Resume data knowledge base update detection method and system

Abstract

The invention relates to the technical field of data processing, and particularly provides a resume data knowledge base update detection method and system.The method comprises the steps that when an update event occurs in a resume data knowledge base, a plurality of changed fields and change types and change amplitudes corresponding to the changed fields are obtained according to the update event; for each changed field, obtaining corresponding preset attribute information, and then obtaining a field update influence score and a field update complexity score according to the preset attribute information and the corresponding change type and change amplitude; determining an updating processing strategy of each changed field according to all field updating influence scores and all field updating complexity scores; for each changed field, carrying out updating processing on the field according to the corresponding updating processing strategy; according to the method, each updated field can be precisely processed matched with the importance, the complexity and the potential influence of the updated field.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a method and system for detecting updates to a resume data knowledge base. Background Technology

[0002] In enterprise information systems, resume data knowledge bases are typically used to integrate resume information from various sources, including user-submitted resumes, external platform synchronization, and internal personnel entry, to efficiently manage candidate resume information and support the optimization of the recruitment process. As data volume continues to grow and data sources become increasingly diversified, system development focuses on improving data accuracy and timeliness to adapt to increasingly complex business needs.

[0003] However, existing fixed detection strategies only focus on the existence of data changes, failing to effectively assess the update type (e.g., addition, modification, deletion), the fields involved (structured or unstructured), the magnitude of the change (minor or major modifications), and the potential business impact (e.g., whether it affects contact, alters user profiles, or involves sensitive information). This leads to high-overhead processing for low-impact, low-complexity updates (e.g., typo correction), wasting computational resources. Furthermore, the system fails to promptly identify the importance of high-impact, high-complexity updates (e.g., changes in critical work experience), failing to initiate rigorous verification or prioritize notification of relevant personnel, thus affecting the timeliness of critical information synchronization. Simultaneously, because fixed detection strategies cannot dynamically allocate resources based on processing complexity, existing technologies may encounter situations where simple updates are blocked by complex updates or high-importance updates are not prioritized when resources are scarce, thereby limiting the improvement of overall system efficiency, optimization of resource utilization, and assurance of accurate synchronization of critical information.

[0004] There is currently no effective technical solution to the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for detecting updates to a resume data knowledge base. This method and system can effectively solve the problems of resource waste and untimely processing of key information caused by fixed detection strategies being unable to adapt to dynamic changes in the impact and complexity of updates, as well as the limitations on improving the overall efficiency of the system, optimizing resource utilization, and ensuring accurate synchronization of key information caused by simple updates being blocked by complex updates or failure to prioritize high-importance updates when resources are scarce.

[0006] Firstly, this application provides a method for detecting updates to a resume data knowledge base, which includes the following steps: S1. When an update event occurs in the resume data knowledge base, obtain several fields that have changed, as well as the change type and magnitude of each changed field, based on the update event. S2. For each field that has changed, obtain the corresponding preset attribute information, and then obtain the field update impact score and field update complexity score based on the preset attribute information, the corresponding change type and change magnitude. S3. Determine the update handling strategy for each changed field based on the update impact score and update complexity score of all fields. S4. For each field that has changed, update the field according to its corresponding update processing strategy.

[0007] Secondly, this application also provides a resume data knowledge base update detection system, which includes: The change information acquisition module is used to acquire several changed fields and the change type and magnitude of each changed field when an update event occurs in the resume data knowledge base. The scoring acquisition module is used to obtain the corresponding preset attribute information for each field that has changed, and then obtain the field update impact score and field update complexity score based on the preset attribute information, the corresponding change type and change magnitude. The update processing strategy acquisition module is used to determine the update processing strategy for each changed field based on the update impact score and update complexity score of all fields. The update processing execution module is used to update each field that has changed, according to its corresponding update processing strategy.

[0008] As can be seen from the above, the resume data knowledge base update detection method and system provided in this application first identifies the specific fields, change types, and change magnitudes that have changed in the resume data knowledge base update event, and quantitatively evaluates the impact and processing complexity of each field update by combining the preset attribute information of the fields. Then, by determining the update processing strategy for each changed field based on the update impact score and update complexity score of all fields, a differentiated update processing strategy is dynamically determined for each changed field. Therefore, this application can ensure that each updated field receives accurate processing that matches its importance, complexity, and potential impact, thereby effectively solving the problems of resource waste and untimely processing of key information caused by fixed detection strategies being unable to adapt to the dynamic changes in update impact and complexity, and the limitations on the overall efficiency improvement, resource utilization optimization, and accurate synchronization of key information caused by simple updates being blocked by complex updates or failure to prioritize high-importance updates when resources are scarce. Attached Figure Description

[0009] Figure 1 A flowchart illustrating a resume data knowledge base update detection method provided in this application embodiment.

[0010] Figure 2 This is a schematic diagram of the structure of a resume data knowledge base update detection system provided in an embodiment of this application.

[0011] Attached reference numerals: 1. Change information acquisition module; 2. Scoring acquisition module; 3. Update processing strategy acquisition module; 4. Update processing execution module. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0013] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] In existing systems that use resume data knowledge bases for resume information storage and management, resume information comes from user submissions or modifications, synchronization from external recruitment platforms, and manual entry or updates by recruiters. To ensure the accuracy of resume information in the knowledge base, existing methods require detection when resume information is updated. However, existing detection methods using fixed strategies cannot evaluate the specific circumstances of the update (e.g., they cannot identify changed fields, change types, change magnitudes, and impacts on business processes). This results in the system requiring computationally intensive processing for low-complexity updates, leading to inefficient resource utilization. Furthermore, updates with high impact scores cannot be identified based on their importance, failing to initiate rigorous verification processes or notify relevant personnel according to priority, thus affecting the timeliness of synchronization for high-impact information. Simultaneously, existing methods cannot allocate resources based on processing complexity, causing the processing of low-complexity updates to be blocked by high-complexity updates, or preventing the priority processing of high-importance updates when resources are scarce. This limits the overall system processing efficiency, resource utilization optimization, and accurate synchronization of high-impact information.

[0015] Suppose we have a large-scale resume data knowledge base, with data sources including user submissions, external platform synchronization, and internal manual entry. For example, a user might correct a character in an address, external synchronization might update work experience, and internal staff might add skill certification information that has a significant impact on the user profile. A detection method using a fixed strategy would perform the same processing flow for all these updates. For example, it would perform comprehensive data format validation, content compliance checks, and trigger synchronization processes for all relevant downstream systems on all updates.

