Dynamic intention layer quantification and BUG early warning mechanism for performance appraisal

Abstract

The invention discloses a dynamic intention quantification and 'BUG' early warning system for enterprise performance management. The system analyzes texts such as work logs and plans of employees, extracts work gravity center and target intention information of the employees, and compares the work gravity center and the target intention information with a preset target intention baseline, so that intention deviation is dynamically quantified. Once the actual attention point of the employee is found to have significant drifting relative to the target, abnormal early warning similar to BUG is triggered, and a manager is prompted to pay attention to possible target deviation and performance risks. The system uses semantic analysis to position the specific aspect of deviation occurrence, explains semantic reasons (such as task gravity center transfer, strategy simplification and the like) of performance deviation, and provides deviation correction suggestions. According to the mechanism, the idea about efficient abstract decision in the BUG theory in the field of artificial consciousness is used for reference, and the high-level intention deviation is regarded as a bug needing to be corrected in time.

Description

Technical Field

[0001] This invention belongs to the field of enterprise performance management and artificial intelligence monitoring. Specifically, it relates to a dynamic intent layer quantification and "BUG" early warning system, which is used to quantify employees' goal intent through semantic analysis in employee performance appraisal, so as to realize intelligent early warning and semantic cause diagnosis of performance deviation. Background Technology

[0002] Performance appraisal is a crucial aspect of corporate management, but traditional performance management often becomes a mere formality, lacking process tracking and timely detection of deviations. Many companies only evaluate results at the end of the appraisal cycle, ignoring changes and deviations in employee goals during the process, leading to the accumulation of problems without early intervention. Especially in knowledge-based organizations, employees' daily work goals and actual behaviors may experience "goal drift"—that is, the execution process gradually deviates from the initially set intentions, ultimately affecting performance results.

[0003] Existing HR performance management systems have begun to incorporate intelligent analytics to assist management, such as providing performance trend analysis and early warning functions for abnormal performance to help identify problems in a timely manner. Some systems can even use AI technology to provide suggestions and help managers automatically generate feedback comments. However, current technology's judgment of "abnormal performance" is mostly based on quantitative indicators (such as performance data not meeting standards or abnormal KPIs), lacking insight into the deeper semantic reasons causing these anomalies. For example, if an employee's performance declines due to a misunderstanding of goals or inappropriate strategies caused by changes in the external environment, traditional systems struggle to automatically identify the underlying intent-level issues.

[0004] Furthermore, in the field of artificial intelligence, the "BUG" theory of consciousness posits that consciousness is essentially an abstract and simplified, highly efficient computational model. It does not pursue completeness but rather actively introduces incomplete abstractions or "bugs" to achieve extremely high computational efficiency and decision-making capabilities. This means that intelligent decision-making systems may intentionally retain some "bias" in exchange for efficiency. However, in the context of corporate management, employees, in pursuit of efficiency or to meet deadlines, may also introduce their own "simplified assumptions" or biases (similar to BUGs at the consciousness level). If these biases get out of control, they can lead to goal deviations and performance problems. Therefore, it is necessary to design a mechanism to introduce the "BUG warning" concept from the field of artificial consciousness into performance management, monitoring changes in employee intent to identify potential problems and issuing warnings before they become serious. Summary of the Invention

[0005] This invention proposes a dynamic intent-layer quantification and "BUG" early warning mechanism for performance appraisal, aiming to address the lack of semantic-level analysis of employee goal deviation and performance anomalies in existing performance management systems. Based on the DIKWP model (Data-Information-Knowledge-Wisdom-Intention), the system divides employee performance management into a multi-layered intent structure, performing full-process semantic tracking and analysis of employee goal setting, execution, and results. By collecting employee goal descriptions and behavioral wording from work plans, weekly reports, meeting minutes, and other texts, the system dynamically quantifies employee goal intent biases at different time points and compares them with pre-set goals. If a significant drift in intent or inconsistency with organizational goals is detected, a "BUG"-style early warning is triggered. Here, "BUG" is used metaphorically to refer to abnormal deviations in employee awareness or strategy; the early warning mechanism mimics the artificial awareness BUG theory to efficiently detect deviations at a high level of abstraction.

