An evaluation system and method for improving participation in Mycoplasma pneumoniae testing through relay testing

Abstract

The present invention discloses an evaluation system and method for improving the participation of Mycoplasma pneumoniae detection by relay detection, which belongs to the field of data analysis. The system includes: a data collection module for collecting behavioral data of a research population; a data preprocessing module for performing data cleaning, integration and preprocessing on the behavioral data; a model training module for obtaining a participation evaluation model; an evaluation import module for obtaining the output results of the participation evaluation model and importing them into the evaluation database of researchers for clustering and segmentation; and a task allocation module for adjusting and allocating detection tasks according to the evaluation and segmentation results of researchers. This application solves the technical problem of the lack of effective methods in the prior art for evaluating and intelligently improving the participation of Mycoplasma pneumoniae detection, and achieves the technical effect of quantitatively evaluating the participation of researchers through a data-driven machine learning model, and optimizing task allocation accordingly, thereby improving the overall efficiency and quality of Mycoplasma pneumoniae detection.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to an evaluation system and method for improving participation in Mycoplasma pneumoniae testing through relay testing. Background Art

[0002] In the detection of Mycoplasma pneumoniae, evaluating and improving the participation of testers is a key factor in ensuring the quality and efficiency of testing. Figure 3 As shown, the relay testing method involves having people undergo free health tests, then informing participants that someone has kindly paid for them and asking them if they are willing to donate to support more people receiving free health testing services. This method can significantly increase participation in various health tests. However, when applied to Mycoplasma pneumoniae testing, relay testing fails to meet practical needs in terms of effectively evaluating and intelligently improving participation in Mycoplasma pneumoniae testing. Currently, methods for assessing tester participation rely primarily on manual statistics and judgment. This method is not only time-consuming and labor-intensive, but also fails to effectively and objectively reflect the actual performance of testers. For example, assessing participation solely based on whether or not a test was taken and the number of tests taken fails to comprehensively measure test quality and efficiency and improve testing indicators through intelligent analysis. Summary of the Invention

[0003] This application aims to solve the technical problem in the existing technology of lack of effective methods to evaluate and intelligently improve the participation rate of Mycoplasma pneumoniae testing by providing an evaluation system and method for relay testing to improve the participation rate of Mycoplasma pneumoniae testing.

[0004] In view of the above problems, the present application provides an evaluation system and method for relay testing to improve the participation rate of Mycoplasma pneumoniae testing.

[0005] The first aspect disclosed in the present application provides an evaluation system for improving the participation of Mycoplasma pneumoniae testing in relay testing, the system comprising: a data collection module for collecting behavioral data of a research population, including testing duration, number of tests, task completion status, and abnormality reporting rate; a data preprocessing module for performing data cleaning, integration, and preprocessing on the behavioral data, constructing a training data set, and segmenting the training data set to determine training data and test data; a model training module for performing model training and testing based on a machine learning algorithm using training data and test data to converge to obtain a participation evaluation model, the participation evaluation model being used to quantitatively evaluate the participation of multiple indicators inputted into the researchers; an evaluation import module for obtaining the output results of the participation evaluation model, importing them into the researchers' evaluation database, and clustering and segmenting all researchers in the evaluation database; and a task allocation module for adjusting and allocating detection tasks according to the evaluation segmentation results of the researchers.

[0006] Another aspect disclosed in the present application provides an evaluation method for improving participation in Mycoplasma pneumoniae testing through relay testing, the method comprising: collecting behavioral data of a research population, including testing duration, number of tests, task completion status, and abnormality reporting rate; performing data cleaning and integration preprocessing on the behavioral data, constructing a training data set, and segmenting the training data set to determine training data and test data; based on a machine learning algorithm, performing model training and testing on the training data and test data, and converging to obtain a participation evaluation model, the participation evaluation model is used to quantitatively evaluate the participation of multiple indicators input to the researchers; obtaining the output results of the participation evaluation model, importing them into the researchers' evaluation database, and clustering and segmenting all researchers in the evaluation database; and adjusting and allocating testing tasks according to the researchers' evaluation and segmentation results.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The data collection module collects behavioral data from the study population, including test duration, number of tests, task completion status, and abnormality reporting rate, obtaining comprehensive raw data to provide a foundation for subsequent analysis. The data preprocessing module cleans, integrates, and preprocesses the collected behavioral data to construct a training dataset, which is then segmented to determine training and test data to ensure data quality and prepare for model training. The model training module uses a machine learning algorithm to train and test the model using training and test data, ultimately converging to obtain a participation assessment model, which is used to quantitatively evaluate the participation of multiple researchers. The evaluation import module obtains the output of the participation assessment model and imports it into the researcher evaluation database. All researchers in the evaluation database are clustered and segmented, the model evaluation results are systematically managed, and researchers are classified. The task allocation module adjusts the allocation of testing tasks according to the researcher's evaluation and segmentation results. This technical solution solves the technical problem of the lack of effective methods to evaluate and intelligently improve the participation of Mycoplasma pneumoniae testing in existing technologies. It achieves the technical effect of quantitatively evaluating researcher participation through a data-driven machine learning model and optimizing task allocation accordingly, thereby improving the overall efficiency and quality of Mycoplasma pneumoniae testing.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1A structural schematic diagram of an evaluation system for improving the participation rate of Mycoplasma pneumoniae detection by relay detection is provided for an embodiment of the present application.

