Big data-based regional industrial innovation talent gap early warning method and system

Abstract

The application relates to the technical field of computer and big data analysis, and discloses a regional industrial innovative talent gap early warning method and system based on big data. The method comprises the following steps: constructing a multi-dimensional talent supply and demand balance domain integrating multi-source data, generating a dynamic ecological boundary benchmark through a Gaussian mixture model and a disturbance test; extracting double time sequence characteristics of an innovation chain and a talent supply side, and jointly predicting by using an LSTM-GBDT hybrid model; mapping the supply and demand state into an ecological niche point, quantifying the imbalance risk by combining a Monte Carlo stress test, and tracing a dominant factor; and starting a closed-loop intervention mechanism of monitoring optimization, accurate supply or emergency response according to a risk level. Through the above technical scheme, the application realizes dynamic benchmark self-adaption, prediction accuracy improvement, accurate risk judgment and differentiated closed-loop intervention, and enhances the foresight and governance efficiency of regional innovative talent supply and demand matching.

Description

Technical Field

[0001] The present invention belongs to the technical field of computers and big data analysis, and specifically relates to a method and system for early warning of regional industrial innovation talent gaps based on big data. Background Technique

[0002] Under the background of the in-depth promotion of the strategies of building a strong scientific and technological country and a strong talent country, the regional industrial innovation system has become increasingly dependent on high-quality talents. As the core link connecting technology R & D, achievement transformation and industrial upgrading, the dynamic adaptation ability of the supply and demand of talents directly determines the resilience and sustainability of the regional innovation ecosystem. At present, talent management around the industrial innovation chain has shifted from static allocation to dynamic regulation. There is an urgent need to build a talent gap early warning mechanism that can perceive in real time, accurately predict and respond intelligently to support the efficient flow and optimal allocation of innovation elements.

[0003] The method for early warning of regional industrial innovation talent gaps driven by big data aims to depict the complex mapping relationship between talent supply and industrial demand by integrating multi-source heterogeneous data, and on this basis, identify potential imbalance risks. Such methods usually rely on multi-dimensional information such as historical recruitment data, patent output, enterprise R & D investment, and education and training structure, and attempt to establish a quantitative index system reflecting the health of the regional talent ecosystem to provide a decision-making basis for strategic formulation and resource scheduling.

[0004] The existing technologies have the following problems when achieving the above goals: First, the early warning models mostly rely on static thresholds or simple statistical rules, and it is difficult to adapt to the sudden change characteristics of talent demand under the rapid iteration of technologies, resulting in late warning or even failure; the analysis frameworks generally process each link of the innovation chain, talent flow paths and external environmental disturbance factors separately, lacking systematic modeling of the multi-factor coupling conduction mechanism, weakening the overall and forward-looking nature of the early warning; the mainstream prediction algorithms mostly use linear regression or shallow machine learning models, which cannot effectively capture the non-linear dependencies and structural mutations in the long-term time series evolution, restricting the improvement of prediction accuracy; finally, the design of intervention strategies tends to be homogenized, without making differential responses according to risk levels, industrial characteristics and gap causes, and lacking a closed-loop feedback mechanism, making it difficult to achieve dynamic optimization of resource allocation and effect tracking.

[0005] These problems together exacerbate the structural contradictions such as the shortage of high-end composite talents and the lack of cross-field collaboration ability, significantly raising the risk of "broken chains" in the regional innovation chain. There is an urgent need for a new early warning technology system that integrates dynamic benchmark construction, high-precision time series modeling, niche risk quantification and hierarchical closed-loop intervention. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for early warning of regional industrial innovation talent gaps based on big data, which can effectively solve the problems in the above background technique.

[0007] The present invention provides a method for early warning of regional industrial innovation talent gaps based on big data, comprising the following steps:

[0008] A multidimensional talent supply and demand equilibrium domain is constructed and an ecological boundary benchmark is dynamically generated. This involves integrating regional industrial innovation chain data, talent supply structure data, and external environmental disturbance factors, selecting representative benchmark samples, and using a Gaussian mixture model to model the probability density of the multidimensional feature space. Furthermore, multidimensional disturbance tests are applied to the benchmark samples to simulate the distribution of talent supply and demand under different industrial evolution paths, thereby generating a dynamic ecological boundary benchmark that evolves over time.

[0009] Extract dual time series features and establish a fusion prediction model. Collect long-term historical data from the innovation chain side and the talent supply side to construct a dual-channel time series input sequence. Use a hybrid architecture of long short-term memory network and gradient boosting decision tree to jointly model the two types of time series. Long short-term memory network is used to capture long-term dependencies, and gradient boosting decision tree is used to fit nonlinear mutation features. Output the predicted value of talent supply and demand gap in the future specified period.

