Visualization engine for multi-dimensional data display

Abstract

The invention relates to the technical field of data processing, in particular to a visualization engine for multi-dimensional data display, which is characterized in that a data acquisition module is responsible for collecting personal work performance and team performance data, and a preprocessing module is used for preprocessing the acquired data; the index system configuration module depends on a preset index library and a configuration rule, calculates preprocessed data, extracts an index template to complete configuration, and generates a personal performance index and a team performance index; the work efficiency visualization module generates an interaction chart from the perspective of time trend, self-comparison and standard benchmarking based on personal efficiency indexes, and realizes visual display and man-machine interaction; the performance ranking visualization module generates an interaction chart from the perspective of hierarchical ranking, composition disassembly and anomaly location based on team performance indicators. According to the invention, the problems of limited data analysis and poor visualization effect during personal work performance and team performance data display in the prior art can be solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to a visualization engine for multidimensional data display. BACKGROUND

[0002] In the process of enterprise digital transformation, business data presents an exponential growth trend, and individual work efficiency and team performance related data are widely scattered in OA systems, CRM systems, project management systems and other independent business platforms, forming a typical "data island" phenomenon.

[0003] There are three major problems in the prior art: first, the data timeliness and accuracy are insufficient, multi-system data synchronization is delayed, and there is a lack of unified data verification mechanism, resulting in data deviation; second, the flexibility of multidimensional analysis is limited, and the existing system mainly supports single-dimensional data display, which cannot meet the needs of user-defined indicators and multidimensional split analysis; third, the visualization experience effect is poor, the chart type is single and the interaction is weak, it is difficult to intuitively present data trends, comparison relationships and abnormal information, and it cannot provide efficient data support for decision-making.

[0004] The core reason for the above problems is that the prior art does not establish a unified multi-source data integration framework, the data collection lacks a standardized cleaning and conversion mechanism; the index system lacks flexibility and traceability, and the calculation rules are not modularized; the visualization design is not optimized for the core needs of individual and team dimensions, and lacks dynamic interaction and intelligent early warning mechanism. SUMMARY

[0005] The technical problem solved by the application is to provide a visualization engine for multidimensional data display to solve the problems of limited data analysis and poor visualization effect in the prior art when displaying individual work efficiency and team performance data.

[0006] The basic scheme provided by the application is a visualization engine for multidimensional data display, comprising a data acquisition module, a preprocessing module, an index system configuration module, a work efficiency visualization module and a performance ranking visualization module. The data acquisition module is used to acquire individual work efficiency data and team performance data. The preprocessing module is used to preprocess the acquired individual work efficiency data and team performance data. The index system configuration module has a preset index library, and the index system configuration module calculates the index values based on the preprocessed individual work efficiency data and team performance data according to the preset index configuration rules, and extracts the index templates from the index library for configuration, to obtain individual performance indicators and team performance indicators respectively. The work performance visualization module generates diversified interactive charts from three core perspectives of time trend, self comparison and standard benchmarking based on the configured personal performance indicators, and performs visualization display and human-computer interaction. The performance ranking visualization module generates diversified interactive charts from three core perspectives of hierarchical ranking, composition disassembly and anomaly positioning based on the configured team performance indicators, and performs visualization display and human-computer interaction.

[0007] Further, the preprocessing of the collected personal work performance data and team performance data in the preprocessing module includes: The collected personal work performance data and team performance data are respectively subjected to basic preprocessing, and the basic preprocessing includes data cleaning, data conversion, and storage through a distributed database; A feature mapping relationship table of sub-entity features and entity features between the personal work performance data and the team performance data is constructed; According to the feature mapping relationship table, the corresponding sub-entity feature data of the personal work performance data of all members in the target team are extracted, and the extracted sub-entity feature data are calculated according to the feature mapping logic in the feature mapping relationship table to obtain theoretical entity feature values; The actual entity feature values of the team actually collected after the basic preprocessing are obtained, and error calculation is performed on the actual entity feature values and the theoretical entity feature values, if the error calculation result is within a preset error threshold, it is determined that the team performance data is normal, otherwise, it is determined that the team performance data is abnormal, and the team performance data is located.

