Kenssman digital evaluation system

Abstract

The invention relates to the technical field of management, in particular to a craftsman digital evaluation system. Comprising an information acquisition module used for acquiring basic information, work performance, skill level and training experience of craftsmen; and the data analysis module is used for analyzing the craftsman information acquired by the information acquisition module by adopting a weight scoring model, setting a related weight according to the craftsman information, calculating a comprehensive score, analyzing professional specialty, project experience and skill weakness of the craftsman by utilizing a machine learning algorithm, and performing image processing and feature extraction so as to obtain a comprehensive score of the craftsman. Key features in craftsman operation are identified, and quantitative scoring is carried out; and the evaluation module is used for generating an evaluation result in combination with the weight scoring model and the machine learning analysis result, and dynamically adjusting the parameters of the weight scoring model according to the evaluation result and the continuous performance of the craftsman. According to the craftsman digital evaluation system provided by the invention, the craftsman information is updated and maintained in real time, the accuracy and timeliness of evaluation are ensured, and the craftsman management and service level is improved.

Description

Technical Field

[0001] The present invention relates to the field of management technology, and in particular to a digital evaluation system for craftsmen. Background Art

[0002] As an important force in rural construction, the management and service level of rural construction craftsmen directly affects the quality and safety of rural housing. However, the traditional manual management method has problems such as information opacity, low efficiency, and extensive management, which can no longer meet the current needs of rural construction. With the rapid development of digital technology, it has become an important trend in the development of the industry to optimize the craftsman evaluation system by introducing digital means to improve evaluation efficiency and fairness.

[0003] For example, the Chinese patent publication number CN116523395A, entitled "A digital evaluation system for model workers and craftsmen based on personal performance", includes a performance entry module, a performance review module, a comprehensive evaluation module and a leadership evaluation module. The performance entry module includes a work performance unit, a technical skill unit, an honorary title unit, an innovation and efficiency unit, a demonstration and leadership unit and a social influence unit. The calculation and processing are performed through a multi-dimensional fusion calculation model. In the multi-dimensional fusion calculation model, the scoring methods for each entered data include direct scoring, scoring according to ranking or award level, scoring according to the year of honors received, and scoring for groups or individuals. The disadvantage is that there is a lack of dynamic update and maintenance mechanism, which makes it difficult to adapt to real-time changes in craftsman information, resulting in limited accuracy and effectiveness of the evaluation. Summary of the invention

[0004] In view of the problem that the craftsman evaluation system in the prior art lacks a dynamic update and maintenance mechanism and is difficult to adapt to the real-time changes of craftsman information, the present invention provides a digital craftsman evaluation system to achieve real-time update and maintenance of craftsman information, ensure the accuracy and timeliness of the evaluation, and further improve the level of craftsman management and service.

[0005] In order to achieve the above technical objectives, the present invention provides a technical solution, which is a digital evaluation system for craftsmen, comprising: The information collection module is used to collect the basic information, work performance, skill level and training experience of the craftsmen; The data analysis module uses a weighted scoring model to analyze the craftsman information collected by the information collection module, sets relevant weights based on the craftsman information, and calculates a comprehensive score. It uses a machine learning algorithm to analyze the craftsman's professional expertise, project experience, and skill shortcomings. Through image processing and feature extraction, it identifies the key features in the craftsman's operation and performs quantitative scoring. The evaluation module combines the weighted scoring model and machine learning analysis results to generate evaluation results, and dynamically adjusts the weighted scoring model parameters based on the evaluation results and the craftsman's ongoing performance.

[0006] In this technical solution, by integrating the three core modules of information collection, data analysis and evaluation, the comprehensive and real-time update and maintenance of craftsman information is achieved. The system can not only collect multi-dimensional data such as the basic information, work performance, skill level and training experience of craftsmen, but also use advanced weight scoring models and machine learning algorithms to conduct in-depth analysis of this information, accurately calculate the comprehensive score of craftsmen, and accurately identify their professional expertise, project experience and skill shortcomings. Through image processing and feature extraction technology, the system further quantifies the key features in the craftsman's operation, making the evaluation more objective and specific. The evaluation module combines the weighted scoring and machine learning analysis results to generate comprehensive and accurate evaluation results, and dynamically adjusts the model parameters according to the continuous performance of the craftsman to ensure the timeliness and accuracy of the evaluation. Therefore, the level of craftsman management and service has been greatly improved, providing strong support for the growth and development of craftsmen.

