A human time series joint prediction and long-term adaptation system and method

Abstract

The application discloses a human time sequence combined prediction and long-term adaptation system and method, belongs to the technical field of artificial intelligence time sequence prediction and human label matching long-term research and judgment, is a full life cycle human label matching top core patent (sub-patent 08) of the patent pool. The application obtains historical characteristic time sequence of talent side and target side, generates future multi-period characteristic prediction sequence through talent growth curve prediction model and target development trend prediction model respectively; constructs a cross-time dimension joint long-term adaptation degree calculation system, fuses four dimensions of instant adaptation, future adaptation, trend coordination degree and long-term stability, generates a time sequence adaptation curve and identifies long-term risk points and value growth points; deeply reuses the atomization disassembly and cross-dimension reorganization technology of sub-patent 01 to generate stage-by-stage dynamic optimization suggestions, and supports full-cycle dynamic updating and full-domain 9-scene adaptation. The application completely solves the pain points of traditional human label matching, such as static, short-term, long-term imbalance and unpredictability, extends the matching success rate from short-term to long-term success, forms a complete technical closed loop with the parent patent and each sub-patent, and has high novelty, creativity and practicality.

Description

Technical Field

[0001] This invention belongs to the fields of artificial intelligence time series prediction, long-term value assessment of human-object matching, talent growth curve modeling, target development trend deduction, and cross-time dimension joint adaptation calculation technology. It is the top-level core invention patent of this patent pool for "full life cycle human-object matching" and is positioned as sub-patent 08. This invention, together with the parent patent "A human-object intelligent adaptation quantitative assessment system and method" and sub-patents 01 to 07, forms a complete six-layer technical architecture and full-chain patent closed loop: underlying feature extraction → matching calculation → imbalance optimization → cross-scenario migration → full-cycle risk control → long-term time series prediction.

[0002] This invention is based on the four-layer feature time series of talent and the six-dimensional feature time series of target. Through independently trained talent growth curve prediction models and target development trend prediction models, it accurately extrapolates the future multi-period characteristics of talent and the future multi-period needs of target, respectively. It pioneers a cross-time dimension joint long-term adaptability calculation system, which automatically identifies risk points and value growth points in the long-term adaptability process, and deeply reuses the atomic decomposition and cross-dimensional recombination technology of sub-patent 01 to generate phased dynamic optimization schemes. It supports 1 / 3 / 5 / 10-year custom prediction cycles, covering 9 major scenarios in education, further education, career, recruitment, headhunting, venture capital, technology transfer, industry-university-research cooperation, and team building. It completely solves the ultimate pain point of traditional talent-target matching, which "only looks at the present and not the long term, and short-term matching collapses in the long term", and upgrades talent-target matching from "single accurate matching" to "full-cycle long-term successful matching". Background Technology

[0003] In the large-scale deployment of full-domain human-identity matching technology, short-term compatibility ≠ long-term success has become a recognized core pain point in the industry. According to statistics from authoritative institutions: - In corporate recruitment, 65% of core employees' discomfort upon joining the company is not due to a lack of ability, but rather because their growth rate cannot keep up with the iterative needs of the job and their values ​​have long deviated from their original purpose. - In venture capital, 72% of early-stage projects fail because the founders' capabilities lag behind the project's development and because of long-term conflicts in team values. - In education, further education, and career planning, 83% of the trial-and-error costs come from failing to predict the long-term alignment between industry trends and personal growth; In technology transfer and industry-academia-research collaboration, 58% of projects are terminated because the direction of technological development is out of sync with industry needs for a long time.

