A method and system for managing cloud databases of human resources information

Abstract

This invention relates to the field of data processing technology, specifically to a method and system for managing a cloud database of human resources information. The method includes: acquiring search request data sent by a user to the cloud database of human resources information; performing data analysis on the search request data to obtain the search information type and information sensitivity; performing data matching on the search information type and information sensitivity to determine the matching data searched by the user; filtering the matching data searched by the user to obtain filtered matching data; and pushing the filtered matching data to the user to complete the user's search request. The purpose of this invention is to solve the problem of low search efficiency caused by data routing errors in existing human resources information management technologies.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for managing a cloud database of human resources information. Background Technology

[0002] With the increasing volume of unstructured HR information such as employee files, skill tags, and project experience, companies are finding it increasingly difficult to respond quickly to talent assessments and job matching, struggling to adapt to rapidly changing business needs. Furthermore, when faced with the highly diverse, multilingual, and unstructured HR data resulting from global business expansion, existing HR information cloud database management methods, due to their relatively fixed understanding of data formats and language encodings, have failed to adapt promptly and adequately to new data formats and language environments. They are unable to quickly integrate new language patterns, identify novel document structures, or understand regional compliance tags.

[0003] Existing technologies are prone to data routing errors when classifying and routing highly diverse data. Persistent data routing errors directly impact the physical distribution of information. This can lead to employee records, including critical personal identification information and sensitive project documents, being incorrectly stored in mismatched physical data centers or storage nodes. This physical management misalignment causes the actual storage location of data to no longer align with its logical classification or intended access patterns, resulting in a fragmented and inconsistent data landscape across the global infrastructure. This significantly reduces search efficiency for enterprises conducting global talent reviews or cross-regional job matching, and greatly increases the risk of serious compliance issues. Summary of the Invention

[0004] The purpose of this invention is to provide a human resources information cloud database management method and system to solve the problem that data routing errors easily occur in the existing technology when managing human resources information, resulting in low search efficiency.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a human resources information cloud database management method, comprising: Obtain search request data sent by users to the human resources information cloud database; Data analysis is performed on the search request data to obtain the search information type and information sensitivity; Data matching is performed on the search information type and information sensitivity to determine the matching data searched by the user; The matching data searched by the user is filtered to obtain filtered matching data; The filtered matching data is pushed to the user to complete the user's search request.

[0006] Furthermore, the step of performing data matching based on the search information type and information sensitivity to determine the matching data searched by the user includes: Using a preset matching model, data matching is performed on the search information type and information sensitivity to obtain initial matching information; The initial matching information is subjected to integrity verification to obtain the matching completeness. Using the matching completeness, the initial matching information is adjusted to obtain the matching data searched by the user.

[0007] Furthermore, the step of adjusting the initial matching information using the matching completeness to obtain the matching data searched by the user includes: The completeness threshold is used to perform comparative analysis on the matching completeness to obtain a completeness comparison value; When the completeness comparison value indicates that the initial matching information is complete, the initial matching information is subjected to information aggregation and encryption processing to obtain the matching data searched by the user. When the completeness comparison value indicates that there is missing information in the initial matching information, the information adjustment parameters are constructed. The parameters are adjusted using the information to modify the initial matching information, thereby obtaining the matching data searched by the user.

[0008] Furthermore, the step of adjusting the parameters using the information to modify the initial matching information and obtain the matching data searched by the user includes: The initial matching information is parsed to obtain each information adjustment element; Each information adjustment element is subjected to element scoring processing to obtain a score for each element. Based on the score and information adjustment parameters of each element, the initial matching information is adjusted to obtain the matching data searched by the user.

[0009] Further, the steps of performing data analysis on the search request data to obtain the search information type and information sensitivity include: Perform semantic analysis on the search request data to identify semantic requirement information and key semantic types; Based on the semantic requirements information and key semantic types, analyze the sensitivity of the search request data and determine the information sensitivity. The search request data is analyzed for information type to obtain the search information type.

[0010] Further, the step of performing information type analysis on the search request data to obtain the search information type includes: The search request data is categorized to obtain each information type; Each information type is integrated to obtain an integrated information type; Type validation is performed on each of the integrated information types to obtain the search information type.

[0011] Further, the step of filtering the matching data searched by the user to obtain the filtered matching data includes: The matching data searched by the user is initially filtered to obtain the raw filtered data. The original screening data is preprocessed to obtain preprocessed original screening data; The confidence level of the preprocessed original screening data is obtained by performing a confidence assessment on the preprocessed original screening data. Using the confidence level of the preprocessed original screening data, the preprocessed original screening data is adjusted to obtain the filtered matching data.

[0012] Further, the step of preprocessing the original screening data to obtain preprocessed original screening data includes: The original screening data is denoised to obtain the denoised original screening data. The denoised original screening data is subjected to text correction processing to obtain corrected original screening data; The corrected original screening data is adjusted to meet compliance requirements to obtain preprocessed original screening data.