[0006] The technical problem to be solved: The core problem this invention aims to address is the lack of quantitative means for analyzing textual semantics and changes in employee intent during the performance process, leading to the inability to promptly detect and explain performance deviations. Existing methods focus on outcome indicators and cannot identify deviations in the employee's target intent layer and their causes. This invention fills this technical gap by introducing semantic analysis of the intent layer, enabling a deeper understanding of the performance process and early warning of abnormal deviations.

[0007] Technical solution: The system architecture of this invention is shown in the appendix. Figure 1 As shown, it consists of multiple functional modules to achieve the above objectives. The modules and their functions are as follows.

[0008] The Performance Intent Semantic Collection Module continuously collects text and data sources related to employee goals and work content. This includes, but is not limited to: individual employee OKR / KPI goal descriptions, project plans, weekly work logs, periodic summaries, and performance feedback records. This module uses Natural Language Processing (NLP) technology to perform semantic analysis and key point extraction on the text, extracting the work intent elements reflected within. For example, from an engineer's weekly work log, it can be extracted that the focus for this week was "solving performance issues with module A," while their quarterly goal is "improving overall system stability."

[0009] The Dynamic Intent Layer Quantification Model: Based on the "intent" layer theory in the DIKWP model, this model quantifies the aforementioned semantic elements. Specifically, it constructs intent vectors or semantic distributions: Several intent dimensions that the company focuses on are predefined (such as cost-oriented, quality-oriented, innovation-oriented, and collaboration willingness). Through keyword matching and contextual analysis, employee text is mapped to scores on these dimensions. Alternatively, NLP techniques such as word embedding and topic modeling can be used to automatically learn implicit intent topics from the data. Ultimately, an employee's intent vector representation for a given period is obtained. For example, a product manager's semantic analysis for this month shows their focus distribution as: "Market expansion 40%, internal processes 20%, technological innovation 5%, customer feedback 35%." These values ​​intuitively reflect the employee's actual energy allocation and goal focus.

[0010] Baseline Goal Intent Library: At the beginning of each employee's performance cycle, a goal intent vector is established as a baseline library based on their job responsibilities and set performance goals. This baseline vector is jointly confirmed by the employee and supervisor when setting goals and can be regarded as the ideal distribution of intent that the employee should maintain. For example, the quarterly goals set by the product manager mentioned above might require "market expansion 50%, customer feedback 30%, technological innovation 10%, internal processes 10%". This baseline distribution is stored in the goal intent library as a reference standard for subsequent comparisons.

[0011] Intent Deviation Calculation and Monitoring: The system periodically (e.g., weekly or in real-time) calculates and continuously monitors the difference between an employee's current intent vector and the baseline target intent vector. Differences are quantified using metrics such as cosine similarity and Euclidean distance to obtain a deviation index. Dimensions with significant differences indicate semantic shifts. For example, in the aforementioned product manager case, if their current score in the "Technological Innovation" dimension is significantly lower than the target value while their score in the "Customer Feedback" dimension is significantly higher, it indicates that they have shifted too much energy from innovation tasks to urgent customer issues, resulting in a target shift. The system will continuously monitor the shift trend and generate an intent trajectory graph to demonstrate the evolution of intent over time.

[0012] The "BUG" early warning trigger mechanism sets deviation thresholds and rules. Once a deviation indicator is detected exceeding a preset threshold or showing a continuously escalating trend, an early warning is immediately triggered. Warning information includes: the dimension of intent involved in the deviation, the time when the deviation began, and possible cause analysis. For example, "Warning: In the past two weeks, employee engagement in the 'innovation' dimension has been only 5%, far below the target of 10%; Suspected cause: A recent surge in customer complaints has consumed innovation time." Here, "BUG" refers to an anomaly in employee intent at the senior management level, similar to a bug in a program, representing an inappropriate deviation in strategy or attention. The early warning mechanism aims to quickly detect such senior management deviations to prevent them from escalating and further impacting performance.

