Employee onboarding multi-business system automatic authorization method based on dynamic mapping of position permissions

Abstract

The present application relates to a method for automatically authorizing employees in a multi-business system based on dynamic mapping of position permissions, aiming to solve the problem of semantic drift identification and the risk of delayed permission synchronization configuration caused by cross-system job function changes. The core solution includes: based on structured master data and hospital management specification documents, constructing a post semantic data set with a trusted label; using hierarchical hash operation to generate organizational semantic snapshot chain, realizing historical state tracking and consistency checking; determining the type of semantic drift through a multi-dimensional rule engine, and triggering permission adjustment strategy according to the drift intensity; using decision tree model to match and fine-tune the permission template, forming atomized operation instructions, and automatically distributing to each business system and checking global synchronization consistency. This solution effectively improves the permission accuracy and response efficiency of cross-system organizational changes, and realizes the closed-loop self-optimization of the permission model and the role adjustment in the organizational structure.

Description

Technical Field

[0001] This invention relates to the fields of identity and permission management, intelligent mapping of organizational structure and rapid semantic alignment, and in particular to an automatic authorization method for employee onboarding in a multi-business system based on dynamic mapping of job permissions. Background Technology

[0002] Current mainstream automated authorization systems generally adopt a role-based access control model, relying on structured fields such as job master tables, department trees, and job grade mapping tables. Roles and permissions are mapped and changed through static rule matching or periodic batch synchronization. These solutions typically have job template encapsulation and permission inheritance capabilities, enabling semi-automatic synchronization between organizational structure adjustments and business permission models through periodic fetching or change-triggered mechanisms. However, when non-standard fields in job descriptions change, existing systems struggle to distinguish between substantive business changes and superficial data corrections, leading to frequent fluctuations in the permission model or synchronization delays. Some solutions introduce multi-rule engines or historical change log tracking, but these mostly optimize static roles. When faced with complex scenarios involving overlapping historical work orders, such as cross-department job transfers or misaligned job grade advancements, manual verification or batch task recalculation is still necessary.

[0003] Existing technologies suffer from the following problems: First, when organizational structure or job master data changes, permission synchronization experiences minute-level delays, failing to meet the compliance requirements of immediate permission activation in sensitive industries. Second, permission change triggering relies on single-field-level change detection, lacking semantic-level business drift identification and tolerance judgment, easily leading to contradictory phenomena of over-response or under-response. Third, when master data is abnormally written or job descriptions become diversified, noise isolation for non-essential changes is impossible, resulting in redundant permission recalculation requests and increasing system maintenance burden. Fourth, there is a lack of automated consistency verification and correction capabilities for permission mapping results, making it difficult for the authorization process to achieve closed-loop adaptiveness, affecting overall robustness and business continuity. Current solutions generally lack real-time consistency verification and high-confidence triggering mechanisms for multi-dimensional semantics of positions, departments, and job levels, making it difficult to adapt to the high efficiency, security, and business needs of permission adjustments in sensitive industries such as healthcare. Figure 1 A comprehensive set of needs for consistency. Summary of the Invention

[0004] This application provides an automatic authorization method for employee onboarding in multiple business systems based on dynamic mapping of job permissions, aiming to solve one of the problems or issues of the existing technology mentioned in the background section above.

[0005] The method for automatic authorization of employees in multiple business systems based on dynamic mapping of job permissions provided in this application specifically includes: S1: Obtain the structured field data of the job master table, department tree and job level mapping table in the master data system, and perform semantic anchoring processing in combination with the hospital management standard document to generate the initial job semantic dataset; S2: Based on the initial job semantic dataset, perform fingerprint calculation on each collected data using hierarchical hashing to generate an organization semantic snapshot chain that is arranged in chronological order and contains semantic consistency verification information; S3: Receive the change notification message sent by the master data platform, and input the change notification message and the previous and next snapshot fingerprints in the organization's semantic snapshot chain into the context-aware comparator to generate a semantic drift detection event packet to be determined; S4: Based on the built-in business rule engine, perform multi-dimensional rule matching operations on the semantic drift detection event package to generate semantic drift determination results; S5: Determine whether the semantic drift determination result exceeds the preset business tolerance threshold. If the determination result is that there is a semantic drift that requires immediate response, trigger the generation instruction of the pending lightweight event package carrying the drift type label, and output the activation signal to the dynamic mapping strategy model. S6: Input the activation signal and historical job transfer order data into the dynamic mapping strategy model built on the decision tree ensemble, retrieve the baseline permission template with the highest matching degree and superimpose fine-tuning instructions to generate an atomic permission operation instruction set.

[0006] S7: Based on the atomic permission operation instruction set, distribute permission configuration requests to multiple business systems, perform incremental update operations on role-permission relationships, and generate permission status feedback logs after synchronization of multiple business systems; S8: Based on the permission status feedback log records and comparing the alignment accuracy indicators of the permission model and the organizational structure, if an abnormal synchronization delay or permission configuration conflict is detected, the decision tree weight parameters of the dynamic mapping strategy model are automatically corrected.

[0007] The method for automatic authorization of employees in multiple business systems based on dynamic mapping of job permissions provided in this application has the following beneficial effects: This solution constructs an organizational semantic snapshot chain and a context-aware comparison mechanism. When fields such as job title, department, and rank change, the triggering conditions of the permission system are upgraded from field-level changes to semantic intent recognition. This effectively distinguishes between substantive business adjustments and non-substantive data corrections, improving the accuracy of semantic drift detection and reducing the frequency of invalid permission recalculation caused by changes in non-core information. This provides a precise semantic basis for multi-business system authorization in scenarios of employee onboarding or job adjustments. Simultaneously, a decision tree ensemble model trained on historical job transfer work orders enables the system to quickly retrieve the most matching baseline permission template based on the drift type and execute fine-grained instruction overlays. This avoids the dependence on full rule traversal in the permission mapping process, reduces end-to-end permission synchronization latency, and improves the responsiveness of multi-business system automatic authorization to dynamic changes in organizational structure. Furthermore, through closed-loop verification of parallel distribution across multiple business systems and permission status feedback logs, the system can automatically correct the decision parameters of the strategy model, improving the consistency of cross-system permission configuration and the adaptability of the authorization process. Ultimately, this forms a closed loop for automatic multi-business system authorization for employee onboarding in the face of dynamic organizational structure adjustments. Attached Figure Description

[0008] Figure 1 This is the main flowchart of the automatic authorization method for multiple business systems during employee onboarding based on dynamic mapping of job permissions; Figure 2 This is a sub-flowchart of the automatic authorization method for multiple business systems during employee onboarding based on dynamic mapping of job permissions; Figure 3 This is another sub-flowchart of the automatic authorization method for multi-business systems for employee onboarding based on dynamic mapping of job permissions. Detailed Implementation

[0009] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0010] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0011] like Figure 1As shown, this application provides a method for automatic authorization of employees onboarding across multiple business systems based on dynamic mapping of job permissions, specifically including: S1: Obtain the structured field data from the job master table, department tree, and job grade mapping table in the master data system, and perform semantic anchoring processing in conjunction with hospital management standard documents to generate an initial job semantic dataset. This initial job semantic dataset includes timestamps and source credibility tags.

[0012] S2: Based on the initial job semantic dataset, perform fingerprint calculation on each collected data using hierarchical hashing to generate an organization semantic snapshot chain that is arranged in chronological order and contains semantic consistency verification information.

[0013] S3: Receive a change notification message from the master data platform, and input the change notification message and the before-and-after snapshot fingerprints in the organization's semantic snapshot chain into the context-aware comparator to generate a semantic drift detection event packet to be determined. The change notification message includes the changed field name, old value, and new value.

[0014] S4: Based on the built-in business rule engine, perform multi-dimensional rule matching operations on the semantic drift detection event package to generate semantic drift determination results marked with substantial change types. The business rule engine includes: rigid semantic constraint rules, flexible semantic equivalence rules, and time-sensitive rules.

[0015] S5: Determine whether the semantic drift determination result exceeds the preset business tolerance threshold. If the determination result is that there is a semantic drift that requires immediate response, trigger the generation instruction of the pending lightweight event package carrying the drift type label, and output the activation signal to the dynamic mapping strategy model.

[0016] S6: Input the activation signal and historical job transfer order data into the dynamic mapping strategy model built on the decision tree ensemble, retrieve the baseline permission template with the highest matching degree and superimpose fine-tuning instructions to generate an atomic permission operation instruction set.

[0017] In this embodiment, this application provides a method for automatic authorization of multiple business systems for employee onboarding based on dynamic mapping of job permissions, which further includes: S7: Based on the atomic permission operation instruction set, distribute permission configuration requests to multiple service systems, perform incremental update operations on role-permission relationships, and generate permission status feedback logs after synchronization of multiple service systems.

[0018] S8: Based on the permission status feedback log records and comparing the alignment accuracy indicators of the permission model and the organizational structure, if an abnormal synchronization delay or permission configuration conflict is detected, the decision tree weight parameters of the dynamic mapping strategy model are automatically corrected to complete the closed-loop optimization of the permission model.

[0019] Step S1: Obtain the structured field data of the job master table, department tree, and job grade mapping table from the master data system, and perform semantic anchoring processing in conjunction with the hospital management standard document to generate an initial job semantic dataset. The initial job semantic dataset includes timestamps and source credibility tags.

[0020] Specifically, it includes: S1.1: Perform structured field extraction operations on the job master table, department tree, and job grade mapping table in the master data system to obtain the original job attribute set containing job code, standard job name, department ID, administrative and technical job grade, effective date, and historical change markers, as the base data source for subsequent semantic processing.

[0021] When performing structured field extraction operations on the job master table, department tree, and job grade mapping table of the master data system, the input objects are set to the job master table, department hierarchy tree file, and job grade mapping table dataset in the master data database, and the access interface provided by the master data system is loaded to achieve batch reading at the field level.

[0022] Perform uniqueness checks and format standardization operations on the job codes recorded in the job master table, remove invalid codes and fill in missing values ​​to form a set of job codes that conform to the hospital's job management specifications.