[0011] Figure 2 A flowchart of a method for evaluating the effectiveness of relay testing to improve participation in Mycoplasma pneumoniae testing is provided for an embodiment of the present application;

[0012] Figure 3 Schematic diagram of the implementation mode of the relay detection method in the embodiment of the present application;

[0013] Explanation of the accompanying drawings: data collection module 11, data preprocessing module 12, model training module 13, evaluation import module 14, task allocation module 15. DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] This application provides an evaluation system and method for improving participation in Mycoplasma pneumoniae testing through relay testing. By leveraging data-driven and machine learning technologies, the system objectively evaluates testing participation and optimizes task allocation. The system employs a modular design and, through the collaborative work of multiple functional modules, automates the entire process from data collection to task allocation.

[0016] Specifically, the data collection module first collects comprehensive behavioral data from the study population, including multi-dimensional information such as test duration, number of tests, task completion status, and abnormality reporting rate. Subsequently, the data preprocessing module cleans and integrates these raw data to construct a high-quality training dataset. On this basis, the model training module uses machine learning algorithms to repeatedly learn from training and test data, converging to a participation assessment model that can quantitatively evaluate multiple indicators of researchers. The assessment results are systematically managed through the assessment import module and used to cluster and segment researchers. Subsequently, the task allocation module realizes the intelligent adjustment and allocation of detection tasks based on these evaluation and classification results.

[0017] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0018] Example 1

[0019] like Figure 1 As shown, an embodiment of the present application provides an evaluation system for improving the participation rate of Mycoplasma pneumoniae detection through relay testing, the system comprising:

[0020] The data collection module 11 is used to collect behavioral data of the research population, including detection time, number of detections, task completion status, and abnormality reporting rate.

[0021] Specifically, the data collection module 11 is used to collect behavioral data of the research population during the Mycoplasma pneumoniae testing process. Among them, the behavioral data includes but is not limited to the test duration, number of tests, task completion status and abnormal reporting rate. The test duration records the time required for each research subject to complete a single Mycoplasma pneumoniae test in minutes. The number of tests counts the total number of Mycoplasma pneumoniae tests completed by each research subject within a specified time period (such as a week or a month). The task completion status records whether the research subject completes all assigned testing tasks according to the predetermined plan, which can be expressed as the completion rate (number of completed tasks / total number of tasks). The abnormal reporting rate counts the proportion of abnormalities or errors in the test reports submitted by the research subjects, and is calculated as the number of abnormal reports divided by the total number of reports. Among them, the data collection module 11 can obtain these behavioral data in a variety of ways, such as automatic recording by the testing equipment, manual input by researchers, or questionnaires. These data will provide a basis for subsequent participation assessment and help to fully understand the performance and participation level of the research population in the Mycoplasma pneumoniae testing process. Among them, the data collection module 11 can obtain these behavioral data in a variety of ways, such as automatic recording by the testing equipment, manual input by researchers, or questionnaires. These data will provide a basis for subsequent engagement assessments and help to fully understand the performance and level of engagement of the study population in the M. pneumoniae testing process.

[0022] The data preprocessing module 12 is used to perform data cleaning and integration preprocessing on the behavior data, construct a training data set, and segment the training data set to determine training data and test data.

[0023] Specifically, the data preprocessing module 12 first performs data cleaning and integration preprocessing on the collected behavioral data, including but not limited to processing missing values, removing outliers, unifying data formats, etc., in order to improve data quality and ensure the accuracy of subsequent analysis. For example, for missing values ​​in the detection time data, the mode of the feature is used to fill in; for obviously unreasonable outliers, such as a negative number of detections, it may be deleted. After the data cleaning is completed, the data preprocessing module 12 will integrate the preprocessed behavioral data to construct a training data set. In this process, an evaluation mapping relationship between features such as detection time, number of detections, task completion status, and abnormal reporting rate and participation is established based on expert experience or predefined rules. Afterwards, the data preprocessing module 12 divides the constructed training data set to determine training data and test data. For example, a random sampling method is used to divide the entire data set into training data and test data according to a certain ratio (such as 7:3 or 8:2). Among them, the training data is used for model learning and parameter adjustment, while the test data is used to evaluate the performance and generalization ability of the model.