[0013] Furthermore, the steps for obtaining the search request data sent by the user to the human resources information cloud database include: Obtain the raw data of the search request sent by the user to the human resources information cloud database; The original data of the search request is subjected to integrity verification to obtain the search request data.

[0014] The present invention also provides a human resources information cloud database management system, the system comprising: The data acquisition module is used to acquire search request data sent by users to the human resources information cloud database; The data analysis module is used to perform data analysis on the search request data to obtain the search information type and information sensitivity; The data matching module is used to perform data matching on the search information type and information sensitivity to determine the matching data searched by the user; The data search module is used to filter the matching data searched by the user to obtain the filtered matching data; The data push module is used to push the filtered matching data to the user to complete the user's search request.

[0015] Compared with the prior art, the human resources information cloud database management method and system of the present invention have the following advantages: This invention acquires user search request data and analyzes it to determine the type and sensitivity of the searched information. This data is then matched to identify the matching data for the user's search. The matched data is then filtered to obtain the final matched data, which is then pushed to the user to complete their search request. Furthermore, through refined analysis, matching, and filtering of search requests, the management efficiency and accuracy of the human resources information cloud database are significantly improved, effectively preventing the leakage of sensitive information and data misalignment. This enhances data processing capabilities, increases search efficiency, avoids compliance risks, and enables enterprises to manage human resources information more efficiently and securely. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart of a cloud database management method for human resources information according to the present invention.

[0018] Figure 2 This is a flowchart illustrating the structure of a cloud database management system for human resources information according to the present invention.

[0019] In the diagram: 210, Data Acquisition Module; 220, Data Analysis Module; 230, Data Matching Module; 240, Data Search Module; 250, Data Push Module.

[0020] The implementation and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms, and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If a combination of technical solutions is contradictory or impossible to implement, such a combination should be considered nonexistent and not within the scope of protection claimed by this invention.

[0024] To further understand the content, features, and effects of this invention, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings: Please see Figure 1 This invention provides a method for managing a cloud database of human resources information, comprising the following steps: S100. Obtain search request data sent by the user to the Human Resources Information Cloud Database. The Human Resources Information Cloud Database refers to a database system on a cloud computing platform used to store, manage, and process various types of information about enterprise employees, including but not limited to employee files, skill tags, project experience, and salary and benefits. It possesses high availability, scalability, and distributed storage capabilities. Search request data refers to the query instructions issued by the user to the cloud database through a front-end interface or API interface, which includes the information content and query conditions the user wishes to obtain. Specifically, users can enter query keywords through a web interface, and after receiving the keywords, they are packaged into search request data. Alternatively, users can submit a query form through a mobile application, and the application converts the form content into structured search request data and sends it to the cloud database. Furthermore, batch query requests from other enterprise management systems (such as HRM systems) can also be received through the API interface; these requests are also considered search request data.

[0025] S200. Perform data analysis on the search request data to obtain the search information type and information sensitivity. The search information type refers to the category of the data requested by the user, such as basic employee information, educational background, work experience, and performance evaluation. Information sensitivity refers to the importance and confidentiality level of the queried information; for example, personal identification information and salary data are usually highly sensitive, while publicly available job descriptions are less sensitive. Specifically, natural language processing can be performed on the user-input text to identify entities (such as Zhang San and Java engineers) and intents (such as querying employee information and searching for skill matches) to determine the search information type. For determining information sensitivity, a set of rules can be preset. For example, if the search request contains keywords such as salary and bank account number, it is automatically marked as high-sensitivity information; if it only contains keywords such as department and position, it is marked as low-sensitivity information.

[0026] S300. Perform data matching on the search information type and information sensitivity to determine the matching data searched by the user. The matching data refers to the original dataset retrieved from the cloud database that matches the search information type and information sensitivity based on the user's search request. Specifically, based on the analyzed search information type, search for the corresponding table or field in the metadata index of the cloud database. Simultaneously, considering the information sensitivity, the system will prioritize retrieving data from data partitions with the corresponding security level. If the user requests highly sensitive information, matching will only be performed from encrypted storage areas or databases with strict access control.

[0027] S400. The matching data searched by the user is filtered to obtain filtered matching data. Filtered matching data refers to the data presented to the user after further refinement processing to remove redundant, inaccurate, or non-compliant data. Specifically, the data can be further filtered based on other conditions in the user's request (such as time range and departmental restrictions). Furthermore, deduplication and format standardization operations can be performed on the matched data to ensure data quality and consistency.

[0028] S500: The filtered matching data is pushed to the user to complete the user's search request. Specifically, the filtered matching data can be presented on the user's web interface in the form of a table, chart, or report. For mobile application users, the data can be displayed in the form of cards or lists. If the user requests data export, the filtered matching data can be generated into CSV, Excel, or other format files for the user to download.