[0007] The present invention is further configured as follows: the dynamic adjustment of the weight scoring model parameters includes: Continuously collect data on the actual work performance of craftsmen; Re-evaluate the evaluation indicators and adjust the weights according to the performance of the craftsmen and changes in market demand during the evaluation period; Determine the optimal weight allocation based on the performance of the craftsmen under the corresponding weight combination; Conduct weight sensitivity analysis and adjust sensitive weights first.

[0008] In this technical solution, by continuously collecting the actual work performance data of the craftsmen, the system can reflect the ability and performance of the craftsmen in real work scenarios in real time, providing the latest and most accurate information basis for evaluation. During the evaluation cycle, the system will flexibly re-evaluate the evaluation indicators and adjust the weights according to the specific performance of the craftsmen and changes in market demand to ensure the timeliness and practicality of the evaluation criteria. Through continuous exploration and experimentation, the system can determine the optimal performance of the craftsmen under different weight combinations, thereby optimizing the weight distribution and making the evaluation results closer to the actual situation. At the same time, weight sensitivity analysis is carried out, and sensitive weights are adjusted preferentially to ensure the accuracy of the evaluation model on key indicators, further improving the accuracy and scientificity of the evaluation. These dynamic adjustment mechanisms work together on the weight scoring model, so that the evaluation results can dynamically reflect the true level of the craftsmen, providing more accurate and effective support for craftsman management and services. The characteristics of the digital evaluation system for craftsmen in dynamically adjusting the parameters of the weight scoring model further enhance its adaptability and accuracy.

[0009] The present invention is further configured as follows: the information acquisition module includes: Basic information collection unit: used to collect the name, gender, age and contact information of the craftsman; Work performance collection unit: records the projects in which the craftsmen participated, the tasks completed, the quality of the project and the customer satisfaction in real time; Skill Level Assessment Unit: Regularly assess the professional skill level of craftsmen through online examinations, skill certification, peer evaluation and monitoring of the actual operations of craftsmen; Training experience tracking unit: records the training courses attended by craftsmen, learning time, learning outcomes and certificates obtained.

[0010] In this technical solution, the information collection module, as an important part of the digital evaluation system for craftsmen, realizes the comprehensive and real-time collection of craftsmen's information through detailed unit design. Specifically, the basic information collection unit ensures the accuracy and completeness of the craftsmen's personal information and provides basic data for subsequent analysis and evaluation. The work performance collection unit comprehensively reflects the work performance and actual contribution of the craftsmen by recording key information such as the craftsmen's project participation, task completion, engineering quality and customer satisfaction in real time. The skill level assessment unit uses online examinations, skill certification, peer evaluation and actual operation monitoring to regularly evaluate the professional skill level of craftsmen, ensuring the objectivity and fairness of the evaluation. At the same time, the training experience tracking unit records in detail the craftsmen's training courses, learning time, learning outcomes and certificate acquisition, providing strong support for the craftsmen's career development and capacity improvement. Together, they constitute the information cornerstone of the system, ensure the comprehensiveness and timeliness of the craftsmen's information, and lay a solid foundation for subsequent data analysis and evaluation.

[0011] The present invention is further configured as follows: the evaluation module comprises: Evaluation result generation unit: combines the weighted scoring model and machine learning analysis results to generate the evaluation results of the craftsman; Career Development Application Unit: Based on the evaluation results, targeted improvement is carried out on the craftsmen’s skill shortcomings; Market access review unit: the evaluation results are used as the basis for craftsmen to participate in market competition and obtain project qualifications; Project acceptance assessment unit: predicts the possibility of a craftsman completing a project through evaluation results.

[0012] In this technical solution, the evaluation result generation unit relies on the weighted scoring model and machine learning analysis results to generate the evaluation results of the craftsmen scientifically and objectively, providing a quantitative measurement standard for the professional performance of the craftsmen. The career development application unit accurately identifies the skill shortcomings of the craftsmen based on the evaluation results, and provides a targeted improvement plan based on this, which promotes the professional growth and career development of the craftsmen. The market access review unit uses the evaluation results as an important basis for craftsmen to participate in market competition and obtain project qualifications, ensuring the fairness and professionalism of market competition. The project acceptance assessment unit further uses the evaluation results to predict the possibility of craftsmen completing the project, providing strong support for project allocation and resource allocation. The application of these evaluation modules not only improves the refinement and scientific level of craftsman management, but also effectively promotes the overall quality and competitiveness of the craftsman team, injecting new vitality into the healthy development of the craftsman industry. As one of the core functions of the digital evaluation system for craftsmen, the evaluation module realizes the comprehensive application and guidance of craftsman evaluation through refined unit design.