[0008] Further, the preprocessing of the collected personal work performance data and team performance data in the preprocessing module includes: From the team performance data after the basic preprocessing, the entity feature values of the target team and the team basic information are extracted; According to the reverse logic of the feature mapping relationship table, the entity feature values are disassembled to obtain theoretical sub-view feature mean values; The actual sub-entity feature mean values of all members in the target team are calculated, and error comparison is performed on the actual sub-entity feature mean values and the theoretical sub-entity feature mean values, if the error comparison result is within a preset error threshold, it is determined that the personal work performance data is normal; otherwise, it is determined that the personal work performance data is abnormal, and the error personal work performance data is located for verification and correction.

[0009] Further, the index system configuration module includes an index library unit, an index calculation unit, a warning threshold setting unit and an index data verification unit, wherein: The indicator library unit includes a built-in indicator library and a custom indicator library. The built-in indicator library includes personal performance indicators and team performance indicators. The custom indicator library is used by users to add custom indicators according to business needs. The custom indicators include indicator name, data source and calculation logic. The indicator calculation unit includes several indicator calculation rules, and the indicator calculation unit is used to calculate individual performance indicators and team performance indicators according to the indicator calculation rules. The warning threshold setting unit is used to configure absolute and relative thresholds for individual performance indicators and team performance indicators, respectively. The indicator data verification unit is used to perform verification based on the feature mapping relationship table of sub-entity features and entity features in the preprocessing module.

[0010] Furthermore, the work efficiency visualization module, which generates diverse interactive charts from the core perspective of time trends, includes a three-part structure with time as the core axis and around the configured personal efficiency indicators, consisting of a basic trend chart, an auxiliary information layer, and an interactive control layer. The diverse interactive charts generated from the core perspective of self-comparison consist of a three-layer structure: a comparison chart, a dimension breakdown layer, and a detail viewing layer, with the differences in indicators at different stages of an individual as the core and the configured personal performance indicators as the basis. The diverse interactive charts generated from the core perspective of standard benchmarking consist of a three-layer structure: a benchmarking subject chart, a reference standard layer, and a difference labeling layer, with the difference between personal indicators and external reference indicators as the core and the configured personal performance indicators as the basis.

[0011] Furthermore, the performance ranking visualization module, which generates diverse interactive charts from the core perspective of hierarchical ranking, includes a three-layer structure: a ranking main chart, a hierarchical filtering layer, and an interactive control layer, with the team indicator value differences as the core and the configured team performance indicators as the basis. The diverse interactive charts generated from the core perspective of decomposition include a three-layer structure: the main chart, the hierarchical drill-down layer, and the numerical annotation layer, with the internal composition of a single team performance indicator as the core. The diverse interactive charts generated from the core perspective of anomaly localization consist of a three-layer structure: an anomaly main chart, an anomaly labeling layer, and an anomaly analysis layer, centered around the abnormal values ​​of team performance indicators and built around the configured team performance indicators.

[0012] The principle and advantages of this invention are as follows: In the technical solution of this application, firstly, multi-source raw data on individual work efficiency and team performance are acquired through a data acquisition module, and the data is pre-processed by a preprocessing module to ensure basic quality; then, based on the preset indicator library and configuration rules of the indicator system configuration module, the numerical calculation and template configuration of individual efficiency indicators and team performance indicators are completed based on the pre-processed data; finally, a hierarchical visualization logic is adopted for the two types of indicators respectively: the work efficiency visualization module generates interactive charts from the perspectives of time trend, self-comparison, and standard benchmarking, and the performance ranking visualization module generates interactive charts from the perspectives of hierarchical ranking, component breakdown, and anomaly location. Through multi-perspective and interactive chart design, accurate data presentation and human-computer interaction are achieved.