[0029] This embodiment ensures users can efficiently and accurately obtain the necessary human resources information through a refined data processing workflow, while also ensuring data security and compliance. First, it acquires the user's search request, such as a query command issued by the user through various front-end interfaces. Then, it conducts in-depth analysis of the request data to identify the type of information the user truly wants to find (search information type) and the sensitivity of the information (information sensitivity). This is crucial for ensuring the accuracy of subsequent data matching. Next, using the analysis results, it performs intelligent matching in the human resources information cloud database to find all data that matches the search information type and information sensitivity. After obtaining the initial matching data, it is not directly pushed to the user but undergoes rigorous screening to remove redundant, inaccurate, or data that does not meet the user's specific needs, ensuring that the data finally pushed to the user is highly relevant and of high quality. Finally, the screened data is securely and promptly pushed to the user, thus efficiently fulfilling the user's search request. This not only significantly improves the accuracy and efficiency of data processing but also enhances search efficiency and avoids compliance risks.

[0030] In some embodiments of this application described above, the step of performing data matching on the search information type and information sensitivity to determine the matching data searched by the user includes: Using a pre-defined matching model, data matching is performed on the search information type and information sensitivity to obtain initial matching information. This step refers to the preliminary data comparison and association of the search information type and information sensitivity obtained after analyzing the user's search request, based on pre-set matching rules, algorithms, or models. This retrieves a data set that initially matches the user's needs from the human resources information cloud database, forming initial matching information. This pre-defined matching model can be implemented based on various technologies such as keyword matching, semantic similarity matching, and structured data matching, with the aim of quickly and effectively identifying potentially relevant data.

[0031] The initial matching information is then subjected to integrity verification to obtain the matching completeness. This step involves a systematic check of the initial matching information to evaluate the completeness, consistency, and validity of its data items. For example, it can check whether key fields are missing, whether the data format conforms to specifications, and whether there are any conflicts in the data logic. Matching completeness is an important indicator for measuring the quality of the initial matching information, and its purpose is to identify potential defects or deficiencies in the initial matching information.

[0032] Using the completeness of the match, the initial matching information is adjusted to obtain the matching data searched by the user. This step refers to optimizing and correcting the initial matching information based on the completeness verification result, i.e., the completeness of the match. When the completeness of the match indicates that the initial matching information has defects, measures such as supplementing missing data, correcting erroneous data, eliminating redundant information, or standardizing data can be taken to improve the quality and usability of the matching data. The purpose is to ensure that the data finally pushed to the user is accurate, complete, and reliable.

[0033] Specifically, after a user sends a search request to the human resources information cloud database, requesting to find the contact information of employees with more than three years of work experience in the sales department, the request is first analyzed to determine that the search information type is employee contact information, work experience, and department, and the information sensitivity is medium. Then, using a preset matching model, such as a model based on SQL queries and keyword matching, the search information type and information sensitivity are matched against the data, retrieving employee information that initially meets the criteria from the database, resulting in initial matching information. For example, it might retrieve employee A (sales department, 5 years of work experience, but the contact information field is empty), employee B (sales department, 3 years of work experience, complete contact information), and employee C (marketing department, 4 years of work experience, complete contact information). Next, the completeness of the initial matching information is checked. For employee A, it is found that their contact information field is empty, so their match completeness is assessed as low. For employees B and C, their information completeness is high. Finally, the initial matching information is adjusted based on the match completeness. Because employee A's contact information is missing, according to the preset adjustment strategy, the system will attempt to supplement their contact information from other related data sources (such as the employee file system), or, if it cannot be supplemented, mark them as having incomplete information and notify the user. For employee C, although their information is complete, their department is the marketing department rather than the sales department, which does not fully match the type of information being searched. Adjustments will be made based on the completeness of the match, either excluding them from the final matching data or lowering their priority. Through these adjustments, the final matching data obtained for the user's search will be more complete, more accurate, and better suited to the user's needs.

[0034] This invention effectively avoids incomplete or inaccurate matching results by introducing a preset matching model, integrity verification, and information adjustment mechanisms. First, the preset matching model can quickly generate initial matching information based on the type and sensitivity of the searched information, laying the foundation for subsequent processing. Second, integrity verification of the initial matching information can promptly identify missing or erroneous data and quantify it as matching completeness, thus providing a basis for decision-making in subsequent information adjustments. Due to the introduction of matching completeness, targeted adjustments can be made to the initial matching information, such as supplementing missing data or correcting erroneous data, thereby ensuring high-quality matching data obtained from the user's search.