[0016] If processing strategies are not determined based on the specific circumstances of the update, updates with low processing complexity may consume a lot of computing resources and processing time. For example, a simple typo correction may consume the same processing resources as a critical work experience change. Furthermore, updates with high impact scores may not be processed according to priority or rigorously verified. For instance, important skills or work experience updates may not be identified in a timely manner and prioritized for synchronization with recruiters, resulting in reduced data accuracy or delayed information dissemination. Consequently, the overall processing efficiency of the system, the optimization of resource utilization, and the accurate synchronization of information with high impact scores will be affected.

[0017] In this regard, firstly, such as Figure 1 As shown, this application provides a method for detecting updates to a resume data knowledge base, which includes the following steps: S1. When an update event occurs in the resume data knowledge base, obtain several fields that have changed, as well as the change type and magnitude of each changed field, based on the update event. S2. For each field that has changed, obtain the corresponding preset attribute information, and then obtain the field update impact score and field update complexity score based on the preset attribute information, the corresponding change type and change magnitude. S3. Determine the update handling strategy for each changed field based on the update impact score and update complexity score of all fields. S4. For each field that has changed, update the field according to its corresponding update processing strategy.

[0018] The update event in step S1 refers to an operation that changes the data content or structure in the resume data knowledge base. This update event can be an operation performed by a user on the data content in the resume data knowledge base, an operation that synchronizes the data content or structure in the resume data knowledge base based on an external recruitment platform, or an operation that allows recruiters to manually enter or update the data content in the resume data knowledge base. This embodiment can use methods such as database transaction logs, message queue notifications, API call records, or file system change monitoring to identify update events occurring in the resume data knowledge base. The changed field in this embodiment refers to a data item whose content or structure actually changes during the update event. This embodiment can use data comparison algorithms (such as field-by-field hash comparison, differential comparison) or database triggers or the dirty data detection mechanism of the ORM framework to identify the changed field. The change type in this embodiment refers to the nature of the change in field content. This change type can be represented by an enumeration value, such as "added" (the field did not exist before and is now added), "modified" (the field content has changed), "deleted" (the field existed before and is now removed), or "unchanged" (the field has not changed, but may be obtained as context information). The magnitude of change in this embodiment refers to the degree of change in the field content or a quantitative indicator. This embodiment can use string similarity (e.g., Levenshtein distance, Jaccard similarity) to measure the magnitude of change in the obtained text field. This embodiment can use numerical difference or percentage change to measure the magnitude of change in the numerical field. This embodiment can also use Boolean values ​​to indicate whether the field has changed or not.

[0019] The preset attribute information in step S2 refers to the inherent attributes predefined for each resume field during system design, such as field importance, field type, and field content sensitivity. Field importance can be divided according to business needs. For example, the importance of the "Contact Number" field is higher than that of the "Hobbies" field. The field type can be text, number, date, etc. For example, modifications to the "Contact Number" field (high importance, high sensitivity) will have a higher impact score; while typo corrections to the "Self-Evaluation" field (low importance, low sensitivity, minor modification) will have a lower impact score and complexity score. The preset attribute information in this embodiment can be stored using a configuration file, database table, or data dictionary. That is, this embodiment can obtain the preset attribute information corresponding to the changed fields by querying the configuration file, database table, or data dictionary. The field update impact score in this embodiment refers to a quantitative assessment of the potential impact of updating a field on business processes, data quality, downstream systems, or user experience. The field update complexity score in this embodiment refers to a quantitative assessment of the resource investment, operational difficulty, or technical complexity required to process the field update. This embodiment can use pre-set scoring rules to obtain the field update impact score and field update complexity score based on the preset attribute information, change type, and change magnitude corresponding to the field. For example, for the "contact number" field (high importance, high sensitivity, and significantly affecting whether the user can be successfully contacted), even a small modification will result in a high field update impact score and field update complexity score. The "Self-Evaluation" field (low importance, low sensitivity, and will not affect contacting users, etc.) is slightly modified (e.g., typo correction), and it will only be assigned a lower field update impact score and field update complexity score. This embodiment can also obtain the field update impact score and field update complexity score by means of a lookup table. Specifically, the data table used by the lookup table method stores multiple sets of data, and each set of data includes the field update impact score and field update complexity score of the same field under different change types and / or change ranges. That is, this embodiment obtains the field update impact score and field update complexity score based on the preset attribute information, change type, and change range corresponding to the field by means of data query. Step S2 is equivalent to combining simple data changes with the inherent characteristics of the data itself by combining preset attribute information with change type and change magnitude. This makes the scoring no longer a simple change detection, but a quantitative assessment of the potential impact of the update on business processes, data quality, and system resources, as well as the resource investment and operational difficulty required to process the update. Therefore, this embodiment can effectively distinguish between high-value, high-risk updates and low-value, low-risk updates, thereby providing a scientific basis for subsequent differentiated processing and solving the problem that existing strategies cannot effectively assess update type, fields involved, change magnitude, and potential business impact.

[0020] The update processing strategy in step S3 refers to a series of specific processing measures and procedures that the system should take for a specific field update event to guide subsequent operations such as data verification, synchronization, notification, or resource allocation for that specific field. This embodiment can use rule sets or decision tree models based on scoring thresholds to determine the update processing strategy for each changed field based on the update impact score and update complexity score of all fields. For example, for high-impact and high-complexity updates, the system may formulate a strategy of "strict verification, urgent notification, and high-priority synchronization"; for low-impact and low-complexity updates, the system may formulate a strategy of "lightweight verification, regular notification, and low-priority synchronization". The update processing strategy of this embodiment may include verification methods (such as data format verification, content legality verification, cross-comparison with external data sources), notification recipients (such as recruitment specialists, system administrators, and downstream systems), data synchronization targets and priorities (such as immediate synchronization to the talent profiling system and delayed synchronization to the BI reporting system), and resource scheduling instructions (such as allocating more computing resources for processing). Since step S3 determines the update processing strategy for each changed field based on the update impact score and update complexity score of all fields, this embodiment is equivalent to customizing the update processing strategy for each changed field based on the update impact score and update complexity score. Therefore, this embodiment can allocate reasonable processing flow and resources to the changed fields to solve the problems of resource waste and untimely processing of key information caused by unreasonable processing flow and resource allocation. Step S4 can use automated scripts, API calls, message queue triggers, or manual intervention to update the corresponding fields according to the update processing strategy. Since the customized strategy generated in step S3 is executed in step S4, this embodiment can ensure that the update of each field receives accurate processing that matches its importance, complexity, and potential impact, effectively avoiding the problems of resource waste (e.g., overprocessing simple updates) and untimely processing of key information (e.g., failure to respond to important updates in a timely manner) caused by traditional fixed strategies, thereby significantly improving the overall efficiency, data accuracy, and system response speed of resume data knowledge base update management.