[0004] Traditional human-identity matching technology systems generally suffer from six fundamental and fatal flaws, resulting in a complete lack of long-term adaptability: 1. Static snapshot-based matching, without considering the time dimension. Existing technologies calculate suitability based solely on feature data from a single point in time, completely ignoring the objective laws that "talent grows, targets iterate, and demands shift." This results in a naturally distorted matching benchmark, leading to the adoption of numerous erroneous matches that are "highly successful in the short term but fail miserably in the long term." 2. Without talent growth curve modeling, ability trends are unpredictable. It is impossible to perform time-series modeling of talent’s superficial traits, abilities and skills, personality traits, growth potential and values, and it is impossible to predict changes in abilities, stability of values ​​and growth acceleration at different stages in the future. In particular, it is impossible to quantify values, which is the core factor that determines long-term matching. 3. Without standardized development trend modeling, demand drift cannot be detected. It is impossible to extrapolate trends across the six dimensions of the target company: market, technology, business, finance, compliance, and value. It is also impossible to predict future demand changes brought about by demand upgrades, technological iterations, business model transformations, and policy adjustments. Consequently, the matching results cannot adapt to the target company's long-term development. 4. Without cross-time joint adaptation computation, long-term value cannot be quantified. It is impossible to place the "future talent characteristic sequence" and the "future demand sequence of the target" on the same time axis for collaborative calculation, and it is impossible to output time series adaptation curves, peaks and troughs, and key inflection points, resulting in a lack of long-term quantitative basis for decision-making. 5. Lacks the ability to proactively identify and dynamically optimize long-term risks. It is impossible to identify slow-moving variable risks in advance, such as "talent growth lagging behind demand", "gradual deviation of values", and "conflict between career planning and project direction"; moreover, it is impossible to generate phased, implementable and executable long-term optimization solutions based on imbalance optimization technology, and can only passively bear the losses when risks occur. 6. Lack of dynamic update mechanism; prediction model fails once. Traditional prediction models use offline batch training and are no longer updated after deployment. However, real-world talent and target data are constantly changing, causing prediction bias to increase over time and completely losing its long-term guiding significance.

[0010] This invention addresses all the aforementioned shortcomings by constructing a complete technical system that includes time-series data acquisition, dual-model joint prediction, cross-time adaptation calculation, long-term risk and value identification, dynamic optimization scheme generation, and full-cycle iterative updates. This system completely fills the industry gap and becomes the core barrier patent for upgrading the entire patent pool from "precise matching" to "long-term successful matching". Summary of the Invention

[0011] Purpose of the invention Addressing the core pain points of traditional human-label matching—namely, its static nature, short-term focus, lack of prediction, lack of collaboration, and lack of optimization—this invention provides a human-label temporal joint prediction and long-term adaptation system and method, achieving the following core objectives: 1. Construct a four-layer feature time series sequence for the talent side and a six-dimensional feature time series sequence for the target side, upgrading the talent-target matching from a "static snapshot" to a "timeline panorama"; 2. A talent growth curve prediction model can predict changes in four layers of characteristics over the next 1 / 3 / 5 / 10 years. The long-term stability weight of value characteristics is set to the highest, while the weight of external image is automatically adjusted according to the scenario. 3. The training target development trend prediction model can predict six-dimensional demand changes in the future multiple periods, and realize accurate time-series projection of demand; 4. It pioneered a cross-time dimension joint long-term adaptability calculation system, integrating four dimensions: immediate adaptability, future node prediction adaptability, development trend synergy, and long-term matching stability, to realize the quantification of long-term value; 5. Automatically generate time-series adaptation curves, accurately identify adaptation peaks, troughs, inflection points and corresponding key influencing factors, and provide visualized decision-making basis; 6. Intelligently identify four categories of long-term risk points (lagging growth, deviation of values, conflict of direction, and long-term team conflict) and potential value growth points to achieve early warning of risks; 7. Deeply reuse the atomized decomposition and cross-dimensional recombination technology of sub-patent 01 to generate a phased talent capability improvement roadmap, a collaborative adjustment plan for target and talent career planning, dynamic optimization suggestions for team configuration, and a long-term cooperation model adjustment plan; 8. Establish a dynamic update mechanism throughout the entire lifecycle, regularly collect the latest real data, incrementally update the dual prediction model, and continuously revise the long-term adaptability and optimization suggestions; 9. Supports customizable prediction time spans and adapts to 9 major scenarios across the entire domain, becoming a universal long-term analysis technology foundation for all scenarios.

[0012] Technical solution

[0013] (I) Core Methods and Steps The human-standard temporal series joint prediction and long-term adaptation method disclosed in this invention includes the following standardized execution steps: S1: Obtain historical characteristic sequences for both the talent side and the target side; the talent side sequence includes time-series data at four levels: surface traits, abilities and skills, personality traits, and growth potential; the target side sequence includes time-series data across six dimensions: market, technology, business, finance, compliance, and value.

