Dynamic reference construction and matching data processing method and system
By constructing dynamic benchmarks, acquiring multi-dimensional user data and attribute tags, and generating dynamic statistical benchmarks, the timeliness and accuracy issues of static analysis reports are resolved, enabling precise resource matching and profile generation, and improving the system's data processing capabilities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BAOSHAN UNIV
- Filing Date
- 2026-04-02
- Publication Date
- 2026-06-26
AI Technical Summary
In existing technologies, static statistical analysis reports cannot meet the needs of complex application scenarios, resulting in a decrease in the timeliness and accuracy of analysis results, an inability to generate accurate segmented group profiles, and low resource matching accuracy, leading to resource waste.
By constructing dynamic benchmarks, multi-dimensional user quantitative data and attribute tag data are obtained to generate dynamic statistical benchmarks. Based on the differences in dimensions, matching and related resources are retrieved from the resource library to form personalized output.
It enables real-time analysis of data distribution, generates accurate segmented group profiles, improves the accuracy of resource matching, and saves system resources.
Smart Images
Figure CN122285668A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a data processing method and system for dynamic benchmark construction and matching. Background Technology
[0002] With the advancement of organizational digital transformation, multi-dimensional quantitative analysis of organizational members (such as digital leadership assessment for cadres) has become an important means for relevant departments to improve management efficiency. Related systems typically employ web-based online data collection to achieve structured data submission, collection, and basic statistical analysis. Their technical structure generally includes: a front-end data collection page, a back-end database, and a basic data analysis module. The working principle is typically as follows: the user completes a structured questionnaire (such as a five-point Likert scale), the system stores the answers in a relational database, then calculates the total or average score for each dimension using a preset algorithm, and generates a static statistical analysis report.
[0003] However, as application scenarios become more complex, traditional static statistical analysis reports are gradually failing to meet the needs of these scenarios. Firstly, as new data continuously floods in over time, the system's statistical baseline remains in its initial state, making it impossible for the calculated individual data deviation to reflect its true position in the real-time data distribution. This leads to a systematic decline in the timeliness and accuracy of the analysis results as the system runs longer. Secondly, the system cannot quickly generate accurate statistical baselines for corresponding subgroups based on the user's multidimensional identity attributes, resulting in coarse-grained and poorly targeted comparative analysis results that fail to meet the technical requirements for refined profiling of complex groups. Thirdly, the system fails to achieve targeted retrieval and push based on precise data differences, leading to low accuracy of the matching engine and wasting computing and storage resources.
[0004] In view of the above-mentioned shortcomings, this application proposes a data processing method based on dynamic benchmark construction and matching, which aims to achieve fine-grained data comparison and accurate resource matching. Summary of the Invention
[0005] The main purpose of this application is to provide a data processing method for dynamic benchmark construction and matching, which aims to solve the problem of how to optimize the system's data processing capabilities.
[0006] To achieve the above objectives, this application provides a data processing method for dynamic benchmark construction and matching, the method comprising: S10, acquire multi-dimensional user quantitative data and attribute label data for grouping collected by the front end, and store them in the original data table. The multi-dimensional user quantitative data and the attribute label data are both obtained by converting and processing the information input by the user in the preset input interface. S20, upon receiving the benchmark calculation instruction, read the historical data in the original data table, perform aggregate calculation to generate a dynamic statistical benchmark, and store the dynamic statistical benchmark in the benchmark data table; S30: Read the user's target multi-dimensional user quantitative data, and call the corresponding dynamic statistical benchmark in the benchmark data table for comparison calculation to determine the user's difference dimension. Based on the difference dimension, retrieve and match related resources from the tagged resource library to form a personalized output list.
[0007] Optionally, in step S20, reading historical data from the original data table and performing aggregation calculations to generate a dynamic statistical benchmark includes: Target historical data that meets the preset reading conditions in the benchmark calculation instruction are filtered from the original data table; An aggregation calculation is performed on each of the target historical data, and the aggregation calculation result is used as the dynamic statistical benchmark.