[0021] The core innovation of this application lies in first identifying the specific fields, change types, and magnitudes of changes in the resume data knowledge base update events, and then quantitatively assessing the impact and processing complexity of each field update by combining the preset attribute information of the fields. Then, by determining the update processing strategy for each changed field based on the update impact score and update complexity score of all fields, this application dynamically determines a differentiated update processing strategy for each changed field. Therefore, this application enables each updated field to receive precise processing that matches its importance, complexity, and potential impact. This effectively solves the problems of resource waste and untimely processing of key information caused by fixed detection strategies failing to adapt to dynamic changes in update impact and complexity, and the limitations on overall system efficiency, resource utilization optimization, and accurate synchronization of key information caused by simple updates being blocked by complex updates or failure to prioritize high-importance updates when resources are scarce.

[0022] Specifically, this method aims to address the inefficiencies and resource waste in existing resume data knowledge base update management by introducing an intelligent evaluation and strategy formulation mechanism to achieve refined and adaptive processing of data updates. First, based on the update event, several fields that have changed are identified, and the change type (and magnitude) for each changed field are obtained. This allows the system to accurately identify the specific content and nature of the update, laying the foundation for subsequent refined evaluation and avoiding blind processing of the entire resume data knowledge base, thereby improving the targeting and efficiency of the processing. For example, when a job seeker updates their resume, the system can accurately identify that only the "contact number" field has been modified, rather than performing a general update check on the entire resume. Next, for each identified changed field, its corresponding preset attribute information is obtained, reflecting the characteristics of the field itself. Then, considering the preset attribute information of the field, as well as the specific change type and magnitude of this update, the update impact score and update complexity score for that field are obtained. The impact score reflects the importance of the field change to business or data quality, while the complexity score reflects the difficulty or resource requirements for processing the change. This embodiment quantifies all changed fields using the above methods. The system obtains comprehensive assessment data about the entire update event through an evaluation method. Based on the update impact score and update complexity score of all changed fields, the system comprehensively analyzes and determines the update processing strategy for each changed field. This dynamically generates the most suitable processing solution for each changed field; for example, for high-impact and high-complexity fields, a more stringent verification process is initiated and notifications are sent to more relevant parties; while for low-impact and low-complexity fields, automated and lightweight processing methods are adopted. Finally, for each changed field, the system strictly executes the specific update processing operation according to the determined update processing strategy. This differentiated processing based on assessment results ensures that high-importance and high-complexity updates receive sufficient attention and resources, while avoiding unnecessary overhead for low-importance and low-complexity updates. Therefore, this embodiment effectively avoids the resource waste (and the problem of untimely processing of key information) caused by traditional fixed strategies, thereby significantly improving the overall efficiency, data accuracy, and system response speed of resume data knowledge base update management.

[0023] As a preferred embodiment, the solution of this application is implemented as follows: Assume there is a resume of job seeker A in the resume data knowledge base, which includes fields such as "name", "contact number", "educational background", "work experience" and "self-evaluation". After job seeker A updates his resume (update time occurs), step S1 identifies the following changes by comparing the resume data before and after the update: "Contact number" field: changed from "138XXXXX678" to "139XXXXX321", the change type is "modification", and the change magnitude is "numerical change"; "Self-evaluation" field: changed "enthusiastic" to "passionate", the change type is "modification", and the change magnitude is "minor text content modification". In step S2, for the "Contact Number" field: the preset attribute information is: field importance "high", field type "numeric", field content sensitivity "high". Combining the change type "modification" and the change magnitude "numerical change", the system calculates the update impact score of the "Contact Number" field as 90 points (out of 100) and the update complexity score as 80 points. For the "Self-Evaluation" field: the preset attribute information is: field importance "low", field type "text", field content sensitivity "low". Combining the change type "modification" and the change magnitude "minor modification of text content", the system calculates the update impact score of the "Self-Evaluation" field as 20 points and the update complexity score as 15 points. In step S3, for the "Contact Number" field (impact score 90, complexity score 80): the system determines its update processing strategy as follows: verification method "strict verification (including format verification, uniqueness verification and cross-comparison with external data sources)", notification recipients "recruitment specialists (urgent notification), system administrators", data synchronization target and priority "talent profiling system (high priority), BI reporting system (medium priority)", resource scheduling instruction "allocate high priority computing resources"; for the "Self-evaluation" field (impact score 20, complexity score 15): the system determines its update processing strategy as follows: verification method "routine verification (format verification only)", notification recipients "no specific notification, included in routine batch notification", data synchronization target and priority "BI reporting system (low priority)", resource scheduling instruction "allocate low priority computing resources". In step S4, for the "Contact Number" field: the system immediately initiates a strict verification process, including verifying whether the format of the new phone number is correct, whether it is duplicated with other job seekers' phone numbers, and attempting to verify the validity of the number through an external interface. After the verification is successful, the system immediately sends an emergency notification to the recruitment specialist responsible for job seeker A and alerts the system administrator via instant messaging. At the same time, the system prioritizes synchronizing the updated contact number to the talent profile system to ensure that the recruitment specialist can obtain the latest contact information as soon as possible, and allocates high-priority computing resources to complete this synchronization task. For the "Self-Evaluation" field: the system only performs routine format verification.After successful verification, the update is included in the daily batch data synchronization task and synchronized to the BI reporting system with low priority. It does not immediately notify recruitment specialists, nor does it consume high-priority computing resources. This embodiment intelligently assesses the impact and complexity of the update based on the specific circumstances of the resume data knowledge base update and dynamically determines differentiated processing strategies. Therefore, this embodiment effectively solves the problems of inefficiency, resource waste, and untimely synchronization of key information caused by existing fixed processing strategies, thereby effectively improving the efficiency of update processing, optimizing the utilization of system resources, and ensuring the accuracy and timely synchronization of key resume information.