[0013] The present invention is further configured as follows: the generation of the weight scoring model includes: Determine evaluation indicators, which include basic information, work performance, skill level and training experience; Data were collected through questionnaire surveys, and initial weights were determined using statistical methods; Adopt iterative algorithm to optimize weight distribution; Quantification of craftsman information, including quantification of basic information, quantification of work performance, quantification of skill level and quantification of training experience.

[0014] In this technical solution, by clarifying the evaluation indicators, multiple dimensions such as the basic information of the craftsman, work performance, skill level and training experience are covered to comprehensively evaluate the comprehensive ability of the craftsman. Subsequently, a large amount of data was collected through questionnaires, and the initial weights of each indicator were preliminarily determined using statistical methods, providing data support for model construction. On this basis, an iterative algorithm was used to continuously optimize the weight distribution, and through multiple iterative adjustments, the weight distribution was made more reasonable and more in line with the actual situation. At the same time, the information of the craftsman was fully quantified, including basic information, work performance, skill level and training experience, to ensure the consistency and comparability of data in the evaluation process. This series of steps works together to generate a weight scoring model, providing a solid foundation for the digital evaluation of craftsmen, so that the evaluation results can truly reflect the actual ability and level of the craftsmen, and provide a scientific basis for the management and service of craftsmen.

[0015] The present invention is further configured as follows: the use of an iterative algorithm to optimize weight allocation includes: finding the optimal weight combination by simulating the genetic mechanism in the biological evolution process, and in each iteration, evaluating its fitness based on the system performance under the current weight combination and adjusting the weight.

[0016] In this technical solution, by simulating the genetic mechanism in the biological evolution process, the algorithm can systematically explore and find the optimal weight combination. This process is similar to the gradual adaptation of species to the environment through evolution in nature. In each iteration, the algorithm will evaluate the fitness of the system based on the performance of the current weight combination, that is, the effectiveness and accuracy of the evaluation model. Based on this feedback, the algorithm will intelligently adjust the weights to gradually approach the optimal solution. This adaptive iterative process not only improves the rationality and scientificity of weight distribution, but also ensures the accuracy and reliability of the evaluation results. By using this iterative algorithm, the digital evaluation system for craftsmen can more accurately reflect the actual ability and performance of craftsmen, and provide more powerful support for the career development and project acceptance of craftsmen.

[0017] The present invention is further configured as follows: the image processing and feature extraction include: Place cameras at the work sites of craftsmen to collect real-time work videos of them; De-noise and enhance contrast of the collected video; Use motion detection algorithms to identify key operation frames in the video; Use edge detection algorithm to extract key feature points in key frames; The key feature points are quantified and matched with the standard operating specifications to calculate the matching degree.

[0018] In this technical solution, by arranging cameras at the work site of the craftsman, the system can capture the work process of the craftsman in real time, providing intuitive and real video data for subsequent quantitative evaluation. The collected video is denoised and contrast enhanced, which effectively improves the video quality and makes the subsequent feature extraction more accurate and reliable. The application of motion detection algorithm can accurately identify the key operation frames in the video. These key frames contain the core information of the craftsman's operation and are an important basis for evaluating the skill level of the craftsman. Furthermore, the edge detection algorithm is used to extract the key feature points in the key frames, and these feature points are quantified and matched with the standard operating specifications to calculate the matching degree. This process not only realizes the quantitative evaluation of the craftsman's operating skills, but also ensures the objectivity and comparability of the evaluation results. Overall, this series of image processing and feature extraction technologies provide strong technical support for the digital evaluation system of craftsmen, making the evaluation results more scientific, accurate and reliable, and providing strong data support for the career development and management of craftsmen.

[0019] The present invention is further configured as follows: the machine learning analysis result includes: Perform cluster analysis on the work performance and skill level data of craftsmen based on clustering algorithms to identify the professional expertise of craftsmen; build a decision tree classification model to predict the skill shortcomings of craftsmen; A regression model is used to predict the likelihood of a craftsman completing related projects, and the weighted scoring model parameters are dynamically adjusted based on the craftsman’s actual performance and market demand.