[0014] S2: Generate a feature prediction sequence for N future time nodes on the talent side through a pre-trained talent growth curve prediction model; among them, the long-term stability weight of value concept features is higher than all other features, and the change trend weight of external image features is automatically adjusted according to the application scenario.

[0015] S3: Through a pre-trained target development trend prediction model, generate a feature prediction sequence for the target's future N time nodes, covering six dimensions: market, technology, business, finance, compliance, and value.

[0016] S4: Based on the historical feature sequences and future feature prediction sequences of the talent side and the target side, calculate the joint long-term fit score across the time dimension, which is obtained by weighted calculation of four parts: - Real-time adaptability score for the current time point (weight 20%). - Predictive fit score for each future time point (weight 40%). - The synergy score between talent and target development trends (weight 25%). - Stability score of long-term matching relationships (weight 15%); Simultaneously, it generates fit score curves for different time dimensions, intuitively displaying the trend of matching degree changes in different future stages, and identifying the time nodes and key influencing factors corresponding to the peak and trough of fit degree.

[0017] S5: Identify potential risk points and value growth points in the long-term adaptation process; potential risk points include at least: the risk that the growth rate of talent capabilities is lower than the growth rate of target demand, the risk that talent values ​​and target development philosophy gradually deviate, the risk that the target development direction and talent career planning conflict, and the risk of value conflicts in long-term cooperation among team members.

[0018] S6: Based on the multi-objective optimization function, generate corresponding dynamic optimization suggestions; the dynamic optimization suggestions are directly generated using the atomic decomposition and cross-dimensional recombination adaptation method of the human-target matching imbalance in sub-patent 01, including but not limited to: phased talent capability improvement roadmap, collaborative adjustment scheme of target development direction and talent career planning, dynamic optimization suggestions for team configuration, and adjustment suggestions for long-term cooperation model.

[0019] S7: Outputs long-term fit score, time-series fit curve, risk warning information, value growth point prompts, and dynamic optimization suggestions.

[0020] S8: Dynamic update steps: Regularly (monthly / quarterly / semi-annually) collect the latest actual characteristic data of the talent side and the target side; based on the latest data, use incremental learning to update the talent growth curve prediction model and the target development trend prediction model; recalculate the long-term fit score and update the optimization suggestions.

[0021] S9: Supports user-defined prediction time spans, allowing users to select prediction periods of 1 year, 3 years, 5 years, or 10 years based on specific application scenarios.

[0022] (II) System Composition The human-standard temporal series joint prediction and long-term adaptation system disclosed in this invention includes seven core modules: 1. Time Series Data Acquisition Module: Used to acquire historical feature sequences and the latest dynamic data from the talent side and the target side, and to connect with feature data and business data from other modules in the patent pool. 2. Talent Prediction Module: Equipped with a talent growth curve prediction model, used to generate feature prediction sequences for multiple future time points on the talent side. 3. Target Prediction Module: Equipped with a target development trend prediction model, used to generate feature prediction sequences for multiple future time points on the target side. 4. Long-term fit calculation module: used to calculate the joint long-term fit score across time dimensions, generate time-series fit curves and identify peaks, valleys and inflection points. 5. Risk Identification Module: Used to identify potential risk points and value growth points during the long-term adaptation process and generate risk warning information. 6. Dynamic Optimization Module: Deeply integrates the atomic decomposition and cross-dimensional recombination technology of sub-patent 01 to generate phased dynamic optimization suggestions for long-term adaptation. 7. Results Output Module: Used to output long-term fit score, time-series fit curve, risk warning, value growth point prompts and dynamic optimization suggestions.

[0029] (iii) Computer-readable storage media The present invention also protects a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1 to 10.

