A title declaration personnel qualification intelligent pre-judgment recommendation method based on historical review big data
Patent Information
- Application Number
- CN202611049354.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-15
- Publication Date
- 2026-11-03
AI Technical Summary
Existing intelligent prediction technology for professional title evaluation fails to effectively perceive the competitive pressure within the same evaluation batch, cannot scientifically and accurately predict the success probability of applicants, and lacks differentiated strategy suggestions, resulting in inaccurate prediction results and a lack of practical value.
Based on historical review big data, a competitive field snapshot sequence is constructed. Using a ensemble attention mechanism and a ListWise ranking learning model, combined with Monte Carlo sampling and Bayesian regression models, the relative ranking and pass probability of applicants are predicted, and competitive situation-sensitive strategy suggestions are generated.
It enables accurate ranking prediction and strategy suggestions under uncertain conditions, improves the robustness and practical value of prediction, and provides specific guidance on application timing, marginal competitiveness enhancement, and track switching.
Smart Images
Figure CN123032207A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent auxiliary technology for professional title evaluation, and more specifically, to a method for intelligent prediction and recommendation of the qualifications of professional title applicants based on historical evaluation big data. Background Technology
[0002] Professional title evaluation is a crucial step in the career development of professionals, directly impacting their interests. With the deepening reform of the professional title system, the evaluation process is becoming increasingly standardized and regulated, leading to a continuous increase in the number of applicants while the number of evaluation slots remains relatively limited, resulting in increasingly fierce competition. Against this backdrop, how to scientifically and accurately predict the probability of applicants passing the evaluation and provide them with effective application strategy suggestions has become an urgent technical problem to be solved.
[0003] However, existing intelligent prediction technologies for professional title evaluation have at least the following shortcomings:
[0004] Current technology equates predicting professional title evaluation with "a comparison of individual ability with evaluation standard thresholds," failing to recognize that professional title evaluation is a quota-based selection mechanism. Passing the evaluation depends not only on absolute compliance but also on relative ranking within the same batch of applicants. This paradigmatic flaw leads to a systematic deviation between predicted results and reality—when an applicant meets the requirements but faces stronger competitors in the same batch, current technology may misjudge the result as "passed," providing incorrect application advice.
[0005] Existing technologies cannot perceive the distribution of qualifications among other applicants within the same review batch, nor can they quantify the impact of "competitive pressure within the same track" on an individual's probability of success. An applicant's probability of success depends not only on their own qualifications but also on the overall level of the applicant pool at the same time, and existing technological solutions have not established an effective mechanism for perceiving the competitive situation within the group.
[0006] While the application window for the target review batch is still open, it is unknown which potential applicants will ultimately participate. Existing technologies lack the means to model the "probability of potential applicants' participation," making it impossible to reasonably predict the composition of the competitive field under conditions of uncertainty, resulting in a lack of robustness in the prediction results.
[0007] Most existing technologies only output single conclusions such as "whether it meets the standard" or "probability of passing", lacking differentiated strategic suggestions with practical decision-making value for applicants, such as "when is the best time to apply", "which weakness should be strengthened first", "whether to switch application channels", etc., which are difficult to meet the actual decision-making needs of applicants.
[0008] Therefore, we designed an intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data. Summary of the Invention
[0009] The purpose of this invention is to provide an intelligent prediction and recommendation method for the qualifications of applicants for professional titles based on historical review big data, so as to solve the problems mentioned in the background art.
[0010] To achieve the above objectives, the present invention aims to provide an intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data, including: S1, Construction of historical competitive field snapshot sequence: taking historical review batches as units, storing the multidimensional feature vectors of all applicants in the same batch and their final review results as a competitive field snapshot, forming a competitive field snapshot sequence arranged by time; wherein, the review results include pass or fail, and all applicants in the same batch are labeled with batch ranking tags based on the review scores.
[0011] S2. Preconstruction of the future competitive field: Before the application window for the target review batch opens, collect the characteristic data of personnel who have submitted their application intentions. At the same time, calculate the probability of potential applicants who have not expressed their intentions to participate in this batch of applications through the absence probability model. Combine the personnel with certain intentions and the personnel with probabilistic potentials to construct a future competitive field distribution with uncertainty.