[0008] Optionally, the aggregation calculation includes: Calculate the mean and standard deviation of each dimension in the target historical data; The aggregation calculation result of the target record subset is determined based on the mean and the standard deviation.
[0009] Optionally, in step S30, the dynamic statistical benchmark corresponding to the benchmark data table is called for comparison calculation to determine the user's difference dimensions, including: Obtain the target score value corresponding to the target historical data in the original data table in the preset attribute label table; Determine whether the target score and the corresponding dynamic statistical benchmark in the target multi-dimensional user quantitative data meet the preset judgment rules; If the conditions are met, the dimension corresponding to the dynamic statistical benchmark is determined to be the user's difference dimension.
[0010] Optionally, the preset determination rule includes: Determine the mean and standard deviation of the dynamic statistical benchmark; Calculate the numerical difference between the target score and the average value, and determine the difference score as the ratio of the numerical difference to the standard deviation; When the difference score is less than a preset threshold, it is determined that the preset judgment rule is met; otherwise, it is not met.
[0011] Optionally, in step S30, based on the difference dimension, related resources are retrieved and matched from the tagged resource library to form a personalized output list, including: Query the target tags associated with the difference dimension, wherein the target tags include at least one of the following: the matching degree between the resource and the weakness dimension, the resource popularity, the release time, and the user's historical learning feedback; The target tags are sorted according to preset weights to generate a personalized resource list and output it.
[0012] Optionally, the benchmark calculation instructions are generated when new and / or updated data, or generated by user input.
[0013] Furthermore, to achieve the above objectives, this application also provides a data processing system for dynamic benchmark construction and matching, the data processing system for dynamic benchmark construction and matching comprising: A multi-dimensional data acquisition module is used to acquire multi-dimensional user quantitative data and attribute label data for grouping collected from the front end, and store them in the original data table. The multi-dimensional user quantitative data and the attribute label data are both obtained by converting and processing the information input by the user in the preset input interface. The dynamic benchmark construction module is used to read historical data from the original data table and perform aggregate calculations to generate a dynamic statistical benchmark when a benchmark calculation instruction is received, and to store the dynamic statistical benchmark in the benchmark data table. The difference analysis module is used to read the user's target multi-dimensional user quantitative data and call the corresponding dynamic statistical benchmark in the benchmark data table for comparison calculation to determine the user's difference dimensions. The resource matching output module is used to retrieve and match related resources from the tagged resource library based on the difference dimension to form a personalized output list.
[0014] This application has at least the following beneficial effects: 1. By constructing a dynamic statistical benchmark, real-time and incremental aggregation logic is introduced into the system, enabling the statistical benchmark to be updated as data accumulates, ensuring that the analysis results can reflect the true situation of the current data distribution, and eliminating systematic errors caused by benchmark lag; 2. Through multi-dimensional grouping and aggregation calculations, the system is able to generate accurate data profiles for data subjects within their specific subgroups; 3. Resource mapping and matching are performed based on the "relative weak dimension" of the data subject relative to the dynamic benchmark, which reduces the interference of irrelevant information, improves the accuracy of personalized content output, and saves system resources. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating the data processing method for dynamic benchmark construction and matching involved in the embodiments of this application; Figure 2This is a schematic diagram illustrating the mapping relationship between analysis dimensions and resource tags involved in the embodiments of this application; Figure 3 This is a schematic diagram of the architecture of the data processing system for dynamic benchmark construction and matching involved in the embodiments of this application.
[0016] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0017] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.
[0018] First Embodiment The fundamental flaw of existing technologies can be attributed to the lack of a dynamic benchmark (norm) construction and update mechanism that can automatically update as data accumulates and support cross-statistics based on multi-dimensional attribute labels.
[0019] Specifically, there are three technical challenges: First, the contradiction between "data dynamism" and "real-time computation," with full recalculation of the benchmark performing poorly when the data volume is large; second, the contradiction between "diverse grouping dimensions" and "storage and query efficiency," with supporting the storage and rapid retrieval of multi-dimensional cross-grouping benchmarks being a major challenge; and third, the disconnect between "difference diagnosis" and "content intervention."