[0020] In this technical solution, the clustering algorithm is used to conduct an in-depth analysis of the work performance and skill level data of the craftsmen. The system can accurately identify the professional expertise areas of the craftsmen, providing strong data support for the career planning and development of the craftsmen. At the same time, a decision tree classification model is constructed to predict the skill shortcomings of the craftsmen, which not only helps the craftsmen themselves identify the skill areas that need to be improved, but also provides targeted training suggestions for training institutions and employers. In addition, the use of regression models to predict the possibility of craftsmen completing related projects provides a scientific basis for project allocation and resource allocation, and effectively improves the success rate of project completion. More importantly, the system can dynamically adjust the parameters of the weighted scoring model based on the actual performance of the craftsmen and market demand, ensuring the timeliness and accuracy of the evaluation results, so that the evaluation system can better adapt to market changes and the needs of craftsmen's growth. Overall, the application of these machine learning analysis results has injected intelligence and vitality into the digital evaluation system of craftsmen, and promoted the scientific management and sustainable development of the craftsman industry.

[0021] The present invention is further configured as follows: the regression model is a linear regression model, and the input parameters of the linear regression model include the craftsman's skill level, work experience and project complexity.

[0022] In this technical solution, by introducing a linear regression model, the digital evaluation of craftsmen no longer relies solely on subjective judgment, but can be quantified through a mathematical model, making the evaluation results more objective and scientific. The input parameters of the model include the craftsman's skill level, work experience and project complexity. These three dimensions comprehensively cover the important aspects of the craftsman's professional ability. The skill level reflects the craftsman's technical proficiency, the work experience reflects the craftsman's accumulated wisdom in solving practical problems, and the project complexity measures the craftsman's ability to cope with difficult tasks. The linear regression model can calculate the craftsman's quantitative evaluation score based on the specific values ​​of the input parameters. This process reduces the impact of human factors on the evaluation results, thereby improving the accuracy and consistency of the evaluation. As the craftsman's skills improve, experience accumulates, and the complexity of the project changes, the value of the input parameter will also be adjusted accordingly. The linear regression model can process these changes in real time and dynamically reflect the latest status of the craftsman's ability, providing strong support for the selection, training and assessment of craftsmen. Through regular digital evaluation, craftsmen can clearly understand their strengths and weaknesses, improve their skills and work experience in a targeted manner, and thus continue to make progress in their careers.

[0023] The beneficial effects of the present invention are as follows: (1) It realizes real-time updating and maintenance of craftsman information, ensures the accuracy and timeliness of evaluation, and further improves the level of craftsman management and service; (2) It can not only collect multi-dimensional data such as basic information, work performance, skill level and training experience of craftsmen, but also use advanced weight scoring models and machine learning algorithms to conduct in-depth analysis of this information, accurately calculate the comprehensive score of the craftsman, and accurately identify his professional expertise, project experience and skill shortcomings; (3) Through image processing and feature extraction technology, the system further quantifies the key features of the craftsman's operation, making the evaluation more objective and specific; the evaluation module integrates the weight score and machine learning analysis results to generate comprehensive and accurate evaluation results, and dynamically adjusts the model parameters according to the continuous performance of the craftsman to ensure the timeliness and accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The present invention is a flowchart of a digital evaluation system for craftsmen. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] like Figure 1 As shown, as a first embodiment of the present invention, a digital evaluation system for craftsmen includes: The information collection module is used to collect the basic information, work performance, skill level and training experience of the craftsmen; The data analysis module uses a weighted scoring model to analyze the craftsman information collected by the information collection module, sets relevant weights based on the craftsman information, and calculates a comprehensive score. It uses a machine learning algorithm to analyze the craftsman's professional expertise, project experience, and skill shortcomings. Through image processing and feature extraction, it identifies the key features in the craftsman's operation and performs quantitative scoring. The evaluation module combines the weighted scoring model and machine learning analysis results to generate evaluation results, and dynamically adjusts the weighted scoring model parameters based on the evaluation results and the craftsman's ongoing performance.

