A method and system for secure sharing of human resource data

Abstract

This invention discloses a method and system for secure sharing of human resource data, specifically relating to the field of human resource data security. The method includes collecting user behavior signal sequences on a human resource identity authentication platform. These behavioral signal sequences include page access paths, interaction types, time rhythm distribution, and device switching events. The behavioral signal sequences are then divided into continuous time-series operation segments using a unified time sliding window mechanism. Feature encoding processing is performed on each time-series operation segment group to extract operation frequency, rhythm changes, device fingerprints, and interface jump features, constructing corresponding behavior embedding vector blocks. By constructing a redundant field filtering mechanism based on the dynamic evolution of trajectory credibility and behavioral stability, this method breaks away from the traditional platform's reliance solely on field format legality verification. It effectively prevents attackers from constructing fake legitimate identities to infiltrate the sharing system, solving the problem of system misjudgment and misidentification of real identities caused by the reverse exploitation of the redundant mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human resource data security, and more particularly to a human resource data security sharing method and system. BACKGROUND

[0002] In the process of sharing human resource data, if the platform only relies on redundant field combination for identity verification and lacks dynamic management and control of the credibility of redundant paths, it may be exploited by attackers to induce the system to make false judgments by constructing a "format legal" fake identity.

[0003] This not only causes false identities to mix into shared data, but also causes false identities to be misidentified and banned, thereby causing the redundancy mechanism to be exploited in reverse, the verification system to fail, and ultimately causing data security risks at the platform level. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide a human resource data security sharing method and system, which establishes a two-way closed loop link from behavior path to field output to verification inversion through a redundancy field generation and screening mechanism based on the combination of behavior trajectory credibility evolution and field reversible verification, to solve the problems of data forgery mixing, identity misidentification and verification system failure caused by the lack of path dynamics and source credibility modeling of the field verification mechanism in the background art.

[0005] To achieve the above object, the present application provides the following technical scheme: a human resource data security sharing method, comprising:

[0006] Collecting a behavior signal sequence of a user on a human resource identity authentication platform, the behavior signal sequence including a page access path, an interaction operation type, a time rhythm distribution and a device switching event;

[0007] Dividing the behavior signal sequence into continuous time sequence operation fragment groups according to a unified time sliding window mechanism; performing feature encoding processing on each group of time sequence operation fragment groups to extract operation frequency, rhythm change, device fingerprint and interface jump features, and constructing a corresponding behavior embedding vector block;

[0008] Taking the behavior embedding vector block as a node of a directed graph, establishing a behavior transition edge between nodes according to time sequence, and constructing a preliminary behavior path graph;

[0009] Performing node mode similarity calculation on a plurality of behavior path graphs, aggregating behavior path graphs with similar structures and uniformly rearranging them into a standardized behavior trajectory structure graph;

[0010] All standardized behavior trajectory structure maps are aggregated, combined with user identification, platform source and time period information, and a trajectory identity matrix index set is constructed to support unified retrieval of global trajectory data;

[0011] By reading the trajectory identity matrix index set, path credibility and behavior stability information are extracted, and trajectory encoding block groups, platform embedding vectors, redundancy mapping vectors and candidate field sets are generated in turn, and finally the behavior-driven redundancy field group is screened to complete the consistency mapping of trajectory to field space.

[0012] In a preferred embodiment, each trajectory path in the trajectory identity matrix index set is read, the jump frequency, behavior period and stability of each trajectory path are counted, and a trajectory credibility vector is generated for path trust evaluation;

[0013] The trajectory credibility vector is input into the path stability analyzer, combined with the trajectory entropy value and periodicity index, and a trajectory encoding block group with consistent structure is constructed; the trajectory encoding block group and the platform preference factor are fused to generate a platform embedding vector reflecting the platform context behavior characteristics; the platform embedding vector is executed redundancy mapping to complete the path feature compression and semantic transformation, and a redundancy mapping vector for field mapping is generated; the redundancy mapping vector is decoded to a structured field space, and a candidate field set meeting the structure rule is output;

[0014] The candidate field set is subjected to stability and disturbance screening to extract the behavior-driven redundancy field group evolved from the target confidence trajectory.