[0023] Perform hierarchical path parsing on the department tree data structure, mapping the unique ID of each department node and the path of its parent node into a flat hierarchical path field, ensuring that subsequent semantic anchoring can accurately identify organizational affiliation.

[0024] The administrative and technical job title fields in the job title mapping table are split and their codes are standardized. Job title descriptions in different formats are converted into standardized job title sequence codes, and the association index between the code and the job title code is calculated.

[0025] The effective date field of the job postings in the three datasets is converted to a timestamp format, parsing the non-standardized date strings into high-precision millisecond-level time series values ​​to ensure the consistency of the time processing chain.

[0026] Perform Boolean value conversion and change count operation on the job history change marker field recorded in the master data system to quantify the job change frequency index, which is convenient for subsequent drift detection.

[0027] Through the chained processing method described above, the master data field set is transformed into a raw job attribute set containing job code, standard job name, department ID, administrative rank code, technical rank code, millisecond-level effective timestamp, and historical change markers, thus achieving the effect of structuring and standardizing the raw data.

[0028] S1.2: Based on the hospital management standard documents, natural language parsing and keyword extraction processing are performed to transform the job descriptions defined in the documents into a standardized semantic rule library containing classification identifiers such as clinical medical positions and information technology positions, as well as attribute values ​​such as professional permission levels and system maintenance permission sets, which are used to guide the semantic alignment of the original data.

[0029] The hospital management standard documents refer to authoritative rules and regulations issued by the hospital that define job positions, responsibilities, and authorities, such as job position management methods, provided in digital text form.

[0030] The baseline input conditions are the original set of job attributes extracted in step S1.1, which includes job code, standard job name, department ID, administrative and technical rank, effective date and historical change markers, and the full-text digital content of the hospital management standard document is obtained as the reference text for semantic processing.

[0031] The hospital management standard documents are subjected to natural language parsing based on word segmentation and part-of-speech tagging. The long sentence structures describing job responsibilities in the documents are broken down into standardized phrase units, and each phrase unit is labeled with a functional category attribute.

[0032] The parsed phrase units are processed by keyword extraction and weighted. High-weight keywords reflecting job categories are selected by term frequency-inverse document frequency (TF-IDF) and used as the candidate set of job category identifiers.

[0033] The candidate keyword set is input into the classification mapping rule generation module. Based on the job categories defined in the hospital's job setting management method, the keywords are mapped to classification labels such as clinical medical positions, information technology positions, and administrative management positions, and attribute values ​​such as professional permission level or system maintenance permission set are attached.

[0034] Context fusion calculation is performed on the mapping results to merge different textual expressions under the same job category into unified semantic rule entries, and a standardized semantic rule library with multi-dimensional attributes such as classification identifier, permission level, and functional module identifier is constructed.

[0035] Through the above-described parsing, extraction, mapping, and fusion processing methods, the original set of job attributes from the previous steps is transformed into a standardized semantic rule base with unified classification identifiers and permission attributes, thereby achieving the ability to provide precise semantic alignment guidance for the original data.

[0036] S1.3: Utilize a standardized semantic rule base to perform multidimensional semantic matching and anchoring calculation on the original set of job attributes, map non-standardized job name texts to unified job function semantic tags, generate intermediate job semantic mapping records carrying standard job function semantic tags, and eliminate semantic ambiguity caused by naming differences.

[0037] Multidimensional semantic matching calculations are performed on the standardized semantic rule base output from step S1.2 and the original job attribute set generated in step S1.1. The job name field, department affiliation field, and rank attribute field are input into the corresponding classification identifier and permission level mapping module of the rule base to construct a preliminary job function semantic matching vector. For the job name field, matching processing based on word vector cosine similarity is applied. The word vector matrix of known standard job names in the rule base is loaded, and similarity is calculated with the word vector of the original job name. Entries with similarity below a set threshold are marked as candidate sets requiring fuzzy matching. Regularization and semantic cluster normalization operations are performed on the job names in the candidate set. Using replacement rules that include different naming conventions, the text is mapped to a unified job function semantic label. For example, "Associate Chief Physician (Cardiology)" and "Associate Chief Physician of Cardiology" are uniformly mapped to "Clinical Medical Position - Senior Title - Practice Permission Level 4". The department affiliation field is aligned hierarchically with the department tree structure in the rule base. Noisy entries with non-actual affiliation changes are eliminated by calculating path similarity and mapped to unique department semantic identifiers. Rule matching based on the job grade index is performed on the job grade attribute, using the job grade sequence table in the rule base to locate the corresponding administrative or technical authority tags, ensuring consistent mapping results for the same job grade under different naming conventions. The job title semantic tags, department semantic tags, and job grade semantic tags are combined and encapsulated to generate a structured intermediate job semantic mapping record with complete fields.

[0038] Through the above multidimensional semantic matching and anchoring calculation, the original set of job attributes in the previous step is transformed into an intermediate job semantic mapping record carrying standard job function semantic labels, thereby eliminating naming differences and enhancing semantic consistency.

[0039] S1.4: Perform timestamp injection and source credibility assessment operations on the intermediate job semantic mapping records, generate time series labels accurate to milliseconds by combining the data collection time, calculate the source credibility score based on the stability index of the data source system, and output enhanced job semantic metadata with time series labels and source credibility scores.

[0040] Upon receiving the intermediate job semantic mapping record as input, the time tag injection module is invoked to obtain the system clock information at the time of data collection and perform millisecond-level time precision enhancement processing. The collection time is then converted into a time series tag that can be used for version management and attached to the corresponding job function semantic tag.

[0041] Based on the enhanced job semantic metadata requirements, the source credibility assessment engine is invoked to read the stability indicators, historical failure frequency, and the interval of the most recent data anomaly of the data source system. The above indicators are then combined into a source credibility value using a weighted scoring method.

[0042] In the scoring calculation, normalization is used to normalize the stability index to the interval [0,1] based on its maximum value, and then the source credibility score is calculated using the following formula: Where S represents the source credibility score. , , These are the weighting coefficients for stability, failure frequency, and data anomaly interval, respectively. , , This corresponds to the normalized index value.

[0043] Bind time series labels to source credibility scores in the data object and perform consistency checks to ensure that the labels and scores correspond to the same data collection batch.

[0044] The metadata enhancement module is invoked to combine the above binding results with the job function semantic tags and encapsulate them into an enhanced job semantic metadata object, ensuring that the output object contains three necessary fields: job semantics, time tag, and credibility score.

[0045] By using the aforementioned millisecond-level timestamp injection and weighted credibility scoring method, the intermediate job semantic mapping record from the previous step is transformed into enhanced job semantic metadata that can support the construction of semantic snapshot chains, thereby achieving a simultaneous improvement in time accuracy and data credibility.

[0046] S1.5: Based on the enhanced job semantic metadata, perform structured encapsulation and integrity verification processing, integrate job function semantic tags, time series tags and source credibility scores into a unified data object, and generate an initial job semantic dataset with timestamps and source credibility tags, which serves as the direct input basis for constructing the organizational semantic snapshot chain.

[0047] Step S2: Based on the initial job semantic dataset, perform fingerprint calculation on each collected data using hierarchical hashing to generate an organization semantic snapshot chain arranged chronologically and containing semantic consistency verification information. Specifically, this includes: S2.1: The structured field data in the initial job semantic dataset is standardized and sorted according to job code, department tree hierarchical path and job grade sequence identifier to eliminate field arrangement noise caused by differences in the writing order of the master data system, and generate a standardized job semantic vector sequence with a definite field arrangement order.

[0048] S2.2: Based on the standardized job semantic vector sequence, the job attribute field, department affiliation field, and job level status field are mapped to independent logical data buckets using a predefined semantic bucketing strategy, so as to construct a multi-level semantic data bucket set with business dimension isolation characteristics, providing a structured input unit for hierarchical hash calculation.

[0049] Perform semantic category recognition operations on each field in the standardized job semantic vector sequence, and establish three business dimension index mapping tables based on the predefined semantic bucketing strategy: job attributes, department affiliation, and job level status.

[0050] The set of job attribute fields is matched and retrieved according to the semantic tags of job functions. Fields with similar tags are aggregated into the logical data bucket of job attributes, and the department or job level information mixed in is removed by the semantic classifier.

[0051] The department affiliation field set is hierarchically categorized according to the department tree hierarchy path. The same department ID and its superior path information are organized into logical data buckets of department affiliation, and an organizational structure hierarchy sequence is attached to each bucket.

[0052] The job grade status field set is categorized and organized according to administrative / technical job grade codes and effective status, and a job grade status logical data bucket is constructed, with a job grade sequence number attached to maintain temporal consistency.

[0053] A data bucket integrity verification mechanism is adopted to detect the number of fields, data types, and semantic consistency within each logical data bucket, and to perform null value filling or consistency correction actions for missing items.

[0054] By using a business dimension isolation method to form a multi-level semantic data bucket set, we can ensure that the data between different business dimensions remain independent in subsequent hierarchical hash calculations and reduce cross-interference.

[0055] By using semantic bucketing and business dimension isolation processing, the standardized job semantic vector sequence output from the previous step is transformed into a multi-level semantic data bucket set with structured grouping, thereby realizing the structured input function of hierarchical hash calculation.

[0056] S2.3: Perform a non-cryptographic strength rolling hash operation on the content of each logical data bucket in the multi-level semantic data bucket set to extract local hash fingerprint values ​​that reflect the content characteristics of each business dimension, and generate a hierarchical local fingerprint set containing hash values ​​of job dimension, department dimension, and job level dimension.

[0057] Each logical data bucket in the multi-level semantic data bucket set receives structured content from the job attribute field, department affiliation field, and job level status field as input conditions.

[0058] For the field sequence within each logical data bucket, a serialization traversal is performed using preset rolling window length and step parameters to map the field encoding stream into a numerical sequence in order to establish a hash calculation benchmark.

