Efficiency evaluation resource information management system

Abstract

The invention relates to the technical field of efficiency evaluation resource information management systems, in particular to an efficiency evaluation resource information management system, which comprises a data access module, a data processing module, a real-time simulation module, an efficiency acquisition module and a decision support module. Through improvement of the above modules step by step, data generated by the efficiency acquisition module and data generated by the real-time simulation module are integrated together through data analysis and mining of the decision module, and statistical analysis and data visualization are applied. The method comprises the steps of extracting valuable information and knowledge from data through technologies such as machine learning, helping decision makers to understand trend relevance and rules of the data, calculating weights through a grey correlation analysis method so as to ensure scientificity and reasonability of evaluation results, facilitating rapid understanding and application of the decision makers, providing required information and suggestions for the decision makers, and improving the decision making efficiency. And the decision support module also ensures that the decision support module always meets the actual demand of the user according to the feedback of the user and the actual situation.

Description

Technical Field

[0001] The present invention relates to the technical field of an efficiency evaluation resource information management system, and in particular to an efficiency evaluation resource information management system. Background Art

[0002] Effectiveness evaluation refers to the quantitative calculation or conclusive evaluation of efficiency indicators such as the quality, effect, and status of the results or processes of a certain thing or system performing a certain task. It is widely used in the military, scientific research, and manufacturing industries, and can also be used to evaluate certain plans and projects.

[0003] In the existing performance evaluation resource information management system, users can use the resources in the system to evaluate the performance of a certain plan. However, in the performance evaluation process, the rationality of the performance of the plan is judged only by comparing the data in the library, which is often limited and unreasonable. Therefore, it is necessary to improve it and propose a performance evaluation resource information management module. Summary of the Invention

[0004] Technical problem solved: A performance evaluation resource information management system.

[0005] In view of the deficiencies of the prior art, the present invention provides a performance evaluation resource information management system, thereby solving the technical problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an efficiency evaluation resource information management system, an efficiency evaluation resource information management system, the system includes a data access module, a data processing module, a real-time simulation module, an efficiency acquisition module, and a decision support module;

[0007] The data access module consists of a text input unit, a speech recognition unit, and an associative processing unit for these information;

[0008] The data processing module is composed of a resource information collection and storage unit, a resource information classification and arrangement unit, a resource information update and maintenance unit, and an information retrieval and comparison unit;

[0009] The real-time simulation module includes real-time data acquisition, dynamic simulation, and data processing and analysis;

[0010] The performance acquisition module compares the data obtained from the real-time case with the data generated by the real-time simulation module to determine whether it is within a reasonable range;

[0011] The decision support module supports the above generated data through weight calculation, including data analysis and mining and visual reporting.

[0012] In one possible implementation, a performance evaluation resource information management system uses a text input module to express the type of evaluation required and keywords. At this time, the association function relies on the keywords given by the operator to enrich this information, thereby making data processing more accurate.

[0013] In a possible implementation, a performance evaluation resource information management system, the dynamic simulation relies on an advanced real-time simulation software platform, especially a professional software tool such as RT-LAB. Seamless integration enables real-time interaction between Simulink models and the real environment.

[0014] In one possible implementation, a performance evaluation resource information management system, in the visual reporting unit, after the weight calculation is completed, the data is visually displayed in the form of charts, reports, dashboards, etc., to facilitate decision makers to quickly understand and apply the data, and provide decision makers with the required information and suggestions.

[0015] In one possible implementation, a performance evaluation resource information management system, the data analysis and mining integrates the data generated by the performance acquisition module with the data generated by the real-time simulation module, uses statistical analysis, data visualization, machine learning and other technologies to extract valuable information and knowledge from the data, helps decision makers understand the trend correlation and rules of the data, and calculates weights through gray correlation analysis to ensure the scientificity and rationality of the evaluation results.

[0016] In one possible implementation, a performance evaluation resource information management system may collect data including system parameters, environmental variables, operating instructions, and other information. Standardization technology is used to unify the data format, or feature engineering is used to extract key features to enhance data usability. The processed data will be more accurate, complete, and representative.