[0015] In a preferred embodiment, the behavior-driven redundancy field group submitted in the identity authentication request is received, the corresponding field hash feature is extracted, and a reverse index vector is constructed as a trajectory inversion entry parameter;

[0016] The trajectory path matching the reverse index vector is retrieved in the trajectory identity matrix index set, and a trajectory candidate path set with the same redundancy mapping mode is obtained; each trajectory candidate path is input into the field simulation reconstruction module, and the reverse mapping of the trajectory encoding block group to the field hash feature is performed to generate a simulation field sequence for verification.

[0017] In a preferred embodiment, the structural consistency between the simulation field sequence and the field group to be verified is compared, the coincidence rate of the simulation path and the historical trajectory path at the redundancy mapping vector layer is calculated, and a path matching score matrix is constructed;

[0018] If there is a unique target confidence score path in the path matching score matrix, mark the behavior-driven redundant field group as a trajectory reversible generation result and pass the verification; if all candidate paths in the path matching score matrix do not reach the confidence threshold, mark the behavior-driven redundant field group as an un-mappable combination, and trigger the field structure offset identification mechanism;

[0019] In the field structure offset identification mechanism, the mapping offset value and the structure deformation feature of the un-mappable field group are extracted, combined to construct a trajectory break feature vector set, which is used for subsequent identification of potential pseudo-constructed field groups in the trajectory evolution level.

[0020] In a preferred embodiment, all binding pairs between the verified trajectory paths in the trajectory identity matrix index set and the behavior-driven redundant field group are traversed, and the corresponding trajectory path set of the field group is extracted as the main index to construct the joint distribution matrix of the redundant mapping vector in the path space, so as to statistically analyze the frequency of the same redundant field group appearing in multiple trajectory paths and its time evolution distribution trend;

[0021] The joint distribution matrix is sliced according to consecutive time windows, and the aggregation intensity of the redundant field group on the trajectory subset is evaluated in each window. Combined with the path confidence rate change rate and the trajectory entropy fluctuation coefficient, the field aggregation dynamic surface across time periods is calculated to identify the field abnormal dense growth area.

[0022] In a preferred embodiment, the abnormal area with a local gradient change rate higher than a preset threshold is extracted on the field aggregation dynamic surface, and all trajectory path nodes involved in the abnormal area are tracked to form an initial set of pseudo-constructed trajectory paths. Then, the redundant field combination in the initial set of pseudo-constructed trajectory paths is compared for coincidence rate, and the target repeated field group with similar redundant mapping vectors appearing in multiple paths is selected to form a pseudo-constructed field clustering cluster.

[0023] In a preferred embodiment, each trajectory path in the pseudo-constructed field clustering cluster is subjected to a path evolution consistency process, the jump density, node entropy fluctuation, and behavior expression offset in the platform embedding vector in the trajectory encoding block group are analyzed dimension by dimension, a path-level structure offset measurement space is constructed, and the offset vector of each path in the space is extracted to form a pseudo-constructed path offset vector set.

[0024] The pseudo-constructed path offset vector set is subjected to feature clustering, and historical path evolution trajectories are introduced as positive example samples. By comparing the behavior stability and platform adaptability differences between the positive example trajectories and the abnormal paths, an offset abnormal evolution group is divided, and the corresponding redundant field group is marked as a trajectory break risk identity group, and the behavior output result of the marked group is marked as derived from a real trajectory.

[0025] In a preferred embodiment, the redundant field group binding path in the trajectory fracture risk identity group is taken as a frozen candidate path, and a multi-level access control measure is performed on the path in the trajectory identity matrix index set, the multi-level access control measure including three freezing strategies of temporary identity authentication block, path write channel suspension and field mapping output locking to block subsequent participation in the system field verification process;

[0026] Finally, the field generation frequency, trajectory inversion failure rate, trajectory path offset gradient and coincidence index of the frozen field group in the past cycle are summarized to construct a redundant mechanism dynamic credibility monitoring report, and the redundant mechanism dynamic credibility monitoring report is pushed to the system for correction to drive the trajectory to field mapping function weight update and offset tolerance adjustment mechanism execution cycle optimization.