[0013] The semantic cause diagnosis module combines multi-source data before and after the warning trigger point, using semantic analysis and association rules to conduct in-depth diagnosis of the causes of deviations. When a deviation occurs, this module automatically captures and analyzes recent relevant texts from employees (such as weekly reports and communication records), looking for keywords or themes with abnormal frequency, and mapping them to predefined intent categories to infer the cause of the deviation. For example, if an employee frequently mentions "customer complaints" or "urgent repairs" in their recent work logs, the system identifies these words as belonging to the "customer feedback" category, inferring that customer issues caused them to deviate from their original innovation tasks. Similarly, if the text contains a large number of "new requirements" or "temporary tasks," it is recorded as a scope creep factor. Finally, the module provides a semantic report on the cause of the deviation, indicating the specific manifestations of the deviation and possible reasons, such as: "Goal drift: Due to an increase in temporary requirements, the employee's main focus shifted to handling new tasks, causing the original planned innovation goals to be shelved."

[0014] Feedback and Corrective Suggestions: The system promptly sends alerts to the employee and their supervisor, along with corresponding improvement suggestions. For example, "It is recommended to adjust the work plan and increase investment in innovation tasks in the next cycle" or "Coordinate with the customer support team to avoid individuals taking on too much customer complaint handling work." Employees and supervisors can provide feedback on the actual situation on the system, such as confirming the alert's validity and adjusting the baseline of their goals accordingly, or explaining that there are legitimate reasons for deviations and adjustments will not be made temporarily. The system records each feedback result to continuously learn and improve the accuracy of deviation detection. This closed-loop feedback and correction mechanism ensures that employee goals and intentions remain continuously aligned with organizational requirements.

[0015] Through the synergistic effect of the above modules and mechanisms, this invention can quantify elusive employee intentions into a computer-understandable form and provide real-time alerts and explanations for deviations in the performance process. This not only improves the timeliness of performance problem detection and the targeted nature of management, but also provides managers with a new semantic perspective to understand and guide employee behavior, achieving continuous goal alignment and performance improvement.

[0016] Compared with the prior art, the present invention has the following beneficial effects.

[0017] 1. Incorporating Semantic Intent into Performance Monitoring: By quantifying employee intent through semantic analysis, this approach overcomes the limitations of traditional performance management that relies solely on quantitative KPIs. It can identify issues such as goal deviation and distraction hidden behind metrics. Early warnings can be issued at the semantic level before performance anomalies manifest in the final data, preventing problems from accumulating.

[0018] 2. Real-time Early Warning and Intervention: Based on a rapid "BUG" detection mechanism for intention deviation, the system can issue an early warning when high-level intentions deviate abnormally, without waiting for a significant deterioration in lower-level performance indicators. Compared to manual post-event analysis, deviations can be detected and corrected earlier, reducing losses caused by deviations from performance goals.

[0019] 3. Intelligent Diagnosis of Performance Deviation Causes: The system can automatically analyze the deep semantic reasons for performance deviations, such as shifts in work focus, imbalances in resource allocation, and external interference. This semantic-level diagnosis provides managers with insights that traditional data reports cannot offer, helping them to take targeted measures (such as reallocating tasks, adjusting goals, or providing support).

[0020] 4. Enhance the scientific rigor of performance communication: Through quantified intent vectors and semantic reports, employees and supervisors can discuss issues in the performance process more objectively. Compared to subjective impressions, the data and analysis provided by this system make performance feedback more transparent and specific, improving communication efficiency and employee trust.

[0021] 5. Fostering Continuous Improvement and Learning: The feedback mechanism following an alert allows the system to continuously improve itself. Each alert and its results are used to train the system, optimize deviation thresholds and analytical models, and continuously improve alert accuracy. In the long run, the organization will gradually develop a data-driven, proactive performance culture through this cycle.

[0022] In summary, this invention, through dynamic intent layer quantification and a "BUG" early warning mechanism, realizes a new way of monitoring and managing employee performance processes. It can effectively make up for the shortcomings of traditional performance appraisal in process monitoring and cause diagnosis, and provide enterprises with a scientific and forward-looking technical means for performance management. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the dynamic intent quantification and "BUG" early warning system architecture for performance appraisal according to the present invention;

[0024] Figure 2 This is a flowchart of the early warning process of the system of the present invention;

[0025] Figure 3 This is a schematic diagram of the closed-loop process of data flow, early warning triggering, cause diagnosis, and feedback baseline update of the system of the present invention. Detailed Implementation

[0026] The embodiments of the present invention will be further described below with reference to the accompanying drawings and specific processes. The present invention can be deployed as a software system on an existing enterprise performance management platform, consisting of a front-end interface and a back-end analysis engine. The back-end includes modules such as text semantic parsing, intent calculation, deviation detection, early warning, and report generation, and its working principle is as described above. Figure 1 and Figure 2 As shown.