[0010] Map ecological sites and perform stress tests. Project the current and predicted talent supply and demand status vectors onto the multidimensional talent supply and demand equilibrium domain to form ecological sites. Based on these ecological sites, use the Monte Carlo simulation method to randomly sample and disturb key disturbance factors. Repeat the simulation multiple times to statistically analyze the frequency and distribution of imbalance critical points, quantify the current supply and demand matching degree, and determine the risk level and trace the dominant imbalance factor.

[0011] A tiered and categorized closed-loop intervention mechanism was launched, which categorizes intervention strategies into three types based on risk level: monitoring and optimization strategy, precise replenishment strategy, and emergency response strategy. Differentiated measures were customized for different industry types and the causes of gaps. Feedback data was continuously collected after the intervention was implemented to update the status of ecological sites and evaluate the intervention effect, thus forming a closed-loop evaluation mechanism.

[0012] Preferably, the construction dimensions of the multidimensional talent supply and demand equilibrium domain are no less than eight, including industrial technology maturity, core job skill demand intensity, high-end talent density, interdisciplinary integration index, regional innovation activity, education supply matching coefficient, net talent flow inflow rate and strategic incentive intensity. The data of each dimension are input into the Gaussian mixture model after standardization processing, and the number of model components is automatically determined according to the Bayesian information criterion.

[0013] Preferably, the perturbation test of the benchmark sample includes applying random perturbations within a specific range to the rate of technological breakthroughs, the level of strategic support, and external economic shocks. The perturbation amplitude is dynamically adjusted according to the historical fluctuation standard deviation. After each perturbation, the equilibrium domain boundary is recalculated, and finally, the boundary envelope within the pre-set confidence interval is taken as the dynamic ecological boundary benchmark.

[0014] Preferably, the dual temporal features are aligned by a sliding window before being input into the model. The window length is a predetermined time period to eliminate phase shifts caused by inconsistent data acquisition frequencies, and missing values ​​are filled using cubic spline interpolation.

[0015] Preferably, the number of hidden layer units in the Long Short-Term Memory Network is a predetermined value, the time step covers several months in the past, and the number of base learners, maximum depth, and learning rate of the gradient boosting decision tree are all predetermined values. The outputs of the two models are integrated through a weighted fusion method, and the weights are determined by the inverse ratio of the mean squared error on the validation set.

[0016] Preferably, the coordinates of the ecological site are composed of the standardized scores of each dimension in the multidimensional talent supply and demand equilibrium domain, and the Euclidean distance with the dynamic ecological boundary benchmark is used as the initial imbalance measure. The disturbance factors in the Monte Carlo simulation include the technology substitution rate, the enterprise failure rate and the lag period of university major adjustment. Each simulation outputs a binary judgment result to determine whether the ecological boundary has been crossed. The imbalance critical point is defined as the disturbance combination with a cumulative failure rate greater than a preset threshold.

[0017] Preferably, the risk level is divided into three levels:

[0018] Level 1 is a green and safe zone, where the ecological sites are located within the dynamic boundary and the probability of imbalance is less than the first preset threshold.

[0019] Level 2 is a yellow warning zone, where the probability of imbalance is between the first and second preset thresholds.

[0020] Level 3 is a red alert zone, where the probability of imbalance is greater than the second preset threshold, and the type of imbalance is located by combining the identification results of the dominant imbalance factor.

[0021] Preferably, the monitoring and optimization strategy is applicable to Level 1 risk, including increasing the data collection frequency, optimizing indicator weights, and fine-tuning model parameters;

[0022] The precise supply strategy is applicable to Level 2 risks, and pushes customized talent introduction lists, university-enterprise cooperation project suggestions and on-the-job training course packages according to industry sub-sectors;

[0023] The emergency response strategy applies to Level 3 risks, activates a cross-departmental emergency coordination mechanism, opens a green channel to introduce urgently needed talents, and temporarily adjusts the direction of regional industrial support.

[0024] Preferably, the closed-loop evaluation mechanism includes setting intervention effect evaluation indicators, such as talent arrival rate, job matching degree and innovation output recovery speed, collecting feedback data according to a predetermined cycle, and if two consecutive evaluations show that the risk level has not decreased, the strategy upgrade process is automatically triggered and the risk assessment step is re-executed.

[0025] This invention also provides a regional industrial innovation talent gap early warning system based on big data, including an ecological boundary construction module, a fusion prediction module, a stress testing module, and a closed-loop intervention module;

[0026] The ecological boundary construction module is used to integrate regional industrial innovation chain data, talent supply structure data and external environmental disturbance factors, select representative benchmark samples, perform probability density modeling on multidimensional feature space through Gaussian mixture model, and generate dynamic ecological boundary benchmarks by combining multidimensional disturbance tests.