[0027] A human resource data security sharing system comprises an acquisition module, a segmentation module, a construction module, an aggregation module, an index module and a mapping module.

[0028] The acquisition module is configured to acquire a behavior signal sequence of a user on an identity authentication platform of human resources, the behavior signal sequence comprising a page access path, an interactive operation type, a time rhythm distribution and a device switching event.

[0029] The segmentation module is configured to segment the behavior signal sequence into continuous time sequence operation segment groups according to a uniform time sliding window mechanism, perform feature coding processing on each group of time sequence operation segment groups, extract operation frequency, rhythm change, device fingerprint and interface jump features, and construct a corresponding behavior embedding vector block.

[0030] The construction module is configured to take the behavior embedding vector block as a node of a directed graph, establish a behavior transition edge between nodes according to time sequence, and construct a preliminary behavior path graph.

[0031] The aggregation module is configured to perform node mode similarity calculation on a plurality of behavior path graphs, aggregate behavior path graphs with similar structures, and uniformly rearrange the behavior path graphs into a standardized behavior trajectory structure graph.

[0032] The index module is configured to aggregate all standardized behavior trajectory structure graphs, combine user identification, platform source and time period information, construct a trajectory identity matrix index set to support unified retrieval of global trajectory data, and perform unified retrieval of global trajectory data.

[0033] The mapping module extracts path credibility and behavior stability information by reading the trajectory identity matrix index set, and generates a trajectory code block group, a platform embedding vector, a redundant mapping vector and a candidate field set in sequence, and finally screens a behavior-driven redundant field group to complete consistent mapping of the trajectory to the field space.

[0034] The technical effects and advantages of the present application are:

[0035] 1. By constructing a redundant field screening mechanism based on the dynamic evolution of trajectory credibility and behavior stability, breaking the traditional platform's way of checking only the legality of field format, effectively preventing attackers from constructing pseudo-legal identities to mix into the sharing system, solving the problem of system misjudgment and real identity misrecognition caused by reverse utilization of the redundant mechanism;

[0036] 2. By mapping the field group mapping results to the trajectory encoding layer in reverse and constructing a path scoring matrix to judge the unique matching path, the reversible mapping closed loop from "field results" to "real behavior path" is realized, thereby providing structural level traceability guarantee for identity authentication and improving the trustworthiness and verifiability of shared field groups;

[0037] 3. By constructing a field-path joint matrix using redundant mapping vectors and path co-occurrence distribution, and introducing a time sliding window mechanism to analyze the change trend of field aggregation intensity, the non-natural growth section of the field at different time periods is effectively identified, and the abnormal behavior signs of potential batch pseudo-structure or field collusion attacks are captured in advance;

[0038] 4. Based on the trajectory offset vector, jump density, node entropy fluctuation and platform embedding bias, a structure offset space is constructed, and combined with historical positive example trajectory execution clustering comparison, the abnormal path highly deviated in behavior expression is effectively divided, and then the broken identity field possibly evolved by non-real users is identified. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The method step flowchart of the present application.

[0040] Figure 2 The behavior signal collection and trajectory generation flowchart of the present application.

[0041] Figure 3 The field inversion and reversibility verification flowchart of the present application.

[0042] Figure 4 The field anomaly aggregation and pseudo-structure clustering identification flowchart of the present application.

[0043] Figure 5 The behavior offset analysis and high-risk identity identification flowchart of the present application.

[0044] Figure 6 The system module diagram of the present application. DETAILED DESCRIPTION

[0045] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of the present application.