[0059] During the field encoding stream traversal, non-cryptographically strong rolling hash processing is used to perform iterative accumulation and modulo operation on the numerical sequence within the window to generate a local hash value that reflects the business semantic state of the window.

[0060] To eliminate the order sensitivity introduced by different window positions, a positional salting operation is performed for each local hash value, combining the logical bucket level index of the field with the window start position information into the object to be hashed.

[0061] A second rolling operation and normalization process is performed on the local hash value with salt applied to output local fingerprints with stable characteristics in the job position dimension, department dimension, and job level dimension. The three types of fingerprints are then integrated to form a hierarchical local fingerprint set.

[0062] By using the rolling hashing and hierarchical fingerprint extraction methods described above, the multi-level semantic data bucket set from the previous step is transformed into a hierarchical local fingerprint set containing hash values ​​of job position, department, and rank, thereby achieving semantic consistency feature extraction across business dimensions.

[0063] S2.4: Based on the hierarchical local fingerprint set, the local hash fingerprint values ​​of each dimension are combined with the current collection timestamp and the data source credibility label by the aggregate hash processing with salt value concatenation to generate a global semantic snapshot fingerprint that can uniquely identify the semantic state of the organization at that moment and has anti-collision characteristics.

[0064] Based on the hash values ​​of job, department, and rank dimensions from a hierarchical local fingerprint set, an ordered set containing local fingerprints of the three business dimensions is constructed, and a combined operation is performed by calling aggregate hashing with salt concatenation. The high-entropy random sequence output from the salt selection process is positionally bound to the current collection timestamp and source credibility label to form an aggregate input vector with a logically fixed concatenation order. The local hash fingerprints of each dimension, along with the timestamp field and source credibility score, are combined into a unified serialized bitstream using a multi-vector concatenation method to provide the complete input data units required for aggregate hashing calculation. The core compression function in aggregate hashing is applied to perform multi-round segmented iterative operations on the serialized bitstream, with salt and timestamps introduced in each iteration to ensure collision resistance of the output. A shift operation and a nonlinear hybrid function are used to strengthen the influence weights of fingerprints from different dimensions in each iteration, ensuring that the contributions of job, department, and rank dimensions in the global fingerprint are preserved and evenly distributed. The final reduction function is called to generate a global hash value that uniquely identifies the organization's semantic state at that moment, and this value is output as the global semantic snapshot fingerprint for reference in subsequent snapshot chain construction steps. By using the above-mentioned salt value concatenation and aggregate hashing methods, the hierarchical local fingerprint set and time and credibility information are transformed into an irreversible global semantic snapshot fingerprint, thereby achieving unique identification and anti-collision detection capabilities.

[0065] S2.5: Attach a reference to the parent node of the previous version and the current version number identifier to the generated global semantic snapshot fingerprint, and link it to the end of the existing historical snapshot sequence in chronological order to build an organizational semantic snapshot chain with complete version evolution trajectory and semantic consistency verification capabilities.

[0066] like Figure 2 As shown, step S3: Receive a change notification message from the master data platform, and input the change notification message and the before-and-after snapshot fingerprints in the organization's semantic snapshot chain into the context-aware comparator to generate a semantic drift detection event packet to be determined. The change notification message includes the changed field name, old value, and new value. Specifically, it includes: S3.1: Standardize the parsing and processing of change notification messages issued by the master data platform, which contain changed field names, old values ​​and new values, and extract key structured elements such as job codes, departmental affiliation identifiers and job grade sequence codes to generate standardized change data payloads, which serve as the benchmark input objects for subsequent comparison operations.

[0067] For change notification messages sent by the master data platform that contain changed field names, old values, and new values, a message receiving buffer is established and the message is parsed according to a fixed byte length and field separator to extract the original text values ​​of key fields.

[0068] The extracted field values ​​are input into the field type matcher, which performs format recognition based on the regular expression templates of predefined field types such as job code, department affiliation identifier, and job grade sequence code to confirm the consistency of their data type with business constraints.

[0069] For the job code, the coder is called to read the dictionary mapping table to convert the coded string into the corresponding job category index value; for the department affiliation identifier field, the department tree indexer is used to locate the unique department level path code; for the job grade sequence code field, the corresponding job grade weight coefficient is extracted by combining the job grade mapping table.

[0070] The job category index value, department level path code, and job grade weight coefficient are combined in the order of the fields to construct a binary structured data object, and the original change timestamp and operator identifier are attached as metadata header information.

[0071] Perform integrity checks on structured data objects, using CRC checks to ensure that the data payload has not been tampered with during transmission, and mark the structured object as a standardized changed data payload after the check passes, so that it can be used directly in subsequent comparison operations.

[0072] Through the above standardized parsing process, the results of the previous step are transformed into a standardized change data payload with a unified coding system, complete metadata header and verifiable payload, so as to achieve data consistency and controllability in the subsequent snapshot chain comparison stage.

[0073] For example, in a hospital's automated authorization system, when the master data platform detects that the "Job Title" field has changed from "Associate Chief Physician (Cardiology)" to "Associate Chief Physician of Cardiology," the message buffer receives a change notification containing: field name = job title, old value = Associate Chief Physician (Cardiology), new value = Associate Chief Physician of Cardiology, job code = MD305, department affiliation identifier = DEPT12, and rank sequence code = LV04. The field type matcher identifies that the job code MD305 conforms to the coding rules, the department affiliation identifier DEPT12 matches the cardiology hierarchical path code, and the rank sequence code LV04 maps to the rank weight coefficient 0.85. The structured object is combined as [MD305, PATH_Cardiology, 0.85] with an appended timestamp 20240615123045 and operator ID 00457. The payload is verified as normal via CRC-32 checksum and marked as a standardized change data payload. In subsequent snapshot chain comparisons, this data payload is directly used to retrieve snapshot fingerprints before and after the change, to ensure that semantic drift detection can significantly improve the accuracy of change determination.

[0074] Here, CRC is the Cyclic Redundancy Check value, and data is the structured change data payload content, ensuring that the check value obtained through byte stream analysis is consistent with the expected check value, thereby guaranteeing data integrity.

[0075] S3.2: Based on the timestamp and job code index in the standardized change data payload, retrieve and obtain the snapshot fingerprint before the change and the snapshot fingerprint after the change from the time-series-arranged organizational semantic snapshot chain to construct a spatiotemporal comparison dataset containing historical state features and current state features that will change, and establish a reference benchmark for semantic drift detection.

[0076] The job code index refers to the key value extracted from the standardized change data payload to uniquely identify a specific job that has undergone a change. It is directly derived from the job code field carried in the change notification message issued by the master data platform.

[0077] Based on the timestamp and job code index in the standardized change data payload, an index retrieval condition and snapshot chain positioning parameter set are constructed to accurately match the timestamp in the payload to the version sequence identifier of the organization's semantic snapshot chain, thereby achieving version positioning.

[0078] By jointly matching the job code index with the timestamp location result, the previous snapshot fingerprint data object corresponding to the change notification is extracted from the historical sequence of the snapshot chain and used as the source of historical status features.

[0079] After locating the previous snapshot fingerprint data object, the latest version node in the snapshot chain that is consistent with or close to the change timestamp is matched with the same job code index, and the subsequent snapshot fingerprint data object is extracted as the source of the current state feature to be changed.

[0080] The extracted historical state feature sources and the current state feature sources that will change are encapsulated in a structured manner, and organized into a two-way state feature matrix containing hash values ​​of job position dimension, department dimension, and job level dimension, and a change timestamp label is attached to ensure temporal consistency.

[0081] An index mapping table is embedded in the bidirectional state feature matrix so that the context-aware comparator can directly read core fields such as job code, department affiliation identifier, and job grade sequence, forming a spatiotemporal comparison dataset containing historical state features and current state features that will change, in order to establish a reference benchmark for semantic drift detection.

[0082] Through the above retrieval and encapsulation processes, the standardized change data payload from the previous step is transformed into spatiotemporal comparison data with temporal correlation and job attribute alignment capabilities, thereby achieving the precise reference effect required for semantic drift detection.

[0083] For example, when a hospital's intelligent authorization system receives a change notification from the master data platform, the payload timestamp is 2024-05-06 14:23:15, the job code index is "CL-MD-005", the department affiliation identifier is "NEUR-01", and the job grade sequence code is "T3". The system retrieves version V102 (timestamp 2024-05-06 14:20:00) as the pre-change snapshot fingerprint and version V103 (timestamp 2024-05-06 14:23:15) as the post-change snapshot fingerprint from the organizational semantic snapshot chain. The job dimension hash value of the pre-change snapshot fingerprint is... The hash value of the department dimension is The hash value of the job level dimension is The job-specific hash value of the subsequent snapshot fingerprint The hash value of the department dimension is The hash value of the job level dimension is The system encapsulates the above values ​​into a two-way state feature matrix: The dataset incorporates a job code and timestamp mapping table, forming a spatiotemporal comparison dataset that can be directly input into a context-aware comparator. Verification has shown that this dataset significantly improves the accuracy of job attribute comparison in subsequent difference quantification calculations, ensuring a significant enhancement in the ability to identify substantial changes during the drift detection process.

[0084] S3.3: Using a pre-set context-aware comparator, the spatiotemporal comparison dataset is input into a multidimensional feature mapping space, and a difference quantification calculation based on business context is performed to identify regular expression matching deviations and logical jumps in administrative affiliation in job attribute descriptions, and to generate a preliminary semantic difference measurement vector.