[0017] Beneficial effects compared with existing technologies:

[0018] 1. In this solution, a performance evaluation resource information management system is used; through real-time data collection, dynamic simulation, and data processing and analysis, first, the original data entered by the operator, which may include system parameters, environmental variables, operating instructions and other information, cannot be used directly for simulation because the original data often has problems such as noise, missing values or inconsistent formats. Therefore, it needs to be cleaned, converted and enriched by the data processing module first. For example, missing values are filled by interpolation algorithms, data formats are unified by standardization technology, or key features are extracted with the help of feature engineering to enhance data availability. The processed data will be more accurate, complete and representative. The large amount of data generated by dynamic simulation will be processed and analyzed again, and statistical analysis methods will be used to extract key indicators such as mean, variance, correlation coefficient, etc. to identify patterns and trends in the data. A prediction model is established through machine learning algorithms to predict the future behavior and performance of the system; visualization technology is used to display complex data in the form of intuitive charts, curves, etc. to help users understand the simulation results more clearly. Ultimately, this information will be used to evaluate the effectiveness of the system, identify potential problems and optimization directions, thereby further improving task execution and program design, ensuring that the system can achieve the expected performance goals in actual operation, and providing strong support for decision-making.

[0019] 2. This solution utilizes a performance evaluation resource information management system. Through the gradual improvement of the above modules, the decision-making module integrates data generated by the performance acquisition module and the real-time simulation module through data analysis and mining. Statistical analysis and data visualization are used, along with other techniques, to extract valuable information and knowledge from the data, helping decision-makers understand trends, correlations, and patterns in the data. Gray correlation analysis is used to calculate weights to ensure the scientific and rationality of the evaluation results, facilitating their rapid understanding and application, and providing the necessary information and recommendations. The decision support module utilizes advanced decision-making algorithms and models, comprehensively considering factors such as system performance, cost, and risk, to provide users with optimal decision-making solutions. These recommendations may include system optimization suggestions, resource allocation adjustments, and risk mitigation strategies. The decision support module also dynamically adjusts recommendations based on user feedback and actual conditions to ensure they consistently meet users' needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings.

[0021] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0022] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can be implemented in various forms, so the present invention is not limited to the embodiments described below.

[0023] The technical solution in the embodiments of the present application is to solve the problems of the above-mentioned background technology, and the overall idea is as follows:

[0024] Example 1:

[0025] Please refer to Figure 1 As shown, this embodiment introduces a performance evaluation resource information management system, including a performance evaluation resource information management system; this system mainly includes a data access module, a data processing module, a real-time simulation module, a performance acquisition module, and a decision support module; the data access module includes text input and voice recognition functions, and users can input information through the interface. User information can be input not only through text input but also through voice input. After the information is input, the association function in the data access module will associate the information, thereby enriching the data and making the data more accurate. The data processing module processes the received data in real time and reasonably accesses it with the data in the application library. The real-time simulation module relies on the inputter's instructions and the resource information in the database to perform diffraction reasoning and conduct multi-faceted evaluation to obtain more accurate and more practical data; the performance acquisition module compares the data obtained in the real-time case with the data generated in the real-time simulation module to determine whether it is within a reasonable range; the decision support module supports the feasibility of this solution by weighting the above-generated data;

[0026] 1. Data input module, including text input and voice input as well as association function for input data; operators can express the type and keywords they need to evaluate by inputting text. At this time, the association function relies on the keywords given by the operator to enrich this information, making data processing more accurate, greatly improving the efficiency of data input and bringing convenience to the operator. Operators can also enrich this data through voice input and association function, making data processing more accurate. At this time, if the operator is not satisfied with the associated data, the association function can be used to associate again, so that these keywords are more in line with the operator's expectations, greatly improving the efficiency of data input. The data input module does not rely solely on user input. The data sources of the input module are relatively diverse, such as real-time data collection from sensor networks, extracting historical records from databases, or receiving manually entered information through the user interface. In order to ensure the quality of the data, the module will perform preliminary data cleaning and verification to eliminate erroneous or incomplete data, laying a solid foundation for subsequent processing.