[0024] The model training module 13 is used to perform model training and testing based on the training data and test data based on a machine learning algorithm, and converge to obtain a participation evaluation model, wherein the participation evaluation model is used to quantitatively evaluate the participation of multiple indicators of input researchers.

[0025] Specifically, the model training module 13 first selects an appropriate machine learning algorithm as a basis. For example, the support vector machine (SVM) is selected as the core algorithm. After selecting the algorithm, the model training module 13 uses the training data to train the model. During the training process, the model will continuously adjust its internal parameters to minimize the error between the predicted results and the actual results. For the support vector machine, this includes selecting an appropriate kernel function (such as the radial basis function RBF), adjusting the kernel function parameters (such as the γ value) and the penalty parameter C value. After training is completed, the model training module 13 uses the test data to evaluate the performance of the model. Evaluation indicators include accuracy, precision, recall rate, etc. If the model performance does not meet the preset goals, the model training module 13 optimizes by adjusting hyperparameters or increasing the number of training rounds until the model performance reaches the expected level. Afterwards, the model training module 13 outputs a converged participation evaluation model. The model can receive multiple indicators from researchers as input, including but not limited to detection time, number of detections, task completion status, abnormality reporting rate, etc., and output a quantitative participation score.

[0026] The evaluation import module 14 is used to obtain the output results of the participation evaluation model, import them into the evaluation database of researchers, and perform clustering and segmentation on all researchers in the evaluation database.

[0027] Specifically, the evaluation import module 14 first obtains the evaluation results of the participation evaluation model for each researcher. For example, the result is a quantitative score or grade that reflects the degree of participation of the researcher in the Mycoplasma pneumoniae detection process. The evaluation import module 14 imports these results into a pre-set researcher evaluation database and stores them in association with other information of the researcher (such as personal basic information, detection history, etc.). After completing the data import, the evaluation import module 14 clusters and segments all researchers in the evaluation database, and groups researchers with similar participation characteristics to facilitate subsequent task allocation and management. Among them, cluster segmentation can adopt a variety of algorithms, such as K-means clustering or hierarchical clustering. In this embodiment, researchers can be divided into multiple levels according to the participation score, such as high participation, moderate participation and low participation.

[0028] In addition, the evaluation import module 14 performs further cluster analysis based on other characteristics of researchers. For example, researchers are re-clustered based on factors such as research location, age, family income, and parental education level. This multi-dimensional cluster analysis can generate more detailed cluster labels, providing a more accurate basis for subsequent task allocation. At the same time, the cluster segmentation results of the evaluation import module 14 can be updated regularly to reflect the dynamic changes in researcher participation. For example, a cluster analysis can be re-performed weekly or monthly to capture the changing trends in researcher participation. This dynamic update mechanism helps to maintain the timeliness and accuracy of the evaluation results.

[0029] Through the processing of the evaluation import module 14, a structured and hierarchical researcher evaluation database is formed, which provides important support for subsequent task allocation and management. It not only helps to identify highly engaged researchers, but also helps to find low-engagement researchers who need additional support or incentives, thereby improving the overall detection efficiency and quality.

[0030] The task allocation module 15 is used to adjust and allocate detection tasks according to the evaluation and segmentation results of researchers.

[0031] Specifically, the task assignment module 15 first obtains the output of the assessment import module 14, including the researcher's participation score, cluster segmentation results, and multi-dimensional cluster labels. Based on this information, the task assignment module 15 establishes a mapping between the segmentation level and cluster labels and the researchers, and presents this information in a visual manner on the task management interface, allowing managers to intuitively understand the overall distribution of researchers.

[0032] When performing specific task assignments, the task assignment module 15 first obtains the preset task assignment information of the detection task. This information may include the number, type, difficulty, etc. of tasks. Subsequently, the task assignment module 15 parses the characteristic information of each detection task, including but not limited to geographical characteristics, difficulty characteristics, support requirement characteristics, etc. For example, certain detection tasks may need to be carried out in a specific area, or require researchers with specific skills to complete. Afterwards, the task assignment module 15 uses these task characteristics as search targets and performs a correlation analysis on the segmentation level and clustering labels of the researchers. Through this analysis, the researchers who are most relevant to a specific detection task can be screened out. For example, for detection tasks with higher difficulty, researchers with high participation and high skill levels are given priority; for tasks that need to be carried out in a specific area, researchers in that area are given priority.