[0020] Based on this, refer to Figure 1 This embodiment provides a data processing method for dynamic benchmark construction and matching, the method including the following steps: S10, acquire multi-dimensional user quantitative data and attribute label data for grouping collected by the front end, and store them in the original data table. The multi-dimensional user quantitative data and the attribute label data are both obtained by converting and processing the information input by the user in the preset input interface. In this embodiment, considering the technical challenge that traditional data acquisition systems cannot meet the need for detailed profiling of complex groups, we propose to use multi-dimensional grouped attribute labels for cross-statistics and flexible analysis.
[0021] Optionally, both multi-dimensional user quantitative data and attribute label data are set based on the required quantitative dimensions for the target user.
[0022] For example, the "Digital Leadership Assessment System" is used as an application scenario for illustration. The system's preset input interface contains 40 questions, each belonging to one of 7 different preset dimension attribute tags. After the user inputs the answers to the 40 questions, the system generates multi-dimensional user quantitative data based on the input results. This multi-dimensional user quantitative data is then associated with the target attribute tags and stored in a labeled wide-table data record table, i.e., the original data table.
[0023] In some alternative implementations, multidimensional quantitative data of organizational members can be collected by configuring a structured data acquisition interface that includes multiple analytical dimensions.
[0024] S20, upon receiving the benchmark calculation instruction, read the historical data in the original data table, perform aggregate calculation to generate a dynamic statistical benchmark, and store the dynamic statistical benchmark in the benchmark data table; Furthermore, considering the technical challenge that the statistical baseline of the system cannot be changed over time, which leads to a systematic decline in the timeliness and accuracy of the analysis results as the system runs longer, i.e. the system cannot adapt to the dynamic changes of the dataset and the evaluation baseline statistical baseline is mismatched with the current data distribution, we propose to perform real-time or periodic grouping and aggregation of historical data based on the attribute labels carried by the data subjects, and generate and maintain dynamic statistical baselines that reflect the current state of each subgroup.
[0025] Optionally, the benchmark calculation instruction is generated when new and / or updated data, or generated by user input. Specifically, the instruction can be manually triggered in the background by users such as administrators, or automatically triggered via an API interface when new / updated data is added.
[0026] Optionally, the generation of dynamic statistical benchmarks can be achieved by the following steps: S21, Select target historical data that meets the preset reading conditions in the benchmark calculation instruction from the original data table; S22, perform aggregation calculation on each of the target historical data, and use the aggregation calculation result as the dynamic statistical benchmark.
[0027] Further and optionally, the aggregation calculation includes: S221, Calculate the mean and standard deviation of each dimension in the target historical data; S222, determine the aggregation calculation result of the target record subset based on the average value and the standard deviation.
[0028] Specifically, the system iterates through all preset read conditions that match the baseline calculation command. These preset read conditions can include preset analysis dimensions and grouping attribute fields. For each combination of "analysis dimension + grouping attribute", a subset of records that meet the conditions is selected from the original data table as the target historical data. Aggregation operations are performed on each target historical data set to calculate the mean μ and standard deviation σ of the set. Finally, the calculation result (μ, σ) is used as the dynamic statistical baseline for the set and stored or updated in the baseline data table.
[0029] For example, taking the "Digital Leadership Assessment System" scenario, the system iterates through various analysis dimensions and attribute label fields. For each combination, such as the "Digital Security Guardian" dimension grouped by "Department-Q43", it performs an SQL aggregation query on the original data table, for example, calculating the "Average Score of Digital Strategy Foresight Grouped by Department". Then, the calculated results, including "Dimension Name, Grouping Field group_type, Group Value group_value, Average_score, Standard Deviation std_dev, and Sample Size", are written to the benchmark data table, which records the real-time benchmarks for all subgroups.
[0030] S30: Read the user's target multi-dimensional user quantitative data, and call the corresponding dynamic statistical benchmark in the benchmark data table for comparison calculation to determine the user's difference dimension. Based on the difference dimension, retrieve and match related resources from the tagged resource library to form a personalized output list.