[0013] The advantages are as follows: On the one hand, through standardized data collection and preprocessing processes, combined with the configuration mechanism of the preset indicator library, the problems of traditional data dispersion and inconsistent indicator logic are effectively solved, ensuring the accuracy and standardization of both individual and team indicator data, and laying a high-quality data foundation for subsequent visualization analysis. On the other hand, differentiated multi-perspective interactive charts are designed for individual and team metrics, which not only meet the needs of trend tracking, self-reflection and benchmarking of individual performance data, but also realize the functions of ranking comparison, composition analysis and anomaly location of team performance data. This greatly improves the intuitiveness and efficiency of data interpretation, provides accurate and efficient data support for user decision-making, and enhances the flexibility and experience of user operation through human-computer interaction design. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of an embodiment of the present invention. Detailed Implementation

[0015] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 As shown: A visualization engine for multidimensional data display includes a data acquisition module, a preprocessing module, an indicator system configuration module, a work efficiency visualization module, and a performance ranking visualization module, wherein: The data acquisition module is used to collect individual work efficiency data and team performance data. In this embodiment, the data acquisition module connects to OA, CRM, project management system and terminal devices through standardized interfaces such as RESTful API, JDBC, and FTP, and extracts target individual work efficiency and team performance related data according to preset fields. The preprocessing module preprocesses the collected individual work performance data and team performance data. The preprocessing process includes: The collected individual work efficiency data and team performance data are subjected to basic preprocessing, including data cleaning and data transformation, and stored in a distributed database. In this embodiment, data cleaning is used to filter abnormal data, such as identifying and processing invalid data through anomaly detection algorithms. Data transformation unifies the format of the cleaned data and classifies and labels it according to data type, such as labeling it as personal data, team data, business data, and performance data.

[0016] Distributed database storage employs a partitioned storage strategy to store the transformed data in a distributed data warehouse, using time-based and department-based partitioning to improve query efficiency.

[0017] Subsequently, a data back-check was performed on the correlation between individual work efficiency data and team performance data. Since team performance data is essentially derived from the aggregation of multiple individual work efficiency data sets, if only a single dimension of data is checked independently, issues may arise where individual data is normal, but deviations appear after aggregation into team data, or team data may be abnormal, but the abnormal individual data cannot be quickly located. This leads to incomplete data verification, affecting the accuracy of subsequent visualization analysis. To address this, an individual-team data back-check algorithm was constructed to establish a two-way verification link, specifically including: The features in individual work performance data are defined as sub-entity features, which are the basic features of individual work performance that constitute the entity features of a team. The features in team performance data are defined as entity features, which are features used to describe the core indicators of team performance. A feature mapping table is constructed between sub-entity features and entity features of individual work performance data and team performance data. In this embodiment, the feature mapping table includes the sub-entity features corresponding to each team entity feature, the mapping logic, and the allowable error range, as shown in Table 1 below: Table 1

[0018] Next, individual-team re-inspections will be conducted, specifically as follows: From the pre-processed individual work performance data, extract the corresponding sub-entity feature data of all members in the target team according to the feature mapping relationship table; for example, when verifying the task completion rate of the technical department team, extract the number of individual completed tasks and the total number of individual tasks of all members of the technical department. Based on the feature mapping logic in the mapping relationship table, the extracted sub-entity feature data is processed to obtain the visiting entity feature value. Taking the task completion rate of the technical department team as an example: If the technical department has 5 members, and their individual task completion counts are 18, 20, 15, 19, and 22 respectively, with a total individual task count of 20 for each member, then... The number of tasks completed by an individual = 18 + 20 + 15 + 19 + 22 = 94; Total number of tasks per person = 20 5 = 100; therefore, the theoretical team task completion rate is... ; After obtaining the actual team entity feature values ​​collected following basic preprocessing, the difference between the two is calculated and compared with a preset error threshold, i.e., the allowable error range in Table 1 above. If the error calculation result is within the preset error threshold, the team performance data is determined to be normal; otherwise, the team performance data is determined to be abnormal, and the team performance data is located. For example: the actual task completion rate of the technical department team is 90%. Calculate the difference between the two as , and the allowable error range If the difference exceeds the allowable error range, the team entity feature data is determined to be abnormal, triggering an alarm. The alarm information includes: target team, entity feature name, theoretical value, actual value, difference, and source of the corresponding sub-entity feature. The abnormal data is prohibited from entering the subsequent indicator configuration module.