[0027] First, the data acquisition module continuously gathers employee performance-related data from various sources. In the implementation, internal enterprise APIs or RPA (Robotic Process Automation) tools can be used to collect data periodically. For example, task descriptions and progress can be obtained from project management systems, key work points can be extracted from emails or instant messaging logs, and commit message comments can be obtained from code repositories, etc. This data undergoes preprocessing upon entering the system, such as removing stop words, word segmentation, and anonymizing sensitive information, to facilitate subsequent analysis.

[0028] Next, the intent modeling process maps the preprocessed text into a quantified intent vector. This process can be implemented using keyword classification or machine learning models. In a simple implementation, we can maintain a predefined set of intent dimensions and their corresponding keyword dictionaries. For example:

[0029] Cost-oriented: Focuses on cost reduction and efficiency improvement. Typical keywords include "schedule compression," "cost control," "efficiency improvement," and "time saving."

[0030] Quality-oriented: Focuses on improving work quality and reliability. Keywords include "testing," "quality optimization," "error reduction," and "process improvement."

[0031] Innovation-oriented: Focuses on innovation and the development of new solutions. Keywords include "new features," "innovative solutions," "research," and "improved algorithms."

[0032] Willingness to collaborate: Focus on team collaboration and communication. Keywords include "communication," "collaboration," "cross-departmental," and "supporting others."

[0033] Customer-oriented: Focuses on customer needs and satisfaction. Keywords include "customer feedback," "complaints," "user experience," and "changes in requirements."

[0034] The dimensions and keywords mentioned above are for illustrative purposes only. In a real system, they can be customized based on job categories and corporate strategic priorities. Different industries and positions may require the addition of other intent dimensions, such as "security and compliance" or "learning and growth."

[0035] To improve accuracy, the intent quantification module can employ machine learning and deep learning techniques. One feasible approach is to train a text classification model: using a large amount of historical performance-related text (already labeled by experts with their respective intent category distributions) for training, such as a BERT-based multi-label classification model. The model input is employee text (such as weekly report content), and the output is the rating for each intent dimension. Another approach is to use unsupervised methods, such as LSA (Latent Semantic Analysis) or LDA (Latent Topic Analysis), to automatically extract topics from the text set, and then manually map these topics to the intent dimensions. Furthermore, word embeddings can be used to calculate the similarity of text to a predefined set of intent keywords to determine the text's tendency across each dimension.

[0036] The current intent vector obtained by quantization is denoted as Each component On behalf of employees at The level of attention in each intent dimension (e.g., expressed as a percentage or a 0-1 normalized value). The corresponding target baseline vector is denoted as... This is derived from the "baseline goal intent library," which is the intent distribution stipulated in the employee performance contract. During system initialization, the system has already translated the employee's key performance objectives into this set of expected intent weights. In practice, OKRs can be manually set by employees and supervisors when formulating goals, or automatically generated based on the company's general requirements for the position. The baseline library supports on-demand adjustments; for example, if goals change during a performance cycle, the corresponding baseline vectors can be updated.

[0037] The deviation calculation module calculates the current vector periodically (e.g., weekly, or in real time when new data arrives). With baseline vector The difference between them. We define a deviation measure. This can be used to represent the distance or similarity between two vectors. For example, cosine similarity can be used. To measure the consistency between the current intent and the target intent, the calculation formula is as follows:

[0038]

[0039] in" " represents the vector dot product, Let represent the Euclidean norm of a vector. When and The closer the similarity, the closer it approaches 1; the greater the deviation, the closer it approaches 0. A deviation index can be further defined from similarity. (Or use a distance metric such as Euclidean distance directly) to visually represent the degree of deviation. In addition to the overall deviation index, the system also calculates the difference in each dimension. In order to identify the dimension of intent with the greatest deviation.

[0040] Threshold settings: The system presets several threshold rules for the deviation index and the differences in each dimension to determine when to trigger an alert. For example, setting the overall similarity... A value below 0.8 that continues to decline for two weeks, or a deviation in a key dimension. An alert is issued when the deviation exceeds a set percentage (e.g., a deviation of 20% or more). Of course, the threshold can be automatically optimized using machine learning based on historical data. For example, it can analyze which deviation levels in the past ultimately led to performance problems, thereby adjusting the threshold to make the alert more sensitive or accurate.