[0035] In some embodiments of this application described above, the step of adjusting the initial matching information using the matching completeness to obtain the matching data searched by the user includes: A completeness threshold is used to compare and analyze the matching completeness, resulting in a completeness comparison value. Specifically, the completeness threshold is a pre-set standard value used to measure the completeness of information, which can be flexibly configured according to the actual application scenario and data quality requirements. For example, it can be set as a percentage, such as 90%, indicating that when the matching completeness reaches or exceeds 90%, the information is complete. This step involves comparing the calculated matching completeness with the preset completeness threshold to determine whether the initial matched information has reached the expected level of completeness. The result of the comparative analysis, i.e., the completeness comparison value, will clearly indicate whether the initial matched information is complete or contains missing information.

[0036] When the completeness comparison value indicates that the initial matching information is complete, the initial matching information is aggregated and encrypted to obtain the matching data searched by the user. Specifically, to ensure data security and transmission efficiency, the initial matching information can be aggregated and encrypted. The aggregation process aims to integrate scattered data into a structured format that is easy to transmit and store; the encryption process uses specific encryption algorithms to securely protect the aggregated data, preventing unauthorized access and leakage, thereby obtaining the matching data searched by the user.

[0037] When the completeness comparison value indicates that there is missing information in the initial matching information, information adjustment parameters are constructed. These information adjustment parameters are a set of rules, weights, or supplementary data guiding the information adjustment process, and can be dynamically generated based on the type and importance of the missing information and a preset adjustment strategy. For example, if a key field is missing, the information adjustment parameters may include instructions or default values ​​for retrieving that field from other related data sources.

[0038] Using the aforementioned information to adjust parameters, the initial matching information is adjusted to obtain the matching data searched by the user. This step refers to supplementing, correcting, or optimizing any missing initial matching information based on the constructed information adjustment parameters. For example, missing data can be retrieved from a backup database according to parameter instructions, or missing fields can be inferred and filled based on business logic, thereby making the initial matching information more complete and accurate, ultimately obtaining the matching data searched by the user.

[0039] Specifically, when a user searches for an employee's complete profile, initial matching information is first obtained based on the search request data. This initial matching information is then validated for completeness to determine the matching score. For example, if the employee's educational background information is missing, the matching score might be 80%. This 80% score is then compared to a preset completeness threshold (e.g., 90%). Since 80% is lower than 90%, the comparison value indicates missing information in the initial matching. Based on the type of missing educational background information, an information adjustment parameter is constructed. For example, this parameter might instruct the extraction of educational background information from a scanned copy of the employee's paper file submitted upon joining the company, or prompt the administrator to manually supplement it. Using this adjustment parameter, the initial matching information is adjusted to complete the missing educational background information, ultimately yielding the complete and accurate matching data sought by the user. Conversely, if the initial matching information completeness reaches 95%, exceeding the 90% threshold, the complete initial matching information is directly aggregated, encrypted, and then pushed to the user.

[0040] This invention, by introducing a completeness threshold and comparing and analyzing the completeness of matches, can accurately determine the completeness status of initial matching information. Through differentiated processing, when the information is complete, efficient and secure aggregation and encryption can be performed to ensure data integrity and confidentiality; while when information is missing, targeted information adjustment parameters can be constructed, and these parameters can be used to fine-tune the initial matching information. This differentiated processing mechanism based on the completeness status of information effectively compensates for the shortcomings of making adjustments solely based on the completeness of matches, avoiding the problem of inaccurate or incomplete matching data caused by missing information.

[0041] In some embodiments of this application described above, the step of adjusting the initial matching information using the information adjustment parameters to obtain the matching data searched by the user includes: The initial matching information is parsed to obtain each information adjustment element. Specifically, information parsing refers to decomposing the initial matching information into multiple independent and processable components, which are the information adjustment elements. For example, for a human resources file, information adjustment elements may include name, gender, age, education, work experience, skills and expertise, and project experience. Element scoring processing for each information adjustment element involves evaluating its importance, completeness, accuracy, or relevance to the search request according to preset rules or models, thereby obtaining a corresponding element score.

[0042] Each information adjustment element is scored to obtain a score for each element. The element score is a quantitative assessment of the quality or value of each information adjustment element.

[0043] Based on the score and information adjustment parameters for each element, the initial matching information is adjusted to obtain the matching data searched by the user. Specifically, this step involves combining the score results of each element with pre-built information adjustment parameters to supplement, correct, or reconstruct missing, inaccurate, or needing-optimization information adjustment elements, thereby making the initial matching information more complete and accurate.

[0044] Specifically, after sending a search request to the human resources information cloud database, the user requests to find engineers with more than five years of Java development experience. After initial matching, initial matching information is obtained, including candidate A's profile. However, after verifying the completeness of candidate A's initial matching information, it is found that their work experience field is missing, but their project experience field is very detailed. At this point, information adjustment parameters are constructed; for example, when work experience is missing, it can be inferred from project experience. Specifically, the initial matching information of candidate A is parsed to obtain multiple information adjustment elements, including name, education, project experience, and skill tags. Next, these information adjustment elements are scored. For example, the work experience element is scored low due to its missing information, while the project experience element is scored high due to its detail and high relevance to work experience. Subsequently, based on the element scores and preset information adjustment parameters, the high-scoring information of the project experience element, combined with the adjustment parameters for inferring work experience from project experience, is used to adjust the information of candidate A's work experience element. For example, by analyzing the duration and complexity of their project experience, it is inferred that they have six years of Java development experience. As a result, the matching data searched by users was effectively and accurately supplemented, improving the quality of the matching results.