[0027] The fusion prediction module is used to construct a dual-channel time-series input sequence and output the predicted value of talent supply and demand gap using a hybrid architecture of long short-term memory network and gradient boosting decision tree.

[0028] The stress testing module is used to project the current and predicted talent supply and demand status vectors onto the multidimensional talent supply and demand equilibrium domain to form ecological sites, and to use Monte Carlo simulation to randomly sample and disturb key disturbance factors to quantify the supply and demand matching degree and determine the risk level.

[0029] The closed-loop intervention module is used to initiate monitoring optimization, precise replenishment or emergency response strategies according to the risk level, and to collect feedback data after intervention to update the status of ecological sites and evaluate the intervention effect.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Breaking through the traditional fixed threshold early warning mode, the multi-dimensional talent supply and demand equilibrium domain constructed by integrating Gaussian mixture model and perturbation test can adapt to the talent demand characteristics of different industry technology life cycle stages, realize the dynamic evolution of ecological boundary benchmark, improve the early warning sensitivity of rapidly iterating industries such as artificial intelligence and biomedicine, and avoid missed or false alarms caused by rigid thresholds.

[0032] 2. The innovative approach adopts a hybrid architecture of long short-term memory network and gradient boosting decision tree, which not only retains the ability to stably capture long-term trends, but also enhances the ability to respond to nonlinear jumps caused by sudden strategic adjustments or technological breakthroughs. In empirical tests, the root mean square error of the prediction of talent gaps in the next few months is reduced to the preset level, which is significantly improved on average compared with single models and outperforms existing linear regression or shallow machine learning methods.

[0033] 3. Through ecological site mapping and Monte Carlo stress testing system, not only can the supply and demand matching degree be quantified, but also the key factors that dominate the imbalance can be identified. For example, it can be distinguished whether the supply shortage is caused by the lag in college training or the surge in demand caused by the outbreak of emerging industries. This deepens the risk level judgment from "whether there is an imbalance" to "why there is an imbalance", providing a scientific basis for precise policy implementation and achieving a high level of accuracy in risk tracing.

[0034] 4. The designed hierarchical and classified closed-loop intervention mechanism automatically matches monitoring and optimization, precise replenishment or emergency response strategies based on risk level and gap causes, and is equipped with dynamic tracking evaluation and strategy upgrade functions to form a complete governance closed loop. In pilot areas, talent allocation efficiency has been improved, the risk of innovation chain disruption has decreased, and the problems of intervention homogenization and resource waste have been effectively solved. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0036] Figure 2 This is a schematic diagram illustrating the core principle framework of the construction of a multidimensional talent supply and demand equilibrium domain and the generation of a dynamic ecological boundary benchmark in this invention.

[0037] Figure 3 This is a flowchart of the logical flow of the dual-temporal feature extraction and fusion prediction model in this invention;

[0038] Figure 4 This is a schematic diagram illustrating the risk assessment principle of ecological site mapping and Monte Carlo stress testing in this invention;

[0039] Figure 5 This is a framework diagram of the strategy response and effect evaluation of the hierarchical and classified closed-loop intervention mechanism in this invention. Detailed Implementation

[0040] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following description is provided in conjunction with the appendix. Figure 1 To be continued Figure 5 The following is a detailed description of the specific implementation methods, structures, features, and effects of the present invention, as well as preferred embodiments.

[0041] Example 1: In a national high-tech industrial development zone, the two leading industries of artificial intelligence and biomedicine are at a critical stage of rapid technological iteration and market expansion. The core challenge facing the region is the surge in demand from enterprises for high-end, interdisciplinary talents with cross-disciplinary backgrounds (such as "AI + bioinformatics" and "chip + medical equipment"); the local university talent training system suffers from structural lag, and talent mobility data is scattered across multiple departments such as human resources, education, and science and technology, lacking a unified dynamic perception and early warning mechanism.

[0042] Traditional early warning methods based on static job shortage statistics or simple linear extrapolation are ineffective in addressing the drastic fluctuations in talent supply and demand caused by technological breakthroughs (such as large-scale technological advancements) or strategic adjustments (such as the injection of special support funds). This has led to repeated instances of long-term vacancies in key R&D positions and delays or even cancellations of innovation projects. To address these issues, this invention deploys a regional industrial innovation talent shortage early warning method and system based on big data.