[0085] The context-aware comparator is a pre-built core analysis module that works collaboratively by integrating a multi-dimensional feature mapping space construction module, a regular expression matching and verification module, an administrative affiliation jump identification module, and a difference quantification calculation module. Specifically, the multi-dimensional feature mapping space construction module maps the historical state features and the currently changing state features in the input spatiotemporal comparison dataset to a unified business dimension space; the regular expression matching and verification module performs pattern matching on job attribute descriptions within this business dimension space to quantify naming bias; the administrative affiliation jump identification module determines logical jumps in department affiliation based on path differences in the organizational structure tree; finally, the difference quantification calculation module integrates the outputs of each module, calculates the difference intensity of each business dimension using a pre-set weighting function, and normalizes it to generate a preliminary semantic difference measurement vector, thus providing a quantitative difference analysis basis for semantic drift detection. The specific workflow of the context-aware comparator is as follows: Based on the historical state features and the current state features that will change, contained in the spatiotemporal comparison dataset, a pre-built context-aware comparator is invoked to map the two types of features to a multi-dimensional feature mapping space, so as to construct a correspondence matrix of business dimensions such as job attributes, department affiliation and job level sequence.

[0086] In the mapping space, regular expression matching and validation operations are performed according to the job code, standard job name and job description fields to extract the matching deviation value of the naming pattern deviation, and to quantify the degree of textual difference between the description of each department and the job level description.

[0087] Perform administrative affiliation jump identification operation on the department affiliation identifier, calculate the distance change value of affiliation nodes based on the hierarchical path difference of the organizational structure tree, and determine whether there is a logical jump behavior across nodes.

[0088] In the multidimensional feature mapping space, the matching deviation value and the change value of the distance between the member nodes are input into the difference quantization function to form a numerical output of the difference intensity of each business dimension. The difference quantization function can be defined as follows: in Let be the weight coefficient for the i-th business dimension. Let represent the difference value in the i-th business dimension, and D represent the multidimensional difference intensity value.

[0089] Normalization is performed on the multidimensional difference intensity values ​​to ensure that the differences in each dimension are comparable under a unified scale, and they are integrated into a preliminary semantic difference measurement vector.

[0090] S3.4: Based on the preliminary semantic difference measurement vector and combined with the rigid semantic constraint boundary conditions defined in the hospital management standard document, non-substantive field noise is filtered out to separate the organizational change feature clusters with substantial business impact, forming a high-confidence potential drift candidate set.

[0091] The rigid semantic constraint boundary conditions refer to the set of rules and thresholds extracted from hospital management standard documents to quantify whether a field change has a substantial business impact. By transforming the rigid management requirements in the hospital management standard documents into specific numerical boundaries such as difference intensity thresholds or logical rules, it provides a direct basis for filtering non-substantial field noise and accurately separating organizational change feature clusters.

[0092] The feature weight ranking operation is performed on the preliminary semantic difference measurement vector. Based on the matching error value and drift intensity score of each feature in the hospital job semantic database, a feature importance matrix is ​​constructed. Features with weights lower than the preset rigid semantic constraint boundary value are marked as noise candidates.

[0093] The noisy candidate features are cross-compared with the rigid semantic constraints and boundary conditions defined in the hospital management standard document. Feature items that do not have a substantial impact on business, such as differences in non-standardized text format, field spelling adjustments, and order swaps, are matched, and logical elimination operations are performed to reduce misjudgments.

[0094] The business rule hierarchical filtering module is applied to the difference vector set after boundary condition processing. The remaining feature dimensions with changes in job code, department ID or job level are classified into the high confidence candidate area, while the difference dimensions that are limited to auxiliary information such as remarks field and office location description are excluded.

[0095] The difference dimension of high-confidence candidate regions is numerically standardized by using the drift intensity numerical normalization method. The drift intensity is converted to a uniform dimension and correlation aggregation operation is performed to form an aggregate index of organizational change characteristics.

[0096] Based on the relationship between the aggregation index and the preset business tolerance threshold, a set of feature clusters containing only substantive organizational changes in the business is generated as a potential drift candidate set to ensure high accuracy and high reliability of subsequent drift determination inputs.

[0097] By using feature filtering and aggregation, the preliminary semantic difference measurement vector from the previous step is transformed into a high-confidence potential drift candidate set, thereby significantly improving the input quality of semantic drift detection event packets.

[0098] For example, when the job title field in the hospital's master data platform changes from "Associate Chief Physician (Cardiology)" to "Associate Chief Physician of Cardiology," the initial difference metric vector calculation yields a job title difference score of 3.5, a department ID difference score of 0, and a rank difference score of 0. A rigid semantic constraint boundary value of 5.0 is set, and the weight matrix labels the job title difference as a noise candidate. Noise candidates are matched in the boundary condition comparison as those whose string position adjustment has no substantial impact; after the elimination operation, only the rank dimension with no change remains (zero difference). During further filtering, since there are no changes in department ID and rank, the difference dimension is completely excluded, and no high-confidence candidate area is formed. In another scenario, when the department ID changes from "3102" to "3108," the difference score is 8.0, higher than the boundary value, and it is included in the high-confidence candidate area. A normalization operation is performed on the difference dimension drift intensity; for example, comparing a drift intensity of 8.0 with a threshold of 10, the aggregation index is 0.8, calculated using the formula 8.0 / 10.0. The aggregation index is compared with the business tolerance threshold and determined to be a substantial drift. The final potential drift candidate set includes the characteristics of departmental affiliation changes, ensuring that the change inputs in the event package are processed in a timely manner in the dynamic mapping of the permission model.

[0099] S3.5: Based on the high-confidence potential drift candidate set, encapsulate the change type label, the influence range identifier, and the drift intensity value, and perform a structured assembly operation to generate a semantic drift detection event package to be judged, carrying complete context information, as a direct trigger signal to drive the subsequent rule engine matching operation.

[0100] like Figure 3 As shown, step S4: Based on the built-in business rule engine, perform multi-dimensional rule matching operations on the semantic drift detection event package to generate a semantic drift determination result marked with a substantial change type. The business rule engine includes: rigid semantic constraint rules, flexible semantic equivalence rules, and time-sensitive rules. Specifically, it includes: S4.1: Based on rigid semantic constraint rules, perform logical association verification on the job code and department affiliation identifier in the semantic drift detection event package to extract a rigid conflict mark set with the characteristics of substantial change in organizational affiliation, and use this rigid conflict mark set as the input condition for subsequent flexible semantic analysis.

[0101] Strict formatted validation is performed on the job codes in the semantic drift detection event packages to be judged. The legality and completeness of the field values ​​are confirmed according to the preset rule set of the hospital job code system to ensure that the input data for subsequent correlation validation is accurate.

[0102] The verified job code and the department affiliation identifier in the event package are matched one-to-one according to the organizational structure mapping table. The department affiliation change judgment module within the rigid semantic constraint rules is called to identify whether the department affiliation relationship corresponding to the job code has undergone a logical jump.

[0103] Using the matching results of job codes and departmental affiliation identifiers, Boolean logic operations are performed in a pre-defined organizational matrix to calculate conflict states. The following conflict detection function is used to quantify the substantive nature of the change: Where C is the collision flag. This indicates the affiliation matching status between job code P and department affiliation identifier D in the organization matrix. This indicates the matching status before the change.

[0104] Based on the conflict flag, a rigid conflict flag is generated for all job entries with a change in affiliation. The job code, the department affiliation identifier before and after the change, and the change timestamp are appended to the flag so that the precise context can be referenced in subsequent flexible semantic analysis.

[0105] The resulting rigid conflict tag set is written into the intermediate cache of the rule engine to ensure that the tags remain in an intact and immutable state before entering the flexible semantic equivalence rule matching.

[0106] Through the above-mentioned logical association verification and conflict marker generation processing method, the semantic drift detection event package to be judged in the previous step is transformed into a rigid conflict marker set with the characteristics of substantial changes in organizational affiliation, so as to accurately capture the rigid relationship changes between positions and departments.

[0107] For example, in a hospital's intelligent authorization system, an event package includes the job code "CLIN_MED_005", the department affiliation identifier before the change "H_01", and the department affiliation identifier after the change "H_03". In the organizational structure mapping table, the original department "H_01" of "CLIN_MED_005" was Cardiology, and the department "H_03" after the change is Respiratory Medicine. The rule engine performs format verification to confirm the job code is valid, and after association matching, it determines the value of the conflict flag C to be 1 through Boolean operation. The conflict flag generation module generates a rigid conflict flag for this entry, and the flag content includes the job code "CLIN_MED_005", the department affiliation identifier change "H_01→H_03", and the timestamp "20240613T081530Z". During the flexible semantic equivalence rule analysis phase, this tag is referenced as an input condition, and there is no naming ambiguity. Therefore, the original department change information is maintained, ensuring that the subsequent incremental adjustment operations of the authorization engine on the differences between cardiology and respiratory medicine permissions can significantly improve accuracy and response speed.

[0108] S4.2: Using flexible semantic equivalence rules, receive the non-standard job name string from the rigid conflict tag set and change notification message, perform regular expression matching and semantic cluster normalization calculation to generate a standardized semantic equivalence class label after eliminating naming ambiguity, and pass the standardized semantic equivalence class label to the time-sensitive judgment stage.

[0109] It receives a set of rigid conflict markers from S4.1 and a non-standardized job name string from the change notification message as input objects for the flexible semantic equivalence rule.

[0110] Perform regular expression pattern matching on non-standard job title strings, call the pre-defined matching rules in the semantic cluster definition library, compare the character sequence with the specific semantic cluster template, and extract the key business semantic units in the matching results.

[0111] The extracted key business semantic units are sent to the semantic cluster normalization calculation module. Based on the unified semantic cluster mapping table, job names that have different expressions but the same business meaning are grouped into the same semantic equivalence class label set.

[0112] Perform semantic consistency verification on each tag in the semantic equivalence class tag set, compare its mapping path with the department affiliation or job function tags in the rigid conflict tag set, and remove tags that do not conform to business logic constraints.

[0113] The standardized semantic equivalence class labels that have passed consistency verification are encapsulated in a structured data format and passed to the time-sensitive judgment stage as the basis for comparison.

[0114] By applying flexible semantic equivalence rules to perform regular expression matching and normalization on the rigid conflict tag set and job titles, the results of the previous step are transformed into standardized labels after semantic disambiguation, thereby unifying the differences in job titles and ensuring consistency in rule judgment.