[0033] Through the intelligent allocation of the task allocation module 15, the efficient allocation and execution of Mycoplasma pneumoniae detection tasks can be achieved, thereby improving the overall detection efficiency and quality. At the same time, appropriate incentives can be given according to the performance of researchers, forming a virtuous cycle of detection ecosystem.

[0034] Furthermore, the data preprocessing module includes the following execution steps:

[0035] Processing missing values ​​and outliers in the behavioral data, unifying data formats from different sources, and completing preprocessing of the behavioral data;

[0036] Based on the pre-processed behavioral data, a mapping relationship between the detection duration, number of detections, task completion status, and abnormality reporting rate and participation evaluation is established to construct the training data set.

[0037] In a feasible implementation, first, the data preprocessing module 12 processes missing values ​​and outliers in the collected behavioral data. For missing values, a variety of strategies are adopted, such as using the mean, median or mode of the feature to fill in, or using more complex interpolation methods. For example, for missing values ​​of detection time, the average value of the researcher's past detection time is used to fill in. For outliers, a reasonable threshold range is set, and data out of the range is regarded as abnormal and processed, such as deleting it or setting it as a boundary value. Secondly, the data preprocessing module 12 performs unified format processing on data from different sources. Because behavioral data may come from multiple channels, such as automatically recorded detection equipment data, data manually entered by researchers, questionnaire data, etc. Unified format processing ensures that all data can be consistently processed and interpreted in subsequent analysis.

[0038] After completing the above preprocessing steps, the data preprocessing module 12 establishes an evaluation mapping relationship between key indicators such as detection duration, number of detections, task completion status, and exception reporting rate, and engagement based on the processed behavioral data. This mapping relationship can be established based on expert experience, historical data analysis, or predefined rules. For example, a weighted formula can be set to assign different weights to each indicator according to its importance, and the engagement score can be calculated comprehensively. Then, based on these mapping relationships, the data preprocessing module 12 constructs a training dataset, which includes the preprocessed behavioral data as input features and the engagement score calculated according to the mapping relationship as the target variable.

[0039] Through preprocessing, the data quality of the input model can be ensured, and the accuracy and reliability of the subsequent participation evaluation model can be improved.

[0040] Furthermore, the data preprocessing module further includes the following execution steps:

[0041] For missing values ​​in the behavioral data, the mode of the feature is used to fill in the missing values ​​to complete the missing value processing;

[0042] Deleting outliers in the behavior data to complete the outlier processing;

[0043] After deleting outliers, the continuity analysis of data features was performed, and when there were missing values, the mode filling process was repeated.

[0044] In one feasible implementation, the data preprocessing module 12 first fills missing values ​​in the behavioral data using the mode of the feature. This method maintains the discrete nature of the data and is particularly suitable for categorical or discrete numerical variables. For example, for the feature of test number, if most researchers perform three tests in a week, then filling missing test number data with 3 can better reflect the central tendency of the data and reduce the bias caused by filling. Secondly, the data preprocessing module 12 removes outliers in the behavioral data. The determination of outliers can be based on preset rules or statistical methods, such as the 3σ principle or the interquartile range (IQR) method. For example, if a researcher's test duration far exceeds the normal range, it will be identified as an outlier and removed from the dataset to remove extreme data points that may affect the accuracy of model training. After the outlier removal is completed, the data preprocessing module 12 performs a continuity analysis of the data features to determine whether the removal of the outliers has resulted in new missing data or discontinuities. If new missing data are found in certain features after the outlier removal, the mode filling method is used again. This iterative processing method ensures the integrity and continuity of the data.

[0045] Through a refined data preprocessing process, we can more effectively handle various data problems that may arise during the actual detection process, provide high-quality data input for subsequent model training, and thus improve the accuracy and reliability of participation assessment.

[0046] Furthermore, the model training module includes the following execution steps:

[0047] Build a support vector machine, select the kernel function and kernel function parameters, set the C value, and train the support vector machine using training data;

[0048] The trained support vector machine is tested for accuracy using test data, and the kernel function parameters and C value are adjusted according to the test results. The training is iterated until the test accuracy reaches the target value, thereby obtaining the engagement evaluation model.