[0046] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of the present application. Figures 1-6 An embodiment of the present application is a human resource data security sharing method, comprising:

[0047] Collecting a behavior signal sequence of a user on a human resource identity authentication platform, the behavior signal sequence comprising a page access path, an interactive operation type, a time rhythm distribution and a device switching event;

[0048] Dividing the behavior signal sequence into continuous time sequence operation fragment groups according to a unified time sliding window mechanism, each group of fragments of the time sequence operation fragment groups maintaining continuity and time sequence integrity of original behavior context; performing feature coding processing on each group of time sequence operation fragment groups, extracting operation frequency, rhythm change, device fingerprint and interface jump features, and constructing a corresponding behavior embedding vector block;

[0049] Taking the behavior embedding vector block as a node of a directed graph, establishing a behavior transition edge between nodes according to time sequence, and constructing a preliminary behavior path graph;

[0050] Performing node mode similarity calculation on a plurality of behavior path graphs, aggregating behavior path graphs with similar structures and uniformly rearranging them into a standardized behavior trajectory structure graph;

[0051] Summarizing all standardized behavior trajectory structure graphs, combining user identification, platform source and time period information, constructing a trajectory identity matrix index set to support unified retrieval of global trajectory data;

[0052] By reading the trajectory identity matrix index set, extracting path credibility and behavior stability information, and sequentially generating a trajectory coding block group, a platform embedding vector, a redundancy mapping vector and a candidate field set, a behavior-driven redundancy field group is finally screened to complete the consistency mapping of the trajectory to the field space;

[0053] Among them, the structured trajectory path data is extracted from the trajectory identity matrix, and after multi-stage processing (trajectory block extraction, platform preference fusion, semantic mapping, disturbance screening), a group of "behavior-driven redundancy field groups" are obtained, which will be used for subsequent data sharing identity verification;

[0054] P m =f 扰 (f 映 (f 嵌 (f块 (M 轨 ))));

[0055] wherein P m is a behavior-driven redundancy field group, the mth field group, in units of field sequence sets, P m represents the final data output set filtered for identity authentication; f 扰 (·) is a noise rejection function, which is used to analyze whether the structural deviation output caused by abnormal trajectory path is contained in the mapped field sequence, and internally scores each field group based on the disturbance gradient and removes those with high volatility; f 映 (·) is a field semantic space mapping function, which is used to project the behavior vector after platform fusion into the field encoding space to generate preliminary field candidate values; f 嵌 (·) is a platform context embedding function, which integrates the user platform preferences, operation frequency and behavior period reflected in the trajectory path, in units of high-dimensional vectors for semantic retention; f 块 (M 轨 ) is a trajectory block generation function, which acts on the trajectory identity matrix to extract time-continuous behavior path segments (referred to as trajectory blocks), in units of graph path segment sets; M 轨 is a trajectory identity matrix index set, in units of path graph sets combined with user identification tuples, used for global path retrieval.

[0056] Read each trajectory path in the trajectory identity matrix index set, count the jump frequency, behavior period and stability of each trajectory path, and generate a trajectory credibility vector for path trust evaluation;

[0057] Input the trajectory credibility vector into the path stability analyzer, combine the trajectory entropy value and periodicity index to construct a trajectory encoding block group with consistent structure representation; perform fusion operation on the trajectory encoding block group and the platform preference factor to generate a platform embedding vector reflecting the platform context behavior characteristics; perform redundancy mapping on the platform embedding vector to complete path feature compression and semantic transformation, and generate a redundancy mapping vector facing field mapping; decode the redundancy mapping vector to a structured field space, and output a candidate field set that meets the structure rule;

[0058] Perform stability and disturbance screening on the candidate field set to extract the behavior-driven redundancy field group evolved from the target confidence trajectory;

[0059] It should be noted that the trajectory path is modeled for credibility, so that the most reliable path is selected for field mapping; the core is: using factors such as behavior jump density, rhythm consistency and platform adaptability, construct a path credibility score vector, and generate a unique semantic mapping structure through embedding fusion;

[0060]

[0061] wherein is the behavioral confidence score value of the ith trajectory path, in unit of dimensionless confidence factor, is the transition density in unit of time (transitions per second) in the path, which is derived from the number of edge connections in the graph structure; is the behavioral rhythm sequence, in unit of seconds; represents the platform ID corresponding to the path i, in unit of platform identifier; φ 跳 (·) is the transition density to score function, which is a monotonically increasing function, used to evaluate the path complexity; φ 节 (·) is the rhythm smoothness score function, which is used to measure the rhythm consistency, and the score decreases with the deviation mutation; φ 设 (·) is the platform trust function, which is used to set the confidence coefficient according to the platform identity, such as self-owned system with high score.