[0012] S3. Intra-batch relative ranking inference based on ensemble attention: The future competitive field distribution is input into a ranking inference model based on ListWise ranking learning. The ranking inference model adopts a ensemble attention mechanism, so that the ranking score of the target applicant depends on the feature distribution of all other applicants in the same competitive field. In view of the uncertainty of potential personnel in the future competitive field distribution, multiple competitive fields are generated through Monte Carlo sampling to form scenarios, and the ranking of the target applicant in each scenario is inferred respectively. The ranking confidence interval is obtained by combining the results.
[0013] S4. Joint inference of quota line prediction and pass probability: Based on the actual number of successful applicants, the total number of applicants and the current policy variables in each batch of the historical competitive field snapshot sequence, predict the quota line probability distribution of the target review batch; perform joint probability integration of the ranking confidence interval obtained in step S3 and the quota line probability distribution to calculate the pass probability of the target applicant.
[0014] S5. Competition-sensitive strategy recommendation: Based on the output of the ranking inference model, generate application strategy recommendations for the target applicant and output them to the applicant's terminal; the application strategy recommendations include the current batch pass probability, the ranking confidence interval, and at least one competition-sensitive decision suggestion.
[0015] As a further improvement to this technical solution, in step S1, the construction of the multidimensional feature vector includes: collecting data on each applicant's papers, research projects, patents, honors and awards, continuing education, and annual assessments.
[0016] A time decay weighting is applied to each set of data, and the weighting formula is as follows:
[0017]
[0018] in, For the first The time of output of the results For the current time, This is the attenuation coefficient.
[0019] The weighted data of each dimension is encoded into a feature vector of fixed dimensions.
[0020] As a further improvement to this technical solution, in step S2, the method for constructing the absence probability model includes: using whether each applicant actually participated in the application in each historical batch as an event marker, and using the time interval from the last application, the length of time of professional title retention, the recent growth rate of achievements, and the application pattern of the applicant's unit as covariates, and using a survival analysis model to fit the conditional probability of the applicant participating in the application in the target batch.
[0021] Potential applicants whose conditional probability is greater than a preset threshold will be included in the future competitive field distribution, and their participation weight will be the corresponding conditional probability value.
[0022] As a further improvement to this technical solution, in step S3, the calculation method of the ensemble attention mechanism includes: mapping the feature vector of each applicant in the competition field to a query vector, a key vector, and a value vector, respectively.
[0023] Calculate attention weights for the query vector of the target applicant and the key vectors of all other applicants in the same session.
[0024] The value vectors of all other applicants in the same field are weighted and aggregated based on the attention weights to obtain the competition field context vector.
[0025] The competitive field context vector is concatenated with the target applicant's own feature vector and then input into the ranking score function to output the target applicant's ranking score.
[0026] As a further improvement to this technical solution, in step S4, the prediction method for the probability distribution of the quota line includes: extracting the pass rate, number of applicants, subject category distribution and corresponding policy variables for each batch from the historical competition snapshot sequence.
[0027] The policy variables include the total number of professional title quotas for the current year, the discipline allocation coefficient, and the policy adjustment direction code.
[0028] Using the historical batch pass rate sequence and policy variables as input, a Bayesian regression model is used to predict the posterior distribution of the target batch pass rate.
[0029] Multiply the posterior distribution of the pass rate by the estimated number of applicants for the target batch to obtain the probability distribution of the quota line.
[0030] As a further improvement to this technical solution, in step S4, the joint probability integral is calculated as follows:
[0031]
[0032] in, The maximum number of applicants is estimated for the target batch. The target applicant's ranking should not exceed the number of The cumulative probability of a name. The quota line is exactly the first The probability of a name.
[0033] As a further improvement to this technical solution, in step S5, the competitive situation-sensitive decision-making suggestion includes at least one of the following: application timing game suggestion: For multiple future optional batches, execute steps S2 to S4 respectively, compare the success probability of the target applicant in each batch, and recommend the batch with the highest success probability as the optimal application timing.
[0034] Marginal competitiveness enhancement suggestions: Apply counterfactual perturbations to each characteristic dimension of the target applicant, calculate the ranking change caused by each enhancement action, sort them from largest to smallest by ranking marginal displacement, and output the top K enhancement suggestions with the highest input-output ratio.
[0035] Track switching suggestion: When the target applicant meets the application requirements of multiple discipline series or review channels at the same time, construct a competition field for each channel and perform ranking inference, and recommend the application channel with the least competitive pressure and the highest probability of passing.