[0019] Then, a team-individual data review was conducted, specifically as follows: From the pre-processed team performance data, extract the entity feature values ​​and basic team information of the target team; for example, when verifying the individual data corresponding to the average customer satisfaction of the marketing department, extract the actual average customer satisfaction of the marketing department and the number of team members. Based on the reverse logic of the feature mapping table, the entity feature values ​​are decomposed to obtain the theoretical sub-entity feature averages. Taking the average customer satisfaction per employee in the marketing department as an example: The mapping logic is: Average customer satisfaction per person = ( Individual customer satisfaction rating (Number of personal reviews) / When decomposing the number of individual reviews in reverse, if we assume all members have the same number of reviews, then the theoretical average individual customer satisfaction equals the actual average customer satisfaction. If the actual average customer satisfaction is 85 points, the allowable error range is... If the score is 83-87, then the theoretical average range for individual customer satisfaction is 83-87. The system calculates the average actual sub-entity characteristics of all members within the target team and compares it with the theoretical average sub-entity characteristics. If the error comparison result is within a preset error threshold, the individual work performance data is determined to be normal; otherwise, the individual work performance data is determined to be abnormal, and the erroneous individual work performance data is located for verification and correction. For example, if the marketing department has 8 members with scores of 82, 86, 84, 88, 80, 85, 83, and 87, the average is 84.375. This is compared with the theoretical sub-entity characteristic average range of 83-87, and is within the range. Therefore, the individual sub-entity characteristic data is determined to be normal.

[0020] The indicator system configuration module has a preset indicator library. Based on preset indicator configuration rules, the module calculates indicator values ​​using pre-processed individual work efficiency data and team performance data, and extracts indicator templates from the indicator library for configuration, resulting in individual efficiency indicators and team performance indicators. The indicator system configuration module includes an indicator library unit, an indicator calculation unit, an early warning threshold setting unit, and an indicator data verification unit. The indicator library unit includes a built-in indicator library and a custom indicator library. The built-in indicator library includes personal performance indicators and team performance indicators. The custom indicator library is used by users to add custom indicators according to business needs. The custom indicators include indicator name, data source and calculation logic. The indicator calculation unit includes several indicator calculation rules. The unit is used to calculate individual performance indicators and team performance indicators according to these rules. In this embodiment, the indicator calculation rules include the calculation methods for each indicator, and a visual formula editor is configured for configuration. An example of individual performance indicator calculation is shown below: Average call duration = Total call duration / Total number of calls (unit: minutes); Daily call volume = Total number of calls made on the day (unit: times); First-time resolution rate = Number of problems resolved on the first attempt / Total number of problems processed 100 ; Customer satisfaction = (Single customer satisfaction rating) / Total number of ratings (unit: points, full score 100 points); Average response time = (Time from receiving the task to starting processing) / Total number of tasks (unit: minutes).

[0021] The following is an example of how team performance indicators are calculated: Team task completion rate = Number of tasks completed by the team / Total number of tasks completed by the team 100 ; Average customer satisfaction per person = Total customer satisfaction score of the team / Number of team members (unit: points); Cross-departmental collaboration efficiency = Number of cross-departmental collaborations completed / Total number of cross-departmental collaborations 100 ; Team performance score = task score Corresponding weight + collaboration score Corresponding weight + innovation score Corresponding weights; where, task score = team task completion rate 100 (unit: points, full score 100), Collaboration score = Cross-departmental collaboration efficiency 100 (unit: points, full score 100 points), Innovation Score = Number of Team Innovation Achievements 20 (Unit: points, full marks 100 points, the number of innovative achievements is manually entered by the user or accessed from the designated system).

[0022] The warning threshold setting unit is used to configure absolute and relative thresholds for individual performance indicators and team performance indicators, respectively; the absolute threshold is used to set fixed upper and lower limits, such as the average daily call volume of an individual. The next level is the upper limit warning. The second is the lower limit warning; the relative threshold is used to set the range of periodic fluctuations, such as team performance fluctuations. The percentage represents the warning range.

[0023] The indicator data verification unit is used to perform secondary verification based on the feature mapping relationship table of sub-entity features and entity features in the preprocessing module. During the verification process, a data traceability link is established for each indicator to record the data source, calculation rules and modification logs. Users can click on the indicator to view the complete traceability information, avoiding analysis deviations caused by inconsistent indicator logic.