[0024] In some preferred embodiments, step S2 includes: S21. Obtain current recruitment information and market talent demand trends; S22. For each field that changes, obtain the corresponding preset attribute information, and then modify the preset attribute information according to the field content, current recruitment information and market talent demand trends to obtain the modified attribute information. S23. For each field that has changed, obtain the field update impact score and field update complexity score based on the corresponding correction attribute information, change type and change magnitude.

[0025] The current recruitment information in this embodiment refers to information such as job postings, company needs, and salary levels in the current job market. The market talent demand trend in this embodiment refers to the changing trends in the demand for specific skills, experience, or positions in the talent market. This embodiment obtains current recruitment information and market talent demand trends by using web crawling technology to acquire real-time or recent job posting data, industry reports, talent supply and demand analysis data, and in-demand skills from public data interfaces of mainstream recruitment websites, industry report publishing platforms, and talent data analysis institutions. This embodiment can also obtain current recruitment information and market talent demand trends by subscribing to professional talent market analysis reports or using internal historical recruitment data for trend analysis to identify changes in the current market demand for specific skills, experience, or education. This current recruitment information and market talent demand trend directly affect the actual value and importance of each field in the resume. The field content in this embodiment refers to the specific text, numerical value, or structured data of the changed fields. This embodiment obtains the field content by parsing and understanding the actual values ​​of the changed fields. For example, for the "Work Experience" field, its content may include detailed descriptions such as company name, position, job responsibilities, and project experience. This embodiment can employ preset correction rules to modify preset attribute information based on field content, current recruitment information, and market talent demand trends. For example, the preset rule could be: if the field content contains "Python programming" and the market talent demand trend shows "Python" as a high-demand skill, then the importance of the preset attribute information for that field would be modified from "medium" to "high." This embodiment combines static preset attributes with a dynamic market environment by modifying preset attribute information based on field content, current recruitment information, and market talent demand trends, ensuring the real-time nature and business relevance of field attribute evaluation.

[0026] This solution incorporates current recruitment information and market talent demand trends, and uses this dynamic information to modify preset attribute information. This allows the acquired field update impact scores and field update complexity scores to more accurately reflect the impact of the current actual business environment on field importance and processing complexity, providing a more reliable basis for determining more effective update processing strategies. Specifically, after obtaining the fields, change types, and change magnitudes from the resume data knowledge base, the solution first acquires current recruitment information and market talent demand trends. This information reflects the dynamic changes in the current recruitment market (e.g., which skills or experiences are currently urgently needed by companies). Then, for each changed field, its preset attribute information is acquired, and the preset attribute information is modified based on the specific content of the field combined with the acquired current recruitment information and market talent demand trends (e.g., if a field contains skills that are currently urgently needed in the market, even if its preset importance is not high, its importance attribute will increase after modification). This modification process allows the field's attribute information to dynamically adapt to market changes and more accurately reflect its actual value and processing priority in the current business environment. After obtaining the dynamically corrected attribute information, for each changed field, an impact score and a complexity score for the field update are obtained based on the corresponding corrected attribute information, change type, and change magnitude. Since the corrected attribute information used to obtain the scores has already considered and corrected for the impact of the current business environment, the obtained field update impact score can more accurately assess the potential impact of the field change on business processes such as recruitment, talent profiling, and decision-making. The obtained field complexity score can more accurately assess the resource investment and technical difficulty required to handle the change. These more accurate scores can effectively guide the subsequent determination of more reasonable and efficient update handling strategies, avoiding the problems of under-handling of important changes or over-handling of unimportant changes.

[0027] In one exemplary implementation, obtaining current recruitment information and market talent demand trends can be achieved by calling the API interface provided by an external recruitment data service provider to obtain data such as the latest number of job postings, average salary levels, and talent application popularity for specific industries, positions, or skills. For the "Work Experience" field, which has changed, its preset attribute information may include importance (e.g., preset to medium) and sensitivity (e.g., preset to low). When the content of this field is modified to include "Artificial Intelligence Algorithm Engineer" and the years of work experience increase, the system can adjust the preset importance attribute of this field from medium to high based on the obtained market talent demand trends (e.g., Artificial Intelligence Algorithm Engineer is a currently high-demand position) and current recruitment information (e.g., the company is recruiting heavily for this position). Simultaneously, if market trends indicate an increase in background check risks related to work experience, the sensitivity attribute may also be adjusted to medium. After obtaining the corrected attribute information (importance: high, sensitivity: medium), combined with the type of change (modification) and the magnitude of the change (significant modification, such as a large change in years of work experience), the system can utilize preset scoring rules (e.g., impact score = importance). The change magnitude coefficient + sensitivity coefficient; complexity score = field type complexity + change magnitude complexity) obtains the update impact score and complexity score of the "work experience" field.

[0028] In some preferred embodiments, step S22 includes: S221. For each field that changes, obtain the corresponding preset attribute information, and then modify the preset attribute information according to the field content, current recruitment information and market talent demand trends to obtain preliminary attribute information. S222. For each field that has changed, obtain the correlation information between that field and other fields, and then correct the preliminary attribute information based on the correlation information to obtain the corrected attribute information.

[0029] The initial attribute information in this embodiment refers to the first adjustment result of the preset attribute information after considering the content of the field itself and external recruitment market environment factors (current recruitment information and market talent demand trends). The correlation information between the field and other fields in this embodiment refers to data describing the degree of correlation or influence between the currently selected changed field and other fields in the resume data knowledge base. For example, a change in the "Years of Work Experience" field may be closely related to fields such as "Position" and "Salary Expectation". This correlation information can be predefined or obtained through data analysis. This embodiment can more comprehensively evaluate the impact of a field change on the entire resume data structure by introducing correlation information, thereby more accurately correcting its attribute information.

[0030] This solution obtains more accurate corrected attribute information by introducing inter-field correlation information to perform secondary modification of the initial attribute information. The entire process can be understood as follows: First, for the changed field, its original preset attribute information is obtained as a basis. Then, in step S221, the preset attribute information is initially adjusted using the field's own content and current recruitment market environment information to generate preliminary attribute information. This step considers the nature of the field itself and the impact of external demands on its importance. Next, in step S222, the correlation information between the changed field and other fields in the resume data knowledge base is obtained. This correlation information reflects how a change in one field may affect or corroborate changes in other fields. For example, if "years of work experience" increases significantly, it is usually associated with changes in "position" or "salary expectation". Finally, this correlation information is used to further refine the preliminary attribute information obtained in step S221 to generate revised attribute information. For example, if a changed field is highly correlated with multiple important fields and these related fields have also changed, then the actual importance or complexity of the changed field may need to be further enhanced; conversely, if the correlation is low or the related fields have not changed, then the preliminary attribute information may not need to be significantly adjusted.