[0041] Warning Judgment and Triggering: When threshold conditions are met, the warning module generates a warning record. The warning record includes: trigger time, involved employees and period, the deviation indicator that triggered the warning (e.g., "Innovation dimension decreased by 5 percentage points, below 50% of the target value"), and a brief explanation of the cause. This information is promptly pushed to relevant managers and employees (e.g., via email, instant messaging, or system notification). In engineering implementation, warning rules can be deployed in the form of streaming computation or event triggering to immediately evaluate conditions upon receiving new data and execute notification logic as soon as conditions are met, avoiding potential delays caused by relying solely on scheduled batch processing.

[0042] The cause diagnosis module is invoked after an alert is generated to further analyze the underlying causes of the deviation. This module reads previously stored analysis results (such as keyword classification results and topic analysis results) and can also re-extract relevant data for focused analysis within the time window covered by the alert. For example, if an engineer's "technical innovation" dimension is significantly lower than the baseline in January, the system will focus on analyzing their January work logs, project comments, and submission records to extract frequently occurring entities and events. If a large number of terms related to "customer bug fixing" are found, it can be inferred that urgent customer issues consumed their time; if "team new employee training" is mentioned, it may indicate that they were undertaking new employee mentoring work. Algorithmically, association rule learning can be used to find frequently occurring sets of terms in the text, or a simple classification model can be used to predict the most likely cause category. To improve credibility, the system also utilizes a domain knowledge base, for example, mapping retrieved terms to predefined cause categories ("external customer factors," "internal process factors," "personal ability / health," etc.). Finally, the cause diagnosis results are presented in the form of a semantic report, clearly listing the aspects of the deviation and the inferred causes. For example: "Analysis of causes: In the past month, there have been very few work records related to your innovation, while there have been many mentions of 'customer complaints' and 'emergency repairs.' It is speculated that the main reason is that a large number of customer issues have prevented you from focusing on innovation tasks."

[0043] Suggestion and Feedback Mechanism: After providing a diagnosis, the system generates targeted improvement suggestions. These suggestions are based on the type of deviation and successful responses to similar situations in the past. For example, if it detects that an employee is spending too much energy on unplanned tasks, the suggestion might be "communicate with the supervisor to rebalance task priorities." If the deviation stems from external interference, the suggestion might be "increase team support to share the burden of urgent matters." Suggestions can be implemented using a knowledge base and rule engine, or automatically summarized from past successful correction cases through machine learning. Subsequently, the system awaits feedback from employees and supervisors regarding the warning. Feedback can be submitted in the form of a form on the system interface, such as selecting "Agree with the warning and take action" or "Consider it normal and no adjustment is needed," and filling in a description. Feedback information will be used in two ways: First, if a deviation is confirmed and adjustment is needed, the baseline intent in the target intent library will be updated accordingly (e.g., adjusting the target distribution for the remaining period) or the actions taken will be recorded. Second, this event will be incorporated into the training set of the machine learning model to optimize the warning algorithm and reduce the possibility of false alarms or missed alarms in similar situations in the future. Through a closed loop of feedback, adjustment, and relearning, the system continuously evolves to adapt to the performance management needs of different organizational cultures and business rhythms.

[0044] Performance and Deployment Considerations: This system is centered on semantic analysis, but since employees typically produce relatively small amounts of text daily (compared to social media data) and the analysis frequency is limited (generally daily or weekly), the computational performance requirements are moderate. NLP analysis tasks can be run asynchronously in the background without affecting user experience. Data security and privacy should be carefully considered in the system design: Employee text content involves sensitive personal and business information, requiring anonymization and ensuring that only authorized personnel can view the analysis results. In actual deployment, a microservice architecture can be adopted, with each module decoupled. For example, semantic analysis services, deviation calculation services, and notification services communicate via message queues. This helps to scale independently according to load, improving system stability.

[0045] In summary, the specific implementation details of each module's algorithms and engineering implementation have been described. From data acquisition, semantic quantization, deviation detection to early warning notification, root cause analysis, and feedback correction, each step is supported by mature technical means. Therefore, the solution of this invention is feasible and efficient in the current technical environment. The following examples from multiple practical application scenarios further illustrate the workflow and effects of this invention.