[0043] The system's operation begins with step (1): constructing a multi-dimensional talent supply and demand equilibrium domain and dynamically generating ecological boundary benchmarks. First, multi-source heterogeneous data is integrated from the regional digital economy governance platform. Specifically, the industrial innovation chain data comes from the Science and Technology Bureau's patent database (containing the IPC classification number, applicant, application date, and citation count of all authorized invention patents in the past 5 years), the Industry and Information Technology Bureau's annual report on enterprise R&D investment (containing internal R&D expenditures, full-time equivalent of R&D personnel, and sales revenue of new products), and the technology conversion rate data (defined as the ratio of technology contract transaction amount to R&D expenditure) in the technology contract registration system.

[0044] The talent supply structure data is integrated from the Education Bureau's database of college graduates' employment destinations (including majors, academic qualifications, skill certificates, and the industry of their first job), the Human Resources and Social Security Bureau's records of migrant population registration and social security contributions (used to infer skill distribution and net inflow rate), and real-time job data from recruitment platforms (anonymized, including job titles, required skill tags, salary range, and the industry of the company). External environmental disturbance factors include the macroeconomic prosperity index released by the National Bureau of Statistics, the amount of regional venture capital investment by local financial regulatory bureaus, and industry strategy documents published on the official websites of relevant local departments (strategic incentive intensity keywords were extracted and quantified using NLP technology).

[0045] In the data preprocessing stage, the eight core dimensions mentioned above—industry technology maturity (calculated by weighted average of patent citation counts and technology conversion rate), core job skill demand intensity (derived from the aggregation of TF-IDF values ​​of high-frequency skill tags in job postings), high-end talent density (defined as the number of people with doctoral degrees or senior professional titles per 10,000 employees), interdisciplinary integration index (Shannon entropy calculated based on the co-occurrence matrix of patent IPC classification numbers), regional innovation activity (characterized by the geometric mean of monthly patent applications and R&D investment intensity), education supply matching coefficient (calculated by the cosine similarity between the professional structure of college graduates and the current industry job demand structure), net talent inflow rate ((number of inflow talents - number of outflow talents) / total talent base), and strategic incentive intensity (normalized product of strategic text sentiment analysis score and fiscal subsidy amount)—were standardized. Data for each dimension was mapped to the [0,1] interval to eliminate the influence of dimensions.

[0046] Subsequently, 20 benchmark enterprises with stable talent supply and demand performance over the past three years were selected as representative samples. These samples cover sub-sectors such as artificial intelligence algorithms, smart hardware, gene sequencing, and innovative drug development, and their historical data is complete with no significant operational anomalies. The state vectors of these samples in 8-dimensional space are input into a Gaussian Mixture Model (GMM). The number of components K in the GMM is not preset but automatically determined using the Bayesian Information Criterion (BIC). The system traverses candidate models from K=2 to K=10, calculates the BIC value for each model, and selects the model with the smallest BIC value as the optimal solution. In this embodiment, the BIC criterion ultimately determines K=5, indicating that there are 5 typical potential patterns in the talent supply and demand status of the region.

[0047] To simulate the boundaries under different industry evolution paths, multi-dimensional perturbation tests were applied to the benchmark samples. Specific perturbation variables included: the rate of technological breakthrough (based on the historical monthly average number of patent applications, a normalized random increment of ±1σ to ±3σ was applied, where σ is the historical standard deviation), the strength of strategic support (the intensity of strategic incentives was multiplied by a random factor of 0.8 to 1.2 based on the original value), and external economic shocks (the macroeconomic prosperity index was replaced with the historical lowest or highest value).

[0048] After each perturbation, the new positions of all benchmark samples in 8-dimensional space are recalculated, and the GMM is fitted again. This process is repeated 1000 times, generating 1000 sets of dynamic equilibrium domains. Finally, the boundary envelope within the 95% confidence interval is taken as the dynamic ecological boundary benchmark. This dynamic ecological boundary is not a fixed hyperplane, but a multi-dimensional surface with probability density gradient that evolves over time, adaptively reflecting changes in the industry technology life cycle.

[0049] Proceed to step (2): Extract dual time-series features and establish a fusion prediction model. The system constructs two independent time-series data channels. The innovation chain side channel collects historical data from the past 24 months, with a time step of 1 month. Each time step contains a vector: [number of patent applications, R&D investment intensity, technology conversion rate, regional innovation activity]. The talent supply side channel also collects data from the past 24 months. Each time step contains a vector: [number of graduates from relevant majors in colleges and universities, net inflow rate of high-end talents, proportion of "AI + biology" composite skills required in job postings, education supply matching coefficient].

[0050] Because there may be slight differences in the data acquisition frequency between the two channels (e.g., patent data is released monthly, while recruitment data is updated weekly), the system uses a sliding window alignment process. The window length is set to one month. High-frequency data (such as recruitment data) is aggregated monthly, while low-frequency or missing data points (such as R&D data missing in a certain month due to statistical delays) are filled using cubic spline interpolation to ensure that the two channels are strictly aligned on the time axis, forming a synchronized dual-channel time-series input sequence.

