Dynamic quantification of career stability and risk rating methods, systems, and procedures

Abstract

The professional stability dynamic quantification and risk rating method, system and program adopt natural language processing to identify associated parameters from professional asset parameter dictionaries, convert professional experience text and post demand text into 120-dimensional initial vectors, analyze external data sources, adopt a double-layer decoupling architecture: independently calculate the static matching tension of user rigid assets and post rigid demand; external environment variables generate market down indexes of each skill, independently settle the environmental friction coefficient based on the asset projection weight of the target post demand space, calculate the effective professional tension, and divide the green / yellow / red three-level risk based on the adaptive statistical threshold. The present application solves the problem of dilution paradox and static threshold failure, improves the objectivity and user sovereignty of risk rating, and realizes the traceability and auditability of the whole life cycle of professional assets through version snapshot and user right mechanism.

Description

Technical Field

[0001] This invention relates to the field of intelligent analysis technology for career planning, and in particular to a method, system, and program for dynamic quantification and risk rating of career stability based on a multidimensional parameter matrix. Background Technology

[0002] With the deepening development of the knowledge economy, professional assets, as a new type of intangible asset, are becoming increasingly important for quantitative management and dynamic evaluation. However, existing technologies have many shortcomings in professional asset management and evaluation, making it difficult to meet the diversified needs of modern career development.

[0003] 1. Static quantification cannot adapt to industry fluctuations: Existing occupational assessment systems generally use static scoring models, which quantify occupational abilities with fixed weights. This fails to reflect the dynamic changes in occupational value across different industries and economic cycles. For example, a certain professional skill may have high value in an emerging industry, while its value may shrink significantly in a declining industry. Static models cannot capture this key difference.

[0004] 2. Lack of classification and analysis mechanism for external environmental signals: Existing technologies often handle external factors such as the macroeconomic environment, industry trends, technological changes, and policies and regulations in a rough manner, lacking refined classification and analysis of various professional competency dimensions and specific types of environmental signals. Environmental regulation lacks pertinence, and the results of regulation deviate from actual market demand.

[0005] 3. Limited Path Matching Dimensions: Traditional career development path recommendation systems match based on only a single ability dimension, lacking the overall tensor computation capability to consider multi-dimensional features and target path requirements, resulting in one-sided recommendation results. For example, they lack the overall tensor computation capability to consider the relationship between multi-dimensional features of candidates' career assets and target path requirements, such as skill similarity or salary level.

[0006] 4. Lack of time and environmental correction for fit: The fit decay model only considers the time factor and ignores the nonlinear amplification effect of environmental fluctuations on occupational stress, and cannot distinguish the real occupational stress under high / low fluctuation environments.

[0007] 5. Lack of version management mechanism for professional assets: The lack of version management for professional assets makes it impossible to trace historical status, quantify changes, and confirm user rights at key change points, resulting in a lack of transparency and traceability in asset management.

[0008] In summary, there is an urgent need for a new technical solution that can achieve structured transformation of professional assets, dynamic adjustment of environmental intelligence sensitivity, multi-dimensional tension calculation, and version management. Summary of the Invention

[0009] The purpose of this invention is to address the shortcomings of existing technologies and provide a technical solution that can convert unstructured professional text into structured parameters, adjust parameters based on multi-category environmental intelligence, achieve multi-dimensional path matching through high-dimensional tensor operations, introduce time decay and environmental fluctuation compensation to calculate professional tension, and support versioned management of professional assets and user rights confirmation.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] A method for dynamic quantification and risk rating of job stability, comprising the following steps:

[0012] S1. Receive unstructured text, extract atomic-level professional behavior elements through natural language processing, establish an action-parameter mapping network, align it with a preset N-dimensional constant parameter model library, and generate a structured professional asset parameter set.

[0013] The text includes career experience text and target text. The career experience text is output as a rigid asset vector A, and the target text is output as a job requirement vector B.

[0014] S2. Acquire external environmental data, including AI substitution index, policies and regulations, industry fluctuations, technology maturity, and talent supply and demand, to map and generate market downturn indices for each capability dimension. ;

[0015] S3. Calculate static matching tension based on directional similarity algorithm and absolute strength gap scoring model. :

[0016]

[0017] in: Cosine similarity reflects the degree of alignment between the user's skill set and the job requirements.

[0018] The sum of the absolute gaps in the user's various capabilities, divided by Obtaining the normalized strength gap,

[0019] and The balance coefficient is set to a default value. ;

[0020] S4, Vectorizing User Rigid Assets The projected weights within the target job demand space are weighted and calculated with the market downturn indexes of various dimensions to output the environmental friction coefficient. :

[0021]

[0022] ,

[0023] in A set of non-zero dimension indices for the job requirement vector. To prevent division by zero for extremely small positive numbers, This is a market downturn index;

[0024] S5. Calculate effective occupational tension using the following formula:

[0025]

[0026] A risk rating is generated by comparing effective occupational stress with risk thresholds.

[0027] The above method also includes step S5: generating a version snapshot containing SHA-256 hash value and timestamp each time the rigid asset vector A changes, and triggering user confirmation when the absolute offset of the dimension is ≥0.1 or the relative offset rate is ≥15%. After confirmation, a new version archive is generated. If the user refuses to confirm the rights, the system rolls back to the previous version snapshot.