[0049] In a preferred embodiment, first, the model training module 13 constructs a support vector machine as the core algorithm for engagement assessment. When constructing the support vector machine, the model training module 13 selects an appropriate kernel function and corresponding kernel function parameters, such as a linear kernel, a polynomial kernel, and a radial basis function kernel. In this embodiment, the RBF kernel is selected, which can effectively handle nonlinear relationships and is suitable for complex engagement assessment scenarios. At the same time, the model training module 13 sets the penalty parameter C value to control the complexity and fault tolerance of the model. Next, the model training module 13 uses the pre-processed training data to perform preliminary training on the support vector machine. During the training process, the optimal decision boundary is sought to maximize the interval between different engagement categories. After the training is completed, the model training module 13 will use the pre-reserved test data to perform an accuracy test on the trained support vector machine, aiming to evaluate the generalization ability of the model, that is, the performance of the model on unseen data. The test results will be used to determine whether the model has achieved the expected performance level. If the test accuracy does not reach the preset target value, the model training module 13 adjusts the kernel function parameters (such as the γ value of the RBF kernel) and the C value according to the test results, such as through grid search, random search or Bayesian optimization. After adjustment, the model training module 13 retrains the support vector machine using the new parameter settings and tests again. The training-testing-parameter adjustment process will be continuously iterated until the test accuracy of the model reaches the preset target value. After that, the model training module 13 outputs the optimized engagement evaluation model.

[0050] Through model training, a highly optimized and stable participation assessment model was constructed, which can not only accurately evaluate the participation of researchers in Mycoplasma pneumoniae testing, but also adapt to the characteristics of different populations and testing environments.

[0051] Furthermore, evaluating the import module includes the following execution steps:

[0052] Segment the evaluation levels according to the evaluation results in the evaluation database and construct multi-level researcher clusters;

[0053] The multi-level researcher clusters are re-clustered based on research location, age, family income, and parents' education level to generate cluster labels.

[0054] In a feasible implementation, the evaluation import module 14 first obtains the evaluation results output by the participation evaluation model and imports these results into a preset evaluation database. Based on these evaluation results, an evaluation grade segmentation operation is performed to divide the participation score into multiple levels, such as high participation (Grade A), moderate participation (Grade B), and low participation (Grade C). This grading method can intuitively reflect the level of participation of researchers, facilitating subsequent management and incentives. Based on this, the evaluation import module 14 constructs a multi-level researcher cluster, laying the foundation for further refined management.

[0055] Subsequently, the assessment import module 14 re-clustered the multi-level researcher clusters, aiming to further consider the individual characteristics of researchers in addition to the level of participation for more accurate classification and management. Specifically, re-clustering was performed based on four key characteristics: research location, age, family income, and parental education. This multi-dimensional feature re-clustering takes into account the environmental, cultural, and resource differences that may exist in different geographic locations; the potential differences in participation among different age groups; the potential relationship between family economic status and researcher participation motivation and ability; and the possible influence of parental education background on researcher participation.

[0056] By re-clustering features across multiple dimensions, the assessment import module 14 generates a comprehensive cluster label for each researcher. For example, a researcher might be labeled: "Class A - Urban - Youth - Middle-income - Higher Education Background." This refined cluster labeling can more comprehensively describe the researcher's characteristics, providing a more accurate basis for subsequent task allocation and management. It also helps to increase participation in testing and provide more appropriate incentives and support measures for researchers with different characteristics, thereby improving overall testing results and research quality.

[0057] Furthermore, the task assignment module includes the following execution steps:

[0058] Establishing the segmentation level and the mapping association between the cluster labels and researchers, performing visual conversion, and sending it to the relay inspection task management interface;

[0059] Obtain preset task allocation information for detection tasks;

[0060] Analyze the characteristic information of the detection task, including regional characteristics, difficulty characteristics, and support requirement characteristics;

[0061] Using the regional characteristics, difficulty characteristics, and support requirement characteristics as search targets, performing correlation analysis on the segmentation levels and cluster labels of the researchers, and screening the correspondence between the researchers and the detection tasks with the highest correlation;

[0062] The researcher relationships in the preset task assignment information are adjusted by using the screening researcher relationships.

[0063] In a feasible implementation, first, the task assignment module 15 establishes a mapping association between the segmentation level and the clustering label and the researcher, and associates the multi-dimensional classification results generated by the evaluation import module 14 with the specific researcher information. Subsequently, these associated information are converted into a visual form, such as a chart or an interactive interface, and sent to the relay inspection task management interface. This visual display enables managers to intuitively understand the distribution and characteristics of researchers, and facilitates subsequent task assignment decisions. Secondly, the task assignment module 15 obtains the preset task assignment information of the inspection task, including basic elements such as the total amount, type, and time schedule of the task, to provide an initial framework for task assignment.