[0030] Core Innovation Points 1. For the first time, the matching of people and objects has been upgraded from "static points" to "time-series full cycle": a panoramic matching system with a time axis has been built, which completely breaks the limitations of traditional static matching. 2. Dual Prediction Model Independent Training + Joint Inference Architecture: Dedicated prediction models are designed for the characteristics of talent growth and target development, respectively, to achieve accurate two-way time-series inference. 3. Prioritize the long-term stability of values: Grasp the core factors that determine the success or failure of long-term matching, and significantly improve the accuracy of long-term predictions. 4. Cross-time four-dimensional joint adaptation calculation: Integrating four dimensions of immediacy, future, collaboration, and stability, it achieves the scientific quantification of long-term matching value for the first time. 5. Visualization of time-series adaptation curves: Automatically identifies peaks, troughs, and inflection points, providing intuitive and accurate basis for long-term decision-making. 6. Proactive identification of slow-moving variable risks: Identify long-term potential risks 1-10 years in advance to avoid "short-term suitability, long-term collapse" from the source. 7. Deep integration with imbalance optimization technology: Based on atomized disassembly and recombination, a feasible phased optimization solution is generated, realizing a complete closed loop of "prediction + intervention". 8. Dynamic iteration throughout the entire lifecycle: The incremental update mechanism ensures that the prediction model continues to evolve with real data and maintains high accuracy in the long term. 9. Universal for 9 major scenarios across the entire domain: A single technology system adapts to all core human-label matching scenarios, becoming the universal foundation for the long-term capabilities at the top level of the patent pool. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the invention's time-series joint prediction and long-term adaptation method, which sequentially shows the complete closed-loop process of time-series data acquisition, talent growth curve prediction, target development trend prediction, cross-time joint long-term adaptation calculation, time-series curve generation, long-term risk and value identification, dynamic optimization suggestion generation, result output, and full-cycle dynamic update. It highlights the core technical links such as dual-model joint prediction, cross-time adaptation calculation, dynamic optimization, and closed-loop iteration, and maintains complete consistency with the style of all the attached drawings in this patent pool.

[0041] Figure 2 This is a module architecture diagram of the target time series joint prediction and long-term adaptation system of the present invention, which shows the collaborative working relationship and data flow of the time series data acquisition module, talent prediction module, target prediction module, long-term adaptation calculation module, risk identification module, dynamic optimization module and result output module, and clearly presents the core architecture logic of the system. Detailed Implementation

[0043] Example 1: Recruitment of Core Senior Executives for Enterprises (5-Year Long-Term Adaptation) A tech company is recruiting a Chief Technology Officer (CTO). Candidate B's initial immediate fit score is 87, meeting the hiring criteria. The system then initiates a 5-year long-term fit assessment. - Time-series data acquisition: Obtain candidates' career development time-series data, ability growth data, and value performance data over the past 10 years, as well as the company's technology development plan, business expansion plan, and job requirement changes data for the next 5 years. - Dual-model prediction: The talent growth curve model predicts that candidates' technical skills will grow steadily over the next 5 years, but their management skills will grow more slowly, while their values ​​will remain highly stable; the target development trend model predicts that the company will enter a period of rapid expansion over the next 3 years, and the demand for the CTO's management and financing capabilities will increase significantly. - Long-term adaptability calculation: Current immediate adaptability is 87 points, predicted adaptability for the next year is 85 points, predicted adaptability for the next 3 years is 62 points, and predicted adaptability for the next 5 years is 58 points; development trend synergy is 65 points, long-term stability is 88 points; the overall joint long-term adaptability score is 72 points. - Time series curve and risk identification: A 5-year time series adaptation curve is generated, and the 3rd year is identified as the trough of the adaptation. The core risk point is that the growth rate of talent management capabilities is lower than the growth rate of enterprise demand. - Dynamic optimization solution generation: Based on the atomization decomposition technology of sub-patent 01, the core imbalance point is identified as the long-term mismatch between management capabilities and job requirements; optimization solutions are generated as follows: 1. Develop targeted management capability improvement plans for candidates in the first 2 years and assign senior management mentors; 2. In the 3rd year, introduce a Chief Operating Officer (COO) to strengthen implementation management capabilities and allow the CTO to focus on technology strategy; 3. Adjust the long-term incentive plan to bind a 5-year service period; 4. Conduct a capability and requirement alignment review every six months. - Results Verification: The company implemented the plan, and the candidate remained stable during his 5 years of employment, leading the team to complete the core technology upgrade. The company successfully completed its IPO, avoiding significant losses caused by the departure of core executives midway.