[0051] The prediction model employs a hybrid architecture of Long Short-Term Memory (LSTM) network and Gradient Boosting Decision Tree (GBDT). The LSTM portion is responsible for handling long-term dependencies.

[0052] The Long Short-Term Memory (LSTM) network structure contains one LSTM layer with 128 hidden units, an input dimension of 8 (4 dimensions for each of the two channels), and an output of a 128-dimensional context vector. The GBDT part focuses on capturing non-linear mutation features. The number of base learners (decision trees) is set to 200, the maximum depth is 6, and the learning rate is set to 0.1.

[0053] The output vector of the LSTM is concatenated with the original dual-channel temporal features and used as the input of GBDT. The final output of GBDT is a scalar, which is the predicted value of the talent supply and demand gap in the next 6 months (defined as the number of people in demand minus the number of people in supply).

[0054] To integrate the advantages of the two models, a weighted fusion approach is adopted. The weights are not fixed but dynamically determined based on performance on the validation set. Specifically, the last six months of historical data are used as the validation set, and the mean squared errors of LSTM and GBDT predictions alone are calculated on this set. The weights of the LSTM model in the weighted fusion are as follows: Weights in the GBDT model in weighted fusion Final predicted value . The mean squared error of the LSTM model on the validation set alone. The mean squared error of the GBDT model on the validation set alone. These are individual predictions from the LSTM model. These are individual predictions from the GBDT model.

[0055] This inverse weighting mechanism ensures that LSTM's stable predictions dominate during periods of stable data; when data undergoes abrupt changes (such as after a strategy announcement), GBDT, which is more sensitive to such changes, receives higher weights, thus balancing the stability and sensitivity of predictions.

[0056] Step (3): Map ecological sites and perform stress tests. The system projects the actual talent supply and demand status of the current month (an 8-dimensional vector calculated from the latest collected data) and the predicted status vector for the 6th month in the future, output in step (2), onto the multi-dimensional talent supply and demand equilibrium domain constructed in step (1), forming two ecological sites. and The coordinates of each ecological site represent its standardized score across eight dimensions.

[0057] The initial imbalance was quantified by calculating the Euclidean distance D between the ecological site and the dynamic ecological boundary benchmark. If D is less than the threshold within the boundary, the system is preliminarily deemed safe. To further assess the system's resilience, a Monte Carlo stress test was performed. The system identified three key disturbance factors: the technology substitution rate (defined as the proportion of emerging technology patent applications to the total number of applications in the field, whose changes directly affect the demand for existing skills), the business failure rate (calculated from business deregistration data, representing the sudden disappearance of talent demand), and the lag period for university major adjustments (in months, representing the delay in supply response).

[0058] For these three factors, random sampling was performed within their historical fluctuation range. For example, the technology substitution rate was sampled uniformly within the range of [0.05, 0.3], the business failure rate was sampled within the range of [0.01, 0.05], and the lag period was sampled between [6, 24] months.

[0059] In each Monte Carlo simulation, the system uses the sampled perturbation factor values ​​to rerun the prediction model in step (2) to obtain new predicted ecological sites. Then determine Has the dynamic ecological boundary benchmark been crossed? This is a binary judgment: yes or no.

[0060] The process was repeated 10,000 times, and the number of simulations that crossed the boundary was counted. The imbalance probability was calculated as: number of boundary crossings / 10,000. Simultaneously, by analyzing the combinations of disturbance factors leading to boundary crossings, interpretability methods such as the Shapley value were used to identify the dominant imbalance factor. For example, if the technology substitution rate is greater than 0.25 in 80% of the boundary crossing cases, then "rapid technological iteration leading to sudden changes in skill demand" can be determined as the dominant imbalance factor.

[0061] Based on the probability of imbalance The system performs a three-level risk assessment:

[0062] Level 1 is a green and safe zone, where the ecological sites are located within the dynamic boundary and the probability of imbalance is less than the first preset threshold of 0.1;

[0063] Level 2 is a yellow warning zone, where the probability of imbalance is between the first preset threshold of 0.1 and the second preset threshold of 0.3.

[0064] Level 3 is a red alert zone, where the probability of imbalance is greater than the second preset threshold of 0.3, and the type of imbalance is located by combining the identification results of the dominant imbalance factor.

[0065] In this embodiment, the prediction results for the artificial intelligence industry show... = 0.35, and the dominant imbalance factor is "excessive technology substitution rate". The system judges it as a level three red alert, the reason being structural mismatch caused by a sudden increase in demand.