[0028] The structured transformation in step S1 above includes: behavioral semantic parsing, action-parameter mapping, constraint impact assessment, and result quantification sub-steps, which transform qualitative professional behavior into standardized dimensional values.

[0029] The mapping rule between external environment data and the market downtrend index in step S2 above is as follows:

[0030] The AI ​​substitution index, if this capability dimension is marked as high-risk in the preset AI substitution mapping table, then... Accumulation ,in To preset weights, For the corresponding capability dimension, the AI ​​substitution index;

[0031] Industry volatility; if this capability dimension is strongly correlated with the user's current industry, then... Accumulation ,in For industry relevance, This refers to the industry volatility corresponding to the capability dimension;

[0032] In terms of talent supply and demand, if the supply of this skill dimension exceeds the demand, then... Accumulation ,in To determine the supply and demand ratio of talent;

[0033] If the capability dimension is supported by policies and regulations, then Deduction ,in To preset weights, To the extent of policy support;

[0034] Technology maturity; if the technology corresponding to this capability dimension is in its nascent stage, then... Cumulative penalties;

[0035] After accumulating all contributions, di is normalized to the [0,1] interval, and the contribution of dimensions with no mapping relationship is 0.

[0036] The N-dimensional constant parameter model library in step S1 above includes an 81-dimensional constant parameter model library and 39-dimensional professional skill parameters;

[0037] The 81-dimensional constant parameter model library contains 17 soft skill dimension groups; the 17 soft skill dimension groups are values, adversity quotient / resilience, self-leadership, growth mindset, psychological capital, interpersonal relationships, cross-cultural competence, influence building, coaching and mentoring skills, agile thinking, innovative thinking, systems thinking, strategic vision, critical thinking, digital literacy, sustainable thinking, and transition skills.

[0038] The target text in step S1 above includes job objectives and competency objectives;

[0039] If it is a job target, then directly extract the requirement vector of the corresponding job.

[0040] If the objective is a competency, the corresponding job position is inferred based on semantic relevance, and the requirement vector for that job position is extracted.

[0041] The above risk ratings include:

[0042]

[0043] in, and These are risk thresholds.

[0044] The aforementioned risk thresholds are dynamically and adaptively converged based on the statistical distribution in historical industry datasets, including... percentile interval and Use green / yellow / red as cutting boundaries; if no historical data is available, use empirical thresholds of 0.35 and 0.65, and continuously calibrate based on these empirical thresholds.

[0045] The above method also outputs key gap indicators and action suggestions corresponding to the skill dimensions;

[0046] The action recommendations include completing courses from a course library that match the skills dimension and obtaining certifications.

[0047] The dynamic quantification and risk rating system for job stability is applicable to the aforementioned methods for dynamic quantification and risk rating of job stability, including:

[0048] Structured Transformation Engine: Converts unstructured career experience text and target text into structured parameter sets, including rigid asset vectors and job requirement vectors;

[0049] Environmental intelligence collection engine: collects five types of environmental intelligence and outputs the environmental friction coefficient;

[0050] Tension Rating Engine: Performs tension calculations, calculates effective career tension, and outputs a three-level risk rating;

[0051] Version management module: generates version snapshots, performs user rights confirmation, and maintains the version chain of professional assets.

[0052] A computer-readable storage medium for storing a computer program that, when executed by a processor, implements the above-described method.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] This invention presents a method, system, and program for dynamic quantification and risk rating of career stability based on a multidimensional parameter matrix. It employs natural language processing technology to identify relevant parameters from a career asset parameter dictionary, transforms career experience text into a 120-dimensional initial vector, analyzes external data sources, and adopts a two-layer decoupled architecture: independently calculating static matching tension between user's rigid assets and job rigid requirements; generating market downturn indices for each skill based on external environmental variables; independently calculating environmental friction coefficients based on asset projection weights within the target job demand space; calculating effective career tension; and classifying risks into green, yellow, and red levels based on adaptive statistical thresholds. This invention solves the vulnerability dilution paradox and the static threshold failure problem, improving the objectivity of risk rating and user sovereignty. Simultaneously, version snapshots and user rights confirmation mechanisms enable full lifecycle traceability and auditability of career assets, allowing career asset quantification to align with dynamic industry changes, improving the real-time performance and fit of career path matching, and effectively achieving dynamic quantification and risk rating of career stability. It possesses strong practicality and wide applicability. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments.

[0056] Example 1: System Overall Architecture

[0057] 1. Structured Transformation Engine

[0058] This engine is deployed at the system front end and is responsible for receiving and processing various types of unstructured professional text data input by users (including professional experience text and target text, where the target text is divided into job targets and competency targets).

[0059] (1) Behavioral semantic parser: Based on a pre-trained occupational domain NLP model, it performs word segmentation, named entity recognition and relation extraction on the input text, and extracts atomic-level occupational behavioral elements containing behavior, object and environmental conditions, including confidence level;

[0060] (2) Action-parameter mapping network: Establish a mapping relationship between behavior labels and 120-dimensional parameter IDs (including at least 81 constant parameters (soft skill parameters) and 39 professional skill parameters). Each mapping contains a correlation strength coefficient to quantify the correlation strength between behavior and target parameters.

[0061] (3) Result Quantification Converter: Converts qualitative descriptions such as grade descriptions and project results into standardized contribution scores, and supports normalization processing of project scale coefficients, result grade coefficients, etc.