[0031] Furthermore, considering the technical deficiency of traditional systems in being unable to perform targeted retrieval and push based on precise data differences, we propose comparing individual data vectors with the dynamic statistical benchmarks of their respective groups. By quantifying the differences, we identify the difference dimensions that are considered "weak points." Based on the identified difference dimensions, we retrieve, sort, and output the most relevant related content from a resource library with structured dimensional labels.
[0032] Optionally, the steps for determining the difference dimensions include: S31, obtain the target score value corresponding to the target historical data in the original data table in the preset attribute label table; S32, determine whether the target score and the corresponding dynamic statistical benchmark in the target multi-dimensional user quantitative data meet the preset judgment rules; S33, if satisfied, determine that the dimension corresponding to the dynamic statistical benchmark is the user's difference dimension.
[0033] Further and optionally, the preset determination rule includes: S321, Determine the mean and standard deviation of the dynamic statistical benchmark; S322, Calculate the numerical difference between the target score and the average value, and determine the ratio of the numerical difference to the standard deviation as the difference score; S323, when the difference score is less than a preset threshold, it is determined that the preset judgment rule is met; otherwise, it is not met.
[0034] For example, let's take the "Digital Leadership Assessment System" scenario. The system retrieves the score set [S1,...,S7] of user Zhang San's latest seven target scores from the original data table. Based on the attribute labels stored in Zhang San's original data table, the system retrieves the corresponding dynamic statistical benchmark from the norms table.
[0035] For example, query the dynamic statistical benchmark (average value) of Zhang San under the "Digital Strategy Foresight" tag in "Teaching and Research Institutions". Standard deviation ).
[0036] Let the difference score be The target score values are Then we have: Assuming the preset threshold is 0.5, if If so, the label "digital strategy foresight" is determined to be a difference dimension in Zhang San's "relative weakness dimension" in the comparison group.
[0037] It should be noted that the preset threshold can be dynamically configured according to different job sequences.
[0038] Further and optionally, after deriving the difference dimension, the target tag associated with the difference dimension is queried, and the target tag includes at least one of the following: the matching degree between the resource and the weakness dimension, the resource popularity, the release time, and the user's historical learning feedback; The target tags are sorted according to preset weights to generate a personalized resource list and output it.
[0039] For example, refer to Figure 2 The diagram illustrates the mapping relationship between analysis dimensions and resource tags. The left side shows several analysis dimensions defined by the system (such as dimension A, dimension B, etc.); the right side shows the resource library, where each resource is identified by a set of boolean tags (or tag fields) to which analysis dimensions it corresponds to, for example: tag_dimension_A=true.
[0040] When the system determines that a member has a weakness in "dimension A", it executes a query like SELECT * FROM resources WHERE tag_dimension_A=true to accurately match all resources for that dimension.
[0041] In the technical solution provided in this embodiment, a dynamic benchmark construction method based on multi-dimensional attribute tag grouping, supporting incremental updates and intelligent triggering is proposed to improve the system's data processing efficiency and real-time performance. On the other hand, relative difference analysis based on subgroup dynamic benchmarks is introduced to achieve quantitative positioning of the data stored in the system. Furthermore, by establishing a precise mapping of "quantitative data differences - structured resource tags", a closed loop from data diagnosis to targeted content output is realized.
[0042] In addition, refer to Figure 3 This embodiment also proposes a data processing system for dynamic benchmark construction and matching, which includes: A multi-dimensional data acquisition module is used to acquire multi-dimensional user quantitative data and attribute label data for grouping collected from the front end, and store them in the original data table. The multi-dimensional user quantitative data and the attribute label data are both obtained by converting and processing the information input by the user in the preset input interface. The dynamic benchmark construction module is used to read historical data from the original data table and perform aggregate calculations to generate a dynamic statistical benchmark when a benchmark calculation instruction is received, and to store the dynamic statistical benchmark in the benchmark data table. The difference analysis module is used to read the user's target multi-dimensional user quantitative data and call the corresponding dynamic statistical benchmark in the benchmark data table for comparison calculation to determine the user's difference dimensions. The resource matching output module is used to retrieve and match related resources from the tagged resource library based on the difference dimension to form a personalized output list.