[0045] This invention refines the initial matching information into independently processable information adjustment elements and scores each element. This transforms the information adjustment process from a general, holistic approach to a more refined one, targeting specific missing or inaccurate elements. This allows the information adjustment parameters to be applied more effectively to each individual element, achieving targeted information supplementation and correction. By using an element-based scoring adjustment method, the accuracy and effectiveness of information adjustment are ensured, preventing a decline in matching data quality due to missing information.

[0046] In some embodiments of this application described above, the step of performing data analysis on the search request data to obtain the search information type and information sensitivity includes: Semantic analysis is performed on the search request data to identify semantic demand information and key semantic types. This semantic analysis aims to gain a deeper understanding of the user's true intent and the context of their query. Semantic demand information refers to the explicit or implicit needs expressed by the user in their search request, such as querying a specific employee's salary information, departmental structure, or performance evaluation. Key semantic types refer to the core words, phrases, or concepts identified during semantic analysis that are directly related to specific categories or attributes of the human resources data. For example, when a user enters a query for Zhang San's salary, Zhang San is the semantic demand information, and salary is the key semantic type.

[0047] Based on the semantic requirements and key semantic types, the sensitivity of the requested data is analyzed to determine the information sensitivity. Specifically, information sensitivity refers to the potential impact of the requested data if it is leaked. For example, information such as salary, health status, and personal performance is generally considered highly sensitive, while department names and job titles may be considered low-sensitivity information. By using pre-defined sensitivity rules or models, combined with the semantic analysis results, the sensitivity level of the information involved in this search request can be accurately determined.

[0048] Information type analysis is performed on the search request data to determine the search information type. Specifically, the user's query is mapped to various data structures or categories stored in the human resources information cloud database. Search information types may include employee basic information, salary and benefits information, performance management information, training and development information, and organizational structure information, etc. By analyzing the keywords, phrases, and grammatical structure in the request, the specific category of information the user wishes to obtain can be identified.

[0049] This invention refines the data analysis process for search request data into semantic analysis, sensitivity analysis, and information type analysis, enabling a more comprehensive and in-depth understanding of the user's search intent and the characteristics of the requested data. First, semantic analysis ensures accurate interpretation of the user's query, avoiding misunderstandings caused by inaccurate keyword matching. Second, sensitivity analysis based on the semantic analysis results allows for differentiated processing strategies for data with different sensitivities during subsequent data matching and filtering, such as stricter access controls or encryption measures, effectively ensuring the security of human resource data. Finally, information type analysis ensures precise location of the corresponding information category in the database, providing clear guidance for subsequent data matching. Thus, the obtained search information type and information sensitivity are accurate and comprehensive, laying a solid foundation for subsequent data matching and filtering.

[0050] In some embodiments of this application described above, the step of performing information type analysis on the search request data to obtain the search information type includes: The search request data is then classified to obtain each information type. Specifically, information classification refers to dividing the original search request data into multiple discrete information categories based on its content, structure, or preset classification rules. For example, a search request may contain information such as employee name, department, and salary range, which can be identified as different information types. The purpose is to perform preliminary structuring and summarization of complex search requests, laying the foundation for subsequent processing.

[0051] Each information type is then integrated to obtain an integrated information type. This integration process involves merging, deduplicating, or standardizing the information types initially classified. For example, if an employee's name and "name" are identified as two different information types that actually refer to the same concept, integration is necessary. The aim is to eliminate redundancy and ambiguity, ensuring the accuracy and consistency of the information types.

[0052] Each integrated information type undergoes type validation to obtain the search information type. Specifically, this step involves comparing the integrated information type with a predefined information type dictionary, business rules, or data model to verify its validity and compliance. For example, it can check whether a certain information type belongs to a permitted field type in the human resources database or whether it conforms to specific data format requirements. The purpose is to ensure that the final search information type is accurate, valid, and conforms to system processing specifications.

[0053] This invention first performs meticulous information classification on the search request data, decomposing the original unstructured request into manageable, discrete information units. Then, by integrating the initially classified information types, potential redundancy and inconsistencies are effectively eliminated, ensuring the refinement and standardization of information types. Finally, a type validation step rigorously verifies the integrated information types, ensuring that the obtained search information types are accurate, effective, and meet the system's processing requirements. This allows for the extraction of precise search information types from the original search request data, providing high-quality input for subsequent data matching operations.