[0031] As a specific implementation method, the above correction process can be achieved as follows: For the "Years of Work Experience" field that has changed in the resume, firstly, its preset attribute information is obtained, such as a preset importance of low. In step S221, assuming that the current recruitment information shows a strong demand for senior positions and that the market talent demand trend favors experienced candidates, the system can adjust the importance of "Years of Work Experience" to medium (i.e., the initial importance is medium) based on this information. Then, in step S222, the system queries the preset field correlation information and finds that "Years of Work Experience" has a strong correlation with the "Position," "Project Experience," and "Salary Expectations" fields. The system also finds that the "Position" field has changed (e.g., from "Junior Engineer" to "Senior Engineer"), and the "Project Experience" field has also added content. Based on this correlation and the changes in related fields, the system further determines that this change in "Years of Work Experience" has a high business impact and data structure complexity. Therefore, based on the correlation information, the importance is adjusted from medium to high (i.e., the importance is adjusted to high) to obtain the corrected attribute information. This embodiment overcomes the limitations of relying solely on field content and external environment for correction by modifying preset attribute information in stages and from multiple dimensions. In other words, the modified attribute information in this embodiment can more comprehensively reflect the importance and complexity of fields, providing more accurate input for obtaining field update impact scores and field update complexity scores. Therefore, this embodiment can make the update processing strategy determined based on field update impact scores and field update complexity scores more targeted and effective, thereby effectively avoiding insufficient processing of important changes or over-processing of unimportant changes, and thus effectively improving the overall efficiency and accuracy of resume data knowledge base update detection and processing.

[0032] In some preferred embodiments, step S23 includes: S231. For each field that has changed, obtain historical processing effect data, and then determine the scoring rules based on the historical processing effect data, current recruitment information, and market talent demand trends. S232. For each field that has changed, use the corresponding scoring rules to obtain the field update impact score and field update complexity score based on the corresponding correction attribute information, change type and change magnitude.

[0033] The historical processing effect data in this embodiment refers to the experience and results of processing similar field updates in the past. The historical processing effect data may include indicators such as processing time, processing success rate, subsequent data quality feedback, and user satisfaction. This embodiment can obtain historical processing effect data by extracting it from historical processing logs or summarizing it from manual feedback records. The preferred scoring rule in this embodiment is an algorithm for calculating the field update impact score and the field update complexity score. This embodiment can use a machine learning model or a lookup table method to determine the scoring rule based on historical processing effect data, current recruitment information, and market talent demand trends. For example, the scoring rule is: Field update impact score = k1 × (score determined based on the attribute information corresponding to the field + score determined based on the change type corresponding to the field + score determined based on the change magnitude corresponding to the field); Field update complexity score = k2 × score determined based on the change type and change magnitude corresponding to the field. This embodiment can pre-train a regression model using a training dataset (including multiple sets of inputs (historical processing effect data, recruitment information, and market trends) and their corresponding labels (specific values ​​of k1 and k2)). When executing step S231, the historical processing effect data, current recruitment information, and market talent demand trends are input into the regression model to obtain the corresponding k1 and k2, thereby determining the scoring rule. Preferably, the scores in the above formulas are all obtained by a lookup table method.

[0034] This solution effectively improves the accuracy of field update impact and complexity scores by incorporating historical processing effect data, current recruitment information, and market talent demand trends, and dynamically determining scoring rules based on this information. Specifically, for each changed field, historical processing effect data is first acquired, reflecting past experience and results in handling similar field updates. Simultaneously, current recruitment information and market talent demand trends are obtained, reflecting the current market's focus on and importance of different field content (such as skills and experience). Then, based on this historical data, current recruitment information, and market talent demand trends, rules for obtaining field update impact and complexity scores are dynamically determined. This rule-determination method, based on historical experience and real-time market information, allows the scoring rules to better adapt to actual conditions and overcomes the limitations of using fixed scoring rules. Next, for each changed field, the determined dynamic scoring rules are combined with corresponding corrective attribute information, change type, and change magnitude to obtain the field update impact and complexity scores.

[0035] For example, in a specific implementation scenario, when a user's "Work Experience" field in the resume data knowledge base changes, the system can first obtain historical processing performance data for that field. This includes, for instance, the average time spent updating the "Work Experience" field, the manual review pass rate, and the number of times subsequent recruitment processes were interrupted due to the field update. Simultaneously, the system obtains current recruitment information, such as whether the company is actively recruiting for positions requiring specific work experience, and market talent demand trends, such as a rapid increase in demand for work experience with specific skills in a particular industry. Then, the system can dynamically adjust the rules for the impact and complexity scores of the "Work Experience" field update based on this historical data, current recruitment information, and market talent demand trends. For example, if historical data shows that updating this field frequently causes problems and the current market demand for relevant experience is high, the impact score weight for the field update is increased; if historical processing of this field was complex, its complexity score weight is increased. Finally, the system uses the adjusted scoring rules combined with the specific corrected attribute information (e.g., high importance, medium sensitivity), change type (e.g., modification), and change magnitude (e.g., significant modification) of the "Work Experience" field update to obtain the field update impact score and field update complexity score. This embodiment effectively overcomes the limitations of using fixed scoring rules by acquiring historical processing effect data, current recruitment information, and market talent demand trends, and dynamically determining the scoring rules based on this information. It also makes the obtained field update impact scores and field update complexity scores more accurate. Therefore, this embodiment provides a reliable foundation for subsequently determining more refined and optimized update processing strategies based on the scores, thereby effectively improving the efficiency and accuracy of resume data knowledge base update processing.

[0036] In some preferred embodiments, step S3 includes: S31. Determine the first preliminary processing strategy for each field that has changed based on the update impact score and the update complexity score of all fields. S32. Obtain current recruitment information and market talent demand trends; S33. Based on the current recruitment information, market talent demand trends, and the content of all changed fields, revise the initial processing strategy for each changed field to obtain the update processing strategy for each changed field.

