to the actual needs of power grid operation. Specific implementation methods include: Demand analysis and prediction: By collecting and analyzing grid operation data, user feedback information, etc., we can understand the actual demand for grid operation and the usage of the knowledge base. Then, based on these data, we can perform demand analysis and prediction to determine the content and frequency of knowledge base updates. The specific implementation includes the following steps: Data Collection: Use sensors, data acquisition systems and other tools to collect real-time grid operation data, such as voltage, current, power and other parameters. At the same time, collect user usage and feedback on the knowledge base through user surveys, online feedback, customer service records and other channels. Ensure the comprehensiveness and accuracy of data collection and provide a reliable basis for subsequent analysis and prediction.

[0063] Data Analysis: Use data analysis tools (such as Excel, Python, etc.) and algorithms (such as statistical analysis, data mining, etc.) to process and analyze the collected data. Identify abnormal patterns, trends and rules in power grid operation, as well as user demand hot spots and pain points for knowledge bases. Through in-depth analysis of data, extract valuable information to provide a scientific basis for demand forecasting.

[0064] Demand Forecast: Based on the results of data analysis, prediction models (such as time series analysis, machine learning models, etc.) are used to predict the power grid operation demand and knowledge base usage demand in the future. The accuracy of the prediction results depends on the selection and training quality of the model, as well as the accuracy and completeness of the data.

[0065] Update strategy formulation: According to the results of demand analysis and prediction, formulate the knowledge base update strategy. The update strategy should include the content, frequency, and method of the update to ensure that the knowledge base can timely and accurately reflect the actual situation of power grid operation. The specific implementation method includes the following steps: Confirm the update content: According to the results of demand analysis and prediction, determine the specific content that needs to be updated in the knowledge base, such as newly added power equipment information, updated power technology standards, revised power grid dispatching rules, etc. Ensure the accuracy and pertinence of the updated content to meet the actual needs of power grid operation and user expectations.

[0066] Set the update frequency: According to the stability of power grid operation and the urgency of knowledge base update, set a reasonable update frequency. For frequently changing data and rules, a higher update frequency can be set; for relatively stable content, a lower update frequency can be set. Through a reasonable update frequency, it is ensured that the knowledge base can reflect the actual situation of power grid operation in a timely manner, while avoiding the extra workload caused by unnecessary updates.

[0067] Select the update method: Choose the appropriate update method based on the complexity and urgency of the update content. Simple update operations, such as adding or deleting an entry, can be performed manually; complex update tasks, such as batch updating of data or restructuring of the knowledge base structure, can be performed by writing automated scripts or using the knowledge base management system for batch processing. By choosing the appropriate update method, you can improve update efficiency and accuracy and reduce update costs.

[0068] Automatic update and monitoring: Establish an automatic update system to automatically update the knowledge base according to the established update strategy. At the same time, establish a monitoring mechanism to monitor and record the update process in real time to ensure the accuracy and stability of the update process. The specific implementation method includes the following steps: Establish an automated update system: Using programming languages ​​and database technology, an automated update system is developed. The system can automatically obtain the latest data and information from the data source and update it to the knowledge base according to the preset update strategy and rules. The automated update system can reduce manual intervention and improve update efficiency and accuracy. At the same time, the status and results of the update process can be tracked and recorded in real time through the system log and monitoring mechanism.

[0069] Implement monitoring mechanisms: A monitoring module is embedded in the automated update system to monitor and record the update process in real time. The monitoring content includes the execution of update tasks, data consistency and integrity, system performance and stability, etc. Through the monitoring mechanism, problems and anomalies that occur during the update process can be discovered and handled in a timely manner to ensure the smooth progress of the update process and the accuracy of the results. At the same time, the monitoring data can also provide a useful reference for subsequent optimization and improvement.

[0070] Furthermore, the import function periodically saves the knowledge base content in stages to ensure that changes to knowledge entries are traceable.

[0071] Knowledge base import and export function: Provides overall knowledge base import and export functions, supports WORD and other formats, and facilitates non-knowledge base managers to conduct professional review and confirmation. The import and export function regularly saves the knowledge base content in stages, such as monthly units, to ensure that changes to knowledge items are traceable. This function contributes to the security and traceability of the knowledge base. Its specific implementation is as follows: Data backup technologies include: Determine the backup cycle: First, determine a reasonable backup cycle based on the frequency and importance of knowledge base updates. For example, as described in the description, backups can be performed on a monthly basis, or a shorter or longer cycle can be selected based on actual needs.

[0072] Select a backup method: Choose an appropriate backup method based on the size, structure, and storage environment of the knowledge base. Common backup methods include full backup, incremental backup, and differential backup. A full backup copies all the contents of the knowledge base, while incremental and differential backups only copy the parts that have changed since the last backup.