[0054] In some embodiments of this application described above, the step of filtering the matching data searched by the user to obtain filtered matching data includes: The matching data searched by the user undergoes preliminary filtering to obtain raw filtered data. This step refers to performing a first round of coarse filtering on the initial matching data based on the basic conditions of the user's search request. For example, based on preset rules such as keyword matching degree, data completeness, and time range, data that is obviously unsuitable or of low quality can be quickly eliminated, thereby reducing the amount of data to be processed in subsequent steps. The purpose is to quickly narrow down the data range and improve the efficiency of subsequent processing.

[0055] The original screened data is preprocessed to obtain preprocessed original screened data. This step involves cleaning, normalizing, and standardizing the data after initial screening. Specifically, this may include removing duplicates, correcting typos, standardizing data format, and handling missing values. The purpose is to eliminate noise and inconsistencies in the data, providing high-quality input for subsequent confidence assessment.

[0056] The preprocessed raw screening data undergoes a confidence assessment to obtain its confidence level. Specifically, this step involves establishing an evaluation model or rules to score the reliability, accuracy, or relevance of each piece of preprocessed data. For example, a comprehensive evaluation can be conducted based on multiple dimensions, such as the authority of the data source, the frequency of information updates, the semantic match with the search request, and user feedback history, assigning a confidence score to each piece of data. The purpose is to quantify the reliability of the data and provide a basis for the final screening adjustments.

[0057] Using the confidence level of the preprocessed original filtered data, the preprocessed original filtered data is adjusted to obtain filtered matching data. This step refers to the final filtering, sorting, or weighting of the data based on the assessed confidence level. For example, a confidence threshold can be set to directly remove data below the threshold; or the data can be sorted in descending order based on confidence level, prioritizing the display of data with high confidence levels; or data with different confidence levels can be weighted to influence their presentation in the final result. The purpose is to ensure that the matching data finally pushed to the user is high-quality, highly relevant, and reliable.

[0058] Specifically, when a user searches for software engineer positions with over five years of Java development experience and familiarity with the Spring Boot framework in the human resources information cloud database, the process begins as follows: First, the matching data from the user's search is preliminarily filtered to obtain raw filtered data. For example, this preliminary filtering quickly eliminates all non-software engineer positions, candidates with less than five years of Java development experience, or those whose resumes do not mention the Spring Boot framework, resulting in a preliminary list of candidates who meet the criteria. Next, the raw filtered data undergoes data preprocessing to obtain preprocessed raw filtered data. During this stage, the resumes of the preliminarily filtered candidates are cleaned, for example, by correcting typos, standardizing the expression of "over five years" experience across different resumes (e.g., 5+ years and over 5 years), and addressing any missing information (e.g., incomplete contact information). Then, the preprocessed raw filtered data undergoes confidence assessment to obtain a confidence score. For example, a confidence score is assigned to each candidate based on factors such as their educational background (prestigious university), work experience (well-known companies), project experience (highly relevant to Spring Boot), and skill certifications (Java or Spring certifications). Candidates with experience working for well-known companies, multiple relevant projects, and authoritative certifications will receive higher confidence levels. Finally, the confidence levels of the preprocessed original screening data are used to adjust the data, resulting in filtered matching data. For example, a confidence threshold can be set to remove candidate data with confidence levels below that threshold; or candidates can be sorted in descending order based on their confidence scores, prioritizing the display of candidates with the highest confidence levels to the user, thus ensuring that the matching data received by the user is the most relevant and reliable.

[0059] This invention effectively avoids inaccurate, redundant, or low-quality information in the screening results by introducing a series of refined steps, including preliminary screening, data preprocessing, confidence assessment, and confidence-based adjustment. First, preliminary screening quickly eliminates a large amount of irrelevant or low-quality initial matching data, laying the foundation for subsequent processing. Second, data preprocessing, through cleaning and normalization, eliminates noise and inconsistencies in the data, ensuring data quality. Next, confidence assessment provides a quantified reliability indicator for each data point, identifying which data are more trustworthy. Finally, the confidence-based adjustment mechanism intelligently filters, sorts, or weights data, ensuring that the matching data ultimately pushed to the user has undergone rigorous quality control, thereby significantly improving the accuracy and effectiveness of the screening results.

[0060] In some embodiments of this application described above, the step of preprocessing the original screening data to obtain preprocessed original screening data includes: The original filtered data is then denoised to obtain denoised original filtered data. Specifically, denoising refers to the process of identifying and removing redundant, erroneous, or irrelevant information in the original filtered data. For example, duplicate records, invalid characters, special symbols, or background information unrelated to the search request can be identified and deleted. The purpose is to improve the purity and accuracy of the data, providing high-quality basic data for subsequent processing.

[0061] The denoised original screening data is then subjected to text correction processing to obtain corrected original screening data. Text correction processing involves standardizing and correcting the text content in the denoised original screening data. Specifically, this may include correcting spelling errors, grammatical errors, standardizing terminology, and handling synonyms or abbreviations. For example, "Python development" and "PythonDev" are standardized as "Python development engineer," or typos in resumes are corrected. The purpose is to ensure the consistency and readability of textual information and avoid biases in matching or screening due to textual differences.