[0062] (4) Comprehensive calculator: The final quantitative value is calculated for each dimension. Combined with the confidence weight, the structured professional asset parameter set is output. That is, the professional experience text output is the rigid asset vector A, and the target text output is the job requirement vector B.

[0063] 2. Dynamic regulatory center

[0064] This central hub, located in the middle layer of the system, is responsible for receiving and processing signals from the external environment. Its core components include:

[0065] (1) AI Substitution Index Collector: Connects to data sources for AI substitution risk research and extracts data on target positions. (0-1) and (Credibility).

[0066] Data sources primarily use data from Chinese research institutions, while Global data sources are marked as "for reference only".

[0067] (2) Policy and Regulation Information Collector: Connects to the government policy database and extracts information. (Policy support level, 0-1) and (User matching degree);

[0068] Support the statutory effective date ( + 15-day personal buffer period mechanism; only data from China (CN) is collected.

[0069] (3) Industry Fluctuation Monitor: Connects to industry economic indicator data sources and extracts... (Volatility index, 0-1) and (Trend: up / down / stable).

[0070] (4) Technology Maturity Assessor: Connects to technology trend research data sources and extracts... (Technology maturity stages: nascent / growth / maturity / decline) and (Adoption rate).

[0071] (5) Talent Supply and Demand Analyzer: Connects to labor market data sources and extracts... (Demand index, >1 indicates supply falling short of demand) and (Salary trends).

[0072] 3. Tension Rating Engine

[0073] This engine, located in the back layer of the system, is responsible for calculating static matching tension and dynamic risk rating. Its core components include:

[0074] (1) Static matching tension module:

[0075] Calculate static matching tension based on directional similarity algorithm and absolute strength gap scoring model. :

[0076]

[0077] in:

[0078] Cosine similarity reflects the degree of alignment between the user's skill structure and the job requirements structure.

[0079] The sum of the absolute gaps in the user's various capabilities, divided by The gap in normalized strength has been identified;

[0080] and The balance coefficient is set to a default value. The specific value can be calibrated based on industry datasets.

[0081] (2) Environmental friction coefficient module:

[0082] The environmental friction coefficient is calculated by weighting the rigid asset vector A in the target job demand space with the market downturn index of each dimension. :

[0083]

[0084] ,

[0085] in A set of non-zero dimension indices for the job requirement vector. This is to prevent division by zero for extremely small positive numbers.

[0086] When users have space for their target job requirements When all assets within the numerator are zero (i.e., the user's reliance on the core skills of the position is zero, and there is no chance of winning), the numerator... It is also zero. At this point, through... Interception, Vulnerability Converging to 0, This means that users do not bear the external competitive friction of this specific skill (because they have not even entered the arena), and their core risk is entirely due to static matching tension. The absolute strength gap term (at which the gap value directly exceeds the limit) is rigidly supported. The system maintains perfect mathematical logical consistency even in extreme conditions.

[0087] (3) Risk rating model:

[0088] Calculate effective occupational tension:

[0089]

[0090] Based on effective occupational tension The comparison result with the preset threshold outputs a dynamic risk rating of red / yellow / green. > Red indicates high risk. < ≤ Yellow indicates medium risk. ≤ It is green and low-risk.

[0091] The risk level classification threshold is not a fixed value, but rather dynamically and adaptively converges based on the statistical distribution in historical industry datasets (e.g., using...). percentile interval and (Serving as the dividing line between green / yellow / red) ensures that the rating system maintains stable differentiation across different economic cycles.

[0092] If historical data is unavailable, an empirical threshold (0.35 / 0.65) can be temporarily used and continuously calibrated. This ensures normal operation even during system cold starts and without industry-wide datasets (meeting the patent law requirements of "full disclosure and feasibility"), while maintaining a long-term technological exclusivity barrier through subsequent adaptive percentile evolution.

[0093] The system outputs a green / yellow / red risk level, but does not provide specific details during user interaction. The numerical values ​​only output the risk level, key gap indicators, and action recommendations.

[0094] 4. Version Management Module

[0095] The version identifier logger generates a version snapshot when vectors change due to user actions on rigid assets (such as completing a new project, obtaining certification, or submitting a new story). The snapshot only records parameter changes caused by user actions and does not include environment offsets. The snapshot includes metadata such as the values ​​before and after adjustments, the type of triggering environment variable, and timestamps; specifically...

[0096] Version snapshot: Includes version number, timestamp, SHA-256 hash value, all parameters, trigger reason, and change summary;

[0097] When a change in a certain capability dimension exceeds a preset threshold (absolute offset ≥ 0.1 or relative offset ≥ 15%), the system pushes a change summary and triggers a user authorization process, with a 72-hour follow-up. After confirmation, the version chain is solidified. During authorization, the user decides whether to accept the change; if the user refuses, the system rolls back to the previous version snapshot; if the user ignores the change, the system does not automatically confirm it.