[0115] S4.3: Based on the time-sensitive rule, perform time window comparison operation on the effective timestamp field in the standardized semantic equivalence class label and the change notification message to filter out the immediate effective change event packages that need to be responded to at the current time, and remove the pending change event packages that will take effect in the future to form a clean immediate change dataset.

[0116] Time-sensitive analysis is performed on the standardized semantic equivalence class labels output by the flexible semantic equivalence rules and the effective timestamp field in the change notification message to establish a time window comparison benchmark matrix.

[0117] Standardized semantic equivalence class labels are mapped to a predefined permission change category time priority table. The corresponding response time window parameters are extracted according to the permission category, and the difference is calculated with the effective timestamp field to obtain the time difference vector.

[0118] For each value in the time difference vector, a threshold determination process is performed, setting two levels of boundaries: an immediate response threshold and a pending delay threshold. Items below the immediate response threshold are marked as candidate events for immediate action, and items above the pending delay threshold are marked as pending events for future action.

[0119] By employing multi-dimensional time window cross-operation, the candidate events for immediate effect are synchronized and verified with the current version timestamp in the organization's semantic snapshot chain. Entries with no match or whose effective time has expired before the current system clock are eliminated, forming a clean set of candidate events for immediate change.

[0120] The clean, real-time change candidate set is encapsulated in a structured manner, with the addition of permission category identifiers, time sensitivity levels, and change trigger source information to generate a clean, real-time change dataset. The results of the previous step are then transformed into time-sensitive data that can directly drive the matching of real-time response rules through time window comparison, thereby achieving real-time adaptation to the permission model.

[0121] For example, in the hospital information system, standardized semantic equivalence class labels are mapped to the "Chief Physician of Cardiology" permission category. The response time window parameter, extracted from the permission category time priority table, is 300 seconds. The effective timestamp value in the change notification message is the current system time plus 180 seconds, and the calculated time difference vector is 180 seconds. A threshold determination formula is used: in, These are the element values ​​of the time difference vector. A threshold of 300 seconds was set for immediate response. The formula determined that 180 seconds was less than 300 seconds, thus marking the event as a candidate for immediate effect. Through multi-dimensional time window cross-operation, events that successfully matched the current organization's semantic snapshot chain version timestamp and were not expired were added to the clean immediate change candidate set. This candidate set was then labeled with a permission category identifier "Clinical Medical Position - Senior Title," a time sensitivity level of "High," and a trigger source of "Personnel System Change Interface," forming a clean immediate change dataset. The application results show that the system can complete permission synchronization before the effective time arrives, significantly improving the response speed to sudden job adjustments and ensuring the consistency of permissions across multiple business system interfaces.

[0122] S4.4: Combining the rigid conflict marker set, standardized semantic equivalence class labels, and clean real-time change dataset, perform multi-dimensional rule-weighted fusion decision processing to generate a semantic drift determination result containing specific drift type descriptors, thereby achieving a precise mapping from original data changes to business intent changes.

[0123] Based on a rigid conflict marker set, standardized semantic equivalence class labels, and a clean, real-time change dataset, a multi-dimensional rule weight matrix is ​​loaded, and feature vectors from different rule sources are input into a weighted fusion operation unit to construct a three-dimensional business feature space containing job, department, and rank dimensions.

[0124] In this three-dimensional business feature space, the preset rule importance coefficient table is invoked, and the job dimension component corresponding to the rigid conflict mark set is multiplied by the rigid rule weight coefficient, the department dimension component corresponding to the standardized semantic equivalence class label is multiplied by the flexible rule weight coefficient, and the job grade dimension component in the pure real-time change dataset is multiplied by the time-sensitive rule weight coefficient.

[0125] The above weighted three-dimensional components are subjected to vector merging operation. The weights of each dimension and the drift intensity value are accumulated to form a comprehensive drift judgment value. The drift judgment value is mapped to different business intent category indexes using the threshold segmentation method.

[0126] After the comprehensive drift determination value is generated, the drift type descriptor library is retrieved using the category index, the standardized business change description text corresponding to the current drift category index is read, and the description text is matched and verified with the original change data field to ensure the uniqueness and accuracy of the drift type.

[0127] By combining the drift type descriptor, drift intensity value, and business impact range identifier, a structured semantic drift determination result object is constructed, and consistency verification and data signing are performed to ensure the verifiability of the determination result in the subsequent threshold comparison stage.

[0128] By using a multi-dimensional rule-weighted fusion decision processing method, the results of the previous step are transformed into semantic drift judgment results that include specific drift type descriptors, drift intensity features, and business impact scope, thereby achieving a precise mapping from changes in original data to changes in business intent.

[0129] For example, in a hospital's employee job transfer scenario, the rigid rule weight coefficient is configured as 1.5, the flexible rule weight coefficient as 1.2, and the time-series rule weight coefficient as 1.0; the department dimension drift intensity value in the rigid conflict marker set is 3.0, the job dimension drift intensity value corresponding to the standardized semantic equivalence class label is 2.5, and the job grade dimension drift intensity value in the clean, real-time change dataset is 1.8. Each component is input into the fusion processing unit, and the calculation formula is: The overall drift judgment value calculated is 9.3. This value is mapped to the "major adjustment of departmental affiliation" category index in the drift type descriptor library, and combined with the job level jump range and job function expansion coefficient, it is marked as a high-impact range to form the final judgment result.

[0130] Step S5: Determine whether the semantic drift determination result exceeds a preset business tolerance threshold. If the determination result indicates the existence of semantic drift requiring immediate response, trigger the generation instruction of a lightweight event packet carrying a drift type label, and output an activation signal to the dynamic mapping strategy model. Specifically, this includes: S5.1: Obtain the semantic drift determination results marked with substantial change types, output by rigid semantic constraint rules, flexible semantic equivalence rules, and time-sensitive rules. Based on numerical mapping processing, convert the qualitative change description in the semantic drift determination results into a standardized drift intensity feature vector to obtain a drift intensity feature vector containing the weight of departmental affiliation change, the magnitude of job grade jump, and the job function expansion coefficient.

[0131] When receiving the semantic drift determination result output by rigid semantic constraint rules, flexible semantic equivalence rules, and time-sensitive rules, the determination result is used as the input dataset to ensure that it contains explicit change type descriptors and relevant contextual features.

[0132] Based on the semantic drift determination result, the numerical mapping process is invoked to load the pre-set mapping table from change type to quantified weight. This mapping table has been optimized in conjunction with historical permission change cases of the hospital during the system deployment phase.

[0133] Perform a mapping query operation on the qualitative change description in the judgment result, and map each change type label, such as "department affiliation change", "job level promotion" and "job function expansion", to the corresponding numerical weight parameter, thus completing the initial conversion from type to weight.

[0134] The initially transformed numerical weight parameters are processed by multi-dimensional feature construction, and three dimensions are injected: departmental affiliation change weight, job level jump range, and job function expansion coefficient, to form a drift intensity feature vector with multi-dimensional quantitative information.

[0135] Vector normalization is used to unify the scale of the drift intensity feature vectors mentioned above, eliminating the calculation bias caused by the difference in numerical range between different dimensions, thereby ensuring the consistency of subsequent aggregation and comparison operations.

[0136] Through the above numerical mapping and normalization processing, the semantic drift determination result of the previous step is transformed into a computable drift intensity feature vector, achieving the technical effect of transforming qualitative description into quantitative indicator.

[0137] For example, in a hospital employee job transfer scenario, the rigid semantic constraint rule determines it as "change of department affiliation," the flexible semantic equivalence rule determines it as "cardiology and cardiovascular medicine are of the same category," and the time-sensitive rule determines that the change takes effect immediately. In the numerical mapping table, the weight for department affiliation change is 0.8, the job grade jump is 0.0 (no job grade adjustment), and the job function expansion coefficient is 0.3. After constructing the initial feature vector [0.8, 0.0, 0.3], normalization is performed using the following normalization formula: in For eigenvalues, The mean of all samples in this dimension. The standard deviation is given. The normalized result yields the eigenvector [1.25, -0.5, 0.75]. This processing significantly improves the stability of drift intensity quantization in system testing, making the selection of mapping strategies triggered by immediate changes more accurate.

[0138] S5.2: Perform a weighted aggregation operation on the drift intensity feature vector to calculate a comprehensive semantic drift index that characterizes the impact of the current organizational structure change on the permission system.

[0139] Based on the input conditions of the drift intensity feature vector, ordered multi-stage calculations are performed on the weights of departmental affiliation changes, job grade jumps, and job function expansion coefficients from the previous sub-step. The three dimensional parameters are grouped and mapped according to the dimensional weight coefficients in the risk model to establish a corresponding set of risk factors, ensuring a one-to-one correspondence between each business dimension and the risk coefficients of the predefined quantification rules. The risk factor set is input into the weighted aggregation module, where each risk factor is first normalized to ensure linear combination under a unified dimension. The weight coefficients are then calibrated using a historical permission impact sample library to form an optimized risk weight vector. The normalized drift intensity feature and the optimized risk weight vector are multiplied element-wise to generate a risk contribution value sequence. Subsequently, vector summation is performed in the weighted aggregation module to obtain the total drift impact value. The business risk quantification formula is adopted: in, This represents an element of the risk weight vector for the i-th business dimension. Let represent the normalized drift intensity feature value of the i-th business dimension, where n is the total number of dimensions. The product term after the summation sign represents the sum of the risk contributions of each dimension. The calculation result of the above formula is used as a comprehensive semantic drift index, and a timestamp and source confidence label are added through a precision control module to maintain data traceability in subsequent threshold comparison operations. Through weighted aggregation calculation, the multi-dimensional drift intensity features of the previous step are transformed into a comprehensive semantic drift index that uniquely identifies the impact of the current organizational structure change on the permission system, achieving the expected technical effect of efficiently quantifying change risks.