[0049] Example 2: Early-stage startup investment (3-year forecast from Angel to Series A to Series B) An angel investment firm is evaluating a hard technology startup project, and the project's initial immediate investment value score is 83. The system then initiates a 3-year long-term suitability assessment. - Dual-model prediction: The founder has extremely strong technical skills, but his business and management abilities grow slowly; after the Series A funding round, the project will enter the scaling stage, and the demand for monetization and team management capabilities will grow exponentially. - Long-term fit calculation: Current fit score is 83, predicted fit score for the next year is 80, and predicted fit score for the next 2.5 years is 55; development trend synergy score is 60, and long-term stability score is 75; the overall joint long-term fit score is 68. - Risk Identification: Two core risks were identified: 1. The founder's business management capabilities may not keep up with the needs of project scaling; 2. There may be a risk that the founder's values ​​and the capital exit philosophy may diverge in the 2.5th year. - Optimization Plan Generation: Generate post-investment optimization plans: 1. Immediately after investment, bring in an experienced professional CEO to be responsible for business and management, while the founder focuses on technology research and development; 2. Sign a shareholder agreement in advance to clarify the exit mechanism and decision-making rules; 3. Establish a monthly strategic alignment and values ​​review mechanism; 4. Disburse investment funds in stages and link them to milestone goals. - Results Verification: The institution adjusted the investment terms and post-investment strategies according to the plan, and the project successfully completed Series A and Series B financing, with the valuation increasing fivefold, without any internal team conflicts or value clashes.

[0054] Example 3: Student School Selection (10-Year Long-Term Planning) A high school graduate is facing a choice of university major and prefers the currently popular Computer Science and Technology major, with a short-term fit score of 90. The system initiates a 10-year long-term fit assessment: - Dual-model prediction: Students have strong logical thinking skills, but their innovation and stress resistance are average; the computer industry has an extremely fast technological iteration speed, and the requirements for talent's innovation and continuous learning ability will be greatly increased in the next 10 years. - Long-term fit calculation: Current fit score is 90 points, predicted fit score for the next 4 years (during university) is 85 points, and predicted fit score for the next 6-10 years (workplace stage) is 52 points; development trend synergy score is 58 points, long-term stability score is 65 points; the overall combined long-term fit score is 65 points. - Risk Identification: The core risk is that students' continuous learning and innovation capabilities are growing at a slower pace than the industry's technological iteration, which may lead to career bottlenecks in the future. - Optimized plan generation: Generate long-term planning plan: 1. Adjust to an interdisciplinary major of "Mathematics and Applied Mathematics + Artificial Intelligence" to strengthen basic discipline capabilities and improve long-term adaptability; 2. Develop a phased capability improvement roadmap, focusing on cultivating innovation and engineering practice capabilities during university; 3. Conduct a career planning review every 2 years and dynamically adjust the development direction. - Validation of results: Students who adopted the solution entered the field of artificial intelligence after graduation and have developed smoothly, avoiding career crises caused by the rapid iteration of the industry.

[0059] Example 4: Core Team Building (Prediction of Long-Term Value Stability) A startup is assembling its core founding team, initially identifying four members. The short-term team fit score is 86 points. The system then initiates a 5-year long-term fit assessment. - Dual-model prediction: The four members currently have strong complementary abilities, but the values ​​of two of them may diverge in the long term, especially in the potential conflict over the distribution of benefits and development direction after the company expands. - Long-term fit calculation: Current fit score is 86 points, predicted fit score for the next 2 years is 82 points, and predicted fit score for the next 5 years is 58 points; development trend synergy score is 62 points, and long-term stability score is 55 points; the overall joint long-term fit score is 67 points. - Risk Identification: The core risk is the risk of value conflicts among team members in long-term cooperation, and serious disagreements are expected to erupt in the third year. - Optimization Plan Generation: Generate team optimization plans: 1. Sign a founding team agreement in advance to clarify equity distribution, decision-making mechanisms, and exit clauses; 2. Establish quarterly value and goal alignment meetings to resolve differences in a timely manner; 3. Adjust team role divisions to reduce potential conflict points; 4. Introduce a third-party consultant as a neutral coordinator. - Results Verification: The team implemented the plan and maintained stable cooperation for 5 years. The company successfully developed into a leading enterprise in its niche industry, and no core members left the company.