[0098] Example 2: Complete Technical Process

[0099] S1. Professional Text Input and Preprocessing

[0100] The methods of structured transformation engines include:

[0101] Step 1: Professional Text Input and Preprocessing. Obtain the user's professional experience document in text format, which includes freely described work experience, project participation, and skill mastery information;

[0102] Step Two: Behavioral Semantic Analysis. Based on a pre-trained professional domain NLP model, the document undergoes automated word segmentation to identify lexical boundaries in sentences. Subsequently, named entity recognition is performed to mark names of people, organizations, positions, and technical terms in the text. Furthermore, structured and standardized professional elements are extracted through key information extraction, specifically including clear job titles, project responsibilities, daily toolchains, and output reports. These are atomic-level professional behavioral elements containing key information such as behavior (e.g., "leading," "collaborating," "optimizing"), objects (e.g., "a project," "a system," "a process"), and environmental conditions (e.g., "cross-departmental," "high pressure," "internationalization").

[0103] Step 3: Action-Parameter Mapping. Load the predefined professional asset parameter dictionary. In the system, this dictionary is defined as a static dataset containing N independent professional ability dimensions (as shown in the table below). Each ability dimension is associated with a standardized ability description phrase and multiple sets of synonyms covering industry aliases and common usages. The synonym sets cover commonly used industry abbreviations and variant spellings.

[0104] The table below shows a static dataset of 81 independent professional competence dimensions (soft skills).

[0105] Values 8 v_ethics, v_balance, v_security, v_growth, v_autonomy, v_social_impact, v_income_priority, v_ai_ethics F_BOUNDARY_VALUES Adversity Quotient / Resilience 4 aq_recovery_speed, aq_problem_focus, aq_support_seeking, aq_risk_tolerance A_STABILITY Self-leadership 3 sl_intrinsic_motivation, sl_self_regulation, sl_personal_brand_awareness A_STABILITY Growth mindset 3 gm_feedback_receptivity, gm_challenge_seeking, gm_failure_reframing A_STABILITY Psychological capital 5 pc_self_efficacy, pc_optimism, pc_hope, pc_resilience, pc_meaning_making A_STABILITY Interpersonal relationships 7 ir_collaboration, ir_boundary_clarity, ir_conflict_style, ir_empathy_level, ir_network_building,ir_communication_directness, ir_psychological_safety C_INTERPERSONAL Cross-cultural competence 3 cc_cultural_curiosity, cc_adaptation_speed, cc_global_mindset D_EXECUTION Influence building 3 ib_argumentation, ib_credibility_building, ib_coalition_building D_EXECUTION Coaching and mentoring abilities 5 co_active_listening, co_powerful_questioning, co_developmental_focus, co_feedback_delivery, co_empowerment D_EXECUTION Agile thinking 7 ag_adaptability, ag_learning_agility, ag_decision_speed, ag_multitasking, ag_pattern_recognition,ag_model_pivoting, ag_feedback_loop_speed B_COGNITION Innovative Thinking 7 in_divergent_thinking, in_cross_domain, in_experimentation, in_future_orientation, in_tolerance_ambiguity, in_ai_human_synergy, in_challenge_status_quo E_EXPLORATION Systemic Thinking 3 st_interconnectedness, st_feedback_loop, st_leverage_point D_EXECUTION Strategic Foresight 4 sf_trend_sensing, sf_scenario_planning, sf_signals_weak, sf_horizon_span D_EXECUTION Critical Thinking 5 ct_assumption_testing, ct_evidence_evaluation, ct_logical_consistency, ct_perspective_taking, ct_intellectual_humility B_COGNITION Digital Literacy 6 df_tech_adoption, df_data_literacy, df_remote_collaboration, df_task_decomposition, df_output_validation, df_prompt_engineering B_COGNITION Sustainable Thinking 3 sm_long_term_thinking, sm_stakeholder_inclusivity, sm_personal_sustainability D_EXECUTION Transition Competence 5 tc_transition_planning, tc_uncertainty_tolerance, tc_identity_reconstruction, tc_reflection_learning, tc_transition_risk_management G_TRANSITION_CAPABILITY total 81 — —

[0106] The table below shows a static dataset of 39 professional skill parameters.

[0107] Analysis, Strategy and Consulting AS-001 Market / Industry Research Analysis, Strategy and Consulting AS-002 Strategic Planning Analysis, Strategy and Consulting AS-003 Business Analysis Analysis, Strategy and Consulting AS-004 Financial Analysis Analysis, Strategy and Consulting AS-005 Policy Analysis Communication, Content and Creativity CC-001 Official Document / Business Writing Communication, Content and Creativity CC-002 Content creation and editing Communication, Content and Creativity CC-003 Public speaking and presentation Communication, Content and Creativity CC-004 Multilingual ability Communication, Content and Creativity CC-005 Fundamentals of Graphic Design Data, Technology and Digitalization DT-001 Python programming Data, Technology and Digitalization DT-002 SQL and Database Queries Data, Technology and Digitalization DT-003 Data visualization Data, Technology and Digitalization DT-004 Basic Statistical Analysis Data, Technology and Digitalization DT-005 Excel Advanced Features Data, Technology and Digitalization DT-006 AI tools application Data, Technology and Digitalization DT-007 Prompt template development Data, Technology and Digitalization DT-008 AI toolchain integration Cutting-edge and emerging skills FE-001 Large Language Model (LLM) Hint Project Cutting-edge and emerging skills FE-002 Low-code / no-code development Cutting-edge and emerging skills FE-003 Sustainable Development (ESG) Assessment Cutting-edge and emerging skills FE-004 User Research (UX Research) Cutting-edge and emerging skills FE-005 Decision Trees and Scenario Analysis Cutting-edge and emerging skills FE-006 Risk assessment and prioritization Cutting-edge and emerging skills FE-007 Decision making under uncertainty (Real Options) Cutting-edge and emerging skills FE-008 System Modeling and Causal Analysis Cutting-edge and emerging skills FE-009 AI Agent Workflow Design Cutting-edge and emerging skills FE-010 Large model fine-tuning and customization Cutting-edge and emerging skills FE-011 AI Ethics and Compliance Assessment Leadership and Team Development LD-001 Team building and motivation Leadership and Team Development LD-002 Performance Management and Coaching Leadership and Team Development LD-003 Conflict Mediation Leadership and Team Development LD-004 Change Management Law, Compliance and Governance LG-001 Compliance process construction Law, Compliance and Governance LG-002 Application of Labor Laws Project Management and Operations PM-001 Agile / Scrum Practice Project Management and Operations PM-002 Project planning and scheduling Project Management and Operations PM-003 Budget and Cost Management Project Management and Operations PM-004 Supplier / stakeholder management