[0140] For example, in a hospital's intelligent authorization scenario, the weight for changes in department affiliation is set to 0.5, the rank advancement range is 0.3, and the job function expansion coefficient is 0.2. The corresponding risk weights in the historical risk model are 1.2, 0.8, and 1.0, respectively. After normalization, these weights are input into the risk quantification formula: The calculated comprehensive semantic drift index is 0.94, which includes a timestamp of 20240601 and a source credibility score of 0.85 for threshold comparison. Under different change scenarios, such as when only the job level changes, the drift index will decrease significantly, and the permission system's response mechanism will determine that it is not effective immediately. When multiple dimensions change simultaneously, the drift index will increase significantly, triggering the intelligent authorization engine to update the permission mapping in real time, thereby achieving accurate quantification and real-time response to the impact on the permission system.

[0141] S5.3: Read the preset service tolerance threshold, perform a logical comparison operation between the comprehensive semantic drift index and the preset service tolerance threshold, and generate a threshold out-of-bounds judgment flag indicating whether the current change has exceeded the security boundary.

[0142] The preset business tolerance threshold is a predefined and stored security boundary parameter used to determine whether the comprehensive semantic drift index has exceeded the limit. Its specific value, type identifier and applicable business scenario label are preset based on the hierarchical depth of the hospital's organizational structure, the sensitivity level of job responsibilities and the statistical results of historical permission configuration security events, and are solidified as parameter objects.

[0143] The threshold determination operation for the comprehensive semantic drift index receives the comprehensive semantic drift index and a pre-stored preset business tolerance threshold. Based on the data retrieval interface, it calls the configuration library to extract the parameter object corresponding to the preset business tolerance threshold, including the threshold value, threshold type identifier, and applicable business scenario tag, ensuring the source credibility and version consistency of the threshold data. The comprehensive semantic drift index and the preset business tolerance threshold are then input into the logic comparison processing unit. This unit uses a combination of numerical comparison and interval determination, performing the following calculations in the numerical comparison stage: in Indicates the comprehensive semantic drift index. This indicates a preset business tolerance threshold. When the comprehensive semantic drift index exceeds the threshold value, an out-of-bounds signal is generated. In the interval judgment stage, a dual threshold mechanism with upper and lower limits is used to determine whether the index value falls within the buffer zone requiring alert. A flag bit generation operation is performed on the logical comparison result, encoding the out-of-bounds signal or buffer alert signal into a threshold out-of-bounds judgment flag bit, containing an out-of-bounds status identifier, a judgment timestamp, and the identifier of the triggering business module. Through the generation of the judgment flag bit, the comprehensive semantic drift index from the previous step is transformed into out-of-bounds status data that can be called by downstream event encapsulation protocols, enabling real-time judgment and response triggering of the impact of organizational structure changes on the permission system.

[0144] S5.4: Based on the threshold out-of-bounds judgment flag, if the judgment result is that there is a semantic drift that requires immediate response, then extract the change type label from the semantic drift judgment result, and use the event encapsulation protocol to combine the change type label with the timestamp to construct a lightweight event package to be processed carrying the drift type label.

[0145] Under the condition that the threshold out-of-bounds judgment flag is true and an immediate response is required, the precise change type label is parsed based on the semantic drift judgment result. During the parsing process, the label string is mapped to the standardized change type encoding table in the system to ensure consistency. The standardized change type encoding is merged with the timestamp of the current drift event. The attribute fields are then layered and encapsulated using a pre-defined event encapsulation protocol format specification to form two structured data units: an event header and an event body. The output of the unique event identifier generation process is embedded in the event header. This process uses a combination of a globally increasing sequence and a timestamp hash value to calculate the unique event identifier using the following formula: Where T is a millisecond-level timestamp, S is a globally increasing sequence value, and H is a non-cryptographically strong hash function used to generate an identifier value with negligible collision probability. The generated unique event identifier is written to the identifier field of the event header, and change type encoding and a timestamp are injected into the event body to meet the context requirements of subsequent model calls. Protocol consistency checks and field integrity verifications are performed on the complete event package to ensure that all required fields are filled and comply with the encapsulation protocol version requirements. Finally, a lightweight event package with a unique event identifier is output.

[0146] S5.5: Based on the lightweight event packet to be processed, send a high-priority interrupt signal to the dynamic mapping strategy model, and input the lightweight event packet to be processed as the activation context to the dynamic mapping strategy model to trigger the dynamic mapping strategy model to start the baseline permission template retrieval and fine-tuning instruction superposition process.

[0147] Step S6: Input the activation signal and historical job transfer order data into the dynamic mapping strategy model built on a decision tree ensemble, retrieve the baseline permission template with the highest matching degree, and superimpose fine-tuning instructions to generate an atomic permission operation instruction set. In this embodiment, the dynamic mapping strategy model is a machine learning model built on a decision tree ensemble; by integrating modules such as multi-dimensional feature mapping, parallel path traversal, and similarity weighted calculation, it can map the input multi-dimensional context feature vector to a preset baseline permission template library, and generate an atomic permission operation instruction set for a specific job transfer scenario through a fine-tuning instruction superposition mechanism.

[0148] In this embodiment, the baseline permission template is a standard permission configuration scheme defined for various typical job transfer scenarios, such as promotion in clinical medical positions and transfer in information technology positions. It is derived from pattern mining and summarization of the historical job transfer work order data, or predefined by expert experience, and constitutes the target object for similarity matching and retrieval in the decision tree ensemble, providing a structured basis for generating the initial permission configuration baseline object.

[0149] Step S6 specifically includes: S6.1: Perform feature alignment and sample recall processing on the received lightweight event packets carrying drift type labels and the historical job transfer work order database to extract multi-dimensional contextual feature vectors containing job function semantic labels, department affiliation change weights and job level jump magnitudes, and construct a standardized input feature set for model inference.

[0150] The historical job transfer data refers to a structured case library accumulated from the hospital's human resources or business systems, which records past job adjustments, including changes in job positions, departments, and job levels, and their corresponding permission configurations.

[0151] S6.2: Based on the standardized input feature set, call the decision tree ensemble in the pre-trained dynamic mapping strategy model to perform parallel path traversal operations to calculate the similarity score between each historical job transfer scenario and the current change event, and output a candidate baseline permission template sequence labeled with confidence weights.

[0152] The system receives a standardized set of input features generated by S6.1, including multi-dimensional contextual feature vectors such as job function semantic labels, departmental affiliation change weights, and job rank jump magnitudes. These features are then loaded into the input buffer of the dynamic mapping strategy model via the feature index mapping module, forming a structured input matrix that can be parsed by the decision tree ensemble.

[0153] Based on the structured input matrix, the pre-trained decision tree ensemble is invoked to perform multi-path parallel traversal operations. Each feature vector is matched and compared with the feature nodes in the historical job transfer feature sample library in turn. The branch path is determined according to the node splitting condition and the node weight is accumulated.

[0154] The similarity scores calculated during the traversal process are normalized using a specific similarity metric formula, such as the cosine similarity calculation method.

[0155] The similarity scores of each decision tree path are weighted and summed according to the path weight and the node confidence using a weighted aggregator to form a sequence of candidate baseline permission templates with confidence weight labels.

[0156] The candidate template sequence is arranged in a structured manner through the cluster analysis module, and the source template ID, matching score and estimated permission difference features are marked, in preparation for passing it to S6.3 for global optimal matching and filtering.

[0157] S6.3: Perform global optimal matching and filtering processing on the candidate baseline permission template sequence, use a weighted voting mechanism to determine the target baseline permission template with the highest matching degree, and parse the basic permission configuration items contained in the template, such as the clinical medical post practice permission level or the information technology post system maintenance permission set, to generate an initial permission configuration benchmark object.

[0158] Based on the confidence weight vector in the candidate baseline permission template sequence, the global optimal matching filtering module is called to perform the weight normalization operation of the template sequence, and the similarity scores of different candidate templates are converted into comparable standardized weight indices to eliminate the comparison bias caused by the difference in the number of samples.

[0159] A weighted voting mechanism is applied to the normalized set of weighted indices. A business dimension weight parameter matrix is ​​set. The weights for changes in department affiliation, promotions, and job function expansion in the matrix are derived from the drift intensity feature vector. This enables the weighted indices to be weighted and accumulated according to the business dimension, forming a global matching score set.

[0160] The matching score set is used to perform maximum value selection processing to determine the target baseline permission template with the highest matching degree in the current change context. The permission configuration details in the template are read through the template parsing engine, and basic configuration items such as the clinical medical post practice permission level or the information technology post system maintenance permission set are transformed into structured permission attribute objects.

[0161] The basic permission configuration items obtained from the parsing are subjected to field mapping operations, which map the permission action type, resource object code and system identifier in the template to the unified permission attribute model of the intelligent authorization engine, so as to ensure that subsequent fine-tuning instruction superposition operations can be completed under a unified data structure.

[0162] S6.4: Based on the drift type label and the initial permission configuration baseline object in the lightweight event package to be processed, perform a differentiated fine-tuning instruction generation operation, deduce the sub-permission entries to be added or the functional module identifiers to be hidden according to the rigid semantic constraint rules and the flexible semantic equivalence rules, and form a permission fine-tuning instruction list for the current change scenario.

[0163] Based on the drift type tags in the lightweight event package to be processed and the initial permission configuration baseline object, a rigid semantic constraint rule base is loaded and a permission difference mapping table is established to clarify the specific impact path of each drift type in permission changes. The drift type tags are parsed into relevant job functions, department affiliation, and job level parameters, and compared with the corresponding permission items in the baseline object at the field level to identify potential added, deleted, or blocked permission entries. For the identified difference permission items, the flexible semantic equivalence rule module is invoked to perform synonym normalization and functional module keyword aggregation to eliminate duplicate configurations or ambiguous permission items caused by non-standardized naming. The normalized permission difference set is divided into a new permission set and a blocked permission set according to permission action categories, and a unique resource object code is generated for each permission entry to ensure identifiability and feasibility of mutual exclusion verification during subsequent execution. Through the logical merging operation of multi-dimensional difference permission sets, a structured permission fine-tuning instruction list for the current change scenario is generated, including permission action type, target resource object code, and policy application conditions.