[0027] Suppose an operator inputs the two keywords "learning" through text input or voice input, the association function at this time will change this keyword to "learning method". If the operator is relatively satisfied with this keyword, the association function will not be performed again. If the operator is not satisfied, the operator will use the association function again to enrich this keyword, and "learning method" will become "how to learn efficiently". Through this association function, the keywords are enriched, thereby improving the input efficiency.

[0028] 2. The data processing module is divided into resource information collection and storage, resource information classification and organization, resource information update and maintenance, and information retrieval and comparison. Resource information collection and storage is responsible for collecting various resource information related to performance evaluation, such as human resources personal information, performance data, and training records; and material resources inventory quantity, usage, and procurement costs. This information is centrally stored and managed to ensure data integrity, accuracy, and traceability, providing a data foundation for subsequent evaluations. Resource information classification and organization: Collected resource information is categorized according to specific rules and standards, such as categorizing human resources into different departments, positions, and ranks; and material resources into different categories, uses, and suppliers. Furthermore, this information is organized and archived to facilitate users' quick search and retrieval of required resource information, improving work efficiency. Resource information update and maintenance: Resource information is updated in real time to ensure data timeliness and accuracy. For example, changes in employee positions or increases or decreases in material inventory are promptly updated in the system. At the same time, outdated or erroneous information is corrected and maintained to ensure the quality of resource information, provide reliable data support for performance evaluation, and ensure the accuracy of data, the integrity of information and the timeliness of updates; information retrieval and comparison: at this time, the data sorted out by the data input module is retrieved and compared based on the information in the data processing module to adjust the data to better meet the actual needs of users, and provide users with personalized data, thereby making the operator's data more in line with reality and more accurate, and identifying unreasonable resource allocation. In addition, the data processing module will also standardize the data so that it can better interact and share with other modules.

[0029] 3. Real-time simulation module: real-time data acquisition, dynamic simulation, and data processing and analysis. First, the original data input by the operator, which may include system parameters, environmental variables, operating instructions and other information. Since the original data often has problems such as noise, missing values or inconsistent formats, it cannot be used directly for simulation. Therefore, it needs to be cleaned, converted and enriched by the data processing module first. For example, the missing values are filled by interpolation algorithms, the data format is unified by standardization technology, or the key features are extracted with the help of feature engineering to enhance the availability of the data. The processed data will be more accurate, complete and representative. Next, the processed data will be passed to the real-time simulation module for collection and preliminary analysis. The real-time simulation module will verify and integrate these data to ensure that they meet the format and quality requirements required for the simulation, and extract the key features and parameters in the data to prepare for subsequent simulations. Then, dynamic simulation relies on advanced real-time simulation software platforms, especially professional software tools like RT-LAB. RT-LAB and Seamless integration enables real-time interaction between Simulink models and the real-world environment. Engineers can leverage its powerful model-building capabilities to quickly build complex system models. Leveraging its efficient simulation engine, they can run models and generate simulation data in real time. Furthermore, through real-time interaction, they can adjust parameters during simulation and observe how the plan responds in real-world scenarios, providing a more intuitive experience and deeper insights. The massive amount of data generated by dynamic simulations is further processed and analyzed. Statistical analysis methods are used to extract key metrics, such as mean, variance, and correlation coefficient, to identify patterns and trends in the data. Machine learning algorithms are then used to build predictive models to predict future system behavior and performance. Visualization techniques then present complex data in intuitive charts and graphs, helping users better understand the simulation results. Ultimately, this information is used to evaluate system performance, identify potential issues, and identify optimization areas. This information helps refine task execution and solution design, ensuring that the system achieves expected performance targets in real-world operation, providing strong support for decision-making and driving continuous improvement in system performance.

[0030] 4. In the performance acquisition module, data sources are first obtained from the real-time simulation module to collect performance data. These data sources are diverse and critical, including but not limited to system logs, application programming interfaces (APIs), databases, and sensor data. System logs can record system operating status and events, APIs facilitate data exchange between different systems, and databases centrally store large amounts of structured data. Collecting this data through multiple channels can maximize the authenticity and comprehensiveness of the acquired data. Comparing and contrasting this data with the data in the real-time simulation module lays a solid foundation for subsequent evaluation.