[0027] In this embodiment, by integrating the three core modules of information collection, data analysis and evaluation, comprehensive and real-time updating and maintenance of craftsman information are achieved. The system can not only collect multi-dimensional data such as basic information, work performance, skill level and training experience of craftsmen, but also use advanced weight scoring models and machine learning algorithms to conduct in-depth analysis of this information, accurately calculate the comprehensive score of craftsmen, and accurately identify their professional expertise, project experience and skill shortcomings. Through image processing and feature extraction technology, the system further quantifies the key features in the craftsman's operation, making the evaluation more objective and specific. The evaluation module combines the weighted scoring and machine learning analysis results to generate comprehensive and accurate evaluation results, and dynamically adjusts the model parameters according to the continuous performance of the craftsman to ensure the timeliness and accuracy of the evaluation. Therefore, the level of craftsman management and service has been greatly improved, providing strong support for the growth and development of craftsmen.

[0028] In one embodiment of the present invention, the dynamically adjusting weight scoring model parameters includes: Continuously collect data on the actual work performance of craftsmen; Re-evaluate the evaluation indicators and adjust the weights according to the performance of the craftsmen and changes in market demand during the evaluation period; Determine the optimal weight allocation based on the performance of the craftsmen under the corresponding weight combination; Conduct weight sensitivity analysis and adjust sensitive weights first.

[0029] In this technical solution, by continuously collecting the actual work performance data of the craftsmen, the system can reflect the ability and performance of the craftsmen in real work scenarios in real time, providing the latest and most accurate information basis for evaluation. During the evaluation cycle, the system will flexibly re-evaluate the evaluation indicators and adjust the weights according to the specific performance of the craftsmen and changes in market demand to ensure the timeliness and practicality of the evaluation criteria. Through continuous exploration and experimentation, the system can determine the optimal performance of the craftsmen under different weight combinations, thereby optimizing the weight distribution and making the evaluation results closer to the actual situation. At the same time, weight sensitivity analysis is carried out, and sensitive weights are adjusted preferentially to ensure the accuracy of the evaluation model on key indicators, further improving the accuracy and scientificity of the evaluation. These dynamic adjustment mechanisms work together on the weight scoring model, so that the evaluation results can dynamically reflect the true level of the craftsmen, providing more accurate and effective support for craftsman management and services. The characteristics of the craftsman digital evaluation system in dynamically adjusting the parameters of the weight scoring model further enhance its adaptability and accuracy.

[0030] It is understandable that different dimensions may have different importance for different craftsmen or in different situations. Therefore, allowing the system to dynamically adjust these weights based on the analysis results can make the scoring more in line with reality.

[0031] In one embodiment of the present invention, the information collection module includes: Basic information collection unit: used to collect the name, gender, age and contact information of the craftsman; Work performance collection unit: records the projects in which the craftsmen participated, the tasks completed, the quality of the project and the customer satisfaction in real time; Skill Level Assessment Unit: Regularly assess the professional skill level of craftsmen through online examinations, skill certification, peer evaluation and monitoring of the actual operations of craftsmen; Training experience tracking unit: records the training courses attended by craftsmen, learning time, learning outcomes and certificates obtained.

[0032] In this technical solution, the information collection module, as an important part of the digital evaluation system for craftsmen, realizes the comprehensive and real-time collection of craftsmen's information through detailed unit design. Specifically, the basic information collection unit ensures the accuracy and completeness of the craftsmen's personal information and provides basic data for subsequent analysis and evaluation. The work performance collection unit comprehensively reflects the work performance and actual contribution of the craftsmen by recording key information such as the craftsmen's project participation, task completion, engineering quality and customer satisfaction in real time. The skill level assessment unit uses online examinations, skill certification, peer evaluation and actual operation monitoring to regularly evaluate the professional skill level of craftsmen, ensuring the objectivity and fairness of the evaluation. At the same time, the training experience tracking unit records in detail the craftsmen's training courses, learning time, learning outcomes and certificate acquisition, providing strong support for the craftsmen's career development and capacity improvement. Together, they constitute the information cornerstone of the system, ensure the comprehensiveness and timeliness of the craftsmen's information, and lay a solid foundation for subsequent data analysis and evaluation.

[0033] Understandably, in the information collection module, a real-time update mechanism is implemented to establish a real-time data interface with the craftsmen’s personal database, project management system and training institution to ensure the timeliness and accuracy of information collection.

[0034] In one embodiment of the present invention, the evaluation module includes: Evaluation result generation unit: combines the weighted scoring model and machine learning analysis results to generate the evaluation results of the craftsman; Career Development Application Unit: Based on the evaluation results, targeted improvement is carried out on the craftsmen’s skill shortcomings; Market access review unit: the evaluation results are used as the basis for craftsmen to participate in market competition and obtain project qualifications; Project acceptance assessment unit: predicts the possibility of a craftsman completing a project through evaluation results.