[0062] Receiving the behavior-driven redundant field group submitted in the identity authentication request, extracting the corresponding field hash feature, and constructing a reverse index vector as a trajectory inversion entry parameter;

[0063] Retrieving the trajectory path matched with the reverse index vector in the trajectory identity matrix index set, obtaining the trajectory candidate path set with the same redundant mapping mode; inputting each trajectory candidate path into the field simulation reconstruction module, performing reverse mapping of the trajectory coding block group to the field hash feature, and generating a simulation field sequence for verification.

[0064] Comparing the structural consistency between the simulation field sequence and the field group to be verified, calculating the coincidence rate of the simulation path and the historical trajectory path in the redundant mapping vector layer, and constructing a path matching score matrix;

[0065] Judging whether there is a unique target high-confidence score path in the path matching score matrix, if there is, marking the behavior-driven redundant field group as a trajectory reversible generation result and passing the verification; if all candidate paths in the path matching score matrix do not reach the confidence threshold, marking the behavior-driven redundant field group as an un-mappable combination, and triggering the field structure offset identification mechanism;

[0066] In the field structure offset identification mechanism, the mapping offset value and the structure deformation feature of the un-mappable field group are extracted, combined to construct a trajectory break feature vector set, which is used for subsequent identification of potential pseudo-structure field groups at the trajectory evolution level;

[0067] Where the field group is reflected to the simulation path, compared with the historical path score, if there is a unique high confidence path, it is verified, otherwise it is considered to be fake.

[0068]

[0069] Where Score i,j is the cosine similarity score of the ith simulation path and the jth historical trajectory, unit is dimensionless ∈ [-1, 1]; is the encoding vector of the simulation trajectory, unit is is the historical trajectory vector, unit is θ 置信 is the preset confidence threshold, unit is dimensionless; indicates the score value of the ith simulation path traversing all historical trajectories j, the maximum score is selected; this maximum score is used to judge whether there is a unique credible path; Valid represents the final trajectory reversible determination result, which is a Boolean variable, including 0 or 1.

[0070] Traverse all verified trajectory paths and binding pairs between behavior-driven redundant field groups in the trajectory identity matrix index set, and extract the corresponding trajectory path set according to the field group as the main index to construct the joint distribution matrix of the redundant mapping vector in the path space, so as to count the frequency of the same redundant field group appearing in multiple trajectory paths and its time evolution distribution trend;

[0071] Slice the joint distribution matrix according to the continuous time window, and perform the aggregation intensity evaluation of the redundant field group on the trajectory subset in each window, combine the path credibility change rate and the trajectory entropy fluctuation coefficient, and calculate the field aggregation dynamic surface across the time period to identify the field abnormal dense growth area.

[0072] Extract the abnormal area with local gradient change rate higher than the preset threshold on the field aggregation dynamic surface, and track all trajectory path nodes involved in the abnormal area to form the initial set of pseudo-constructed trajectory paths, and then perform coincidence rate comparison on the redundant field combination in the initial set of pseudo-constructed trajectory paths, and select the target repeated field group that appears similar redundant mapping vector in multiple paths to form the pseudo-constructed field clustering cluster.

[0073] Perform the path evolution consistency process on each trajectory path in the pseudo-constructed field clustering cluster, perform dimension-by-dimension analysis on the jump density, node entropy fluctuation, and behavior expression offset in the platform embedding vector in the trajectory encoding block group, construct the path-level structure offset measurement space, extract the offset vector of each path in the space, and combine to generate the pseudo-constructed path offset vector set;

[0074] Perform high-dimensional feature clustering on the pseudo-constructed path offset vector set, and introduce the historical path evolution trajectory as the positive example sample. By comparing the behavior stability and platform adaptability difference between the positive example trajectory and the abnormal path, the high offset abnormal evolution group is divided, and the corresponding redundant field group in the trajectory is marked as the trajectory fracture high-risk identity group, and the behavior output result may come from the real trajectory.