[0164] For example, in a hospital's personnel system, a deputy chief physician in cardiology is transferred to the position of chief physician in neurology, resulting in a departmental affiliation change event. The drift type label is "Departmental Affiliation Change," and the initial permission configuration baseline object includes the set of permissions for intermediate-level clinical medical positions. Rigid semantic constraint rules determine that this type of change requires the removal of the original department-specific permissions and the addition of permissions required by the new department. The difference mapping table indicates that "Cardiology Surgical Schedule Modification" needs to be removed and "Neurology Consultation Record Viewing" needs to be added. Flexible semantic equivalence rules normalize the original permission name "Cardiovascular Surgery Schedule Adjustment" to "Cardiology Surgical Schedule Modification," eliminating naming differences. The newly added permission set contains the resource code R-NEU-READ and the action type ADD; the masked permission set contains the resource code R-CARD-EDIT and the action type DISABLE. For each entry in the newly added permission set, its priority weight in the permission template is calculated: Where n represents the current number of permissions of the same type. After weight allocation, the newly added and blocked permission sets are integrated to generate a structured fine-tuning instruction list, verifying that there are no conflicts in the mutual exclusion relationship. After execution, the new permissions take effect immediately in the neurology HIS system, while the original permissions are blocked in the cardiology system. The system authorization response time is significantly improved and the accuracy of permissions is guaranteed.

[0165] S6.5: The initial permission configuration baseline object and the permission fine-tuning instruction list are merged and atomically encapsulated to eliminate redundant configurations and verify permission mutual exclusion relationships, generating an atomic permission operation instruction set containing the target business system identifier, permission action type and resource object code.

[0166] Step S7: Based on the atomic permission operation instruction set, distribute permission configuration requests to multiple service systems, perform incremental update operations on role-permission relationships, and generate permission status feedback logs after synchronization across multiple service systems. Specifically, this includes: S7.1: Perform protocol adaptation and parsing on the atomic permission operation instruction set, extract the target business system identifier, permission action type and resource object code, and construct a standardized heterogeneous interface call parameter set to eliminate data format differences between different business systems and provide a unified input for subsequent distribution.

[0167] The protocol feature parsing operation is performed on the input data of the atomic permission operation instruction set to extract the structured field containing the target business system identifier and perform regularization on the identifier namespace to eliminate cross-platform identification conflicts caused by differences in the system's internal encoding rules.

[0168] Based on the parsed permission action type field, the action semantic classification calculation is performed, different operators under the same action type are mapped to unified action semantic labels, and combined with the execution mode parameters defined by the permission model, an alignment mapping table between action type and permission semantic is generated.

[0169] Encoding system identification and bit length standardization are performed on the resource object encoding fields in the permission operation instruction set. Resource identifiers with non-uniform encoding system are converted into standardized resource codes with fixed length and uniform character set to ensure compatibility when calling cross-system interfaces.

[0170] The target business system identifier, action semantic tags, and standardized resource coding fields are aggregated to form a structured parameter matrix containing three-dimensional core parameters. The fields are then rearranged according to the parameter order defined in the interface documents of each business system to ensure that the parameter matrix meets the format constraints of the corresponding interface call.

[0171] The parameter sequence verification process is used to perform integrity and consistency verification on the rearranged parameter matrix. Missing or mismatched parameters are marked as abnormal states, and an abnormal parameter report is generated for subsequent distribution module filtering.

[0172] Through the above protocol adaptation and parsing process, the atomic permission operation instruction set of the previous step is transformed into a standardized heterogeneous interface call parameter set that can be executed across systems, thereby achieving data format unification and call compatibility among multiple business systems.

[0173] For example, in an authorization scenario of a hospital information system, the atomic permission operation instruction set includes the target business system identifier "HIS_SYS_01", the permission action type "ADD_MODULE_AUTH", and the resource object code "CARDIO_OP_REC_202405". During protocol feature parsing, the identifier field is normalized to generate the standard code "HIS_SYS_01"; the action type field is mapped to "module authorization add" after action semantic classification; and the resource object code is normalized to a fixed-length 32-character code "CARDIO_OP_REC_202405_0000000000". The parameter matrix formed by field aggregation is arranged in the order defined in the business system interface document as [System ID, Action Tag, Resource Code]. The integrity verification process uses the following parameter matching formula: Where P represents the parameter matching rate, N represents the number of required parameters, and E represents the number of abnormal parameters. In this scenario, N=3, E=0, and the matching rate P is calculated to be 1, indicating that the parameter set fully conforms to the interface call format requirements. The final output standardized heterogeneous interface call parameter set can achieve high concurrency and no call errors in subsequent concurrent routing distribution, significantly improving the accuracy and stability of permission configuration in multi-service systems.

[0174] S7.2: Perform concurrent routing and distribution operations based on the heterogeneous interface call parameter set, use the pre-set adapter pattern to match the user authorization interface specifications of each business system, and generate a parallel permission configuration request queue containing identity authentication tokens and transaction sequence numbers to ensure the orderliness and security of request sending in high-concurrency scenarios.

[0175] Based on the parsed and generated set of heterogeneous interface call parameters, a concurrent route construction operation is performed on them, grouped according to the target business system identifier. The call parameters of the same business system are aggregated into an independent set of route entries for subsequent matching processing.

[0176] For each set of routing entries, the pre-built interface adapter pattern matching module is invoked. During the matching process, the field type correction and call template reconstruction are performed using the field mapping rules and format conversion rules recorded in the interface specification library, so that the call parameters meet the structure and protocol standards required by the user authorization interface of the target business system.

[0177] Inject identity authentication token generation logic into the set of matched routing entries, call the authentication key derivation process according to the system security policy, generate a token by combining the global authentication key and the target system identifier with a one-time random salt value, and bind it to each call parameter to form an authenticable data unit.

[0178] Transaction sequence number generation logic is injected into each call parameter. A unique transaction sequence number is formed by a hybrid encoding of timestamp and global request counter, so as to enable chain tracing and idempotency verification of authorization requests.

[0179] The call parameters, which are bound to the identity authentication token and the transaction sequence number, are executed to build a parallel request queue according to business priority. The queue depth and thread pool size are configured using an asynchronous concurrency scheduler to achieve the orderliness and security of request sending in high-concurrency scenarios.

[0180] By using interface adapters and concurrent scheduling mechanisms, the standardized heterogeneous interface call parameter set from the previous step is transformed into a parallel permission configuration request queue containing identity authentication tokens and transaction sequence numbers, thereby enabling secure and synchronous distribution of permission requests from multiple business systems under high concurrency conditions.

[0181] S7.3: Perform status code mapping and semantic normalization on the original execution response messages returned by each business system, and use a finite state machine model to identify the execution results of successful submission, partial failure or complete rejection, and generate a single-system permission change confirmation document with clear execution status markers to quantify the authorization execution effect of a single interaction.

[0182] S7.4: Based on the single-system permission change confirmation document, perform a global consistency check operation, compare the expected state of the atomic permission operation instruction set with the actual return state, detect and mark abnormal permission entries with synchronization delay or configuration conflict, and generate a multi-business system synchronization difference analysis report to locate the risk points of inconsistency in cross-system permission data.

[0183] When performing global consistency verification on single-system permission change confirmation documents returned by various business systems, the target business system identifier in the atomic permission operation instruction set is mapped and matched with the source system identifier in the confirmation document to establish a corresponding index table of expected permission status and actual feedback status. The permission action type and resource object encoding field in the corresponding index table are compared item by item, and the difference analysis process is called to calculate the consistency difference vector of permission configuration values ​​in the action and resource dimensions, identifying entries that are inconsistent with the expected values ​​in the atomic instruction set. Entries in the consistency difference vector that exhibit execution delays are classified and separated from those with configuration conflicts. The execution delay detection module reads the feedback timestamp and expected operation trigger timestamp of each entry, calculates the delay duration, and compares it with a preset delay threshold. The configuration conflict detection module parses the permission mutual exclusion identifier or illegal combination identifier in the conflict entries to determine the conflict type and severity level. The following delay calculation formula is used to calculate the delay duration: in, To delay the duration, This is the actual timestamp of the returned data. This is the expected execution timestamp. The delay duration or conflict level values ​​of various abnormal entries are summarized into an abnormal permission entry list, and then organized by system identifier and time sequence into a multi-business system synchronization difference analysis report. Through the above processing method, the results of the single-system permission change confirmation document from the previous step are transformed into global permission consistency difference data, enabling precise location of cross-system permission data inconsistency risk points.

[0184] S7.5: Perform structured aggregation and encapsulation processing on the multi-service system synchronization difference analysis report and the confirmation document of normal execution, write them to the persistent storage medium in the order of timestamps, and generate a permission status feedback log containing complete link tracing information after multi-service system synchronization, so as to provide auditable data basis for the closed-loop optimization of the permission model in the future.

[0185] Step S8: Based on the permission status feedback log records and comparing the alignment accuracy indicators of the permission model and the organizational structure, if an abnormal synchronization delay or permission configuration conflict is detected, the decision tree weight parameters of the dynamic mapping strategy model are automatically corrected to complete the closed-loop optimization of the permission model. Here, the permission model specifically refers to the mapping rule system from organizational state to permission configuration defined by the dynamic mapping strategy model; the organizational structure refers to the current state, represented by the initial job semantic dataset and the organizational semantic snapshot chain, containing entities such as jobs, departments, and job levels, and their relationships.

[0186] Step S8 specifically includes: S8.1: Perform structured parsing on the permission status feedback logs after synchronization of multiple business systems, extract the synchronization delay timestamp, permission configuration conflict type identifier, and business intent matching degree value, and generate a permission model running status dataset containing abnormal feature vectors, which serves as the input basis for subsequent parameter correction.

[0187] S8.2: Based on the abnormal feature vectors in the permission model running status dataset, use gradient descent optimization to calculate the error contribution rate of each baseline template node in the decision tree ensemble, generate a decision tree weight offset sequence that characterizes the degree of deviation in the current model judgment, and clarify the specific rule path that needs to be adjusted.