[0108] The aforementioned parameter system can also be expanded to include specific parameters for recent graduates and status parameters. Specific parameters for recent graduates are evaluation dimensions extended for those just entering the workforce (such as internship quality, graduation project outcomes, etc.); status parameters are system operation metadata (such as data update time, user activity, etc.). The specific list of these extended dimensions can be configured according to the application scenario and can be expanded as needed, such as to 155 dimensions or more.

[0109] The extracted behaviors are associated with the corresponding dimensions of a 120-dimensional constant parameter model library to establish a mapping relationship between core action tags and dimension IDs in the 120-dimensional constant parameter model library, i.e., action-parameter mapping. Professional behavior elements are standardized and aligned with the pre-defined 120-dimensional constant parameter model library to generate a structured set of professional asset parameters. Each mapping includes a correlation strength coefficient to quantify the semantic correlation between the action and the target parameter.

[0110] The extracted standardized occupational elements (behavioral actions) are sequentially compared with the standardized descriptive phrases and their synonym sets for each ability dimension in the occupational asset parameter dictionary. A semantic similarity score between 0 and 1 is obtained.

[0111] For each capability dimension, determine whether there are standardized occupational elements whose similarity scores exceed a preset threshold (e.g., 0.75). If the condition is met, record an initial vitality score on the initial vector component corresponding to that dimension. This score is determined by the product of the similarity score and the preset weight coefficient of that type of occupational element. The formula for calculating the initial vitality score is:

[0112]

[0113] in: Indicates the first The initial vitality score corresponding to each professional ability dimension. Indicates the first The first standardized occupational element and the first Semantic similarity scores for each professional competence dimension This indicates the preset weight coefficient of the standardized occupational elements in this category. The difference in the weight coefficients of standardized occupational elements in different categories reflects the difference in their importance in occupational assessment.

[0114] For all dimensions where the similarity does not reach the threshold, zero values ​​are filled into the vector components, thereby transforming the unstructured professional experience text into a structured 120-dimensional initial vector.

[0115] Step 3: Contribution Measurement. Dimensions are assigned values ​​based on project size, outcome level, constraint factors, and confidence weights. Specifically:

[0116] The weighting coefficients of standardized occupational elements can be adjusted according to actual assessment needs. The default weight for project responsibility elements is set to 0.8, the default weight for tool usage elements is set to 0.4, the default weight for output elements is set to 0.6, and the default weight for job title elements is set to 0.5. The above weighting reflects the differences in the contribution of different occupational elements to the representation of occupational ability. Semantic similarity calculation uses cosine similarity to measure the distance in the word vector space. The similarity score is normalized to the range of 0 to 1, and the threshold setting balances the requirements of recall and precision.

[0117] Step 4: Comprehensive Calculator: A final quantitative value is calculated for each dimension, and after L1 normalization, a structured professional asset parameter set is output, combining confidence weights. The professional experience text is output as a rigid asset vector A.

[0118] Similar to the steps described above, NLP parsing is performed on the target job (job description) or competency target text to extract key phrases and demand intensity, generating a target vector and constructing the job demand vector B for the target job. Specifically, when the user input is a job target, the corresponding job demand vector is directly extracted; when the user input is a competency target, the system infers the corresponding job based on semantic relevance, i.e., after converting it into a job target, the job demand vector B is output.

[0119] S2. Environmental Intelligence Acquisition: The system synchronously acquires external data across five intelligence categories, processes them independently, and uses this data to map and generate market downturn indices for each capability dimension. , ...

[0120] (1) AI Substitution Index Collection: Obtain the AI ​​substitution index of corresponding capabilities (dimensions) from AI substitution risk data sources. ;

[0121] (2) Policy and regulatory intelligence gathering: Obtain policy support levels for corresponding capabilities (dimensions) from policy databases. ;

[0122] (3) Industry volatility monitoring: Obtain industry volatility corresponding to the capability (dimension) from industry economic indicator data sources. Industry relevance ;

[0123] (4) Technology maturity assessment: whether the technology corresponding to the capability dimension is in its infancy, based on the technology trend data source;

[0124] (5) Talent supply and demand analysis: Obtain the talent supply and demand ratio for corresponding abilities (dimensions) from labor market data sources. .