[0046] To more clearly illustrate the functions and effects of this invention, the following description of the system's working process is based on practical application examples from different positions and team levels. These embodiments cover three typical scenarios: R&D personnel, sales personnel, and team management, but are not limited to these. This invention is also applicable to performance management in other positions and business areas.

[0047] Example 1: Target deviation warning for R&D personnel.

[0048] In a software development company, the system of this invention was introduced to monitor and improve the performance of R&D team employees. One of the quarterly goals of development engineer E was to "complete the performance optimization of module X and increase system throughput by 20%". Based on this goal, the system established a corresponding baseline intent vector for E, in which dimensions such as "performance optimization" and "system testing" had significant weight. In the first month after the performance cycle began, the system analyzed E's daily work logs and code commit records and found that the frequency of mentions related to "performance optimization" in his actual intent vector was low, while the proportion of mentions related to "new feature development" was surprisingly high. In other words, E had not devoted enough energy to performance optimization, but instead spent a lot of time on unplanned new feature development.

[0049] Further intent deviation calculations revealed that the similarity between E's current intent distribution and the baseline target had dropped to only about 60%, far below the preset threshold (e.g., 75%). The system therefore triggered an alert, notifying E's direct supervisor: "Alert: Module X optimization goals may be neglected; attention is being diverted to new feature development (a high frequency of 'New Feature Y' was detected in recent work records)." Upon receiving the alert, the supervisor immediately communicated with E and learned that E had been responding to an urgent client request to develop feature Y, thus deviating from the optimization focus. In response, the supervisor adjusted the task allocation, assigning feature Y to other engineers and urging E to quickly refocus on performance optimization tasks.

[0050] In the following weeks, the system continued to monitor E's intent distribution and found that E refocused its main efforts on performance optimization. Its current intent vector gradually realigned with the original target baseline, and the similarity rose above the threshold. As the deviation was eliminated, the warning status was automatically lifted. This example demonstrates that without the system's alert, E's target deviation might not have been apparent until the end of the quarter when performance indicators failed to meet targets; the warning mechanism allowed the problem to be detected and corrected early, buying valuable time for subsequent performance targets to be met.

[0051] Example 2: Diagnosis of abnormal sales performance.

[0052] Sales manager F of a company was tasked with achieving a 15% sales increase within a quarter. Halfway through the quarter, the system analyzed F's work activity records (such as visit minutes and CRM logs) to construct his intent vector for that period. The results showed that, compared to the set target, F's weight in the "developing new customers" dimension significantly decreased, while the weight in the "internal collaboration / training" dimension increased. Simultaneously, actual sales performance fell short of the target progress for the same period.

[0053] The system issued a "BUG"-like warning: "Warning: F's core performance objectives have shifted—insufficient effort in new customer development, with attention diverted to internal affairs, which may lead to a deviation from performance targets." Subsequent semantic diagnostic reports further pointed out that F's records over the past month frequently mentioned "training new employees" and "internal meetings," while mentioning almost nothing about "expanding" or "visiting new customers." This indicated that a significant amount of F's time was occupied by internal management work. Upon learning of this situation, the company's sales director intervened promptly, adjusting F's work allocation—allowing other team members to share some internal tasks, freeing up time for F to return to the front lines of market expansion, and providing him with more support in external development.

[0054] In the following period, the system detected a rebound in the weighting of F's new customer development intent dimension, and sales activities refocused on market expansion. At the end of the quarter, F barely met its performance targets. Through the system's diagnosis, the company realized that F's performance decline was not due to a lack of ability, but rather a deviation from its goals caused by role conflict, and thus took targeted organizational measures. This example demonstrates that the present invention can not only provide early warnings of performance problems, but also help management identify the underlying causes of the problems, providing a basis for formulating corrective strategies.

[0055] Example 3: Early warning of team collaboration deviation.

[0056] A product development team set quarterly goals emphasizing "cross-departmental collaboration" and "user satisfaction." At the beginning of the cycle, the system created a collective intent profile for the team as a whole, which represents the distribution of goals across the team's main focus dimensions. For example, the system hoped the team would dedicate approximately 40% of its energy to "cross-departmental communication," 30% to "user research," and the remaining 30% to "internal development process optimization." These proportions serve as the baseline intent vector at the team level and are stored in the goal database.