[0073] Perform backup operations: Use the backup tools provided by the database management system (DBMS) or write custom scripts to perform backup operations. During the backup process, it is necessary to ensure the integrity and consistency of the data to avoid data corruption or loss during the backup process.

[0074] Storing backup data: Store backup data on safe and reliable storage media, such as hard disks, tapes, cloud storage, etc. At the same time, backup data needs to be verified and tested regularly to ensure the availability and integrity of backup data.

[0075] Through the above steps, the knowledge base content can be backed up and stored regularly to ensure the security and traceability of the knowledge base. When the knowledge base changes, the backup data can be restored to the state before the change to avoid losses caused by data loss or damage.

[0076] Data recovery techniques include: Determine recovery requirements: When you need to recover data, first clarify the recovery requirements and goals. For example, whether to recover the entire knowledge base or only a specific knowledge item, or to recover to the most recent backup or a specific point in time.

[0077] Select a recovery method: Select an appropriate recovery method based on the recovery requirements. Common recovery methods include full recovery, partial recovery, and point-in-time recovery. Full recovery will restore the contents of the entire knowledge base, while partial recovery will only restore specific knowledge items or data tables. Point-in-time recovery allows users to recover to a specific point in time.

[0078] Perform recovery operations: Use the recovery tools provided by the database management system (DBMS) or write custom scripts to perform recovery operations. During the recovery process, it is necessary to ensure the integrity and consistency of the data to avoid data corruption or loss during the recovery process.

[0079] Verify the recovery results: After the recovery is complete, you need to verify and test the recovery results to ensure that the recovered data is consistent with the original data and that the knowledge base can operate normally.

[0080] Through the above steps, the backed-up data can be restored when needed, thus ensuring the recoverability of the knowledge base and the integrity of the data. When the knowledge base is damaged or lost, the data can be restored to the state at the time of backup through the recovery operation to avoid the loss caused by data loss.

[0081] Data backup and recovery algorithms: Data backup and recovery algorithms are usually implemented by database management systems (DBMS) or custom scripts. These algorithms perform corresponding operations based on the backup and recovery requirements. For example, the data backup algorithm will traverse all the data in the knowledge base and copy it to the backup storage medium. The data recovery algorithm will restore the data to its original state or a specified time point based on the metadata information in the backup data.

[0082] By implementing data backup and recovery algorithms, data backup and recovery operations can be completed automatically, improving operational efficiency and accuracy. At the same time, these algorithms can also be customized and optimized according to actual needs to meet different backup and recovery needs.

[0083] At the same time, cloud storage technology can be introduced to store backup data on the cloud storage platform to achieve remote backup and recovery of the knowledge base. This can not only improve the reliability and security of backup data, but also facilitate cross-regional data backup and recovery operations. Alternatively, automated tools for data backup and recovery can be developed by using programming languages ​​and API interfaces provided by database management systems. These tools can automatically perform backup and recovery operations according to preset backup and recovery strategies, reducing the possibility of manual intervention and errors.

[0084] Through the implementation of the above extension plan, the security and operability of the knowledge base import and export functions can be further improved. The introduction of cloud storage technology can ensure the reliability and security of backup data; the development of automated tools can improve the efficiency and accuracy of backup and recovery operations. These measures together provide a strong guarantee for the security and traceability of the knowledge base.

[0085] Furthermore, the intelligent recommendation system adopts collaborative filtering algorithm or content-based recommendation algorithm to provide users with personalized knowledge recommendation services.

[0086] Intelligent recommendation and early warning system: Based on the user's usage habits and power grid operation requirements, collaborative filtering algorithms or content-based recommendation algorithms are used to provide users with personalized knowledge recommendation services. This helps to improve the utilization rate of the knowledge base and user satisfaction. An early warning system is also established to issue early warning signals in a timely manner. The specific implementation methods of the system are as follows: System architecture design: First, the overall architecture of the intelligent recommendation system needs to be designed. This includes determining the system input (such as user behavior data, knowledge item content, etc.), output (such as the recommended knowledge item list), and processing flow (such as data preprocessing, algorithm application, result generation, etc.).

[0087] Algorithm selection and implementation: According to the design of the system architecture, select the appropriate recommendation algorithm for implementation. For collaborative filtering algorithms, it is necessary to implement functions such as user-knowledge item rating matrix construction, similarity calculation, and recommendation list generation. For content-based recommendation algorithms, it is necessary to implement content feature extraction of knowledge items, user preference modeling, and content-based matching.

[0088] System integration and testing: Integrate the implemented recommendation algorithm into the intelligent recommendation system and conduct system testing. The testing content includes functional testing, performance testing, and user experience testing. Through testing, we can ensure that the system can run stably and meet user needs.