[0024] The work performance visualization module generates diverse interactive charts based on configured personal performance indicators, from three core perspectives: time trends, self-comparison, and benchmarking. These charts are then visualized and interact with by the user. The diverse interactive charts generated from the core perspective of time trends consist of a three-part structure built around the configured personal performance index, with time as the core axis. This structure includes a basic trend chart, an auxiliary information layer, and an interactive control layer. The basic trend chart is used to present the continuous change curve or numerical coverage area of ​​the personal performance index within a selected time period, representing the fluctuation trajectory of the personal performance index over time. The auxiliary information layer includes data annotation processing and period division processing. The data annotation processing automatically annotates specific values ​​at key nodes of the curve; the period division processing adds dividing lines to the horizontal axis according to preset period rules to divide the index changes in different time stages. The interactive control layer provides time range filters, indicator switching buttons, and data granularity adjustment options.

[0025] The diverse interactive charts generated from the core perspective of self-comparison consist of a three-layer structure: a comparison chart, a dimension breakdown layer, and a detail viewing layer, all centered around the differences in indicators at different stages of an individual's life. The comparison chart layer is the core of the chart, with the horizontal axis representing the comparison dimensions and the vertical axis representing the configured personal performance indicator values. The chart itself uses multiple groups of bars arranged side-by-side or stacked to visually present the numerical differences of the same indicator under different comparison dimensions. The dimension splitting layer includes a dimension selector and splitting rule configuration options. The dimension selector allows you to select the target dimension from preset comparison dimensions (time period, business type, etc.); the splitting rule configuration options allow you to stack and split the bars by sub-dimensions (such as splitting "business type" into multiple specific business sub-categories), displaying the composition ratio of the indicator under the sub-dimension; The detailed viewing layer includes numerical labels and detailed jump entries. The numerical labels indicate the specific value and the difference / difference rate with the baseline dimension above each bar. The detailed jump entries allow you to click on the bar or the sub-dimension stack block to jump to the original data details list of the indicator under that dimension (e.g., clicking on the stack block corresponding to "a certain business type" will allow you to view all task records under that business type).

[0026] The diverse interactive charts generated from the core perspective of standard benchmarking consist of a three-layer structure: a benchmarking main chart, a reference standard layer, and a difference labeling layer, centered on the differences between personal indicators and external reference indicators and built around the configured personal performance indicators. Among them, the core of the benchmarking main chart is a line chart, bar chart, or radar chart, with the horizontal axis representing the analysis dimension and the vertical axis representing the configured personal performance indicator values. The main body of the chart presents the numerical distribution or change curve of the personal indicator under the target dimension. The reference standard layer includes a standard selector and a standard area. The standard selector allows you to select one or more target standards from preset reference standards (such as personal historical average, departmental average, industry benchmark, and preset target value). The standard area is presented in the chart as a fixed line (such as a horizontal reference line in a line chart) or a shaded area (such as the "benchmark area" in a radar chart) as a benchmark. The difference labeling layer includes difference value labels and status indicators. The difference value labels indicate the specific difference / difference rate between the individual indicator and each reference standard (e.g., "above the department average"). The status indicators distinguish the compliance status by color (e.g., green for values ​​above the standard, orange for values ​​below the standard, and red for values ​​exceeding the warning threshold), and trigger visual reminders (e.g., flashing labels and thickened borders) when the indicator exceeds the preset range.

[0027] The performance ranking visualization module, based on configured team performance indicators, generates diverse interactive charts from three core perspectives: hierarchical ranking, component breakdown, and anomaly localization, and provides visual display and human-computer interaction; among which: The diverse interactive charts generated from the core perspective of hierarchical ranking consist of a three-layer structure: a ranking main chart, a hierarchical filtering layer, and an interactive control layer, all centered around the differences in team indicator values ​​and configured team performance indicators. The core of the ranking main chart is a sorted bar chart, with the horizontal axis representing team / department identifiers (such as department name and group number) and the vertical axis representing the configured team performance indicator values. The main chart presents bars sorted by indicator values ​​from high to low or low to high, while differentiating the bars in specific ranking intervals with visual markings (such as highlighting the higher-ranked intervals with brighter colors and marking the lower-ranked intervals with warning colors) to intuitively distinguish the performance levels of different teams. The tiered filtering layer includes a tier selector and a range filter. The tier selector supports filtering ranking objects by organizational structure level (such as company level, department level, team level), enabling tiered ranking viewing from macro to micro. The range filter allows manual selection of the target ranking range (such as "top 10" or "bottom 5") or filtering by performance score range (such as "excellent" or "needs improvement"), quickly focusing on teams at a specific level. The interactive control layer provides a ranking dimension switching button (supports switching ranking dimensions between multiple configured team performance indicators, such as switching from "total performance" to "per capita performance"), a sorting direction adjustment item (supports ascending / descending sorting), and a details viewing entry (clicking the bar will trigger a pop-up window to display supplementary information related to the team's ranking, such as ranking change trends and key influencing factors).