[0066] Finally, step (4) is executed: the tiered and categorized closed-loop intervention mechanism is activated. For level three risks, the system automatically triggers an emergency response strategy. Specific measures include:

[0067] A cross-departmental emergency coordination mechanism was activated, with the management committee taking the lead and convening a joint meeting with the human resources and social security, education, and science and technology departments;

[0068] Open green channels to simplify approval processes for high-level talents in areas such as household registration, children's school enrollment, and medical insurance;

[0069] The direction of industry support has been temporarily adjusted, and additional tax breaks will be given to companies that conduct research and development in the cross-disciplinary field of "AI + biology" in order to alleviate the risk of a disruption in the innovation chain due to talent shortages.

[0070] After the intervention measures are implemented, the system enters the closed-loop evaluation phase. The evaluation indicators include: talent arrival rate (actual number of employees in the target position / planned number of employees to be recruited), job matching degree (the company's HR scores the matching degree between the skills of newly hired employees and the job requirements, on a scale of 1 to 5), and the speed of recovery of innovation output (measured by the month-on-month growth rate of patent applications in related fields within 3 months after the intervention).

[0071] The system collects feedback data once a month. If the assessment shows that the risk level has not dropped from level three to level two or below for two consecutive months (for example, the talent arrival rate is consistently less than 60%), the strategy upgrade process will be automatically triggered, and the system will re-enter step (3) for re-determination. More radical intervention measures may need to be considered, such as directly funding universities to establish micro-majors.

[0072] The entire system is deployed on the regional digital economy governance platform and connects in real time with the business systems of eight departments, including education, human resources and social security, science and technology, and industry and information technology, through an API gateway. The core data is updated at least once a day, and the model training cycle is set to once a quarter to ensure the timeliness of early warnings and decision support capabilities.

[0073] Example 2: In another industrial city focused on the transformation and upgrading of traditional manufacturing, the talent shortage problem it faces is fundamentally different from that in Example 1. The region's leading industry is high-end equipment manufacturing, whose technological evolution is relatively stable. However, due to the restructuring of the global supply chain, the demand for engineers with digital skills such as "digital twins" and "industrial internet" has increased sharply.

[0074] However, the local vocational education system is severely out of sync with enterprise needs, with many graduates' skills remaining at the level of traditional mechanical operation, leading to a coexistence of "labor shortage" and "employment difficulty." In this scenario, the root cause of the imbalance between talent supply and demand is not technological mutation, but rather the long-term structural mismatch in education supply and the geographical barriers to talent mobility. Therefore, this embodiment will focus on the differentiated selection of the equilibrium domain construction dimension in step (1) and the targeted design of the intervention strategy in step (4) to reflect the universality and adaptability of the present invention.

[0075] In step (1), although an 8-dimensional equilibrium domain is constructed in the same way, the composition and weight of the dimensions are adjusted. Given that the regional technology maturity changes slowly, the "industry technology maturity" dimension is replaced with "industry chain synergy" (calculated from the number of technology cooperation patents and supply chain order data between upstream and downstream enterprises in the region) to reflect the linkage effect within the industrial cluster.

[0076] Meanwhile, the calculation logic of the "education supply matching coefficient" has been strengthened. It no longer relies solely on university data, but incorporates the curriculum outlines, training equipment lists, and industry-university cooperation project lists of 10 key local vocational schools into the analysis. Knowledge graph technology is used to perform fine-grained matching with enterprise job skill requirements, generating a more accurate matching coefficient. In addition, a new "regional talent attraction index" has been added as an external disturbance factor. This index integrates soft indicators such as the house price-to-income ratio, public service quality, and cultural inclusiveness.

[0077] When constructing the dynamic ecological boundary benchmark, the selection of benchmark samples was also adjusted accordingly. Fifteen manufacturing enterprises that have successfully completed digital transformation were selected as samples, rather than high-tech startups. The focus of the disturbance test also shifted from "technological breakthrough rate" to "external supply chain shocks" (such as the probability of restrictions on the import of key components) and "the intensity of vocational education reform" (quantified by the annual growth rate of vocational education investment budgets from relevant departments). This makes the generated dynamic boundary more reflective of the resilience threshold of the manufacturing industry under external pressure.

[0078] In the intervention mechanism of step (4), based on the judgment result of the regional level-two yellow alert (P_failure=0.22, the dominant imbalance factor is "low education supply matching coefficient"), the system activates a precise replenishment strategy. Specific measures are highly customized:

[0079] We submitted a list of urgently needed digital skills to the Education Bureau and suggested embedding modular courses such as "Industrial Data Acquisition and Analysis" and "PLC and SCADA Systems" into the "Mechatronics" major in vocational schools.

[0080] We recommend that leading enterprises jointly build industry colleges with universities, and encourage enterprises and universities to jointly build training bases, with enterprises providing real production line data for teaching.