[0075] The redundant field group in the trajectory fracture high-risk identity group is bound to the path as the frozen candidate path, and the multi-level access control measures are performed on these paths in the trajectory identity matrix index set. The multi-level access control measures include: temporary identity authentication blockage, path writing channel suspension, field mapping output locking three freezing strategies to block its subsequent participation in the system field verification process.

[0076] Finally, the field generation frequency, trajectory inversion failure rate, trajectory path offset gradient and coincidence index of all frozen field groups in the past period in the redundant mapping space are summarized to construct the redundant mechanism dynamic trustworthiness monitoring report, and the redundant mechanism dynamic trustworthiness monitoring report is pushed to the system for correction, driving the trajectory to field mapping function weight update and offset tolerance adjustment mechanism execution cycle optimization.

[0077] Among them, by analyzing the path distribution density of the field group at different times, whether it appears abnormal aggregation, that is, whether it appears in a large number of unrelated trajectories in a short period of time, so as to judge whether it may be used by pseudo-constructed attacks;

[0078] Where D 共 (P m ,t) represents the redundant field group P m The trajectory distribution density at time t, the unit is path number / second; Indicates the derivative with respect to time, which is used to calculate the growth trend; ε 异常 is the aggregation anomaly threshold, the unit is density change rate.

[0079] Path offset vector analysis and clustering identification, the path set suspected of pseudo-constructed is converted into a high-dimensional offset vector, and whether it comes from a real behavior trajectory is identified by clustering method. If the difference with the historical behavior is large, it is classified as a high-risk pseudo-constructed trajectory;

[0080]

[0081] Where is the offset vector of the i-th path, and the offset vector represents the difference between the current encoding and the reference encoding, the unit is Euclidean distance (encoding space unit); is the behavior encoding vector of the current suspicious trajectory, the unit is is the historical or positive example trajectory vector; Ψ 轨For the path clustering result, the path clustering result is used to output a cluster center structure combined with a class to which each path belongs; Cluster(·) represents a cluster function of a trajectory offset vector space, and the cluster function of the trajectory offset vector space is used for cluster analysis on a set of offset vectors of all pseudo-constructed paths in a high-dimensional behavior encoding difference space, so as to identify whether there is an abnormal mode clustering trend in the behavior evolution process, thereby delimiting a suspicious trajectory break identity group.

[0082] A human resource data security sharing system comprises an acquisition module, a segmentation module, a construction module, an aggregation module, an index module and a mapping module.

[0083] The acquisition module is used to acquire a behavior signal sequence of a user on an identity authentication platform of human resources, and the behavior signal sequence comprises a page access path, an interactive operation type, a time rhythm distribution and a device switching event.

[0084] The segmentation module is used to segment the behavior signal sequence into continuous time sequence operation fragment groups according to a uniform time sliding window mechanism; feature encoding processing is performed on each group of time sequence operation fragment groups, operation frequency, rhythm change, device fingerprint and interface jump features are extracted, and a corresponding behavior embedding vector block is constructed.

[0085] The construction module is used to take the behavior embedding vector block as a node of a directed graph, establish a behavior transition edge between nodes according to time sequence, and construct a preliminary behavior path graph.

[0086] The aggregation module is used to perform node mode similarity calculation on a plurality of behavior path graphs, aggregate behavior path graphs with similar structures, and uniformly rearrange the behavior path graphs into a standardized behavior trajectory structure graph.

[0087] The index module is used to summarize all standardized behavior trajectory structure graphs, combine user identification, platform source and time period information, construct a trajectory identity matrix index set to support unified retrieval of global trajectory data, and construct a trajectory identity matrix index set to support unified retrieval of global trajectory data.

[0088] The mapping module extracts path credibility and behavior stability information by reading the trajectory identity matrix index set, and generates a trajectory encoding block group, a platform embedding vector, a redundant mapping vector and a candidate field set in sequence, and finally screens a behavior-driven redundant field group to complete consistent mapping of the trajectory to the field space.