[0028] The diverse interactive charts generated from the core perspective of decomposition include a three-layer structure centered on the internal composition of a single team performance indicator, and built around the configured team performance indicator. The core of the main composition chart layer is a pie chart or a donut chart. The main body of the chart is divided into sectors according to the internal composition dimensions of the team performance indicator (such as the sub-scores of the indicator and the contribution ratio of different business types). The area ratio of each sector corresponds to the proportion of the corresponding composition dimension in the total indicator. Different colors are used to distinguish each composition dimension, intuitively presenting the internal composition relationship of the indicator. The drill-down hierarchy includes a drill-down trigger control and drill-down level configuration. The drill-down trigger control supports clicking any sector to trigger a drill-down operation and view the secondary subdivisions of that dimension (e.g., clicking the "Task Score" sector will drill down to view the secondary subdivisions of "Core Task Score" and "Ordinary Task Score"). The drill-down level configuration supports preset maximum drill-down levels (e.g., supporting 2-3 levels of drill-down) to avoid excessive breakdown that could lead to information confusion. The numerical labeling layer includes percentage labels and raw numerical labels. The percentage labels indicate the percentage of each sector (e.g., "60%)"; the raw numerical labels indicate the raw numerical value of each sector (e.g., "60 points"). In some scenarios, a hover pop-up window is supported to display more detailed calculation logic (e.g., the specific calculation method and data source of this constituent dimension).

[0029] The diverse interactive charts generated from the core perspective of anomaly localization consist of a three-layer structure: an anomaly main chart, an anomaly labeling layer, and an anomaly analysis layer, centered around the abnormal values ​​of team performance indicators. The anomaly main chart layer is essentially a scatter plot or line chart (adapted to the anomaly analysis scenario). The horizontal axis represents the correlation analysis dimension (such as team size and time period), and the vertical axis represents the fluctuation value or actual value of the team performance indicators. The main chart shows the distribution of all teams in the "correlation dimension-indicator value" coordinate system, providing a basic coordinate framework for anomaly identification. The anomaly labeling layer includes anomaly threshold lines and anomaly markers; the anomaly threshold lines are based on preset rules (such as...). The fluctuation range and fixed upper / lower limits are presented in the chart as a baseline for anomaly detection; anomaly markers are used to mark teams that exceed the threshold range using special styles (such as red scatter dots and flashing lines), and the abnormal team's identifier (such as department name) is also marked to quickly locate the abnormal object. The anomaly analysis layer includes an anomaly details pop-up and an entry point for anomaly cause association; the anomaly details pop-up can be triggered by hovering over or clicking the anomaly marker, displaying specific anomaly information from the anomaly team (such as "fluctuation value"). "Exceeding the threshold" The entry point for anomaly cause association supports jumping to the data traceability module or the anomaly cause entry interface to view the source of the abnormal data (such as data synchronization deviation) or enter the anomaly cause (such as "change of core members").

[0030] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A visualization engine for multidimensional data display, characterized in that: It includes a data acquisition module, a preprocessing module, an indicator system configuration module, a work efficiency visualization module, and a performance ranking visualization module, among which: The data acquisition module is used to collect individual work efficiency data and team performance data; The preprocessing module is used to preprocess the collected individual work performance data and team performance data; The indicator system configuration module has a preset indicator library. Based on the preset indicator configuration rules, the indicator system configuration module calculates the indicator values ​​according to the preprocessed individual work efficiency data and team performance data, and extracts indicator templates from the indicator library for configuration, thereby obtaining individual efficiency indicators and team performance indicators respectively. The work performance visualization module generates diverse interactive charts based on configured personal performance indicators from three core perspectives: time trend, self-comparison, and standard benchmarking, and then displays and interacts with them visually. The performance ranking visualization module generates diverse interactive charts based on the configured team performance indicators from three core perspectives: hierarchical ranking, component breakdown, and anomaly localization. These charts are then visualized and interact with the user.