[0125] The system establishes real-time data connection channels with multiple external data sources, configures a periodic polling mechanism and API call permissions, and the external data sources include:

[0126] a. Industry benchmark data:

[0127] 1. GDP growth rate data by industry released by the National Bureau of Statistics

[0128] 2. Urban unemployment rate and average wage data released by the Ministry of Human Resources and Social Security.

[0129] 3. Industry job demand indexes released by recruitment platforms such as 51job and Liepin

[0130] b. Occupational classification and skills update data:

[0131] 1. National Vocational Qualification Directory

[0132] 2. China Government Website Policy Document Repository Open API

[0133] 3. Occupational Classification Directory of the Ministry of Human Resources and Social Security

[0134] 4. NET database

[0135] c. Data on AI substitution and technology maturity:

[0136] 1. China Academy of Information and Communications Technology (CAICT) Artificial Intelligence Development Index Report

[0137] 2. World Economic Forum's Future of Jobs Report

[0138] 3. China Academy of Information and Communications Technology (CAICT) ICT Industry Maturity Index

[0139] 4. Gartner Hype Cycle

[0140] d. Policy and regulatory data:

[0141] 1. Macroeconomic data from the National Bureau of Statistics

[0142] 2. Aggregated Data Policy Information API

[0143] All the above data sources are publicly available statistical data and do not involve any personal privacy data. The system will only call the corresponding interfaces as needed when supplementary industry benchmark information is required to assist in analysis or evaluation.

[0144] Data source interface description:

[0145] Industry fluctuation data source: Call the public data interface of the monthly industry prosperity index of the National Bureau of Statistics. The collection period is every day at midnight. JSON format data can be obtained through preset HTTP GET requests. The returned indicators include the growth rate of industry added value, the change rate of employment, etc. A forward filling strategy is used when data is missing.

[0146] AI Substitution Index Data Source: Connects to annual skill substitution risk rating reports published by publicly available research institutions. The substitution risk score for each skill category is obtained through the report parsing interface. The data collection period is once per quarter; missing data is replaced with the most recent valid value.

[0147] Policy and regulation data source: The database interface of the Ministry of Human Resources and Social Security and local labor authorities is called. It is updated every day at midnight. The returned content includes text data such as industry regulatory policies and changes in skill qualification requirements. After NLP semantic filtering, it is mapped to relevant parameter dimensions.

[0148] Technology maturity data source: Connects to the API of a public technology trend monitoring platform, with a collection cycle of once a week, to obtain quantitative indicators such as maturity scores and adoption rate changes in various technology fields; low-relevance data is filtered out through preset thresholds.

[0149] Labor market data source: It calls the public data interface of job demand and salary level published by public employment service agencies in various regions. The collection cycle is once a day, and it returns structured data such as recruitment demand, median salary, and application ratio. After cleaning, it is used by the matching and analysis module.

[0150] The system maps five types of external environmental data (AI substitution index, industry volatility, policy and regulatory support, technology maturity, and talent supply-demand ratio) to the market downturn index for each capability dimension according to the following rules. The mapping rules for the market downtrend index are shown in the table below:

[0151] AI Substitution Index If this capability dimension is marked as high-risk in the preset AI substitution mapping table, then Accumulation ,in Preset weight (default 1.0) Industry volatility If this capability dimension is strongly correlated with the user's current industry, then Accumulation ,in For industry relevance Talent supply and demand ratio If the supply of this capability exceeds demand (supply-demand ratio) ),but Accumulation Policies and Regulations If this capability dimension is supported by policy, then Deduction (Reduce downside risk), among which The preset weight (default 0.5) Technology maturity If the technology corresponding to this capability dimension is in its nascent stage, then Cumulative penalty (default 0.2)

[0152] After accumulating all contributions, the result is normalized to the interval [0,1], and the dimension di with no mapping relationship is 0.

[0153] Taking skill DT-006 (AI tool application) as an example, calculate its market downturn index di:

[0154] According to external intelligence in May 2026:

[0155] AI substitution index: 0.8 (high risk), contribution 0.8 × 1.0 = 0.8.

[0156] Industry volatility: Internet industry 0.7, correlation 0.9, contribution 0.7 × 0.9 = 0.63.

[0157] Talent supply and demand ratio: AI positions are in short supply (supply-demand ratio 1.4), not exceeding demand, contributing 0.

[0158] Policy and regulations: AI ethics are supported by policy (0.6), minus 0.6 × 0.5 = 0.3.

[0159] Technology maturity: Large-scale model technology is in its growth stage, with no nascent stage penalty and a contribution of 0.

[0160] The cumulative value = 0.8 + 0.63 - 0.3 = 1.13, and after normalization to [0,1], di = 1.0.

[0161] This example demonstrates that the application skills of AI tools are under extremely high downward pressure in the current market environment.

[0162] S3, Dynamic Adjustment and Version Management: Rights confirmation is triggered when the threshold is exceeded.

[0163] By using the version identifier recorder, compare the current rigid asset vector A with the previous version snapshot V1.0 (assuming it was 30 days ago).

[0164] A version snapshot is generated for each adjustment operation, including metadata such as the values ​​before and after the adjustment, the type of triggering environment variable, and the timestamp. When the adjustment magnitude in a certain dimension meets one of the following conditions, the system pushes a user confirmation request:

[0165] absolute offset (Absolute value)

[0166] relative offset ,

[0167] User ownership confirmation process: When the system detects an asset change that meets the ownership confirmation conditions, the following process is executed: Change notification → Details display → Confirmation operation → Snapshot generation → Exception handling (72-hour reminder).