[0031] As data is collected, it is classified and organized into different categories based on characteristics such as type, source, and timestamp. For example, system logs can be categorized by severity level, and API data can be organized by request type. This not only facilitates subsequent queries and analysis, but also clarifies the data structure, improves data processing efficiency, and enables real-time monitoring of the status of each data source. Closely monitor the data generation, transmission, and storage processes to promptly identify and resolve issues such as missing data, delays, or anomalies to ensure data integrity and accuracy. For example, if a sensor data interruption is detected, the system can immediately issue an alarm, prompting relevant personnel to check and repair it. To facilitate subsequent calculations and analysis, the collected data in various formats will be integrated into a unified data format. This may involve operations such as data cleaning, conversion, and standardization to eliminate inconsistencies and redundancies between data, so that the data can be processed and analyzed under the same evaluation framework. Throughout the performance data acquisition process, strict error control measures are taken to ensure that the deviation between the collected data and the actual value is within the allowable range through data verification, comparison, and correction. For example, data verification algorithms are used to calibrate sensor data in real time, and historical data and statistical models are used to check the rationality of API return data, etc., so as to ensure data quality and improve the reliability and accuracy of performance evaluation.

[0032] 6. Decision Support Module: This includes data analysis and mining, and visual reporting. Data analysis and mining integrates data generated by the Performance Acquisition Module with data generated by the Real-Time Simulation Module, applying statistical analysis and data visualization. Machine learning and other technologies extract valuable information and knowledge from the data, helping decision makers understand trends, correlations, and patterns. Gray correlation analysis is used to calculate weights to ensure the scientific and reasonable nature of the evaluation results. Once the weights are calculated, visual reports present the data in charts, reports, dashboards, and other formats, facilitating quick understanding and application by decision makers, providing them with the necessary information and recommendations. The Decision Support Module utilizes advanced decision-making algorithms and models, comprehensively considering factors such as system performance, cost, and risk, to provide users with optimal decision solutions. These recommendations may include system optimization, resource allocation adjustments, and risk mitigation strategies. The Decision Support Module dynamically adjusts its recommendations based on user feedback and actual conditions to ensure they consistently meet user needs.

[0033] The data is improved step by step through the data input module, data processing module, real-time simulation module, performance acquisition module and decision support module. The existing resource information is compared and simulated, and finally reasonable suggestions are provided to each user through the decision support module. Thus, the performance evaluation process is completed. Such processing will be more scientific and reasonable, the data obtained will be more accurate, and the results of the performance evaluation process will be more in line with reality.

[0034] To summarize, the data input module is the foundation of the entire process. It is responsible for collecting raw data from various sources. This data may include system operating parameters, resource usage, historical performance records, and specific user requirements. The data input module acquires data through various methods, such as real-time data collection from sensor networks, historical records extracted from databases, or manual input through the user interface. To ensure data quality, this module performs preliminary data cleaning and verification to eliminate erroneous or incomplete data, laying a solid foundation for subsequent processing.

[0035] Next, the data processing module performs in-depth processing on the input data. It uses advanced algorithms and data processing techniques to classify, organize, and analyze the data. During this process, the module identifies patterns and trends within the data, uncovering valuable insights hidden within. For example, data analysis can reveal problems within a plan or identify inappropriate resource allocation. Furthermore, the module standardizes the data to facilitate interaction and sharing with other modules.

[0036] The real-time simulation module then uses the processed data to construct a virtual model of the system and simulate it in real time. By simulating the system's operation, this module can predict the plan's performance under different conditions, providing a dynamic reference for performance evaluation. The real-time simulation module can simulate various complex scenarios, such as system response time under high load and performance under resource constraints. Through simulation, users can understand in advance the potential problems that may arise in the plan's actual operation and take appropriate measures to optimize it.