[0064] Next, the task assignment module 15 performs feature analysis on the detection task. The key features of each detection task are extracted, mainly including three aspects: geographical features (such as the location of task execution), difficulty features (such as technical requirements, time limits) and support demand features (such as required resources, auxiliary personnel). The analysis of these features lays the foundation for subsequent accurate matching. Based on the task features obtained by analysis, the task assignment module 15 uses geographical features, difficulty features and support demand features as search targets, and performs correlation analysis on the segmentation level and clustering labels of researchers, aiming to find the most suitable researchers for performing specific tasks. For example, for high-difficulty detection tasks, priority is given to researchers with high participation (Grade A) and strong technical background; for tasks in a specific region, priority is given to matching researchers in the region. Through correlation analysis, the researchers most relevant to the detection task are screened out, and a preliminary correspondence between tasks and personnel is established. Afterwards, the task assignment module 15 uses the screened researcher relationship to adjust the researcher relationship in the preset task assignment information, ensuring that the final task assignment plan not only takes into account the preset task requirements, but also makes full use of the optimization results based on participation evaluation and feature clustering.

[0065] Through a refined task allocation process, intelligent and personalized allocation of Mycoplasma pneumoniae detection tasks can be achieved, which not only improves the efficiency and quality of task execution, but also provides appropriate task challenges and support based on the characteristics and performance of researchers, forming a virtuous cycle of detection ecosystem, thereby comprehensively improving the participation and effectiveness of Mycoplasma pneumoniae detection.

[0066] Furthermore, the detection tasks are adjusted and allocated according to the researchers' evaluation and segmentation results, and the following steps are also included:

[0067] Tracking the execution results of the detection task adjustment;

[0068] Evaluate the researchers in the task based on the execution results and determine the characteristics of the researchers whose execution results meet the execution requirements;

[0069] The researcher characteristics are fed back to the relay inspection task management interface, and the researcher relationship search and adjustment are re-performed based on the fed-back researcher characteristics as the search target.

[0070] In a preferred embodiment, first, the execution results of the adjusted detection tasks are continuously tracked, including indicators in multiple aspects such as task completion rate, detection quality, and time efficiency. By monitoring these indicators in real time, the actual situation of task execution can be grasped in a timely manner, providing data support for subsequent evaluation and optimization. Secondly, based on the collected execution results, the researchers involved in the task are evaluated. This evaluation process not only considers the completion of the task, but also analyzes the special skills, problem-solving ability, team spirit, etc. demonstrated by the researchers during the execution process. Through comprehensive evaluation, researchers who perform well in actual execution and meet or exceed the execution requirements are identified, and the common characteristics of these researchers are summarized.

[0071] The characteristics of these high-performing researchers are then fed back to the Relay Inspection task management interface. These characteristics include skill level, work attitude, adaptability, and other aspects. By visually displaying these characteristics on the management interface, managers are provided with a richer basis for decision-making. Subsequently, based on these feedback, researcher relationship searches and task assignment adjustments are re-performed based on these researcher characteristics as new search targets, continuously optimizing the accuracy and effectiveness of task assignments.

[0072] Through continuous tracking, evaluation and optimization, it can continuously learn and adapt to changes in the actual testing environment, improve the accuracy and effectiveness of task allocation, and significantly enhance the participation of researchers.

[0073] In summary, the evaluation system for improving the participation rate of Mycoplasma pneumoniae testing through relay testing provided by the embodiments of the present application has the following technical effects:

[0074] The data collection module collects behavioral data from the study population, including test duration, number of tests, task completion, and exception reporting rates. This multi-dimensional data collection provides comprehensive data support for objective engagement assessment. The data preprocessing module cleans and integrates the behavioral data, constructs a training dataset, and segments it into training and test data. This construction and segmentation of the training dataset paves the way for subsequent machine learning model training, ensuring the effectiveness of model training. The model training module trains and tests the model using training and test data based on a machine learning algorithm, ultimately converging to an engagement assessment model. The engagement assessment model quantitatively assesses the engagement of multiple researcher input indicators. Through iterative learning from training and test data, a model is constructed that objectively and quantitatively assesses researcher engagement. The assessment import module captures the output of the engagement assessment model and imports it into the researcher assessment database. Clustering and segmenting all researchers in the database allows for effective organization and management of the assessment results, providing a data foundation for subsequent task allocation. The task allocation module is used to adjust and allocate detection tasks according to the researchers' evaluation and segmentation results. It can allocate tasks based on the researchers' actual performance and capabilities, thereby improving the overall detection efficiency and quality.