[0168] Each time user rights confirmation is triggered, the system generates a version snapshot containing the following information: version number (an automatically assigned, incrementing sequence number), timestamp (UTC format), SHA-256 hash value (the hash value of the full 120-dimensional parameter vector, used to verify version data integrity and support trusted data exchange with third-party systems), full parameter values, trigger reason, and change summary. Version snapshots are stored in a chained structure, forming an immutable version chain.

[0169] S4. Static matching tension calculation:

[0170] The static matching tension is calculated by using a directional similarity algorithm and an absolute strength gap scoring model to compare the user's rigid asset vector A with the job demand vector B. :

[0171]

[0172] in: Cosine similarity reflects the degree of alignment between the user's skill structure and the job requirements structure.

[0173] The sum of the absolute gaps in the user's various capabilities, divided by The gap in normalized strength has been identified;

[0174] and The balance coefficient is set to a default value. Further values ​​can be calibrated based on industry datasets.

[0175] S5. Risk rating: Calculation of environmental friction coefficient → effective tension → Level 3 rating.

[0176] Vector user rigid assets The projected weights within the target job demand space are weighted and calculated with the market downturn indexes of various dimensions to output the environmental friction coefficient. :

[0177]

[0178] ,

[0179] in, A set of non-zero dimension indices for the job requirement vector. This is to prevent division by zero for extremely small positive numbers.

[0180] Extreme state: When the user's target job requirement space When all assets within the numerator are zero (i.e., the user's reliance on the core skills of the position is zero, and there is no chance of winning), the numerator... It is also zero. At this point, through... Interception, Vulnerability Converging to 0, .

[0181] Effective career tension:

[0182]

[0183] Based on effective occupational tension The comparison result with the preset threshold outputs a three-level dynamic risk rating: red, yellow, and green. Specifically, red (high risk): Yellow (Medium Risk): Green (low risk): .

[0184] The risk level classification threshold is not a fixed value, but rather dynamically and adaptively converges based on the statistical distribution in historical industry datasets (e.g., using...). percentile interval and (Serving as the dividing line between green / yellow / red) ensures that the rating system maintains stable differentiation across different economic cycles.

[0185] If historical data is unavailable, an empirical threshold (0.35 / 0.65) can be temporarily used and continuously calibrated. This ensures normal operation even during system cold starts and without industry-wide datasets (meeting the patent law requirements of "full disclosure and feasibility"), while maintaining a long-term technological exclusivity barrier through subsequent adaptive percentile evolution.

[0186] The system outputs a three-tiered risk level (green / yellow / red), but does not display specific risk levels during user interaction. The numerical values ​​only output the risk level, key gap indicators, and action recommendations.

[0187] Example 3

[0188] Risk assessment for backend development engineer → AI algorithm engineer

[0189] 1. User rigid asset vector The generation

[0190] User Li, with 6 years of backend development experience, has the following professional experience text: ("Responsible for backend service development at a large internet company, using Java and Python, led the refactoring of a high-concurrency system with millions of daily active users, a project cycle of 8 months, a team of 5, and the system response time was reduced by 40% after the refactoring. In the past two years, he has been self-studying machine learning and completed a Kaggle house price prediction competition (ranked in the top 15%), but it has not been applied in actual projects."). After S1 parsing and L1 normalization, the key dimension values ​​for the rigid asset portion are:

[0191] df_tech_adoption Technology adoption capability 0.0902 df_task_decomposition AI task breakdown capability 0.0188 in_ai_human_synergy Human-machine co-creation capability 0.0602 ag_learning_agility Learning acumen 0.0815 ir_collaboration interpersonal cooperation 0.0727 v_ai_ethics AI ethical awareness 0.0501 DT-001 Python programming 0.1128 DT-006 AI tools application 0.0752 …(Other dimensions are zero or omitted)

[0192] 2. Vector of rigid job requirements The generation

[0193] The target position, "AI Algorithm Engineer," yields the original requirements (without any environmental discounts or gains). This is the set of non-zero dimension indices for the job requirement space. df_tech_adoption, in_ai_human_synergy, DT-001, DT-006 :

[0194] df_tech_adoption 0.85 in_ai_human_synergy 0.78 DT-001 (Python) 0.95 DT-006 (AI tool) 0.90

[0195] The demand intensity for the remaining 116 dimensions is 0.

[0196] 3. Static Matching Tension calculate

[0197] Cosine similarity → Structural misalignment item .

[0198] Strength Gap Calculation (Traversal Only) (Dimensions in the text)

[0199] .

[0200] .

[0201] Normalized gap term .

[0202] .

[0203] 4. Environmental friction coefficient calculate

[0204] The skills decline index is mapped based on external intelligence (rising AI substitution index, increased industry volatility). Standard dimension codes are used:

[0205] , .

[0206] Total asset projection of job requirements:

[0207] .

[0208] Vulnerability calculation (traversal only) (Dimensions in the text)

[0209] .

[0210] .

[0211] 5. Effective Occupational Tension and Risk Rating

[0212] .

[0213] Statistical distribution based on historical industry datasets (this example assumes median ≈ 0.5), , ), The risk level is red (high risk).