[0037] The first preliminary processing strategy in this embodiment refers to the initial processing plan set for each changed field based on internal assessments (field update impact score and field update complexity score). This embodiment can use a preset strategy matrix, decision tree, or threshold-based rule set to determine the first preliminary processing strategy for each changed field based on the impact score and complexity score of all field updates. For example, a strategy matrix can be set: when the impact score is high and the complexity score is low, the preliminary strategy may be "immediate synchronization, strict verification"; when the impact score is low and the complexity score is high, the preliminary strategy may be "delayed synchronization, manual review". The modification of the first preliminary processing strategy in this embodiment refers to the process of adjusting the first preliminary processing strategy based on external recruitment market environment information and field content. This embodiment can use a modification rule pre-set based on expert experience to modify the first preliminary processing strategy of each changed field according to the current recruitment information, market talent demand trends, and the field content of all changed fields. For example, if the first preliminary processing strategy is "routine verification", but based on the current recruitment information and field content (such as updating the "key skills" field to the currently urgently needed skills), the modification of the first preliminary processing strategy according to the modification rule pre-set based on expert experience results in an updated processing strategy of "high-priority verification and immediate notification to the relevant recruitment manager".

[0038] This solution first determines an initial processing strategy for each field based on the update impact score and update complexity score of all changed fields. Building on this, the solution further acquires current recruitment information and market talent demand trends reflecting the external recruitment market environment. This external recruitment market environment information is then combined with the specific content of the fields themselves to revise the initially determined strategy. For example, a field with minimal change may have its processing priority significantly increased if its content is highly relevant to urgently needed positions; conversely, a field with significant change may have its processing priority appropriately reduced if its content is irrelevant to current business priorities. This embodiment, by utilizing current recruitment information, market talent demand trends, and the content of changed fields to revise the initial processing strategy determined based on field update impact and complexity scores, ensures that the final update processing strategy considers not only the characteristics of data change itself but also real-time business needs and market dynamics. Therefore, this embodiment effectively improves the flexibility and accuracy of the update processing strategy, enabling smarter allocation of processing resources and prioritizing updates with higher current business value. This ensures timely synchronization of key information and avoids resource waste, effectively addressing the shortcomings of relying solely on static scoring to determine the strategy.

[0039] In one embodiment, assuming the "Skills" field of a candidate in the resume data knowledge base is updated, changing from "Java" to "Java, Python, Docker", the system obtains the update impact score and update complexity score of the field based on the preset attributes of the "Skills" field and the type and magnitude of the change. Step S31 determines a first preliminary processing strategy based on the update impact score and update complexity score (e.g., initial priority is medium, requiring format verification and automatic synchronization to the internal recruitment system). In step S32, the system obtains current recruitment information (e.g., the company is urgently recruiting for positions requiring "Python" and "Docker" skills) and market talent demand trends (e.g., showing that talent with these skills is in high demand in the market). In step S33, the system modifies the first preliminary processing strategy based on the acquired current recruitment information, market talent demand trends, and specific changes in the "Skills" field. Specifically, considering that the newly added skills highly match the current urgent recruitment needs and the market talent shortage, the system modifies the first preliminary processing strategy for this field to: increase the priority to high, in addition to format verification, trigger notifications to relevant recruitment teams, prioritize synchronization to all external recruitment platforms, and reserve more computing resources for processing. This embodiment enables the update processing strategy to more accurately reflect current business needs and actual data conditions by incorporating current business environment information and specific field content to modify the first preliminary strategy.

[0040] In some preferred embodiments, step S33 includes: S331. Based on the current recruitment information, market talent demand trends, and the content of all changed fields, the first preliminary processing strategy for each changed field is modified to obtain the second preliminary processing strategy for each changed field. S332. Obtain system operating status information, which includes the current system load, available computing resources, and available storage resources. S333. Based on the system operating status information and all preset attribute information, the second preliminary processing strategy for each changed field is modified to obtain the update processing strategy for each changed field.

[0041] The system operation status information in this embodiment refers to real-time data on the system's current processing capacity and resource usage. This embodiment can utilize indicator data collected by system monitoring tools to obtain system operation status information. The current system load refers to the amount of tasks the system is processing or the degree of resource consumption. The current system load can be measured using indicators such as CPU utilization, memory utilization, and queue length. Available computing resources refer to the processing capacity that the system can currently use to execute computing tasks. This available computing resource can be measured using indicators such as the number of idle CPU cores and available memory capacity. Available storage resources refer to the space that the system can currently use to store data. This available storage resource can be measured using indicators such as remaining disk space and available database space.

[0042] This solution first modifies the initial processing strategy determined by scoring based on current recruitment information, market talent demand trends, and the content of changed fields to obtain a second initial processing strategy. Subsequently, system operating status information is acquired, including the current system load, available computing resources, and available storage resources. This system operating status information provides information on the system's current actual processing capacity and resource availability. Finally, the second initial processing strategy, after its first modification, is further modified based on the acquired system operating status information and all preset attribute information to obtain the final update processing strategy. This embodiment makes the update processing strategy flexible and adaptable to dynamic changes in system resources by modifying the second initial processing strategy based on system operating status information and preset attribute information. For example, when system resources are scarce, the processing priority of some non-critical updates can be appropriately reduced or fewer resources allocated; when resources are sufficient, updates can be processed more aggressively. This embodiment, by comprehensively considering system operating status and preset attribute information to further modify the second initial processing strategy, enables the final update processing strategy to more intelligently guide subsequent update processing execution and optimize resource allocation, thereby improving processing efficiency and accuracy. This multi-stage strategy revision process starts with basic scoring and gradually incorporates business context, data content, system status, and inherent data attributes. This allows the final strategy to comprehensively balance multiple factors and overcomes the limitations of relying solely on fixed rules or limited information to determine the strategy, thereby improving the rationality and execution efficiency of the strategy.

