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CN-122022821-A - Tourism skill knowledge management and optimization system

CN122022821ACN 122022821 ACN122022821 ACN 122022821ACN-122022821-A

Abstract

The invention relates to the technical field of travel skill knowledge management, and discloses a travel skill knowledge management and optimization system, wherein a knowledge acquisition module of the system determines the integrity of a knowledge coverage area based on skill update frequency; the system comprises a rule generation module, a rule matching module, an optimization verification module, a system calibration module and a system calibration module, wherein the rule matching module selects a management rule type according to the integrity and establishes an association relation between knowledge and application based on skill requirement conformity, the rule generation module combines knowledge redundancy and response hysteresis quantity to determine an optimization target value and a judgment mode thereof, the optimization verification module evaluates the conformity of the knowledge and the application through knowledge transformation degree, and the system calibration module automatically generates an adjustment scheme when the target value or transformation degree does not reach standards. The invention realizes dynamic acquisition, intelligent matching, self-adaptive optimization and closed-loop calibration of travel skill knowledge, effectively improves knowledge management efficiency and application effect, and provides accurate and real-time skill knowledge support for the travel industry.

Inventors

  • YANG LU
  • SUN YAQI
  • ZHOU YU

Assignees

  • 山东理工职业学院

Dates

Publication Date
20260512
Application Date
20251118

Claims (10)

  1. 1. A travel skill knowledge management and optimization system, comprising: The knowledge acquisition module is used for determining the integrity of the knowledge coverage based on the skill update frequency; The requirement matching module is connected with the knowledge acquisition module and used for determining the management rule type based on the integrity of the knowledge coverage area and determining the association relation between the knowledge and the application according to the skill requirement fit degree under the corresponding management rule; The rule generation module is connected with the requirement matching module and is used for determining a judging mode of the optimization target value according to the knowledge redundancy under the condition of the corresponding management rule and determining the consistency of the optimization target value according to the response lag in the adjustment process; The optimization verification module is connected with the rule generation module and used for determining the consistency of knowledge and application according to the knowledge conversion degree of each application scene under the condition of corresponding management rules; The system calibration module is connected with the rule generation module and the optimization verification module and is used for determining a lifting target value according to the difference value between the preset response delay amount and the actual response delay amount under the condition that the optimization target value is not in accordance with the rule generation module, determining an expansion knowledge according to the ratio of the knowledge conversion degree to the preset conversion degree under the condition that the knowledge is not in accordance with the application, or determining a simplified knowledge according to the difference between the knowledge conversion degree and the preset conversion degree.
  2. 2. The system according to claim 1, wherein the requirement matching module determines that the knowledge coverage is complete based on a comparison result that the skill update frequency is smaller than the preset frequency, determines that the number of application scenarios of the knowledge coverage under the continuous management rule expands with the increase of the effective knowledge amount based on a comparison result that the skill requirement fit is greater than or equal to the preset fit, and determines the optimization target value according to a comparison result of the knowledge redundancy and the preset redundancy.
  3. 3. The system according to claim 1, wherein the requirement matching module determines that the knowledge coverage is incomplete based on a comparison result of a skill update frequency greater than or equal to a preset frequency, determines that the depth adaptation degree of knowledge in a single application scene is improved with the increase of effective knowledge quantity under a segment management rule based on a comparison result of a skill requirement fit degree greater than or equal to a preset fit degree, and determines the optimization target value according to a comparison result of knowledge redundancy and preset redundancy.
  4. 4. The system according to claim 3, wherein the rule generating module determines that the optimization target value is not met based on a comparison result of the response delay amount being greater than or equal to a preset delay amount under the condition of determining the optimization target value, and determines to raise the target value by a first preset target adjustment coefficient based on a comparison result of a difference between the preset response delay amount and an actual response delay amount being less than or equal to a preset error.
  5. 5. The system according to claim 3, wherein the rule generating module determines that the optimization target value is not met based on a comparison result that the response delay amount is greater than or equal to the preset delay amount under the condition that the optimization target value is determined, and determines to raise the target value by a second preset target adjustment coefficient based on a comparison result that a difference between the preset response delay amount and the actual response delay amount is greater than a preset error.
  6. 6. The system according to claim 5, wherein the system calibration module determines that the knowledge is not consistent with the application based on a comparison result that the knowledge conversion degree of each application scenario is smaller than a preset conversion degree under a condition that the corresponding management rule is determined to be executed on the knowledge, and determines that the knowledge is expanded by a first preset knowledge expansion coefficient according to a comparison result that the ratio of the knowledge conversion degree to the preset conversion degree is greater than or equal to a preset threshold.
  7. 7. The system according to claim 6, wherein the system calibration module determines that the knowledge is not in line with the application based on a comparison result that the knowledge conversion degree of each application scenario is less than a preset conversion degree under a condition that it is determined that the corresponding management rule is executed on the knowledge, and determines to expand the knowledge by a second preset knowledge expansion coefficient according to a comparison result that the ratio of the knowledge conversion degree to the preset conversion degree is less than a preset threshold.
  8. 8. The system according to claim 7, wherein the optimization verification module determines that the knowledge is not consistent with the application based on a comparison result that the knowledge conversion degree of each application scenario is smaller than a preset conversion degree under a condition that the corresponding management rule is determined to be executed on the knowledge, and determines to reduce the knowledge by a first preset knowledge reduction coefficient according to a comparison result that a difference between the knowledge conversion degree and the preset conversion degree is smaller than or equal to a preset difference.
  9. 9. The system according to claim 8, wherein the optimization verification module determines that the knowledge is not consistent with the application based on a comparison result that the knowledge conversion degree of each application scenario is smaller than a preset conversion degree under a condition that the corresponding management rule is determined to be executed on the knowledge, and determines to reduce the knowledge by a second preset knowledge reduction coefficient according to a comparison result that the difference between the knowledge conversion degree and the preset conversion degree is larger than a preset difference.
  10. 10. The system according to claim 6, wherein the system calibration module determines knowledge and application compliance based on a comparison result of the knowledge conversion degree being equal to a preset conversion degree under a condition that a corresponding management rule is determined to be executed on the knowledge, and keeps current knowledge and application parameters unadjusted.