[0075] Example 2

[0076] Based on the same inventive concept as the evaluation system for improving the participation rate of Mycoplasma pneumoniae detection in the aforementioned embodiment, Figure 2 As shown, the embodiment of the present application provides a method for evaluating the improvement of participation in Mycoplasma pneumoniae detection through relay testing, the method comprising:

[0077] Collect behavioral data of the study population, including test duration, number of tests, task completion, and abnormality reporting rate;

[0078] Performing data cleaning and integration preprocessing on the behavioral data to construct a training data set, and segmenting the training data set to determine training data and test data;

[0079] Based on a machine learning algorithm, model training and testing are performed using the training data and the test data to converge to obtain a participation evaluation model, wherein the participation evaluation model is used to quantitatively evaluate the participation of multiple indicators of input researchers;

[0080] Obtaining the output results of the engagement assessment model, importing them into the researcher assessment database, and performing clustering and segmentation on all researchers in the assessment database;

[0081] The detection tasks are adjusted and allocated according to the evaluation and segmentation results of the researchers.

[0082] Furthermore, the behavioral data is cleaned and integrated and pre-processed to construct a training data set, including:

[0083] Processing missing values ​​and outliers in the behavioral data, unifying data formats from different sources, and completing preprocessing of the behavioral data;

[0084] Based on the pre-processed behavioral data, a mapping relationship between the detection duration, number of detections, task completion status, and abnormality reporting rate and participation evaluation is established to construct the training data set.

[0085] Furthermore, the behavior data is processed for missing values ​​and outliers, including:

[0086] For missing values ​​in the behavioral data, the mode of the feature is used to fill in the missing values ​​to complete the missing value processing;

[0087] Deleting outliers in the behavior data to complete the outlier processing;

[0088] After deleting outliers, the continuity analysis of data features was performed, and when there were missing values, the mode filling process was repeated.

[0089] Furthermore, obtaining the participation evaluation model includes:

[0090] Build a support vector machine, select the kernel function and kernel function parameters, set the C value, and train the support vector machine using training data;

[0091] The trained support vector machine is tested for accuracy using test data, and the kernel function parameters and C value are adjusted according to the test results. The training is iterated until the test accuracy reaches the target value, thereby obtaining the engagement evaluation model.

[0092] Furthermore, all researchers in the evaluation database are clustered and segmented, including:

[0093] Segment the evaluation levels according to the evaluation results in the evaluation database and construct multi-level researcher clusters;

[0094] The multi-level researcher clusters are re-clustered based on research location, age, family income, and parents' education level to generate cluster labels.

[0095] Furthermore, the adjustment and allocation of detection tasks according to the evaluation and segmentation results of researchers include:

[0096] Establishing the segmentation level and the mapping association between the cluster labels and researchers, performing visual conversion, and sending it to the relay inspection task management interface;

[0097] Obtain preset task allocation information for detection tasks;

[0098] Analyze the characteristic information of the detection task, including regional characteristics, difficulty characteristics, and support requirement characteristics;

[0099] Using the regional characteristics, difficulty characteristics, and support requirement characteristics as search targets, performing correlation analysis on the segmentation levels and cluster labels of the researchers, and screening the correspondence between the researchers and the detection tasks with the highest correlation;

[0100] The researcher relationships in the preset task assignment information are adjusted by using the screening researcher relationships.

[0101] Furthermore, the detection task is adjusted and allocated according to the evaluation and segmentation results of the researchers, and then further includes:

[0102] Tracking the execution results of the detection task adjustment;

[0103] Evaluate the researchers in the task based on the execution results and determine the characteristics of the researchers whose execution results meet the execution requirements;

[0104] The researcher characteristics are fed back to the relay inspection task management interface, and the researcher relationship search and adjustment are re-performed based on the fed-back researcher characteristics as the search target.