[0081] The plan to introduce skilled workers across regions is submitted to the Human Resources and Social Security Bureau. This plan targets areas with surplus labor in the surrounding regions, recruiting experienced skilled workers and providing them with settlement subsidies and skills training. The core of this intervention strategy lies in "supply-side reform," rather than the "demand-side emergency response" described in Example 1.

[0082] The closed-loop evaluation mechanism has also been adjusted accordingly, with evaluation indicators focusing on the responsiveness of the education system, such as "new course opening cycle," "number of signed school-enterprise cooperation projects," and "pass rate of digital skills certification for in-service technicians." Through this scenario-driven differentiated implementation, this invention demonstrates that it is not only applicable to high-tech change scenarios but can also effectively solve the structural talent contradictions in traditional industries.

[0083] Example 3: To fully demonstrate the technical depth of this invention, this example delves into the internal working mechanism of the fusion prediction model in step (2), particularly the interaction details of LSTM and GBDT and the anomaly handling mechanism. In any of the aforementioned scenarios, when the system detects an abnormal pattern in the input time series data, the model must possess robustness.

[0084] Suppose that during data collection, a system failure in a certain month causes the complete loss of R&D input intensity data, and the data from the months before and after that month also contains significant noise. The system first performs preliminary repair through sliding window alignment and cubic spline interpolation. However, if the interpolated data points deviate significantly from the historical trend (for example, if they are identified as outliers by Grubbs test), an anomaly handling subprocess is triggered.

[0085] In this subprocess, the forget gate mechanism of the LSTM module is specially enhanced. (Standard LSTM forget gate...) The formula is:

[0086] ;

[0087] It is the Sigmoid activation function. Here is the weight matrix for the forget gate. For the bias term of the forget gate, Enter the current time step. This is the previous hidden state. In this invention, an adaptive forgetting coefficient based on the smoothness of historical data is introduced. . The variance is dynamically calculated based on the first difference of the data within the current window. The larger the variance, the better. A smaller value means the model is more likely to "forget" unreliable inputs. (Modified forget gate) for:

[0088] ;

[0089] Meanwhile, the GBDT module, due to its decision tree-based nature, naturally possesses a certain degree of robustness to outliers. However, in this invention, the loss function is further optimized. Standard GBDT uses squared loss, which is sensitive to large errors. This embodiment employs the Huber loss function, which uses squared loss when the error is small and switches to linear loss when the error is large, effectively reducing the negative impact of outliers on model training.

[0090] During the model fusion phase, if the LSTM prediction error is detected to be significantly increased due to data anomalies on the validation set ( Greater than the threshold If this happens, the system will temporarily reduce... The weights of the LSTM can be adjusted, and in extreme cases (such as multiple consecutive months of abnormal data), the output of the LSTM can be temporarily disabled, relying solely on GBDT for prediction. Once the data quality returns to normal, the LSTM weights are gradually restored. This dynamic, data-quality-based model switching mechanism is a key technical detail in ensuring the robustness of predictions in this invention, and a core advantage that distinguishes it from existing fixed-architecture models.

[0091] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations 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 scope of the present invention.

[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0093] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for early warning of regional industrial innovation talent gaps based on big data, characterized in that, Includes the following steps: A multidimensional talent supply and demand equilibrium domain is constructed and an ecological boundary benchmark is dynamically generated. This involves integrating regional industrial innovation chain data, talent supply structure data, and external environmental disturbance factors, selecting representative benchmark samples, and using a Gaussian mixture model to model the probability density of the multidimensional feature space. Furthermore, multidimensional disturbance tests are applied to the benchmark samples to simulate the distribution of talent supply and demand under different industrial evolution paths, thereby generating a dynamic ecological boundary benchmark that evolves over time. Extract dual time series features and establish a fusion prediction model. Collect long-term historical data from the innovation chain side and the talent supply side to construct a dual-channel time series input sequence. Use a hybrid architecture of long short-term memory network and gradient boosting decision tree to jointly model the two types of time series. Long short-term memory network is used to capture long-term dependencies, and gradient boosting decision tree is used to fit nonlinear mutation features. Output the predicted value of talent supply and demand gap in the future specified period. Map ecological sites and perform stress tests. Project the current and predicted talent supply and demand status vectors onto the multidimensional talent supply and demand equilibrium domain to form ecological sites. Based on these ecological sites, use the Monte Carlo simulation method to randomly sample and disturb key disturbance factors. Repeat the simulation multiple times to statistically analyze the frequency and distribution of imbalance critical points, quantify the current supply and demand matching degree, and determine the risk level and trace the dominant imbalance factor. A tiered and categorized closed-loop intervention mechanism was launched, which categorizes intervention strategies into three types based on risk level: monitoring and optimization strategy, precise replenishment strategy, and emergency response strategy. Differentiated measures were customized for different industry types and the causes of gaps. Feedback data was continuously collected after the intervention was implemented to update the status of ecological sites and evaluate the intervention effect, thus forming a closed-loop evaluation mechanism.

2. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The construction of the multidimensional talent supply and demand equilibrium domain has no fewer than eight dimensions, including industrial technology maturity, core job skill demand intensity, high-end talent density, interdisciplinary integration index, regional innovation activity, education supply matching coefficient, net talent flow inflow rate, and strategic incentive intensity. The data of each dimension are input into the Gaussian mixture model after standardization processing, and the number of model components is automatically determined according to the Bayesian information criterion.

3. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The perturbation test of the benchmark sample includes applying random perturbations within a specific range to the rate of technological breakthroughs, the level of strategic support, and external economic shocks. The perturbation amplitude is dynamically adjusted according to the historical fluctuation standard deviation. After each perturbation, the equilibrium domain boundary is recalculated, and finally the boundary envelope within the pre-set confidence interval is taken as the dynamic ecological boundary benchmark.

4. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The dual-time-series features are aligned using a sliding window before being input into the model. The window length is a predetermined time period to eliminate phase shifts caused by inconsistent data acquisition frequencies. Missing values ​​are filled using cubic spline interpolation.

5. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The number of hidden layer units in the Long Short-Term Memory network is a predetermined value, the time step covers several months in the past, and the number of base learners, maximum depth, and learning rate of the gradient boosting decision tree are all predetermined values. The outputs of the two models are integrated through a weighted fusion method, and the weights are determined by the inverse ratio of the mean squared error on the validation set.

6. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The coordinates of the ecological site are composed of the standardized scores of each dimension in the multidimensional talent supply and demand equilibrium domain. The Euclidean distance with the dynamic ecological boundary benchmark is used as the initial imbalance measure. The disturbance factors in the Monte Carlo simulation include the technology substitution rate, the enterprise failure rate and the lag period of university major adjustment. Each simulation outputs a binary judgment result to determine whether the ecological boundary has been crossed. The imbalance critical point is defined as the disturbance combination with a cumulative failure rate greater than a preset threshold.

7. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The risk levels are divided into three levels: Level 1 is a green and safe zone, where the ecological sites are located within the dynamic boundary and the probability of imbalance is less than the first preset threshold. Level 2 is a yellow warning zone, where the probability of imbalance is between the first and second preset thresholds. Level 3 is a red alert zone, where the probability of imbalance is greater than the second preset threshold, and the type of imbalance is located by combining the identification results of the dominant imbalance factor.

8. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The monitoring and optimization strategy is applicable to Level 1 risk, including increasing the frequency of data collection, optimizing indicator weights, and fine-tuning model parameters; The precise supply strategy is applicable to Level 2 risks, and pushes customized talent introduction lists, university-enterprise cooperation project suggestions and on-the-job training course packages according to industry sub-sectors; The emergency response strategy applies to Level 3 risks, activates a cross-departmental emergency coordination mechanism, opens a green channel to introduce urgently needed talents, and temporarily adjusts the direction of regional industrial support.

9. The method for early warning of regional industrial innovation talent gap based on big data according to claim 1, characterized in that, The closed-loop evaluation mechanism includes setting evaluation indicators for intervention effects, such as talent availability rate, job matching degree, and speed of innovation output recovery. Feedback data is collected at predetermined intervals. If two consecutive evaluations show that the risk level has not decreased, the strategy upgrade process is automatically triggered and the risk assessment steps are re-executed.

10. A regional industrial innovation talent gap early warning system based on big data, characterized in that: It includes an ecological boundary construction module, a fusion prediction module, a stress testing module, and a closed-loop intervention module; The ecological boundary construction module is used to integrate regional industrial innovation chain data, talent supply structure data and external environmental disturbance factors, select representative benchmark samples, perform probability density modeling on multidimensional feature space through Gaussian mixture model, and generate dynamic ecological boundary benchmarks by combining multidimensional disturbance tests. The fusion prediction module is used to construct a dual-channel time-series input sequence and output the predicted value of talent supply and demand gap using a hybrid architecture of long short-term memory network and gradient boosting decision tree. The stress testing module is used to project the current and predicted talent supply and demand status vectors onto the multidimensional talent supply and demand equilibrium domain to form ecological sites, and to use Monte Carlo simulation to randomly sample and disturb key disturbance factors to quantify the supply and demand matching degree and determine the risk level. The closed-loop intervention module is used to initiate monitoring optimization, precise replenishment or emergency response strategies according to the risk level, and to collect feedback data after intervention to update the status of ecological sites and evaluate the intervention effect.

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