[0036] As a further improvement to this technical solution, the specific methods of counterfactual perturbation in the marginal competitiveness enhancement suggestion include: keeping the characteristics of other applicants in the competitive arena unchanged, and for the target applicant's first... Apply a preset increment to the dimensional feature .
[0037] The perturbed competitive field is then re-inputted into the ranking inference model to obtain the perturbed ranking values. .
[0038] Calculate the marginal displacement of the ranking as follows .
[0039] Calculate the increment Corresponding estimated input costs .
[0040] according to Sort the data in descending order and output the top K improvement suggestions.
[0041] As a further improvement to this technical solution, the proposed application timing game is generated using a sequential decision model, specifically including: modeling the multiple application opportunities faced by the target applicant as an optimal stopping problem in a finite time domain.
[0042] The state of each period is defined as the current competitive landscape composition and the current profile vector of the target applicant. The benefit is the value obtained through the process, and the cost is the time loss and uncertainty risk of waiting for one period.
[0043] The application / waiting decision thresholds for each period are solved using backward induction.
[0044] The current status of the target applicant is compared with the decision threshold, and the conclusion of "It is recommended to apply in this period" or "It is recommended to wait until the tth period to apply" and the corresponding expected probability of approval are output.
[0045] As a further improvement to this technical solution, an incremental model update step is also included: after each review batch is completed, the actual review results of that batch are obtained.
[0046] This batch will be added as a new competitive field snapshot to the historical competitive field snapshot sequence.
[0047] The ranking inference model is incrementally fine-tuned using newly added competitive field snapshots.
[0048] The prior parameters of the quota line prediction model are updated synchronously.
[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0050] This invention is the first to transform the prediction of professional title evaluation from "absolute compliance assessment" to "relative ranking inference within the competitive arena." It constructs a snapshot sequence of the competitive arena based on historical evaluation batches, and models all applicants in the same batch as a whole. Through a ranking inference model based on ListWise ranking learning, it directly predicts the ranking distribution of target applicants within the same batch, fundamentally solving the paradigmatic defect of existing technologies that ignore the competitive nature of quota-based selection.
[0051] This invention employs a collective attention mechanism, which makes the ranking score of the target applicant depend on the feature distribution of all other applicants in the competitive arena. By calculating the attention weights of the query vector and the key vector, the influence of group features on individual ranking is accurately captured.
[0052] This invention uses a survival analysis model to fit the participation probability of potential applicants, combines Monte Carlo sampling to generate multiple competitive field scenarios, infers the rankings of each scenario, and then synthesizes the ranking confidence intervals. This effectively handles the uncertainty of the future competitive field composition, making the prediction results statistically robust and reliable.
[0053] This invention predicts the posterior distribution of the target batch pass rate using a Bayesian regression model and performs joint probability integration with the ranking confidence interval, thus achieving an organic fusion of the two dimensions of "individual relative ranking" and "macro quota restriction". The probability calculation is more scientific and accurate.
[0054] This invention not only outputs the probability of passing and the confidence interval of the ranking, but also further generates suggestions for application timing (solving the optimal application timing through a sequential decision-making model), suggestions for improving marginal competitiveness (calculating the input-output ratio of improvements in each dimension through counterfactual perturbations), and suggestions for switching tracks (comparing competitive pressures across multiple channels). These strategic suggestions directly address the core decision-making pain points of applicants and have high practical value and operability. Attached Figure Description
[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0056] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation
[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0058] Example: Please refer to Figure 1 As shown, a method for intelligent prediction and recommendation of professional title applicant qualifications based on historical review big data is provided, including: S1, Construction of historical competition field snapshot sequence: taking historical review batches as units, the multidimensional feature vectors of all applicants in the same batch and their final review results are stored as a competition field snapshot, forming a competition field snapshot sequence arranged by time; wherein, the review results include pass or fail, and all applicants in the same batch are marked with batch ranking labels based on the review scores.
[0059] In one specific embodiment, step S1, the construction of the multidimensional feature vector includes: collecting data on each applicant's papers, research projects, patents, honors and awards, continuing education, and annual assessments.
[0060] A time decay weighting is applied to each set of data, and the weighting formula is as follows:
[0061]
[0062] in, For the first The time of output of the results For the current time, This is the attenuation coefficient.
[0063] The weighted data of each dimension is encoded into a feature vector of fixed dimensions.