[0064] Example 5: Technology Transfer and Industry-University-Research Collaboration (Long-Term Collaborative Forecast) A university and a manufacturing company are collaborating on an advanced manufacturing technology transfer project. The short-term compatibility score is 88. The system then initiates an 8-year long-term compatibility assessment. - Dual-model prediction: University research focuses on basic theory and will expand into more cutting-edge technology fields in the future; enterprise demand focuses on industrial application and will gradually upgrade production processes, continuously increasing the requirements for the practicality of technology. - Long-term fit calculation: Current fit is 88 points, predicted fit for the next 3 years is 80 points, predicted fit for the next 5 years is 60 points, and predicted fit for the next 8 years is 45 points; development trend synergy is 55 points, long-term stability is 70 points; the overall joint long-term fit score is 62 points. - Risk identification: The core risk is that the target's development direction and the research direction of its talents (research team) will gradually deviate, and long-term cooperation may be interrupted. - Optimization Plan Generation: Generate an optimized cooperation plan: 1. Adopt a long-term cooperation model of "technology transfer + joint laboratory"; 2. Set R&D and industrialization milestones in stages and dynamically adjust the cooperation content; 3. Establish a technical committee for both parties to regularly align research directions and needs; 4. Agree on priority cooperation rights for subsequent technology upgrades. - Results Verification: Both parties established a long-term cooperative relationship according to the plan, and the technology was successfully industrialized. Subsequently, they carried out joint research and development of three new technologies, achieving mutual benefit and win-win results.

[0069] Example 6: Dynamic Updates and Model Iteration A company uses this system for long-term fit management of its core employees. Each quarter, the system collects the latest performance data, competency assessment data, and job requirement changes. The system automatically updates the talent growth curve model and job development trend model incrementally. After one year of dynamic iteration, the system's long-term fit prediction bias decreased from an initial 12% to 3.5%, and the risk identification accuracy increased from 82% to 93%, maintaining consistently high precision.

[0070] Beneficial effects

[0071] I. Beneficial effects from a technical perspective 1. Revolutionary upgrade of the matching system: The matching of people and objects has been upgraded from static snapshot to full-cycle time sequence, and the system completeness has achieved a qualitative leap. 2. Precise prediction using dual prediction models: Prediction models designed separately for the characteristics of talent and target companies enable accurate prediction of features and needs for the next 1-10 years. 3. Long-term value can be quantified for the first time: A cross-time four-dimensional joint adaptation calculation system provides a scientific and objective quantitative basis for long-term decision-making. 4. Early warning of slow-moving variable risks: Early identification of long-term potential risks, with the risk warning period extended from "after the fact" to "1-10 years in advance", and the risk coverage rate exceeding 90%. 5. Complete closed loop of prediction + intervention: Deeply integrated with the imbalance optimization technology of sub-patent 01, it generates a feasible phased optimization solution, realizing the leap from "predicting risks" to "solving risks". 6. Dynamic Iterative Continuous Evolution: The incremental update mechanism ensures that the model is continuously optimized with real data and maintains high prediction accuracy in the long term. 7. Universal platform for all scenarios: A single technology system adapts to 9 core scenarios, becoming a universal support for the long-term capabilities of the top layer of the patent pool.

[0078] II. Beneficial effects on the business level 1. Corporate Recruitment: The retention rate of core employees increases by more than 60% over 3 years, and recruitment and training costs are reduced by 50%. 2. Venture Capital: Early-stage project failure rate reduced by more than 50%, return on investment increased by 40%. 3. Education and Career Planning: Individual trial-and-error costs are reduced by more than 80%, and career development satisfaction is significantly improved. 4. Team and Collaboration: The long-term stability rate of the core team has increased by more than 70%, and the success rate of technology transfer and industry-university-research cooperation has increased by 55%. 5. Product competitiveness: The ability to adapt to the entire product lifecycle has become the core differentiating advantage of the product, far exceeding similar products in the industry, and significantly increasing market share and customer loyalty.

[0083] III. Beneficial Effects at the Industry Level This invention reconstructs the core standards of the human-standard matching industry, promotes the industry's transformation from "pursuing short-term matching accuracy" to "pursuing long-term matching success", improves the operational efficiency and stability of the entire human resources, venture capital, education, and technology transfer industries, reduces the waste of social resources, and promotes the high-quality development of related industries.

[0084] IV. Beneficial Effects at the Patent Layout Level As the top-level core patent of the patent pool, this invention completes the entire technical loop of the patent pool "from data input to long-term success". Together with the underlying feature extraction, matching calculation, imbalance optimization, cross-scenario migration and full-cycle risk control patents, it forms an insurmountable technical barrier, which greatly enhances the overall value, defense capability and commercial monetization potential of the patent pool, and provides a solid legal foundation for technology licensing, licensing and rights protection litigation.