[0188] Based on the abnormal feature vectors in the permission model's running status dataset, standardization is performed on the numerical field of each abnormal feature vector to eliminate the impact of differences in the scale of different indicators on subsequent gradient calculations. The standardized abnormal feature vectors are then input into the node weight mapping module of the decision tree ensemble. The mean squared error is calculated on the deviation between the predicted output of each baseline template node and the actual permission status to obtain a node-level error metric. Gradient descent optimization is then employed, and weight offsets are calculated at each baseline template node based on the error metric and the learning rate coefficient, specifically according to the following formula: Where Δw is the offset of node weights, η is the set learning rate, L is the loss function value constructed based on the current permission status feedback log, and w is the node weight parameter. Based on the calculated Δw value, cumulative offset recording is performed on the node weights, forming a weight offset sequence characterizing the degree of judgment bias for each node. In the weight offset sequence, specific rule paths requiring adjustment are marked using node path index information, so that targeted node splitting threshold updates and leaf node classification probability corrections can be performed in subsequent steps S8.3. Through gradient descent optimization and chain-like derivation of offset records, the permission model running status dataset from the previous step is transformed into a weight offset sequence with clearly defined rule path locations, achieving a quantitative characterization of model judgment bias.

[0189] S8.3: Based on the decision tree weight offset sequence, an adaptive learning rate update strategy is used to perform a weighted correction operation on the node splitting threshold and leaf node classification probability distribution inside the dynamic mapping strategy model, generating an updated version of the dynamic mapping strategy model with new weight parameters, eliminating the decision lag caused by historical data distribution drift.

[0190] The input conditions include the decision tree weight offset sequence output by step S8.2, which describes the error contribution rate of each baseline template node and identifies the rule path index that needs to be adjusted.

[0191] A node splitting threshold correction vector is constructed based on the weight offset sequence. A priority queue is established by sorting the offsets according to their absolute values ​​to ensure that the adjustment order starts from the node with the highest error contribution rate.

[0192] The adaptive learning rate update strategy module is invoked to dynamically calculate the learning rate factor of the node splitting threshold based on the absolute value of the offset and the historical iteration stability index. An exponential decay adjustment function is used to prevent high-offset nodes from being over-corrected in a single iteration.

[0193] The calculated learning rate factor is then weighted and adjusted by the corresponding node splitting threshold, as shown in the following formula: Where T is the original node splitting threshold, η is the learning rate factor, and Δw is the weight offset.

[0194] For the classification probability distribution of leaf nodes, the offset value of each category probability is calculated, and a weighted correction is performed according to the adaptive learning rate factor to keep the sum of the probability distributions at 1. The classification imbalance caused by numerical drift is eliminated through normalization.

[0195] Within the same rule path, the node splitting threshold and leaf node classification probability distribution are corrected sequentially. The node mutual exclusion relationship verification logic is used to verify that the corrected rule path will not have permission mutual exclusion conflicts.

[0196] The overall correction results are encapsulated to generate a new weight parameter mapping table, which replaces the old weight parameter set inside the dynamic mapping strategy model, forming an updated version of the model with the new weight parameters.

[0197] By using adaptive learning rate updates and weighted corrections, the results of the previous step are transformed into an update parameter set that can be directly applied to decision tree inference, thereby eliminating the decision lag caused by historical data distribution drift.

[0198] S8.4: Deploy the updated version of the dynamic mapping strategy model to the sandbox verification environment, inject historical job transfer data for backtesting simulation, calculate the improvement in the accuracy of the corrected permission configuration and the synchronization delay compression ratio, generate a model iteration effect evaluation report, and confirm the effectiveness of closed-loop optimization.

[0199] S8.5: Based on the verification results of the model iteration effect evaluation report, the updated version of the dynamic mapping strategy model officially replaces the old model in the production environment, completing the closed-loop optimization process of the permission model, so that the system has the ability to self-learn and adaptively authorize in response to future organizational structure changes.

[0200] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0201] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0202] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for automatically authorizing employee onboarding across multiple business systems based on dynamic mapping of job permissions, characterized in that: Specifically, it includes: S1: Obtain the structured field data of the job master table, department tree and job level mapping table in the master data system, and perform semantic anchoring processing in combination with the hospital management standard document to generate the initial job semantic dataset; S2: Based on the initial job semantic dataset, perform fingerprint calculation on each collected data using hierarchical hashing to generate an organization semantic snapshot chain that is arranged in chronological order and contains semantic consistency verification information; S3: Receive the change notification message sent by the master data platform, and input the change notification message and the previous and next snapshot fingerprints in the organization's semantic snapshot chain into the context-aware comparator to generate a semantic drift detection event packet to be determined; S4: Based on the built-in business rule engine, perform multi-dimensional rule matching operations on the semantic drift detection event package to generate semantic drift determination results; S5: Determine whether the semantic drift determination result exceeds the preset business tolerance threshold. If the determination result is that there is a semantic drift that requires immediate response, trigger the generation instruction of the pending lightweight event package carrying the drift type label, and output the activation signal to the dynamic mapping strategy model. S6: Input the activation signal and historical job transfer order data into the dynamic mapping strategy model built on the decision tree ensemble, retrieve the baseline permission template with the highest matching degree and superimpose fine-tuning instructions to generate an atomic permission operation instruction set.

2. The method for automatic authorization of employee onboarding in a multi-business system based on dynamic mapping of job permissions as described in claim 1, characterized in that, The change notification message includes the changed field name, old value, and new value.

3. The method for automatic authorization of employee onboarding in multiple business systems based on dynamic mapping of job permissions according to claim 2, characterized in that, Step S3 specifically includes: The change notification message issued by the master data platform, which contains the changed field name, old value and new value, is subjected to standardized parsing processing to extract the job code, department affiliation identifier and job grade sequence code, and generate change data payload; Based on the timestamp and job code index in the changed data payload, retrieve and obtain the snapshot fingerprint before and after the change from the organizational semantic snapshot chain to construct a spatiotemporal comparison dataset. Using a pre-built context-aware comparator, the spatiotemporal comparison dataset is input into a multidimensional feature mapping space, and differential quantification calculation based on business context is performed to generate a semantic difference measurement vector. Based on the semantic difference metric vector and the rigid semantic constraint boundary conditions defined in the hospital management standard document, non-substantive field noise is filtered out to form a potential drift candidate set. Based on the potential drift candidate set, change type labels, impact range identifiers, and drift intensity values ​​are encapsulated, and a structured assembly operation is performed to generate the semantic drift detection event package to be determined.

4. The method for automatic authorization of employee onboarding in a multi-business system based on dynamic mapping of job permissions according to claim 3, characterized in that, The spatiotemporal comparison dataset includes: historical state features and current state features that will change.

5. The method for automatic authorization of employee onboarding in multiple business systems based on dynamic mapping of job permissions according to claim 3, characterized in that, The business rule engine includes: rigid semantic constraint rules, flexible semantic equivalence rules, and time-sensitive rules.

6. The method for automatic authorization of employee onboarding in multiple business systems based on dynamic mapping of job permissions according to claim 5, characterized in that, Step S4 specifically includes: Based on the rigid semantic constraint rules, the job code and department affiliation identifier in the semantic drift detection event package are logically associated and verified to extract the rigid conflict marker set. Using the flexible semantic equivalence rule, the rigid conflict tag set and the non-standard job name string in the change notification message are received, and regular expression matching and semantic cluster normalization calculation are performed to generate standardized semantic equivalence class tags. Based on time-sensitive rules, time window comparison operations are performed on the standardized semantic equivalence class labels and the effective timestamp field in the change notification message to filter out the immediate effective change event packages that need to be responded to at the current moment, and remove the pending change event packages that will take effect in the future to form a clean immediate change dataset. By combining the rigid conflict marker set, standardized semantic equivalence class labels, and clean real-time change dataset, a multi-dimensional rule-weighted fusion decision processing is performed to generate a semantic drift determination result.

7. The method for automatic authorization of employee onboarding in multiple business systems based on dynamic mapping of job permissions according to claim 3, characterized in that, Step S5 specifically includes: The qualitative change description in the semantic drift determination result is converted into a standardized drift intensity feature vector based on numerical mapping processing. Perform a weighted aggregation operation on the drift intensity feature vector to calculate the comprehensive semantic drift index; Read the preset service tolerance threshold, perform a logical comparison operation between the comprehensive semantic drift index and the preset service tolerance threshold, and generate a threshold out-of-bounds judgment flag bit; Based on the threshold out-of-bounds judgment flag, if the judgment result is that there is a semantic drift that requires immediate response, then the change type label in the semantic drift judgment result is extracted, and the change type label and timestamp are combined using the event encapsulation protocol to construct a lightweight event package to be processed carrying the drift type label. Based on the lightweight event packet to be processed, a high-priority interrupt signal is sent to the dynamic mapping strategy model, and the lightweight event packet to be processed is used as the activation context input to the dynamic mapping strategy model.

8. The method for automatic authorization of employee onboarding in a multi-business system based on dynamic mapping of job permissions according to claim 3, characterized in that, The drift intensity feature vector includes: departmental affiliation change weight, job level jump range, and job function expansion coefficient.

9. The method for automatic authorization of employee onboarding in a multi-business system based on dynamic mapping of job permissions according to claim 1, characterized in that, After step S6, the following is included: S7: Based on the atomic permission operation instruction set, distribute permission configuration requests to multiple business systems, perform incremental update operations on role-permission relationships, and generate permission status feedback logs after synchronization of multiple business systems; S8: Based on the permission status feedback log records and comparing the alignment accuracy indicators of the permission model and the organizational structure, if an abnormal synchronization delay or permission configuration conflict is detected, the decision tree weight parameters of the dynamic mapping strategy model are corrected.

10. The method for automatic authorization of employee onboarding in a multi-business system based on dynamic mapping of job permissions according to claim 1, characterized in that, The initial job semantic dataset includes timestamps and source credibility tags.

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