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CN-121981690-A - Intelligent augmentation and recruitment management method and system based on cross verification

CN121981690ACN 121981690 ACN121981690 ACN 121981690ACN-121981690-A

Abstract

The invention relates to the technical field of data management, and particularly discloses an intelligent augmentation and recruitment management method and system based on cross verification, wherein the method comprises the steps of acquiring multidimensional feature data corresponding to entry source basic data in a current recruitment period to construct a source feature portrait; the invention can improve the intelligent management efficiency of the increment, and comprises the steps of carrying out cross check on the pre-matched source set and the current residual balance to generate an increment adjustment index, and updating the configuration of the balance of the current increment period to obtain the final confirmation state of the source of the current increment period.

Inventors

  • HUANG YUANXIONG
  • LI SAIHUA

Assignees

  • 长沙汇德安全技术咨询有限公司

Dates

Publication Date
20260505
Application Date
20260403

Claims (10)

  1. 1. An intelligent augmentation management method based on cross verification, which is characterized by comprising the following steps: step 1, acquiring multi-dimensional characteristic data corresponding to entry source basic data in a current recruitment period, and constructing a source characteristic image of the current recruitment period based on the multi-dimensional characteristic data; Step 2, carrying out matching degree evaluation on the source feature image and a preset sign target image to obtain a matching degree coefficient of the sign source and the sign target in the current sign period; Step 3, screening a pre-matching source set meeting a preset matching threshold value from the registration source basic data based on the matching degree coefficient, and determining the current residual nominal value of the current nominal period based on the real-time nominal state data of the current nominal period; Step 4, cross checking is carried out on the pre-matching source set and the current residual nominal value, and an increment adjustment index aiming at the current increment period is generated; And 5, updating the name allocation of the current recruitment period according to the increment adjustment index to obtain the final confirmation state of the source of the current recruitment period.
  2. 2. The intelligent augmentation management method based on cross verification according to claim 1, wherein in step 1, the steps of obtaining multidimensional feature data corresponding to the registered source base data in the current augmentation period, and constructing the source feature portrait of the current augmentation period based on the multidimensional feature data are as follows: Acquiring entry source basic data in the current recruitment period; performing multidimensional feature analysis on the registering source basic data to obtain basic attribute feature data, academic capability feature data and intention preference feature data of a registering source in the registering source basic data; Performing feature structured combing on the basic attribute feature data, the academic capability feature data and the intention preference feature data to obtain multidimensional feature images of the registration source basic data in basic attribute dimensions, academic capability dimensions and intention preference dimensions; and carrying out association integration on the multi-dimensional feature images to obtain the source feature images of the current recruitment period.
  3. 3. The intelligent augmentation management method based on cross verification according to claim 2, wherein the association and integration of the multidimensional feature images are performed to obtain the source feature image of the current augmentation period, and the process is as follows: Based on a preset feature association mapping rule, carrying out cross analysis on feature data of each dimension in the multi-dimensional feature image to obtain a cross-dimension feature corresponding relation of the multi-dimensional feature image; Performing feature attribution consistency verification on the corresponding relation of the cross-dimension features to obtain feature association patterns of the multi-dimension feature images belonging to the same source individual; Based on the feature association map, carrying out homologous feature aggregation on feature data of each dimension in the multi-dimensional feature image to obtain a fused feature image of the current incurring period; And carrying out structural recombination on the fusion feature images to obtain the source feature images of the current generation period.
  4. 4. The intelligent augmentation management method based on cross verification according to claim 1, wherein in step 2, the matching degree evaluation is performed on the source feature image and a preset augmentation target image to obtain a matching degree coefficient of the registration source and the augmentation target in the current augmentation period, and the process is as follows: performing dimension-by-dimension mapping analysis on the source feature image based on a preset target representation, so as to obtain a corresponding relationship between feature dimensions in the source feature image and dimensions of the target representation; Carrying out matching degree quantitative evaluation on the source feature images according to the dimension corresponding relation to obtain the sub-item matching degree of the registration source in each matching dimension in the current recruitment period; tensor construction is carried out on the item matching degree to obtain a multidimensional matching degree characteristic vector of the registration source in the current recruitment period; and carrying out fusion analysis on the multidimensional matching degree feature vector to obtain a matching degree coefficient of the registration source and the registration target in the current registration period, wherein the matching degree coefficient has the following calculation formula: ; In the formula, The degree of matching coefficient is represented by a coefficient, Representing the total number of matching dimensions in the multi-dimensional matching degree feature vector, Indicating that the reporting source is in the first time period The degree of match of the sub-items in the matching dimension, Representing a preset matching dispersion penalty factor, A standard deviation representing the degree of matching of the terms, And representing the average value of the item matching degree.