[0035] In this technical solution, the evaluation result generation unit relies on the weighted scoring model and machine learning analysis results to generate the evaluation results of the craftsmen scientifically and objectively, providing a quantitative measurement standard for the professional performance of the craftsmen. The career development application unit accurately identifies the skill shortcomings of the craftsmen based on the evaluation results, and provides a targeted improvement plan based on this, which promotes the professional growth and career development of the craftsmen. The market access review unit uses the evaluation results as an important basis for craftsmen to participate in market competition and obtain project qualifications, ensuring the fairness and professionalism of market competition. The project acceptance assessment unit further uses the evaluation results to predict the possibility of craftsmen completing the project, providing strong support for project allocation and resource allocation. The application of these evaluation modules not only improves the refinement and scientific level of craftsman management, but also effectively promotes the overall quality and competitiveness of the craftsman team, injecting new vitality into the healthy development of the craftsman industry. As one of the core functions of the digital evaluation system for craftsmen, the evaluation module realizes the comprehensive application and guidance of craftsman evaluation through refined unit design.

[0036] Understandably, a questionnaire was designed for key stakeholders in craftsman management, including the craftsmen themselves, employers, and project managers, to collect their views on the importance of each dimension of craftsman evaluation (such as basic information, work performance, skill level, and training experience); the AHP method was used to decompose complex decision factors into different components, and the relative importance of the factors in the hierarchy was determined by pairwise comparison. The data collected through the questionnaire was used to construct a judgment matrix and calculate the relative weights of each factor; the weights were tested for significance to ensure the stability and reliability of the weights.

[0037] Preferably, the generation of the weight scoring model includes: Determine evaluation indicators, which include basic information, work performance, skill level and training experience; Data were collected through questionnaire surveys, and initial weights were determined using statistical methods; Adopt iterative algorithm to optimize weight distribution; Quantification of craftsman information, including quantification of basic information, quantification of work performance, quantification of skill level and quantification of training experience.

[0038] In this technical solution, first of all, by clarifying the evaluation indicators, covering multiple dimensions such as the basic information of the craftsman, work performance, skill level and training experience, the comprehensive ability of the craftsman is comprehensively evaluated. Subsequently, a large amount of data is collected through questionnaires, and the initial weights of each indicator are preliminarily determined using statistical methods, providing data support for model construction. On this basis, an iterative algorithm is used to continuously optimize the weight distribution, and through multiple iterative adjustments, the weight distribution is made more reasonable and more in line with the actual situation. At the same time, the information of the craftsman is fully quantified, including basic information, work performance, skill level and training experience, to ensure the consistency and comparability of data in the evaluation process. This series of steps work together to generate a weight scoring model, providing a solid foundation for the digital evaluation of craftsmen, so that the evaluation results can truly reflect the actual ability and level of the craftsmen, and provide a scientific basis for the management and service of craftsmen.

[0039] The iterative algorithm used to optimize weight allocation includes: finding the optimal weight combination by simulating the genetic mechanism in the biological evolution process, and in each iteration, evaluating the fitness of the system according to the performance of the current weight combination and adjusting the weight.

[0040] In this technical solution, by simulating the genetic mechanism in the biological evolution process, the algorithm can systematically explore and find the optimal weight combination. This process is similar to the gradual adaptation of species to the environment through evolution in nature. In each iteration, the algorithm will evaluate the fitness of the system based on the performance of the current weight combination, that is, the effectiveness and accuracy of the evaluation model. Based on this feedback, the algorithm will intelligently adjust the weights to gradually approach the optimal solution. This adaptive iterative process not only improves the rationality and scientificity of weight distribution, but also ensures the accuracy and reliability of the evaluation results. By using this iterative algorithm, the digital evaluation system for craftsmen can more accurately reflect the actual ability and performance of craftsmen, and provide more powerful support for the career development and project acceptance of craftsmen.

[0041] The image processing and feature extraction include: Place cameras at the work sites of craftsmen to collect real-time work videos of them; De-noise and enhance contrast of the collected video; Use motion detection algorithms to identify key operation frames in the video; Use edge detection algorithm to extract key feature points in key frames; The key feature points are quantified and matched with the standard operating specifications to calculate the matching degree.