[0105] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0106] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. An evaluation system for improving participation in Mycoplasma pneumoniae testing through relay testing, characterized in that: include: The data collection module is used to collect behavioral data of the research population, including test duration, number of tests, task completion status, and abnormality reporting rate; A data preprocessing module is used to perform data cleaning and integration preprocessing on the behavioral data, construct a training data set, and segment the training data set to determine training data and test data; A model training module is used to train and test the model based on the training data and the test data based on a machine learning algorithm, and converge to obtain a participation evaluation model. The participation evaluation model is used to quantitatively evaluate the participation of multiple indicators of input researchers; An evaluation import module is used to obtain the output results of the participation evaluation model, import them into the researcher evaluation database, and perform clustering and segmentation on all researchers in the evaluation database; The task allocation module is used to adjust and allocate detection tasks according to the researchers' evaluation and segmentation results; Evaluating an import module involves the following execution steps: Segment the evaluation levels according to the evaluation results in the evaluation database and construct multi-level researcher clusters; Re-clustering the multi-level researcher clusters based on research location, age, family income, and parental education level to generate cluster labels; The task assignment module includes the following execution steps: Establishing the segmentation level and the mapping association between the cluster labels and researchers, performing visual conversion, and sending it to the relay inspection task management interface; Obtain preset task allocation information for detection tasks; Analyze the characteristic information of the detection task, including regional characteristics, difficulty characteristics, and support requirement characteristics; Using the regional characteristics, difficulty characteristics, and support requirement characteristics as search targets, performing correlation analysis on the segmentation levels and cluster labels of the researchers, and screening the correspondence between the researchers and the detection tasks with the highest correlation; The researcher relationships in the preset task assignment information are adjusted by using the screening researcher relationships.

2. The evaluation system for improving participation in Mycoplasma pneumoniae testing by relay testing according to claim 1, characterized in that: The data preprocessing module includes the following execution steps: Processing missing values ​​and outliers in the behavioral data, unifying data formats from different sources, and completing preprocessing of the behavioral data; Based on the pre-processed behavioral data, a mapping relationship between the detection duration, number of detections, task completion status, and abnormality reporting rate and participation evaluation is established to construct the training data set.

3. The evaluation system for improving participation in Mycoplasma pneumoniae detection by relay testing according to claim 2, characterized in that: The data preprocessing module also includes the following execution steps: For missing values ​​in the behavioral data, the mode of the feature is used to fill in the missing values ​​to complete the missing value processing; Deleting outliers in the behavior data to complete the outlier processing; After deleting outliers, the continuity analysis of data features was performed, and when there were missing values, the mode filling process was repeated.

4. The evaluation system for improving participation in Mycoplasma pneumoniae detection by relay testing according to claim 2, characterized in that: The model training module includes the following execution steps: Build a support vector machine, select the kernel function and kernel function parameters, set the C value, and train the support vector machine using training data; The trained support vector machine is tested for accuracy using test data, and the kernel function parameters and C value are adjusted according to the test results. The training is iterated until the test accuracy reaches the target value, thereby obtaining the engagement evaluation model.

5. The evaluation system for improving participation in Mycoplasma pneumoniae testing through relay testing according to claim 1, characterized in that: The detection task is adjusted and allocated according to the evaluation and segmentation results of the researchers, and then it also includes: Tracking the execution results of the detection task adjustment; Evaluate the researchers in the task based on the execution results and determine the characteristics of the researchers whose execution results meet the execution requirements; The researcher characteristics are fed back to the relay inspection task management interface, and the researcher relationship search and adjustment are re-performed based on the fed-back researcher characteristics as the search target.

6. A method for evaluating the effectiveness of relay testing to improve participation in Mycoplasma pneumoniae testing, characterized in that: include: Collect behavioral data of the study population, including test duration, number of tests, task completion, and abnormality reporting rate; Performing data cleaning and integration preprocessing on the behavioral data to construct a training data set, and segmenting the training data set to determine training data and test data; Based on a machine learning algorithm, model training and testing are performed using the training data and the test data to converge to obtain a participation evaluation model, wherein the participation evaluation model is used to quantitatively evaluate the participation of multiple indicators of input researchers; Obtaining the output results of the engagement assessment model, importing them into the researcher assessment database, and performing clustering and segmentation on all researchers in the assessment database; Adjust and allocate detection tasks according to the researchers' evaluation and segmentation results; Clustering of all researchers in the evaluation database includes: Segment the evaluation levels according to the evaluation results in the evaluation database and construct multi-level researcher clusters; Re-clustering the multi-level researcher clusters based on research location, age, family income, and parental education level to generate cluster labels; The detection task adjustment and allocation according to the evaluation and segmentation results of the researchers include: Establishing the segmentation level and the mapping association between the cluster labels and researchers, performing visual conversion, and sending it to the relay inspection task management interface; Obtain preset task allocation information for detection tasks; Analyze the characteristic information of the detection task, including regional characteristics, difficulty characteristics, and support requirement characteristics; Using the regional characteristics, difficulty characteristics, and support requirement characteristics as search targets, performing correlation analysis on the segmentation levels and cluster labels of the researchers, and screening the correspondence between the researchers and the detection tasks with the highest correlation; The researcher relationships in the preset task assignment information are adjusted by using the screening researcher relationships.

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