  5. 5. The intelligent augmentation management method according to claim 1, wherein in step 3, based on the matching degree coefficient, a pre-matching source set satisfying a pre-matching threshold is selected from the entry source base data, and based on the real-time recruitment status data of the current recruitment period, the current remaining markable amount of the current recruitment period is determined by the following procedures: Based on a preset matching threshold, carrying out threshold comparison screening on the matching degree coefficient to obtain a preset matching source set, wherein the matching degree coefficient in the entry source basic data meets the preset matching threshold; the real-time recruitment state data of the current recruitment period are called, and the current confirmed recorded title and the total title of the current recruitment period are extracted from the real-time recruitment state data; and performing name difference analysis on the total name of the current recruitment period and the currently confirmed recorded name to obtain the current residual recruitable name of the current recruitment period.
  6. 6. The intelligent augmentation management method based on cross-checking of claim 1, wherein in step 4, the cross-checking the pre-matching source set with the current remaining marketable names generates an augmentation adjustment index for the current augmentation period, and the process is as follows: sorting the matching priorities of the pre-matching source sets to obtain a pre-matching source priority sequence of the pre-matching source set; performing name occupation state analysis on the current residual available names to obtain the allocable name capacity of the current residual available names; Performing progressive qualification verification on the pre-matching sources in the pre-matching source priority sequence according to the allocatable nominal capacity to obtain an increment candidate source set matched with the allocatable nominal capacity in the pre-matching source set; and carrying out index representation on the increasing demand of the current recruitment period based on the increasing candidate source set to obtain an increasing adjustment index for the current recruitment period.
  7. 7. The intelligent augmentation management method based on cross-validation of claim 6, wherein said performing admission qualification progression on pre-match sources in said pre-match source priority sequence according to said allocable nominal capacity, to obtain an augmentation candidate source set in said pre-match source set that matches said allocable nominal capacity, comprises the steps of: performing entry-by-entry traversal on the pre-matching source in the pre-matching source priority sequence to obtain the entry-by-entry judgment state of the pre-matching source; Performing admission qualification accumulation statistics on the admission qualification judging state according to the allocable denomination capacity to obtain the accumulated source number with the admission qualification in the pre-matching source priority sequence; performing capacity matching verification on the accumulated source number and the allocatable nominal capacity, and terminating admission qualification verification on a subsequent pre-matching source when the accumulated source number reaches the allocatable nominal capacity; performing source-aggregating aggregation on pre-matching sources with admission qualification to obtain an increment candidate source set matched with the allocatable nominal capacity in the pre-matching source set.
  8. 8. The intelligent augmentation-recruitment management method based on cross-validation according to claim 7, wherein the indexing characterization is performed on the augmentation-recruitment demand of the current recruitment period based on the augmentation-recruitment candidate source set to obtain an augmentation-recruitment adjustment index for the current recruitment period, and the process is as follows: Performing augmentation source feature analysis on the augmentation candidate source set to obtain source configuration feature information of the augmentation candidate source set; Performing demand mapping on the increasing and recruiting scale of the current recruitment period based on the source composition characteristic information to obtain an increasing and recruiting scale demand representation of the current recruitment period; performing increment priority division on the increment candidate sources in the increment candidate source set to obtain increment priority information of the increment candidate source set; And carrying out index integration on the increment execution strategy of the current recruitment period based on the increment scale requirement representation and the increment priority grading information to obtain an increment adjustment index aiming at the current recruitment period.
  9. 9. The intelligent augmentation management method based on cross-checking of claim 8, wherein in step 5, the updating of the name allocation of the current recruitment period according to the augmentation adjustment index, to obtain the final source confirmation status of the current recruitment period, comprises the following steps: Performing name increment analysis on the name configuration of the current recruitment period based on the name increment adjustment index to obtain the name increment configuration requirement of the current recruitment period; performing differential updating on the recruitment denomination configuration according to the demand of the increased recruitment denomination configuration to obtain updated recruitment denomination configuration of the current recruitment period; performing admission name allocation on the augmentation and recruitment candidate source set based on the updated recruitment name configuration to obtain an augmentation and recruitment source confirmation state of the current recruitment period recruit; and carrying out source state fusion on the source verification state of the increment recruit and the verified record source state of the current recruitment period to obtain a source final verification state of the current recruitment period.
  10. 10. An intelligent augmentation management system based on cross-verification, characterized in that it is used to implement the intelligent augmentation management method based on cross-verification according to any one of claims 1 to 9, said system comprising: The portrait construction module is used for acquiring multidimensional feature data corresponding to the registering source basic data in the current recruitment period and constructing a source feature portrait of the current recruitment period based on the multidimensional feature data; The matching evaluation module is used for evaluating the matching degree of the source feature image and a preset sign target image to obtain a matching degree coefficient of the sign source and the sign target in the current sign period; The source screening module is used for screening a pre-matching source set meeting a preset matching threshold value from the registering source basic data based on the matching degree coefficient, and determining the current residual signable amount of the current signup period based on the real-time signup state data of the current signup period; the cross checking module is used for carrying out cross checking on the pre-matching source set and the current residual tenderable name and generating an increment and acceptance adjustment index for the current acceptance period; And the configuration updating module is used for updating the name allocation of the current recruitment period according to the increment adjustment index to obtain the final confirmation state of the source of the current recruitment period.