[0037] Based on the simulation, the Performance Acquisition Module quantitatively evaluates the effectiveness of the plan. It comprehensively measures the system's effectiveness through a series of scientific evaluation indicators, such as performance metrics, resource utilization, and cost-effectiveness. These evaluation indicators are carefully designed based on the specific needs and goals of the system and accurately reflect its operational effectiveness. Based on the simulation results, the Performance Acquisition Module calculates the values of each evaluation indicator and generates a detailed performance evaluation report. This report provides an in-depth analysis of each key indicator and identifies the system's strengths and weaknesses.

[0038] Finally, the decision support module is the core of the entire process. Based on the data and evaluation results provided by the previous modules, it provides users with scientific and reasonable recommendations. Using advanced decision-making algorithms and models, the module comprehensively considers factors such as system performance, cost, and risk to provide users with optimal decision solutions. These recommendations may include system optimization suggestions, resource allocation adjustments, and risk mitigation strategies. The module also dynamically adjusts recommendations based on user feedback and actual circumstances to ensure they consistently meet users' needs.

[0039] Through this complete process, consisting of a data input module, data processing module, real-time simulation module, performance acquisition module, and decision support module, we can conduct a comprehensive, scientific, and accurate assessment of system performance. This assessment method not only fully utilizes existing resource information but also, through simulation and analysis, predicts system performance under different conditions. Ultimately, the decision support module provides users with reasonable advice to help them make informed decisions, thereby ensuring efficient operation and continuous optimization of the system. This performance evaluation process is undoubtedly more scientific and reasonable, the data obtained is more accurate, and the evaluation results are more realistic, providing tangible value to users.

[0040] Finally, it should be noted that the above embodiments are merely examples for the purpose of illustrating the present invention and are not intended to limit the embodiments. Those skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. It is not necessary and impossible to provide an exhaustive list of all possible embodiments. However, obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A performance evaluation resource information management system, characterized in that: An efficiency evaluation resource information management system, comprising a data access module, a data processing module, a real-time simulation module, an efficiency acquisition module, and a decision support module; The data access module consists of a text input unit, a speech recognition unit, and an associative processing unit for these information; The data processing module is composed of a resource information collection and storage unit, a resource information classification and arrangement unit, a resource information update and maintenance unit, and an information retrieval and comparison unit; The real-time simulation module includes real-time data acquisition, dynamic simulation, and data processing and analysis; The performance acquisition module compares the data obtained from the real-time case with the data generated by the real-time simulation module to determine whether it is within a reasonable range; The decision support module supports the above generated data through weight calculation, including data analysis and mining and visual reporting.

2. The efficiency evaluation resource information management system according to claim 1, wherein: A performance evaluation resource information management system, wherein the text input module is used to express the type of evaluation required and keywords. At this time, the association function relies on the keywords given by the operator to enrich the information, thereby making the data processing more accurate.

3. The efficiency evaluation resource information management system according to claim 1, wherein: A performance evaluation resource information management system, wherein the dynamic simulation relies on an advanced real-time simulation software platform, especially a professional software tool such as RT-LAB. Seamless integration enables real-time interaction between Simulink models and the real environment.

4. The efficiency evaluation resource information management system according to claim 1, wherein: A performance evaluation resource information management system, in which, after the weight calculation is completed, the visual report unit displays the data in the form of charts, reports, dashboards, etc., so as to facilitate decision makers to quickly understand and apply the data and provide them with the required information and suggestions.

5. The efficiency evaluation resource information management system according to claim 1, wherein: A performance evaluation resource information management system, wherein the data analysis and mining integrates the data generated by the performance acquisition module with the data generated by the real-time simulation module, and uses statistical analysis, data visualization, machine learning and other technologies to extract valuable information and knowledge from the data, helping decision makers understand the trend correlation and laws of the data, and calculating the weights through gray correlation analysis to ensure the scientificity and rationality of the evaluation results.

6. The efficiency evaluation resource information management system according to claim 1, wherein: A performance evaluation resource information management system, wherein the data collected may include system parameters, environmental variables, operating instructions and other information. Standardization technology is used to unify the data format, or feature engineering is used to extract key features to enhance data usability. The processed data will be more accurate, complete and representative.

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