[0043] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
[0044] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A data processing method for dynamic benchmark construction and matching, characterized in that, The method includes the following steps: S10, acquire multi-dimensional user quantitative data and attribute label data for grouping collected by the front end, and store them in the original data table. The multi-dimensional user quantitative data and the attribute label data are both obtained by converting and processing the information input by the user in the preset input interface. S20, upon receiving the benchmark calculation instruction, read the historical data in the original data table, perform aggregate calculation to generate a dynamic statistical benchmark, and store the dynamic statistical benchmark in the benchmark data table; S30: Read the user's target multi-dimensional user quantitative data, and call the corresponding dynamic statistical benchmark in the benchmark data table for comparison calculation to determine the user's difference dimension. Based on the difference dimension, retrieve and match related resources from the tagged resource library to form a personalized output list.
2. The data processing method for dynamic benchmark construction and matching as described in claim 1, characterized in that, In step S20, the dynamic statistical benchmark generated by reading historical data from the original data table and performing aggregation calculations includes: Target historical data that meets the preset reading conditions in the benchmark calculation instruction are filtered from the original data table; An aggregation calculation is performed on each of the target historical data, and the aggregation calculation result is used as the dynamic statistical benchmark.
3. The data processing method for dynamic benchmark construction and matching as described in claim 2, characterized in that, The aggregation calculation includes: Calculate the mean and standard deviation of each dimension in the target historical data; The aggregation calculation result of the target record subset is determined based on the mean and the standard deviation.
4. The data processing method for dynamic benchmark construction and matching as described in claim 1, characterized in that, In step S30, the corresponding dynamic statistical benchmark in the benchmark data table is called for comparison calculation to determine the user's difference dimensions, including: Obtain the target score value corresponding to the target historical data in the original data table in the preset attribute label table; Determine whether the target score and the corresponding dynamic statistical benchmark in the target multi-dimensional user quantitative data meet the preset judgment rules; If the conditions are met, the dimension corresponding to the dynamic statistical benchmark is determined to be the user's difference dimension.
5. The data processing method for dynamic benchmark construction and matching as described in claim 4, characterized in that, The preset determination rules include: Determine the mean and standard deviation of the dynamic statistical benchmark; Calculate the numerical difference between the target score and the average value, and determine the difference score as the ratio of the numerical difference to the standard deviation; When the difference score is less than a preset threshold, it is determined that the preset judgment rule is met; otherwise, it is not met.
6. The data processing method for dynamic benchmark construction and matching as described in claim 1, characterized in that, In step S30, based on the difference dimension, related resources are retrieved and matched from the tagged resource library to form a personalized output list, including: Query the target tags associated with the difference dimension, wherein the target tags include at least one of the following: the matching degree between the resource and the weakness dimension, the resource popularity, the release time, and the user's historical learning feedback; The target tags are sorted according to preset weights to generate a personalized resource list and output it.
7. The data processing method for dynamic benchmark construction and matching as described in claim 1, characterized in that, The baseline calculation instructions are generated when new and / or updated data, or generated by user input.
8. A data processing system for dynamic benchmark construction and matching, characterized in that, The data processing system for dynamic benchmark construction and matching includes: A multi-dimensional data acquisition module is used to acquire multi-dimensional user quantitative data and attribute label data for grouping collected from the front end, and store them in the original data table. The multi-dimensional user quantitative data and the attribute label data are both obtained by converting and processing the information input by the user in the preset input interface. The dynamic benchmark construction module is used to read historical data from the original data table and perform aggregate calculations to generate a dynamic statistical benchmark when a benchmark calculation instruction is received, and to store the dynamic statistical benchmark in the benchmark data table. The difference analysis module is used to read the user's target multi-dimensional user quantitative data and call the corresponding dynamic statistical benchmark in the benchmark data table for comparison calculation to determine the user's difference dimensions. The resource matching output module is used to retrieve and match related resources from the tagged resource library based on the difference dimension to form a personalized output list.