[0064] S2. Preconstruction of the future competitive field: Before the application window for the target review batch opens, collect the characteristic data of personnel who have submitted their application intentions. At the same time, calculate the probability of potential applicants who have not expressed their intentions to participate in this batch of applications through the absence probability model. Combine the personnel with certain intentions and the personnel with probabilistic potentials to construct a future competitive field distribution with uncertainty.
[0065] In a specific embodiment, in step S2, the method for constructing the absence probability model includes: using whether each applicant actually participated in the application in each historical batch as an event marker, and using the time interval from the last application, the length of time of professional title retention, the recent growth rate of achievements, and the application pattern of the applicant's unit as covariates, and using a survival analysis model to fit the conditional probability of the applicant participating in the application in the target batch.
[0066] Potential applicants whose conditional probability is greater than a preset threshold will be included in the future competitive field distribution, and their participation weight will be the corresponding conditional probability value.
[0067] S3. Intra-batch relative ranking inference based on ensemble attention: The future competitive field distribution is input into a ranking inference model based on ListWise ranking learning. The ranking inference model adopts a ensemble attention mechanism, so that the ranking score of the target applicant depends on the feature distribution of all other applicants in the same competitive field. In view of the uncertainty of potential personnel in the future competitive field distribution, multiple competitive fields are generated through Monte Carlo sampling to form scenarios, and the ranking of the target applicant in each scenario is inferred respectively. The ranking confidence interval is obtained by combining the results.
[0068] In one specific embodiment, in step S3, the calculation method of the ensemble attention mechanism includes: mapping the feature vector of each applicant in the competition field to a query vector, a key vector, and a value vector, respectively.
[0069] Calculate attention weights for the query vector of the target applicant and the key vectors of all other applicants in the same session.
[0070] The value vectors of all other applicants in the same field are weighted and aggregated based on the attention weights to obtain the competition field context vector.
[0071] The competitive field context vector is concatenated with the target applicant's own feature vector and then input into the ranking score function to output the target applicant's ranking score.
[0072] In step S3, the specific process of Monte Carlo sampling includes:
[0073] Based on the participation probability of each potential applicant in step S2, Bernoulli sampling is performed independently to determine whether each potential applicant appears in the competitive field in this sampling.
[0074] Repeat the sampling N times to generate N different competitive fields to form a scenario;
[0075] Run the ranking inference model for each scenario to obtain the ranking value of the target applicant in that scenario;
[0076] Statistically analyze N rank values and calculate the rank mean, standard deviation, and confidence interval at a pre-set confidence level.
[0077] S4. Joint inference of quota line prediction and pass probability: Based on the actual number of successful applicants, the total number of applicants and the current policy variables in each batch of the historical competitive field snapshot sequence, predict the quota line probability distribution of the target review batch; perform joint probability integration of the ranking confidence interval obtained in step S3 and the quota line probability distribution to calculate the pass probability of the target applicant.
[0078] In one specific embodiment, step S4, the method for predicting the probability distribution of the quota line includes: extracting the pass rate, number of applicants, subject category distribution and corresponding policy variables for each batch from the historical competition snapshot sequence.
[0079] The policy variables include the total number of professional title quotas for the current year, the discipline allocation coefficient, and the policy adjustment direction code.
[0080] Using the historical batch pass rate sequence and policy variables as input, a Bayesian regression model is used to predict the posterior distribution of the target batch pass rate.
[0081] Multiply the posterior distribution of the pass rate by the estimated number of applicants for the target batch to obtain the probability distribution of the quota line.
[0082] In one specific embodiment, the joint probability integral is calculated in step S4 as follows:
[0083]
[0084] in, The maximum number of applicants is estimated for the target batch. The target applicant's ranking should not exceed the number of The cumulative probability of a name. The quota line is exactly the first The probability of a name.
[0085] S5. Competition-sensitive strategy recommendation: Based on the output of the ranking inference model, generate application strategy recommendations for the target applicant and output them to the applicant's terminal; the application strategy recommendations include the current batch pass probability, the ranking confidence interval, and at least one competition-sensitive decision suggestion.
[0086] In one specific embodiment, in step S5, the competitive situation-sensitive decision suggestion includes at least one of the following: application timing game suggestion: For multiple future optional batches, steps S2 to S4 are executed respectively, the pass probability of the target applicant in each batch is compared, and the batch with the highest pass probability is recommended as the optimal application timing.