[0043] For example, in a specific implementation scenario, when an update event occurs in the resume data knowledge base, such as a candidate updating their work experience field, the system first identifies the change in the work experience field. Based on preset attribute information (e.g., the work experience field is marked as "high importance"), as well as the type of change (modification) and the magnitude of the change (significant content modification), the system obtains an initial field update impact score and complexity score, and determines a first preliminary processing strategy based on these scores, such as suggesting manual review and synchronization to multiple downstream systems. Next, the system obtains current recruitment information (e.g., the company is urgently recruiting for positions related to this work experience) and market talent demand trends (e.g., the talent market in this field is highly competitive), and modifies the first preliminary processing strategy based on this information to obtain a second preliminary processing strategy, such as increasing the priority of manual review and suggesting immediate notification of relevant recruiters. Subsequently, the system obtains current operational status information and finds that the system is currently under high load and that available computing and storage resources are relatively scarce. Finally, the system makes a final correction to the second preliminary processing strategy by combining the preset attribute information of high importance and the tense system operating state. Specifically, considering the high importance of the fields, the system decides to prioritize the allocation of resources for automatic verification even when resources are scarce, and to place manual review tasks in a high-priority queue. At the same time, the data synchronization strategy is adjusted to ensure that the most critical downstream systems are synchronized first, while non-critical synchronization is delayed until the system load is reduced. This embodiment can generate an update processing strategy that is more in line with current business needs, data characteristics, actual system conditions, and inherent data attributes by first correcting the preliminary processing strategy based on current recruitment information, market talent demand trends, and field content, and then further correcting it a second time based on system operating state information and preset attribute information. Therefore, this embodiment can make the final update processing strategy more adaptable and robust, optimize resource allocation and prioritize the processing of important and sensitive data updates when system resources change dynamically, thereby improving the overall efficiency and accuracy of resume data knowledge base update processing and effectively solving the problems of low strategy execution efficiency and insufficient resource utilization that may occur when only business needs and data characteristics are considered to determine the strategy.

[0044] In some preferred embodiments, step S1 includes: S11. When an update event occurs in the resume data knowledge base, determine the change identification rules based on the source of the update event; S12. Using change recognition rules, obtain several fields that have changed based on the update event, as well as the change type and magnitude of each changed field.

[0045] The source of an update event refers to the external or internal entity or mechanism that triggers the update event. In this embodiment, the source of the update event can be identified by means of request header information, message queue metadata, or file paths. The change identification rule refers to the logic or algorithm used to parse the update event and determine the changed fields, types, and magnitudes. This change identification rule can be a preset parsing template, a field comparison algorithm, or a machine learning model, etc.

[0046] Specifically, when an update event occurs in the resume data knowledge base, the system first determines a change identification rule based on the source of the update event. For example, the system can identify whether the update event originates from modifications made by a user through a web interface or from batch data imports via an API interface. Based on the identification result, the system can select or generate a change identification rule that matches the data structure and change pattern of that specific source. Subsequently, the system uses the change identification rule to parse the specific content of the update event, thereby obtaining the changed fields, change types, and change magnitudes. This embodiment, by first determining the change identification rule based on the source of the update event and then performing change identification based on that rule, allows the change identification process to be adjusted for the characteristics of different sources, obtaining change information that is more consistent with the actual situation. Therefore, this embodiment can effectively improve the accuracy of the changed fields and the corresponding change types and change magnitudes for each changed field.

[0047] For example, when the resume data knowledge base detects an update event, the system first identifies the source of the update event. If the update event originates from a single resume modification made by a user through a web interface, the system can determine to use the "field difference comparison rule" as the change identification rule. This rule identifies the changed fields and their change types (such as modification) and change magnitudes (such as the amount of text content change) by comparing the complete resume data records before and after the update and comparing the content of each field one by one. If the update event originates from batch data import through an API interface, the system can determine to use the "API data packet parsing rule" as the change identification rule. This rule directly parses the structured update data carried in the API request body according to the preset API data format specification and extracts the fields marked as changed, the change types (such as addition, modification, deletion), and the change magnitudes (such as the number of new entries and numerical changes). This embodiment can dynamically determine the change identification rules based on the source of the update time, thereby enabling the adoption of appropriate identification methods for data characteristics from different sources. Therefore, this embodiment can make the identification results more consistent with the actual changes and reduce false alarms or false negatives. In other words, this embodiment can adapt the change identification process to diverse update scenarios, thus providing more realistic input for subsequent field update impact scoring and complexity scoring, and effectively improving the accuracy and reliability of the update processing strategy.

[0048] In some preferred embodiments, the update processing strategy includes a verification method, notification recipients, data synchronization targets and priorities, and resource scheduling instructions. The verification method refers to the technical means used to verify the accuracy, completeness, or compliance of the updated data. This verification method can be data format verification, business rule verification, cross-field verification, or manual review. The notification recipients refer to the individuals, systems, or departments that need to receive notifications after the data update is completed. These recipients can be represented by email addresses, system user IDs, message queue topics, or API interface addresses. The data synchronization targets and priorities refer to the downstream systems or modules to which the updated data needs to be synchronized, as well as the order or importance of synchronization. These targets and priorities can be defined using a list of target system identifiers, synchronization queue names, or priority values. Resource scheduling instructions refer to the configuration or indication of the computing resources (such as CPU time and memory) and storage resources (such as disk space and database connections) needed to complete the update processing task for this field. These resource scheduling instructions can be resource quota parameters, task priority flags, or scheduling queue types.

[0049] In some preferred embodiments, the preset attribute information includes field importance, field type, and field content sensitivity. Field importance refers to the business value and criticality of a field within the resume data knowledge base. This embodiment can obtain field importance by querying a preset field importance level table or by analyzing the frequency of use and scope of influence of the field in the recruitment process. Field type refers to the data format and content characteristics of a field. This embodiment can obtain field type by querying the data type identifier defined in the database schema or by identifying the field type through data sampling and analysis of the field content. Field content sensitivity refers to whether a field contains sensitive data such as personal privacy or confidential information. This embodiment can obtain field content sensitivity by querying a preset list of sensitive fields or by using a content matching algorithm (e.g., based on regular expressions or keyword matching).

[0050] As can be seen from the above, the resume data knowledge base update detection method provided in this application first identifies the specific fields, change types, and change magnitudes that have changed in the resume data knowledge base update event, and then quantitatively evaluates the impact and processing complexity of each field update by combining the preset attribute information of the fields. Then, by determining the update processing strategy for each changed field based on the update impact score and update complexity score of all fields, a differentiated update processing strategy is dynamically determined for each changed field. Therefore, this application can ensure that each updated field receives accurate processing that matches its importance, complexity, and potential impact, thereby effectively solving the problems of resource waste and untimely processing of key information caused by fixed detection strategies being unable to adapt to the dynamic changes in update impact and complexity, and the limitations on the overall efficiency improvement, resource utilization optimization, and accurate synchronization of key information caused by simple updates being blocked by complex updates or failure to prioritize high-importance updates when resources are scarce.