[0214] 6. User output (do not output score)

[0215] The system presents the following to the user:

[0216] Your skill set is significantly different from that of an AI algorithm engineer (especially in AI tool application and task breakdown), and the current external environment is demanding higher AI skills, intensifying competition. The overall transition risk is high. It is recommended that you prioritize completing advanced Python training and TensorFlow / PyTorch certifications, and pay attention to policy-supported training resources.

[0217] 7. Version Snapshots and Ownership Confirmation

[0218] If the user subsequently completes authentication and upgrades DT-006 to 0.60, the system will detect an absolute offset ≥0.1, generate a new snapshot, and push an authorization notification, which will take effect after the user confirms it.

[0219] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for dynamic quantification of professional stability and risk rating, characterized in that, The method includes the following steps: S1. Receive unstructured text, extract atomic-level professional behavior elements through natural language processing, establish an action-parameter mapping network, align it with a preset N-dimensional constant parameter model library, and generate a structured professional asset parameter set. The text includes career experience text and target text. The career experience text is output as a rigid asset vector A, and the target text is output as a job requirement vector B. S2, acquire external environment data, including AI substitution index, policy and regulation, industry fluctuation, technology maturity, talent supply and demand, for mapping and generating market down index of each capability dimension ; S3, calculating the static matching tension based on the direction similarity algorithm and the absolute strength gap scoring model : ， in: For cosine similarity, The sum of the absolute gaps in the user's various capabilities, divided by Obtaining the normalized strength gap, and This is the balance coefficient; S4. Calculate the environmental friction coefficient by weighting the projection weight of the rigid asset vector A into the target job demand space with the market downturn index of each dimension. : ， ， in A set of non-zero dimension indices for the job requirement vector. To prevent division by zero for extremely small positive numbers; S5. Calculate effective occupational tension using the following formula: ， A risk rating is generated by comparing effective occupational stress with risk thresholds.

2. The method according to claim 1, characterized in that, The above method also includes step S5: generating a version snapshot containing SHA-256 hash value and timestamp each time the rigid asset vector A changes, and triggering user confirmation when the absolute offset of the dimension is ≥0.1 or the relative offset rate is ≥15%. After confirmation, a new version archive is generated. If the user refuses to confirm the rights, the system rolls back to the previous version snapshot.

3. The method according to claim 1, characterized in that, The mapping rule for external environment data to the market downturn index in step S2 is as follows: The AI ​​substitution index, if this capability dimension is marked as high-risk in the preset AI substitution mapping table, then... Accumulation ,in To preset weights, For the corresponding capability dimension, the AI ​​substitution index; Industry volatility; if this capability dimension is strongly correlated with the user's current industry, then... Accumulation ,in For industry relevance, This refers to the industry volatility corresponding to the capability dimension; In terms of talent supply and demand, if the supply of this skill dimension exceeds the demand, then... Accumulation ,in To determine the supply and demand ratio of talent; If the capability dimension is supported by policies and regulations, then Deduction ,in To preset weights, To the extent of policy support; Technology maturity; if the technology corresponding to this capability dimension is in its nascent stage, then... Cumulative penalties; After accumulating all contributions, di is normalized to the [0,1] interval, and the contribution of dimensions with no mapping relationship is 0.

4. The method according to claim 1, characterized in that, The N-dimensional constant parameter model library in step S1 includes an 81-dimensional constant parameter model library and 39-dimensional professional skill parameters; The 81-dimensional constant parameter model library contains 17 soft skill dimension groups; the 17 soft skill dimension groups are values, adversity quotient / resilience, self-leadership, growth mindset, psychological capital, interpersonal relationships, cross-cultural competence, influence building, coaching and mentoring skills, agile thinking, innovative thinking, systems thinking, strategic vision, critical thinking, digital literacy, sustainable thinking, and transition skills.

5. The method according to claim 1, characterized in that, The target text in step S1 includes job objectives and competency objectives; If it is a job target, then directly extract the requirement vector of the corresponding job. If the objective is a competency, the corresponding job position is inferred based on semantic relevance, and the requirement vector for that job position is extracted.

6. The method according to claim 1, characterized in that, The risk rating includes: ， in, and These are risk thresholds.

7. The method according to claim 6, characterized in that, The risk threshold is dynamically and adaptively converged based on the statistical distribution in the industry's historical dataset, including... percentile interval and As a cutting boundary for green / yellow / red; If no historical data is available, empirical thresholds of 0.35 and 0.65 are used, and the system is continuously calibrated based on these empirical thresholds.

8. The method according to claim 7, characterized in that, The output also includes key gap indicators and action suggestions corresponding to the skill dimensions; The action recommendations include completing courses from a course library that match the skills dimension and obtaining certifications.

9. A dynamic quantification and risk rating system for job stability, characterized in that: The method applicable to the dynamic quantification and risk rating of occupational stability as described in any one of claims 1-8 includes: Structured Transformation Engine: Converts unstructured career experience text and target text into structured parameter sets, including rigid asset vectors and job requirement vectors; Environmental intelligence gathering engine: Collects external environmental intelligence and outputs the environmental friction coefficient; Tension Rating Engine: Performs tension calculations, calculates effective career tension, and outputs a three-level risk rating; Version management module: generates version snapshots, performs user rights confirmation, and maintains the version chain of professional assets.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1-8.

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