[0089] The above only describes preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A human resource data security sharing method, comprising: collecting a behavior signal sequence of a user on a human resource identity authentication platform, the behavior signal sequence comprising a page access path, an interactive operation type, a time rhythm distribution, and a device switching event; characterized in that: the behavior signal sequence is divided into continuous time sequence operation fragment groups according to a unified time sliding window mechanism; feature coding processing is performed on each time sequence operation fragment group to extract operation frequency, rhythm change, device fingerprint, and interface jump features, and a corresponding behavior embedding vector block is constructed; the behavior embedding vector block is taken as a node of a directed graph, and a behavior transition edge between nodes is established according to time sequence to construct a preliminary behavior path graph; node mode similarity calculation is performed on multiple behavior path graphs, and behavior path graphs with similar structures are aggregated and uniformly rearranged into standardized behavior trajectory structure graphs; all standardized behavior trajectory structure graphs are summarized, user identification, platform source, and time period information are combined, a trajectory identity matrix index set is constructed to support unified retrieval of global trajectory data; by reading the trajectory identity matrix index set, path credibility and behavior stability information are extracted, and trajectory encoding block groups, platform embedding vectors, redundancy mapping vectors, and candidate field sets are generated in sequence, and finally behavior-driven redundancy field groups are screened to complete consistent mapping of trajectories to field space. 2.The human resource data security sharing method of claim 1, characterized in that: each trajectory path in the trajectory identity matrix index set is read, and the jump frequency, behavior period, and stability of each trajectory path are counted to generate a trajectory credibility vector for path trust evaluation; the trajectory credibility vector is input into a path stability analyzer, the trajectory entropy value and periodicity index are combined to construct a trajectory encoding block group with consistent structure, the trajectory encoding block group and platform preference factor are subjected to fusion operation to generate a platform embedding vector reflecting the context behavior characteristics of the platform, the platform embedding vector is subjected to redundancy mapping to complete path feature compression and semantic transformation, and a redundancy mapping vector for field mapping is generated; the redundancy mapping vector is decoded to a structured field space, and a candidate field set satisfying the structure rule is output; stability and disturbance screening is performed on the candidate field set to extract behavior-driven redundancy field groups evolved from target confidence trajectories. 3.The human resource data security sharing method of claim 2, characterized in that: the behavior-driven redundancy field groups submitted in the identity authentication request are received, corresponding field hash features are extracted, and a reverse index vector is constructed as a trajectory inversion entry parameter; the trajectory paths matching the reverse index vector in the trajectory identity matrix index set are searched to obtain a trajectory candidate path set with the same redundancy mapping mode; each trajectory candidate path is input into a field simulation reconstruction module to perform reverse mapping of the trajectory encoding block group to the field hash feature, and a simulation field sequence used for verification is generated. 4.The human resource data security sharing method of claim 3, characterized in that: The structure consistency between the simulated field sequence and the to-be-verified field group is compared, the coincidence rate of the simulation path and the historical trajectory path in the redundant mapping vector layer is calculated, and a path matching score matrix is constructed; It is judged whether there is a unique target confidence score path in the path matching score matrix. If there is, mark the behavior-driven redundant field group as a trajectory reversible generation result and pass the verification. If all candidate paths in the path matching score matrix do not reach the confidence threshold, mark the behavior-driven redundant field group as an unmappable combination, and trigger the field structure offset identification mechanism; In the field structure offset identification mechanism, the mapping offset value and the structure deformation feature of the unmappable field group are extracted, combined to construct a trajectory break feature vector set, which is used for subsequent identification of potential pseudo-structure field groups at the trajectory evolution level.