[0051] Secondly, such as Figure 2 As shown, this application also provides a resume data knowledge base update detection system, which includes: The change information acquisition module 1 is used to acquire several changed fields and the change type and magnitude of each changed field when an update event occurs in the resume data knowledge base. The scoring acquisition module 2 is used to obtain the corresponding preset attribute information for each field that has changed, and then obtain the field update impact score and field update complexity score based on the preset attribute information, the corresponding change type and change magnitude. The update processing strategy acquisition module 3 is used to determine the update processing strategy for each changed field based on the update impact score of all fields and the update complexity score of all fields. The update processing execution module 4 is used to update each field that has changed, according to its corresponding update processing strategy.

[0052] This application provides a resume data knowledge base update detection system, which includes a change information acquisition module 1, a scoring acquisition module 2, an update processing strategy acquisition module 3, and an update processing execution module 4. The resume data knowledge base update detection system provided in this embodiment is used to execute the steps in the resume data knowledge base update detection method provided in the first aspect above. The principle of the resume data knowledge base update detection system provided in this embodiment is the same as the principle of the resume data knowledge base update detection method provided in the first aspect above, and will not be discussed in detail here.

[0053] As can be seen from the above, the resume data knowledge base update detection method and system provided in this application first identifies the specific fields, change types, and change magnitudes that have changed in the resume data knowledge base update event, and quantitatively evaluates the impact and processing complexity of each field update by combining the preset attribute information of the fields. Then, by determining the update processing strategy for each changed field based on the update impact score and update complexity score of all fields, a differentiated update processing strategy is dynamically determined for each changed field. Therefore, this application can ensure that each updated field receives accurate processing that matches its importance, complexity, and potential impact, thereby effectively solving the problems of resource waste and untimely processing of key information caused by fixed detection strategies being unable to adapt to the dynamic changes in update impact and complexity, and the limitations on the overall efficiency improvement, resource utilization optimization, and accurate synchronization of key information caused by simple updates being blocked by complex updates or failure to prioritize high-importance updates when resources are scarce.

[0054] In the embodiments provided in this application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of the above units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another robot, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0055] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0056] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0057] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for detecting updates to a resume data knowledge base, characterized in that, The resume data knowledge base update detection method includes the following steps: S1. When an update event occurs in the resume data knowledge base, obtain several fields that have changed, as well as the change type and change magnitude of each changed field, based on the update event. S2. For each field that has changed, obtain the corresponding preset attribute information, and then obtain the field update impact score and field update complexity score based on the preset attribute information and the corresponding change type and change magnitude. S3. Determine the update processing strategy for each changed field based on the update impact score and update complexity score of all the fields. S4. For each field that has changed, update the field according to its corresponding update processing strategy.

2. The resume data knowledge base update detection method according to claim 1, characterized in that, Step S2 includes: S21. Obtain current recruitment information and market talent demand trends; S22. For each field that changes, obtain the corresponding preset attribute information, and then modify the preset attribute information according to the field content, the current recruitment information, and the market talent demand trend to obtain modified attribute information. S23. For each field that has changed, obtain the field update impact score and field update complexity score based on the corresponding correction attribute information, change type and change magnitude.

3. The resume data knowledge base update detection method according to claim 2, characterized in that, Step S22 includes: S221. For each field that changes, obtain the corresponding preset attribute information, and then modify the preset attribute information according to the field content, the current recruitment information, and the market talent demand trend to obtain preliminary attribute information. S222. For each field that has changed, obtain the correlation information between that field and other fields, and then correct the preliminary attribute information based on the correlation information to obtain the corrected attribute information.

4. The resume data knowledge base update detection method according to claim 2, characterized in that, Step S23 includes: S231. For each field that has changed, obtain historical processing effect data, and then determine the scoring acquisition rules based on the historical processing effect data, the current recruitment information, and the market talent demand trend. S232. For each field that has changed, use the corresponding scoring rules to obtain the field update impact score and field update complexity score based on the corresponding correction attribute information, change type and change magnitude.

5. The resume data knowledge base update detection method according to claim 1, characterized in that, Step S3 includes: S31. Determine the first preliminary processing strategy for each changed field based on the update impact score and the update complexity score of all said fields; S32. Obtain current recruitment information and market talent demand trends; S33. Based on the current recruitment information, the market talent demand trend, and the content of all changed fields, the first preliminary processing strategy for each changed field is modified to obtain the update processing strategy for each changed field.

6. The resume data knowledge base update detection method according to claim 5, characterized in that, Step S33 includes: S331. Based on the current recruitment information, the market talent demand trend, and the content of all changed fields, the first preliminary processing strategy for each changed field is modified to obtain the second preliminary processing strategy for each changed field. S332. Obtain system operating status information, which includes the current system load, available computing resources, and available storage resources; S333. Based on the system operating status information and all the preset attribute information, the second preliminary processing strategy for each changed field is modified to obtain the update processing strategy for each changed field.

7. The resume data knowledge base update detection method according to claim 1, characterized in that, Step S1 includes: S11. When an update event occurs in the resume data knowledge base, determine the change identification rules based on the source of the update event; S12. Using the change identification rules, obtain several fields that have changed, as well as the change type and change magnitude corresponding to each changed field, based on the update event.

8. The resume data knowledge base update detection method according to claim 1, characterized in that, The update processing strategy includes verification methods, notification objects, data synchronization targets and priorities, and resource scheduling instructions.

9. The resume data knowledge base update detection method according to claim 1, characterized in that, The preset attribute information includes field importance, field type, and field content sensitivity.

10. A resume data knowledge base update detection system, characterized in that, The resume data knowledge base update detection system includes: The change information acquisition module is used to acquire, based on the update event, several changed fields and the change type and magnitude of each changed field when an update event occurs in the resume data knowledge base. The scoring acquisition module is used to acquire the corresponding preset attribute information for each field that has changed, and then acquire the field update impact score and field update complexity score based on the preset attribute information and the corresponding change type and change magnitude. The update processing strategy acquisition module is used to determine the update processing strategy for each changed field based on the update impact score of all the fields and the update complexity score of all the fields. The update processing execution module is used to update each field that has changed, according to its corresponding update processing strategy.

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