2. A visualization engine for multidimensional data display according to claim 1, characterized in that: The preprocessing module performs preprocessing on the collected individual work performance data and team performance data, including: The collected individual work efficiency data and team performance data are subjected to basic preprocessing, including data cleaning and data transformation, and stored in a distributed database. Construct a feature mapping table between sub-entity features and entity features between individual work efficiency data and team performance data; Based on the feature mapping relationship table, extract the corresponding sub-entity feature data of the personal work performance data of all members in the target team, and calculate the theoretical entity feature value by means of the feature mapping logic in the feature mapping relationship table. After basic preprocessing, the actual entity feature values ​​of the team are obtained and the error is calculated between them and the theoretical entity feature values. If the error calculation result is within the preset error threshold, the team performance data is determined to be normal; otherwise, the team performance data is determined to be abnormal, and the team performance data is located.

3. A visualization engine for multidimensional data display according to claim 2, characterized in that: The preprocessing module further includes the following steps for preprocessing the collected individual work performance data and team performance data: Extract entity feature values ​​and basic team information from the preprocessed team performance data. Based on the reverse logic of the feature mapping table, the entity feature values ​​are decomposed to obtain the theoretical sub-view feature average value; Calculate the average actual sub-entity characteristics of all members in the target team and compare it with the average theoretical sub-entity characteristics. If the error comparison result is within the preset error threshold, the individual work performance data is determined to be normal; otherwise, the individual work performance data is determined to be abnormal, and the erroneous individual work performance data is located for verification and correction.

4. A visualization engine for multidimensional data display according to claim 3, characterized in that: The indicator system configuration module includes an indicator library unit, an indicator calculation unit, an early warning threshold setting unit, and an indicator data verification unit, wherein: The indicator library unit includes a built-in indicator library and a custom indicator library. The built-in indicator library includes personal performance indicators and team performance indicators. The custom indicator library is used by users to add custom indicators according to business needs. The custom indicators include indicator name, data source and calculation logic. The indicator calculation unit includes several indicator calculation rules, and the indicator calculation unit is used to calculate individual performance indicators and team performance indicators according to the indicator calculation rules. The warning threshold setting unit is used to configure absolute and relative thresholds for individual performance indicators and team performance indicators, respectively. The indicator data verification unit is used to perform verification based on the feature mapping relationship table of sub-entity features and entity features in the preprocessing module.

5. A visualization engine for multidimensional data display according to claim 4, characterized in that: The work efficiency visualization module consists of a variety of interactive charts generated from the core perspective of time trends. These charts are structured around the time dimension as the core axis and the configured personal efficiency indicators. The structure comprises three parts: a basic trend chart, an auxiliary information layer, and an interactive control layer. The diverse interactive charts generated from the core perspective of self-comparison consist of a three-layer structure: a comparison chart, a dimension breakdown layer, and a detail viewing layer, with the differences in indicators at different stages of an individual as the core and the configured personal performance indicators as the basis. The diverse interactive charts generated from the core perspective of standard benchmarking consist of a three-layer structure: a benchmarking subject chart, a reference standard layer, and a difference labeling layer, with the difference between personal indicators and external reference indicators as the core and the configured personal performance indicators as the basis.

6. A visualization engine for multidimensional data display according to claim 5, characterized in that: The performance ranking visualization module consists of a variety of interactive charts generated from the core perspective of hierarchical ranking. These charts are composed of three layers: a ranking main chart, a hierarchical filtering layer, and an interactive control layer, with the differences in team indicator values ​​as the core and the configured team performance indicators as the basis. The diverse interactive charts generated from the core perspective of decomposition include a three-layer structure: the main chart, the hierarchical drill-down layer, and the numerical annotation layer, with the internal composition of a single team performance indicator as the core. The diverse interactive charts generated from the core perspective of anomaly localization consist of a three-layer structure: an anomaly main chart, an anomaly labeling layer, and an anomaly analysis layer, centered around the abnormal values ​​of team performance indicators and built around the configured team performance indicators.

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