Description

Intelligent augmentation and recruitment management method and system based on cross verification Technical Field The invention relates to the technical field of data management, in particular to an intelligent augmentation management method and system based on cross verification. Background The existing recruitment management mode is dependent on manual statistics and single-dimension screening, multidimensional feature analysis and accurate image construction of an registering source are difficult to develop, efficient matching of source features and an recruitment target cannot be achieved, the whole matching evaluation process lacks quantification standard and scientific algorithm support, and matching results are not accurate and stable enough. The traditional increment management does not carry out cross check on the source screening result and the real-time increment denomination, the increment adjustment index lacks data basis, the increment denomination configuration can not dynamically adapt to the source quality and the residual denomination condition, the problems of high-quality source omission or denomination waste easily occur, the overall management efficiency and the intelligent level are low, and the fine increment management requirement is difficult to meet. Disclosure of Invention In view of the above problems, the present invention aims to provide an intelligent augmentation management method and system based on cross verification, so as to solve the problems of low management efficiency, poor matching accuracy, easy omission of high-quality sources and waste of famous resources caused by incomplete construction of source images, lack of scientific quantization method for matching and evaluation of sources and the augmentation targets, non-cross verification of pre-matched sources and the rest of the famous, no data support of augmentation and adjustment indexes, and non-dynamic optimization of the allocation of the famous. The invention provides an intelligent augmentation management method based on cross verification, which comprises the following steps: step 1, acquiring multi-dimensional characteristic data corresponding to entry source basic data in a current recruitment period, and constructing a source characteristic image of the current recruitment period based on the multi-dimensional characteristic data; Step 2, carrying out matching degree evaluation on the source feature image and a preset sign target image to obtain a matching degree coefficient of the sign source and the sign target in the current sign period; Step 3, screening a pre-matching source set meeting a preset matching threshold value from the registration source basic data based on the matching degree coefficient, and determining the current residual nominal value of the current nominal period based on the real-time nominal state data of the current nominal period; Step 4, cross checking is carried out on the pre-matching source set and the current residual nominal value, and an increment adjustment index aiming at the current increment period is generated; And 5, updating the name allocation of the current recruitment period according to the increment adjustment index to obtain the final confirmation state of the source of the current recruitment period. Preferably, in step 1, the step of acquiring multidimensional feature data corresponding to the registered source base data in the current recruitment period and constructing a source feature portrait of the current recruitment period based on the multidimensional feature data includes the following steps: Acquiring entry source basic data in the current recruitment period; performing multidimensional feature analysis on the registering source basic data to obtain basic attribute feature data, academic capability feature data and intention preference feature data of a registering source in the registering source basic data; Performing feature structured combing on the basic attribute feature data, the academic capability feature data and the intention preference feature data to obtain multidimensional feature images of the registration source basic data in basic attribute dimensions, academic capability dimensions and intention preference dimensions; and carrying out association integration on the multi-dimensional feature images to obtain the source feature images of the current recruitment period. Preferably, the multi-dimensional feature images are associated and integrated to obtain the source feature image of the current recruitment period, and the process is as follows: Based on a preset feature association mapping rule, carrying out cross analysis on feature data of each dimension in the multi-dimensional feature image to obtain a cross-dimension feature corresponding relation of the multi-dimensional feature image; Performing feature attribution consistency verification on the corresponding relation of the cross-dimension features to obtain feature asso