[0062] The corrected original filtered data undergoes compliance adjustments to obtain preprocessed original filtered data. Specifically, compliance adjustments refer to formatting and content modifications to the corrected original filtered data according to preset data standards, privacy policies, or laws and regulations. For example, sensitive information may be anonymized, date formats standardized, and the integrity of specific fields ensured, or specific data structure requirements are met. The aim is to guarantee the legality, security, and standardization of the data, enabling it to meet the requirements of internal management and external supervision.

[0063] Specifically, after a user sends a search request to the human resources information cloud database, aiming to find candidates with advanced Java development experience, the initial screening data is obtained. This data may contain resumes and personal information from various channels. First, the raw screening data undergoes noise reduction. For example, duplicate resumes can be identified and deleted, or descriptions of personal hobbies unrelated to professional skills and some garbled characters can be filtered out, resulting in noise-reduced raw screening data. Next, the noise-reduced raw screening data undergoes text correction. For example, "Jave" in resumes is corrected to "Java," "Senior Development Engineer" is standardized to "Advanced Development Engineer," or different date formats (such as 2020-01-01 and Jan. 1, 2020) are standardized to a standard format, resulting in corrected raw screening data. Finally, the corrected raw screening data undergoes compliance adjustments. For example, sensitive information such as ID card numbers and bank account numbers in resumes are anonymized or de-identified according to data privacy policies, or all candidates' contact information is ensured to comply with specific storage format requirements, ultimately resulting in preprocessed raw screening data. Through meticulous preprocessing steps, we ensure that subsequent confidence assessments and data adjustments are based on high-quality, standardized, and compliant data, thereby more accurately matching candidates who meet user needs.

[0064] This invention refines data preprocessing into denoising, text correction, and compliance adjustment. First, denoising effectively removes redundant and erroneous information from the data, ensuring that the data processed subsequently is clean and relevant. Second, text correction further unifies and corrects the text content, eliminating matching errors that may arise from inconsistent wording. Finally, compliance adjustment ensures at a higher level that the data complies with established regulations and legal requirements, avoiding potential risks. This ensures that the original screened data reaches a high quality standard before entering subsequent confidence assessment and adjustment, thus laying a solid foundation for the accurate completion of the entire search request.

[0065] In some of the embodiments described above in this application, the step of obtaining search request data sent by a user to the human resources information cloud database includes: Obtain the raw data of the search request sent by the user to the human resources information cloud database. This step refers to the system directly receiving the initial user query information without any processing. This raw search request data may contain keywords, query conditions, and target information types entered by the user, and its form may vary, such as text, voice commands, or structured query statements.

[0066] The original data of the search request is subjected to integrity verification to obtain the search request data. This step refers to performing a series of checks on the received original data of the search request to ensure its integrity and validity. For example, it can check whether the request contains all required fields, whether the field values ​​conform to preset data type or format requirements, and whether there are illegal characters or malicious code. The purpose is to filter out incomplete or erroneous data, thereby ensuring the accuracy and reliability of subsequent data processing.

[0067] Specifically, after a user sends a search request to the HR information cloud database through the front-end interface, such as querying Zhang San's start date and department information, the system first receives this request as the raw data for the search request. Then, the raw data undergoes an integrity check. Specifically, it checks whether the request contains key information such as name and query type, and verifies the correct format of the information. If the query type is missing, or the name field is empty, the raw data for the search request is considered incomplete, and the user may be prompted to re-enter the information or for auto-completion. Only when the raw data for the search request passes all integrity checks is it confirmed as valid search request data and passed to the data analysis module for further processing.

[0068] This invention first acquires the raw data of the search request sent by the user to the human resources information cloud database, and then performs an integrity check on this raw data, thereby ensuring that the search request data entering subsequent processing is complete and valid. This pre-emptive verification mechanism effectively avoids the accumulation of errors in subsequent data analysis, matching, and filtering stages caused by problems with the quality of the raw data, thus improving the robustness and reliability of the entire management method from the source. Because of the strict quality control of the input data, subsequent operations can be based on high-quality data, thereby guaranteeing the accuracy of the final search results.

[0069] Based on any of the above embodiments, please refer to the human resources information cloud database management method. Figure 2 The present invention also provides a human resources information cloud database management system, which includes a data acquisition module 210, a data analysis module 220, a data matching module 230, a data search module 240 and a data push module 250.

[0070] The data acquisition module 210 is used to acquire the search request data sent by the user to the human resources information cloud database.

[0071] The data analysis module 220 is used to perform data analysis on the search request data to obtain the search information type and information sensitivity.

[0072] The data matching module 230 is used to perform data matching on the search information type and information sensitivity to determine the matching data searched by the user.