5. The human resource data security sharing method according to claim 4, characterized in that: All binding pairs between the verified trajectory path and the behavior-driven redundant field group in the trajectory identity matrix index set are traversed, and the corresponding trajectory path set is extracted according to the field group as the main index to construct the joint distribution matrix of the redundant mapping vector in the path space, so as to statistically analyze the frequency and time evolution distribution trend of the same redundant field group appearing in multiple trajectory paths; The joint distribution matrix is sliced according to the continuous time window, and the aggregation intensity of the redundant field group on the trajectory subset is evaluated in each window. Combined with the path confidence rate change rate and the trajectory entropy fluctuation coefficient, the field aggregation dynamic surface across time periods is calculated to identify the field abnormal dense growth area.

6. The human resource data security sharing method according to claim 5, characterized in that: Abnormal areas with local gradient change rate higher than the preset threshold are extracted on the field aggregation dynamic surface, and all trajectory path nodes involved in the abnormal areas are tracked to form an initial set of pseudo-structure trajectory paths. The redundant field combination in the initial set of pseudo-structure trajectory paths is compared for coincidence rate, and the target repeated field group with similar redundant mapping vectors appearing in multiple paths is selected to form a pseudo-structure field clustering cluster.

7. The human resource data security sharing method according to claim 6, characterized in that: Each trajectory path in the pseudo-structure field clustering cluster is subjected to a path evolution consistency process, the jump density, node entropy fluctuation, and behavior expression offset in the platform embedding vector in the trajectory coding block group are analyzed dimension by dimension, a path-level structure offset measurement space is constructed, and the offset vector of each path in the space is extracted to form a pseudo-structure path offset vector set; The pseudo-structure path offset vector set is subjected to feature clustering, and historical path evolution trajectories are introduced as positive example samples. By comparing the behavior stability and platform adaptability differences between the positive example trajectories and the abnormal paths, the offset abnormal evolution group is divided, and the corresponding redundant field group is marked as a trajectory break risk identity group, and the behavior output result is marked as derived from a real trajectory.

8. The human resource data security sharing method according to claim 7, characterized in that: The redundant field group binding path in the trajectory fracture risk identity group is taken as a frozen candidate path, and multi-level access control measures are performed on the path in the trajectory identity matrix index set, the multi-level access control measures including three freezing strategies of temporary identity authentication blockage, path write channel suspension, and field mapping output locking to block subsequent participation in the system field verification process; Finally, the field generation frequency, trajectory inversion failure rate, trajectory path offset gradient, and coincidence index in the redundant mapping space of all frozen field groups in the past period are summarized to construct a redundant mechanism dynamic credibility monitoring report, and the redundant mechanism dynamic credibility monitoring report is pushed to the system for correction to drive the trajectory to field mapping function weight update and offset tolerance adjustment mechanism execution cycle optimization.

9. A human resource data security sharing system, comprising an acquisition module, a segmentation module, a construction module, an aggregation module, an index module, and a mapping module, characterized in that: The acquisition module is configured to acquire a behavior signal sequence of a user on a human resource identity authentication platform, the behavior signal sequence including a page access path, an interactive operation type, a time rhythm distribution, and a device switching event. The segmentation module is configured to segment the behavior signal sequence into continuous time sequence operation fragment groups according to a unified time sliding window mechanism, perform feature coding processing on each group of time sequence operation fragment groups, extract operation frequency, rhythm change, device fingerprint, and interface jump features, and construct a corresponding behavior embedding vector block. The construction module is configured to take the behavior embedding vector block as a node of a directed graph, establish a behavior transition edge between nodes according to time sequence, and construct a preliminary behavior path graph. The aggregation module is configured to perform node mode similarity calculation on multiple behavior path graphs, aggregate behavior path graphs with similar structures, and uniformly rearrange the behavior path graphs into a standardized behavior trajectory structure graph. The index module is configured to aggregate all standardized behavior trajectory structure graphs, combine user identification, platform source, and time period information, construct a trajectory identity matrix index set to support unified retrieval of global trajectory data, and perform unified retrieval of global trajectory data. The mapping module is configured to read the trajectory identity matrix index set, extract path credibility and behavior stability information, generate trajectory code block groups, platform embedding vectors, redundant mapping vectors, and candidate field sets in sequence, and finally filter behavior-driven redundant field groups to complete consistent mapping of trajectories to field space.

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