Description

Tourism skill knowledge management and optimization system Technical Field The invention relates to the technical field of travel skill knowledge management, in particular to a travel skill knowledge management and optimization system. Background In the development process of the current travel industry, management and optimization of travel skill knowledge face a plurality of problems to be solved urgently. With the continuous expansion of the travel market and the increasing diversification of industry demands, the travel skill knowledge presents the characteristic of quick update, however, the existing knowledge management system often has difficulty in timely and accurately determining the integrity of the knowledge coverage according to the skill update frequency, so that the knowledge collection has loopholes and cannot fully cover the required travel skill knowledge. In the aspect of matching knowledge and application, the traditional system lacks a scientific and reasonable management rule type determining mechanism, and is difficult to accurately match skill requirements based on the completeness of knowledge coverage, so that the association relation between knowledge and application is fuzzy, and the requirements of actual application scenes cannot be met. In the rule generation link, the conventional management system cannot reasonably determine the judgment mode of the optimization target value according to the knowledge redundancy based on the condition of the corresponding management rule, and is difficult to accurately judge the compliance of the optimization target value according to the response delay in the adjustment process, so that knowledge optimization lacks effective guidance and basis, and the optimization effect is poor. In terms of the verification of the correspondence between the knowledge and the application, the conventional system cannot accurately judge the correspondence between the knowledge and the application according to the knowledge transformation degree of each application scene based on the condition of the corresponding management rule, so that the transformation effect of the knowledge in the actual application cannot be effectively evaluated and fed back. When the optimization target value is not in line with or knowledge and application are not in line with, the traditional system lacks a perfect calibration mechanism, the lifting target value cannot be determined according to the difference value between the preset response lag amount and the actual response lag amount, and the expansion or simplification knowledge cannot be reasonably determined according to the ratio or the difference of the knowledge conversion degree and the preset conversion degree, so that the adaptability and the optimization capability of the system are poor, and the requirements of the rapid development of the tourism industry on skill knowledge management and optimization are difficult to meet. In view of the foregoing, there is a clear need for a new system for solving these problems in the existing knowledge management system for travel skills, such as knowledge collection, matching, rule generation, optimization verification and system calibration. Disclosure of Invention The invention aims to provide a travel skill knowledge management and optimization system for solving the problems in the background technology. To achieve the above object, the present invention provides a travel skill knowledge management and optimization system, the system comprising: The knowledge acquisition module is used for determining the integrity of the knowledge coverage based on the skill update frequency; The requirement matching module is connected with the knowledge acquisition module and used for determining the management rule type based on the integrity of the knowledge coverage area and determining the association relation between the knowledge and the application according to the skill requirement fit degree under the corresponding management rule; The rule generation module is connected with the requirement matching module and is used for determining a judging mode of the optimization target value according to the knowledge redundancy under the condition of the corresponding management rule and determining the consistency of the optimization target value according to the response lag in the adjustment process; The optimization verification module is connected with the rule generation module and used for determining the consistency of knowledge and application according to the knowledge conversion degree of each application scene under the condition of corresponding management rules; The system calibration module is connected with the rule generation module and the optimization verification module and is used for determining a lifting target value according to the difference value between the preset response delay amount and the actual response delay amount under the condition that the optimi