[0073] The data search module 240 is used to filter the matching data searched by the user to obtain the filtered matching data.

[0074] The data push module 250 is used to push the filtered matching data to the user to complete the user's search request.

[0075] In this embodiment, the configuration of the data acquisition module 210, data analysis module 220, data matching module 230, data search module 240, and data push module 250 enables accurate understanding and efficient response to user search requests. The data analysis module 220 performs in-depth semantic analysis and sensitivity assessment of search requests, ensuring the accuracy of subsequent matching; the data search module 240 rigorously filters the matching results, further guaranteeing the quality and compliance of the pushed data. This significantly improves the management efficiency and data accuracy of the human resources information cloud database, providing more reliable and efficient technical support for talent management in a globalized business context.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention specification.

Claims

1. A method for managing a cloud database of human resources information, characterized in that, include: Obtain search request data sent by users to the human resources information cloud database; Data analysis is performed on the search request data to obtain the search information type and information sensitivity; Data matching is performed on the search information type and information sensitivity to determine the matching data searched by the user; The matching data searched by the user is filtered to obtain filtered matching data; The filtered matching data is pushed to the user to complete the user's search request.

2. The human resources information cloud database management method according to claim 1, characterized in that, The steps of matching the search information type and information sensitivity to determine the matching data searched by the user include: Using a preset matching model, data matching is performed on the search information type and information sensitivity to obtain initial matching information; The initial matching information is subjected to integrity verification to obtain the matching completeness. Using the matching completeness, the initial matching information is adjusted to obtain the matching data searched by the user.

3. The human resources information cloud database management method according to claim 2, characterized in that, The steps of adjusting the initial matching information using the matching completeness to obtain the matching data searched by the user include: The completeness threshold is used to perform comparative analysis on the matching completeness to obtain a completeness comparison value; When the completeness comparison value indicates that the initial matching information is complete, the initial matching information is subjected to information aggregation and encryption processing to obtain the matching data searched by the user. When the completeness comparison value indicates that there is missing information in the initial matching information, the information adjustment parameters are constructed. The parameters are adjusted using the information to modify the initial matching information, thereby obtaining the matching data searched by the user.

4. The human resources information cloud database management method according to claim 3, characterized in that, The steps of adjusting parameters using the aforementioned information to adjust the initial matching information and obtain the matching data searched by the user include: The initial matching information is parsed to obtain each information adjustment element; Each information adjustment element is subjected to element scoring processing to obtain a score for each element. Based on the score and information adjustment parameters of each element, the initial matching information is adjusted to obtain the matching data searched by the user.

5. The human resources information cloud database management method according to claim 1, characterized in that, The steps of performing data analysis on the search request data to obtain the search information type and information sensitivity include: Perform semantic analysis on the search request data to identify semantic requirement information and key semantic types; Based on the semantic requirements information and key semantic types, analyze the sensitivity of the search request data and determine the information sensitivity. The search request data is analyzed for information type to obtain the search information type.

6. The human resources information cloud database management method according to claim 5, characterized in that, The steps for performing information type analysis on the search request data to obtain the search information type include: The search request data is categorized to obtain each information type; Each information type is integrated to obtain an integrated information type; Type validation is performed on each of the integrated information types to obtain the search information type.

7. The human resources information cloud database management method according to claim 1, characterized in that, The steps for filtering the matching data searched by the user to obtain the filtered matching data include: The matching data searched by the user is initially filtered to obtain the raw filtered data. The original screening data is preprocessed to obtain preprocessed original screening data; The confidence level of the preprocessed original screening data is obtained by performing a confidence assessment on the preprocessed original screening data. Using the confidence level of the preprocessed original screening data, the preprocessed original screening data is adjusted to obtain the filtered matching data.

8. The human resources information cloud database management method according to claim 7, characterized in that, The steps for preprocessing the original filtered data to obtain preprocessed original filtered data include: The original screening data is denoised to obtain the denoised original screening data. The denoised original screening data is subjected to text correction processing to obtain corrected original screening data; The corrected original screening data is adjusted to meet compliance requirements to obtain preprocessed original screening data.

9. A human resources information cloud database management method according to claim 1, characterized in that, The steps to obtain the search request data sent by the user to the human resources information cloud database include: Obtain the raw data of the search request sent by the user to the human resources information cloud database; The original data of the search request is subjected to integrity verification to obtain the search request data.

10. A cloud database management system for human resources information, characterized in that, The system includes: The data acquisition module is used to acquire search request data sent by users to the human resources information cloud database; The data analysis module is used to perform data analysis on the search request data to obtain the search information type and information sensitivity; The data matching module is used to perform data matching on the search information type and information sensitivity to determine the matching data searched by the user; The data search module is used to filter the matching data searched by the user to obtain the filtered matching data; The data push module is used to push the filtered matching data to the user to complete the user's search request.

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