[0042] In this technical solution, by arranging cameras at the work site of the craftsman, the system can capture the work process of the craftsman in real time, providing intuitive and real video data for subsequent quantitative evaluation. The collected video is denoised and contrast enhanced, which effectively improves the video quality and makes the subsequent feature extraction more accurate and reliable. The application of motion detection algorithm can accurately identify the key operation frames in the video. These key frames contain the core information of the craftsman's operation and are an important basis for evaluating the skill level of the craftsman. Furthermore, the edge detection algorithm is used to extract the key feature points in the key frames, and these feature points are quantified and matched with the standard operating specifications to calculate the matching degree. This process not only realizes the quantitative evaluation of the craftsman's operating skills, but also ensures the objectivity and comparability of the evaluation results. Overall, this series of image processing and feature extraction technologies provide strong technical support for the digital evaluation system of craftsmen, making the evaluation results more scientific, accurate and reliable, and providing strong data support for the career development and management of craftsmen.

[0043] It can be understood that the motion detection algorithm uses background difference method or optical flow method to detect motion changes in the video. First, continuous frames are extracted from the video, and then the difference between adjacent frames is calculated, and the motion area is identified by setting a threshold.

[0044] Preferably, the edge detection algorithm is the Canny algorithm, which performs edge detection on the key frames obtained after motion detection to extract contour information in the image. The Canny algorithm accurately detects the edges in the image through multi-level threshold processing and double threshold detection, and uses SIFT (Scale Invariant Feature Transform) or SURF (Speeded Up Robust Features) algorithms to extract local feature points in the key frames, which have certain invariance to image rotation, scaling, brightness changes, etc.

[0045] The machine learning analysis results include: Perform cluster analysis on the work performance and skill level data of craftsmen based on clustering algorithms to identify the professional expertise of craftsmen; build a decision tree classification model to predict the skill shortcomings of craftsmen; A regression model is used to predict the likelihood of a craftsman completing related projects, and the weighted scoring model parameters are dynamically adjusted based on the craftsman’s actual performance and market demand.

[0046] In this technical solution, the clustering algorithm is used to conduct an in-depth analysis of the work performance and skill level data of the craftsmen. The system can accurately identify the professional expertise areas of the craftsmen, providing strong data support for the career planning and development of the craftsmen. At the same time, a decision tree classification model is constructed to predict the skill shortcomings of the craftsmen, which not only helps the craftsmen themselves identify the skill areas that need to be improved, but also provides targeted training suggestions for training institutions and employers. In addition, the use of regression models to predict the possibility of craftsmen completing related projects provides a scientific basis for project allocation and resource allocation, and effectively improves the success rate of project completion. More importantly, the system can dynamically adjust the parameters of the weighted scoring model based on the actual performance of the craftsmen and market demand, ensuring the timeliness and accuracy of the evaluation results, so that the evaluation system can better adapt to market changes and the needs of craftsmen's growth. Overall, the application of these machine learning analysis results has injected intelligence and vitality into the digital evaluation system of craftsmen, and promoted the scientific management and sustainable development of the craftsman industry.

[0047] It can be understood that the model divides the craftsman's information layer by layer according to each dimension (work experience or training experience), and finally outputs the prediction results of skill shortcomings.

[0048] The regression model is a linear regression model, and the input parameters of the linear regression model include the skill level, work experience and project complexity of the craftsman.

[0049] In this technical solution, by introducing the linear regression model, the digital evaluation of craftsmen no longer relies solely on subjective judgment, but can be quantified through mathematical models, making the evaluation results more objective and scientific. The input parameters of the model include the craftsman's skill level, work experience and project complexity. These three dimensions comprehensively cover the important aspects of the craftsman's professional ability. The skill level reflects the craftsman's technical proficiency, the work experience reflects the craftsman's accumulated wisdom in solving practical problems, and the project complexity measures the craftsman's ability to cope with difficult tasks. The linear regression model can calculate the craftsman's quantitative evaluation score based on the specific values ​​of the input parameters. This process reduces the impact of human factors on the evaluation results, thereby improving the accuracy and consistency of the evaluation. As the craftsman's skills improve, experience accumulates, and the complexity of the project changes, the value of the input parameter will also be adjusted accordingly. The linear regression model can process these changes in real time and dynamically reflect the latest status of the craftsman's ability, providing strong support for the selection, training and assessment of craftsmen. Through regular digital evaluation, craftsmen can clearly understand their strengths and weaknesses, improve their skills and work experience in a targeted manner, and thus continue to make progress in their careers.