[0057] A few weeks later, the system analyzed the team's meeting minutes and project discussion records to generate a comprehensive intent vector for the team. It found that the team's overall semantic focus had shifted during execution: compared to the target, the proportion of mentions of "internal development processes" and "technical discussions" increased significantly, while mentions of "user research" decreased markedly, falling below the expected target value. In other words, the team may have been too engrossed in internal technical details, neglecting user needs research.

[0058] The system then issued a warning at the team level: "Warning: The team may have deviated from a user-centric approach. Current discussions are overly focused on internal technology. It is recommended to refocus on user needs." Upon seeing the warning, the team immediately held a review meeting. The discussion revealed that, under the pressure of the development schedule, team members had indeed neglected the planned user research phase and failed to dedicate sufficient time to communicating with real users. Realizing this deviation, the team immediately arranged additional user interviews and usability testing to correct the course. In the following period, the system detected an improvement in the team's intent distribution, with the proportion of the "user research" dimension returning to the target range.

[0059] This case demonstrates that the mechanism of this invention is not only applicable to individual performance but can also be extended to the monitoring and early warning of intent at the team or project level. When collective behavior shows signs of deviating from strategic direction, the system can provide early signals to help the team adjust its course in time before the deviation leads to major mistakes.

[0060] The above embodiments fully demonstrate the effectiveness of the present invention in different scenarios. Whether at the individual or team level, when there is a deviation between the intended goal and actual behavior, the system can detect it early and provide root cause analysis and improvement suggestions. This capability is of great significance for modern enterprises to maintain consistency between organizational goals and employees' daily work.

Claims

1. A dynamic intent-level quantification and "BUG" early warning system for performance appraisal, comprising: The semantic data acquisition module obtains work-related text data from employees and extracts semantic elements reflecting work goals and behaviors. The intent quantification module maps semantic elements into multidimensional intent vector representations and dynamically updates the current intent distribution of employees; The target intent library stores pre-defined target intent vectors by employees as a reference baseline; The deviation monitoring module compares the current intent vector with the target baseline, quantifies the degree of deviation, and monitors its changing trend. The warning trigger module generates a warning signal when the deviation exceeds a preset threshold or shows a continuous increasing trend. The cause diagnosis module uses semantic analysis technology to locate and explain the possible causes of deviations, and generates an early warning report that includes semantic offset dimensions and cause analysis.

2. The system according to claim 1, characterized in that: The intent quantification module is based on the intent layer theory of the DIKWP model. It extracts and classifies themes from employee texts, maps the implicit focus to predefined intent dimensions, calculates the weight of each dimension, and generates corresponding intent vectors to realize the digital representation of employee intent.

3. The system according to claim 1, characterized in that: The warning triggering module draws on the concept of "bugs" in human consciousness, treating abnormal deviations from high-level abstract intentions as "bugs" for rapid detection. When a significant deviation in employee behavior from the target in an abstract pattern is detected, an alert is issued in advance without waiting for lower-level KPI indicators to completely fail, thereby improving the timeliness of performance problem detection.

4. The system according to claim 1, characterized in that: The cause diagnosis module is based on semantic association analysis. When an alert is triggered, it automatically retrieves the employee's text records within a predetermined time window before and after the deviation occurs, extracts high-frequency keywords and matches them with predefined deviation cause categories, thereby locating the semantic causes of performance deviations and providing specific explanations and corresponding improvement suggestions in the alert report.

5. The system according to claim 1, characterized in that: It also includes a feedback recording and baseline adjustment module, which is used to receive feedback information from employees or supervisors regarding the warning; when the deviation is confirmed to be real-time, the feedback information is used to update the baseline intent vector in the target intent library or adjust the threshold rules for triggering the warning, and the feedback result is stored to improve the accuracy of subsequent deviation detection.

6. The system according to claim 1, characterized in that: The system is suitable for monitoring the collective performance of teams or projects. It constructs the overall intent vector of the team by aggregating the semantic data of multiple team members and compares it with the target intent baseline at the team level. When the overall intent distribution of the team deviates significantly from the collective goal, the early warning triggering module generates a team-level deviation warning to alert the management team that the team may have deviated from the established strategic direction.

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