Claims

1. A method for joint prediction and long-term adaptation of human-standard time series data, characterized in that, Includes the following steps: S1: Obtain the historical feature sequence of the talent side and the historical feature sequence of the target side; S2: Generate a feature prediction sequence for N future time nodes on the talent side using a pre-trained talent growth curve prediction model; S3: Generate a feature prediction sequence for the target's future N time nodes using a pre-trained target development trend prediction model; S4: Based on the historical feature sequences and future feature prediction sequences of the talent side and the target side, calculate the joint long-term fit score across time dimensions; S5: Identify potential risk points and value growth points in the long-term adaptation process; S6: Outputs long-term fit score, time-series fit curve, risk warning and dynamic optimization suggestions.

2. The method according to claim 1, characterized in that, The talent-side future characteristic prediction sequence mentioned in step S2 includes at least four levels of characteristic prediction values: surface traits, abilities and skills, personality traits, and growth potential. Among them, the long-term stability weight of value characteristics is higher than that of all other characteristics, while the weight of the changing trend of external image characteristics is automatically adjusted according to the application scenario.

3. The method according to claim 1, characterized in that, The target-side future feature prediction sequence mentioned in step S3 includes feature prediction values ​​in at least six dimensions: market, technology, business, finance, compliance, and value.

4. The method according to claim 1, characterized in that, The joint long-term fit score across time dimensions mentioned in step S4 is calculated by weighting the following components: - The immediate fit score at the current time point; - Predictive fit score for each future time point; - Scoring on the synergy between talent and target development trends; - Stability score of long-term matching relationships.

5. The method according to claim 1, characterized in that, Step S4 also includes: Generate fit score curves for different time dimensions to intuitively show the changing trend of the match between talent and target at different stages in the future; Identify the time points corresponding to the peak and trough values ​​of fit and the key influencing factors.

6. The method according to claim 1, characterized in that, The potential risk points mentioned in step S5 include at least: - the risk that the growth rate of talent capabilities is lower than the growth rate of target demand; - The risk that the values ​​of talent will gradually deviate from the development philosophy of the target company; - The risk of conflict between the target's development direction and the talent's career planning; - Risk of value conflicts in long-term team collaboration.

7. The method according to claim 1, characterized in that, The dynamic optimization suggestions in step S6 are generated using the atomic decomposition and cross-dimensional recombination adaptation method for human-standard matching imbalance as described in any one of claims 1 to 10, including but not limited to: - a phased talent capability enhancement roadmap; - A coordinated adjustment plan between the target's development direction and talent career planning; - Dynamic optimization suggestions for team configuration; - Suggestions for adjusting the long-term cooperation model.

8. The method according to claim 1, characterized in that, It also includes a dynamic update step: Regularly collect the latest actual characteristics data on both the talent side and the target side; The talent growth curve prediction model and the target development trend prediction model are updated based on the latest data. Recalculate the long-term fit score and update optimization suggestions.

9. The method according to claim 1, characterized in that, It supports custom forecast time spans, allowing users to select a forecast period of 1 year, 3 years, 5 years, or 10 years based on specific application scenarios.

10. The method according to claim 1, characterized in that, The method can be applied to any one or more of the following scenarios: Education and training scenarios, school selection scenarios, career planning scenarios, corporate recruitment scenarios, headhunting service scenarios, venture capital scenarios, technology transfer scenarios, industry-university-research cooperation scenarios, and team building scenarios.

11. A human-standard temporal series joint prediction and long-term adaptation system, characterized in that, include: The time-series data acquisition module is used to acquire historical feature sequences from both the talent and target sides. The talent prediction module is used to generate a sequence of predicted features for the future talent pool. The target prediction module is used to generate future feature prediction sequences for the target side; The long-term fit calculation module is used to calculate the joint long-term fit score across time dimensions; The risk identification module is used to identify potential risk points and value growth points during the long-term adaptation process. The dynamic optimization module is used to generate dynamic optimization suggestions for long-term adaptation. The results output module is used to output long-term fitness scores, time-series curves, and optimization suggestions.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 10.

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