[0087] Marginal competitiveness enhancement suggestions: Apply counterfactual perturbations to each characteristic dimension of the target applicant, calculate the ranking change caused by each enhancement action, sort them from largest to smallest by ranking marginal displacement, and output the top K enhancement suggestions with the highest input-output ratio.
[0088] Track switching suggestion: When the target applicant meets the application requirements of multiple discipline series or review channels at the same time, construct a competition field for each channel and perform ranking inference, and recommend the application channel with the least competitive pressure and the highest probability of passing.
[0089] In one specific embodiment, the counterfactual perturbation method in the marginal competitiveness enhancement proposal includes: keeping the characteristics of other applicants in the competitive arena unchanged, and applying the first... Apply a preset increment to the dimensional feature .
[0090] The perturbed competitive field is then re-inputted into the ranking inference model to obtain the perturbed ranking values. .
[0091] Calculate the marginal displacement of the ranking as follows .
[0092] Calculate the increment Corresponding estimated input costs .
[0093] according to Sort the data in descending order and output the top K improvement suggestions.
[0094] In one specific embodiment, the application timing game suggestion is generated using a sequential decision model, which specifically includes: modeling the multiple application opportunities faced by the target applicant as an optimal stopping problem in a finite time domain.
[0095] The state of each period is defined as the current competitive landscape composition and the current profile vector of the target applicant. The benefit is the value obtained through the process, and the cost is the time loss and uncertainty risk of waiting for one period.
[0096] The application / waiting decision thresholds for each period are solved using backward induction.
[0097] The current status of the target applicant is compared with the decision threshold, and the conclusion of "It is recommended to apply in this period" or "It is recommended to wait until the tth period to apply" and the corresponding expected probability of approval are output.
[0098] In one specific embodiment, the model incremental update step is also included: after each review batch is completed, the actual review results of that batch are obtained.
[0099] This batch will be added as a new competitive field snapshot to the historical competitive field snapshot sequence.
[0100] The ranking inference model is incrementally fine-tuned using newly added competitive field snapshots.
[0101] The prior parameters of the quota line prediction model are updated synchronously.
[0102] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.
Claims
1. A method for intelligent prediction and recommendation of qualifications for professional title applicants based on historical review big data, characterized in that, include: S1. Construction of Historical Competition Field Snapshot Sequence: Taking historical review batches as units, the multidimensional feature vectors of all applicants in the same batch and their final review results are stored as a competition field snapshot, forming a competition field snapshot sequence arranged by time; wherein, the review results include pass or fail, and all applicants in the same batch are marked with batch ranking labels based on the review scores; S2. Preconstruction of the future competitive field: Before the application window for the target review batch opens, collect the characteristic data of personnel who have submitted their application intentions. At the same time, calculate the probability of potential applicants who have not expressed their intentions to participate in this batch of applications through the absence probability model. Combine the personnel with certain intentions and the personnel with probabilistic potentials to construct a future competitive field distribution with uncertainty. S3. Batch-based relative ranking inference based on ensemble attention: The future competitive field distribution is input into a ranking inference model based on ListWise ranking learning. The ranking inference model adopts a ensemble attention mechanism, so that the ranking score of the target applicant depends on the feature distribution of all other applicants in the same competitive field. In view of the uncertainty of potential personnel in the future competitive field distribution, multiple competitive fields are generated through Monte Carlo sampling to form scenarios, and the ranking of the target applicant in each scenario is inferred respectively. The ranking confidence interval is obtained by combining the results. S4. Joint inference of quota line prediction and pass probability: Based on the actual number of successful applicants, the total number of applicants and the current policy variables in each batch of the historical competitive field snapshot sequence, predict the quota line probability distribution of the target review batch; perform joint probability integration of the ranking confidence interval obtained in step S3 and the quota line probability distribution to calculate the pass probability of the target applicant; S5. Competition-sensitive strategy recommendation: Based on the output of the ranking inference model, generate application strategy recommendations for the target applicant and output them to the applicant's terminal; the application strategy recommendations include the current batch pass probability, the ranking confidence interval, and at least one competition-sensitive decision suggestion.
2. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: In step S1, the construction of the multidimensional feature vector includes: Data on each applicant's papers, research projects, patents, honors and awards, continuing education, and annual assessments will be collected. A time decay weighting is applied to each set of data, and the weighting formula is as follows: in, For the first The time it takes for the results to be produced For the current time, The attenuation coefficient; The weighted data of each dimension is encoded into a feature vector of fixed dimensions.
3. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: In step S2, the method for constructing the absence probability model includes: Using whether each applicant actually participated in the application in each historical batch as an event marker, and the time interval from the last application, the length of time of professional title retention, the recent growth rate of achievements, and the application pattern of the applicant's unit as covariates, a survival analysis model was used to fit the conditional probability of the applicant participating in the application in the target batch. Potential applicants whose conditional probability is greater than a preset threshold will be included in the future competitive field distribution, and their participation weight will be the corresponding conditional probability value.
4. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: In step S3, the calculation method of the ensemble attention mechanism includes: Map the feature vector of each applicant in the competitive arena to a query vector, a key vector, and a value vector, respectively. Calculate attention weights between the query vector of the target applicant and the key vectors of all other applicants in the same session; Based on the attention weights, the value vectors of all other applicants in the same field are weighted and aggregated to obtain the competitive field context vector; The competitive field context vector is concatenated with the target applicant's own feature vector and then input into the ranking score function to output the target applicant's ranking score.
5. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: In step S4, the prediction method for the probability distribution of the quota line includes: Extract the pass rate, number of applicants, subject category distribution, and corresponding policy variables for each batch from the historical competitive field snapshot sequence; The policy variables include the total number of professional title quotas for the current year, the discipline allocation coefficient, and the policy adjustment direction code. Using the historical batch pass rate sequence and policy variables as input, a Bayesian regression model is used to predict the posterior distribution of the target batch pass rate; Multiply the posterior distribution of the pass rate by the estimated number of applicants for the target batch to obtain the probability distribution of the quota line.
6. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: In step S4, the joint probability integral is calculated as follows: in, The maximum number of applicants is estimated for the target batch. The target applicant's ranking should not exceed the number of The cumulative probability of a name. The quota line is exactly the first The probability of a name.
7. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: In step S5, the competitive situation-sensitive decision-making recommendation includes at least one of the following: Application timing strategy suggestion: Perform steps S2 to S4 for multiple future optional batches, compare the success probability of the target applicant in each batch, and recommend the batch with the highest success probability as the optimal application time; Marginal competitiveness enhancement suggestions: Apply counterfactual perturbations to each characteristic dimension of the target applicant, calculate the ranking change caused by each enhancement action, sort them from largest to smallest by ranking marginal displacement, and output the enhancement suggestions with the highest input-output ratio; Track switching suggestion: When the target applicant meets the application requirements of multiple discipline series or review channels at the same time, construct a competition field for each channel and perform ranking inference, and recommend the application channel with the least competitive pressure and the highest probability of passing.
8. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: The specific methods of counterfactual perturbation in the proposed suggestions for enhancing marginal competitiveness include: While keeping the characteristics of other applicants in the competitive arena unchanged, the characteristics of the target applicant are... Apply a preset increment to the dimensional feature ; The perturbed competitive field is then re-inputted into the ranking inference model to obtain the perturbed ranking values. ; Calculate the marginal displacement of the ranking as follows ; Calculate the increment Corresponding estimated input costs ; according to Sort in descending order and output improvement suggestions.
9. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: The proposed timing strategy for submitting applications is generated using a sequential decision-making model, specifically including: The multiple application opportunities faced by the target applicant are modeled as an optimal stopping problem in a finite time domain; The state of each period is defined as the current competitive landscape composition and the current profile vector of the target applicant, the benefit is the value obtained, and the cost is the time loss of waiting for one period and the uncertainty risk. The application / waiting decision thresholds for each period are solved using backward induction. The current status of the target applicant is compared with the decision threshold, and the conclusion of "It is recommended to apply in this period" or "It is recommended to wait until the tth period to apply" and the corresponding expected probability of approval are output.
10. The intelligent prediction and recommendation method for the qualifications of professional title applicants based on historical review big data as described in claim 1, characterized in that: It also includes the incremental model update step: After each review batch is completed, obtain the actual review results for that batch; This batch will be added as a new competitive field snapshot to the historical competitive field snapshot sequence. The ranking inference model is incrementally fine-tuned using newly added competitive field snapshots; The prior parameters of the quota line prediction model are updated synchronously.