[0050] The specific description of the present invention in the above embodiments is only used to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Technical engineers in this field may make some non-essential improvements and adjustments to the present invention based on the contents of the above invention, which fall within the scope of protection of the present invention.

Claims

1. A digital evaluation system for craftsmen, characterized by: include: The information collection module is used to collect the basic information, work performance, skill level and training experience of the craftsmen; The data analysis module uses a weighted scoring model to analyze the craftsman information collected by the information collection module, sets relevant weights based on the craftsman information, and calculates a comprehensive score. It uses a machine learning algorithm to analyze the craftsman's professional expertise, project experience, and skill shortcomings. Through image processing and feature extraction, it identifies key features in the craftsman's operation and performs quantitative scoring. The evaluation module combines the weighted scoring model and the results of machine learning analysis to generate evaluation results, and dynamically adjusts the weighted scoring model parameters based on the evaluation results and the craftsman's ongoing performance.

2. A digital craftsman evaluation system according to claim 1, characterized in that: The dynamic adjustment of weight scoring model parameters includes: Continuously collect data on the actual work performance of craftsmen; Re-evaluate the evaluation indicators and adjust the weights according to the performance of the craftsmen and changes in market demand during the evaluation period; Determine the optimal weight allocation based on the performance of the craftsmen under the corresponding weight combination; Conduct weight sensitivity analysis and adjust sensitive weights first.

3. The digital evaluation system for craftsmen according to claim 1 is characterized in that: The information collection module includes: a basic information collection unit: used to collect the name, gender, age and contact information of the craftsman; Work performance collection unit: records the projects in which the craftsmen participated, the tasks completed, the quality of the project and the customer satisfaction in real time; Skill Level Assessment Unit: Regularly assess the professional skill level of craftsmen through online examinations, skill certification, peer evaluation and monitoring of the actual operations of craftsmen; Training experience tracking unit: records the training courses attended by craftsmen, learning time, learning outcomes and certificates obtained.

4. The digital evaluation system for craftsmen according to claim 1 is characterized in that: The evaluation module includes: Evaluation result generation unit: combines the weighted scoring model and machine learning analysis results to generate the evaluation results of the craftsman; Career Development Application Unit: Based on the evaluation results, targeted improvement is carried out on the craftsmen’s skill shortcomings; Market access review unit: the evaluation results are used as the basis for craftsmen to participate in market competition and obtain project qualifications; Project acceptance assessment unit: predicts the possibility of a craftsman completing a project through evaluation results.

5. A digital craftsman evaluation system according to claim 1 or 2, characterized in that: The generation of the weight scoring model includes: Determine evaluation indicators, which include basic information, work performance, skill level and training experience; Data were collected through questionnaire surveys, and initial weights were determined using statistical methods; Adopt iterative algorithm to optimize weight distribution; Quantification of craftsman information, including quantification of basic information, quantification of work performance, quantification of skill level and quantification of training experience.

6. A digital craftsman evaluation system according to claim 5, characterized in that: The iterative algorithm used to optimize weight allocation includes: finding the optimal weight combination by simulating the genetic mechanism in the biological evolution process, and in each iteration, evaluating the fitness of the system according to the performance of the current weight combination and adjusting the weight.

7. The digital evaluation system for craftsmen according to claim 1 is characterized in that: The image processing and feature extraction include: Place cameras at the work sites of craftsmen to collect real-time work videos of them; De-noise and enhance contrast of the collected video; Use motion detection algorithms to identify key operation frames in the video; Use edge detection algorithm to extract key feature points in key frames; The key feature points are quantified and matched with the standard operating specifications to calculate the matching degree.

8. A digital craftsman evaluation system according to claim 1 or 4, characterized in that: The machine learning analysis results include: Perform cluster analysis on the work performance and skill level data of craftsmen based on clustering algorithms to identify the professional expertise areas of craftsmen; build a decision tree classification model to predict the skill shortcomings of craftsmen; A regression model is used to predict the likelihood of a craftsman completing related projects, and the weighted scoring model parameters are dynamically adjusted based on the craftsman’s actual performance and market demand.

9. A digital craftsman evaluation system according to claim 8, characterized in that: The regression model is a linear regression model, and the input parameters of the linear regression model include the skill level, work experience and project complexity of the craftsman.

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