[0089] Through the above implementation, the functions of the intelligent recommendation system can be specifically implemented to provide users with personalized knowledge recommendation services, which helps to improve the utilization rate of the knowledge base and user satisfaction, thereby achieving the technology or purpose described.

[0090] When implementing the collaborative filtering algorithm, it is necessary to collect historical behavior data of users (such as browsing, clicking, and collecting) and build a rating matrix of users and knowledge items. Then, similar users are found using similarity calculation methods (such as cosine similarity and Pearson correlation coefficient), and knowledge items are recommended based on the behaviors of similar users.

[0091] When implementing a content-based recommendation algorithm, it is necessary to extract content features of knowledge items (such as keyword extraction, text vectorization, etc.) and build a user preference model. Then, a matching algorithm (such as cosine similarity, Euclidean distance, etc.) is used to match the content features of knowledge items with user preferences, thereby recommending knowledge items that meet user preferences.

[0092] When implementing user behavior analysis technology, it is necessary to collect user behavior data (such as browsing time, number of clicks, number of favorites, etc.) when using the intelligent recommendation system, and use data analysis tools (such as Excel, Python, etc.) to analyze and model these data. By analyzing user behavior data, we can understand user usage habits and needs, thereby providing a basis for optimizing the recommendation algorithm.

[0093] When implementing collaborative filtering algorithms, it is necessary to continuously optimize the performance and accuracy of the algorithms. For example, you can use matrix decomposition techniques (such as SVD, NMF, etc.) to reduce the dimension of the rating matrix and improve computational efficiency; or use machine learning techniques (such as support vector machines, random forests, etc.) to optimize similarity calculation methods and improve the accuracy of recommendations.

[0094] When implementing content-based recommendation algorithms, it is necessary to continuously update and optimize the content feature extraction methods of knowledge items and the user preference modeling methods. For example, natural language processing techniques (such as word segmentation, part-of-speech tagging, sentiment analysis, etc.) can be used to extract the content features of knowledge items; or machine learning techniques (such as neural networks, decision trees, etc.) can be used to build user preference models to improve the personalization of recommendations.

[0095] In addition to the above-mentioned technologies and algorithm technologies, deep learning technology can also be introduced, and deep learning models (such as convolutional neural networks, recurrent neural networks, deep neural networks, etc.) can be used to replace traditional recommendation algorithms. By training deep learning models, the complex relationship between user behavior data and knowledge item content can be automatically learned, thereby improving the accuracy and personalization of recommendations.

[0096] It is also possible to combine real-time data analysis technology to build a real-time data analysis system to monitor and analyze the real-time behavior data of users when using the intelligent recommendation system. Through real-time data analysis, it is possible to timely discover changes in user needs and behavior trends, thereby providing a basis for the dynamic adjustment of the recommendation algorithm. At the same time, real-time data analysis technology can also be used to implement dynamic knowledge recommendation services, that is, to recommend corresponding knowledge items based on the user's real-time needs and behaviors.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Intelligent management and adaptive updating method of power grid standard semantic knowledge base, characterized in that: The method comprises: S1. Divide the power grid standard semantic knowledge base into several modules, each module contains a specific knowledge domain; S2. Use machine learning algorithms to intelligently review the knowledge in the power grid standard semantic knowledge base, identify and mark the knowledge content that needs to be updated; S3. Establish an adaptive update mechanism based on the actual needs of power grid operation to dynamically adjust the update frequency and content of the knowledge base; S4. Provides the function of importing and exporting files in various formats of the entire knowledge base, facilitating professional review and confirmation by non-knowledge base managers; S5. Based on the user's usage habits and grid operation requirements, relevant knowledge items are intelligently recommended, and an early warning system is established to issue early warning signals in a timely manner.

2. The method for intelligent management and adaptive updating of a power grid standard semantic knowledge base according to claim 1, characterized in that: The modules include an electric power equipment information module, an electric power technology standard module and a power grid dispatching rule module.

3. The intelligent management and adaptive updating method of the power grid standard semantic knowledge base according to claim 1, characterized in that: The machine learning algorithm includes a support vector machine, a random forest or a deep learning algorithm.

4. The intelligent management and adaptive updating method of the power grid standard semantic knowledge base according to claim 1, characterized in that: The adaptive update mechanism dynamically adjusts the update strategy of the knowledge base according to multi-dimensional information.

5. The intelligent management and adaptive updating method of the power grid standard semantic knowledge base according to claim 1, characterized in that: The import function periodically saves the knowledge base content in stages to ensure that changes to knowledge entries are traceable.

6. The method for intelligent management and adaptive updating of a power grid standard semantic knowledge base according to claim 1, characterized in that: The intelligent recommendation system adopts a collaborative filtering algorithm or a content-based recommendation algorithm to provide users with personalized knowledge recommendation services.

Images