A multi-source data integration management platform for professional construction

Abstract

This invention discloses a multi-source data integration management platform for professional development, relating to the field of professional development data processing technology. This multi-source data integration management platform for professional development includes a data acquisition module, which collects and preprocesses structured, behavioral, and interactive response data to construct a multi-role access behavior dataset; a data structure-level anonymization module, which analyzes field display conditions and triggers structural reconstruction; a collaboration scenario awareness module, which evaluates and adjusts field permission hierarchies; a permission drift path identification module, which comprehensively analyzes sharing stability and dynamically adjusts sharing frequency; and a cross-organizational data flow monitoring module, which marks cross-organizational access behaviors and generates permission reconstruction suggestions. It solves the problem in current vocational education management platforms where it is difficult to balance permission granularity and data visibility, and where platforms facing multiple types of users both inside and outside the school easily lead to overly fine-grained permission allocation, resulting in information silos and ineffective data sharing and flow.

Description

Technical Field

[0001] This invention relates to the field of professional construction data processing technology, specifically to a multi-source data integration and management platform for professional construction. Background Technology

[0002] With the continuous advancement of industry-education integration and digital education, professional development activities are gradually evolving from a decentralized, experience-driven organizational approach to a data-driven, structurally transparent, and collaboratively visible integrated management model. Various teaching resources, curriculum structures, talent training programs, and teaching process data are continuously flowing among multiple roles both inside and outside the school, forming a cross-organizational, multi-source data interaction pattern. To improve the effectiveness of educational governance and the quality of collaborative professional development, an increasing number of applied universities and vocational education institutions are deploying multi-source data perception and processing mechanisms to achieve data collection, access control, process tracking, and dynamic evaluation during the professional development process.

[0003] For example, the invention patent with announcement number CN116701707B discloses an educational big data management system, belonging to the field of data management technology. The system includes a video acquisition unit, a summary extraction unit, a user information acquisition unit, and a course matching unit. The video acquisition unit acquires a set of teaching videos and generates keyframes for each video. The summary extraction unit generates teaching summaries for each keyframe. The user information acquisition unit acquires a set of user course search keywords. The course matching unit matches the optimal teaching video for the user based on the teaching summaries corresponding to each keyframe and the user's course search keyword set. This educational big data management system determines the optimal teaching video by calculating a search weight sequence and a summary weight sequence. Its comparison method is simple and easy to implement, recommending the best teaching video to the user and providing effective support for online educational big data management.

[0004] For example, the invention patent with announcement number CN117149988B discloses a data management and processing method and system based on digital education. Applying embodiments of this invention, historical digital education service session logs are extracted through coarse semantic matching of digital education service session text, improving the timeliness of the solution. Subsequent joint analysis uses refined semantics for structured storage to support decision-making. Given that the semantic features carried by refined digital education service session text are more complete and accurate, the structured storage support decision-making results for the digital education service session logs to be managed can be accurately and reliably determined. Therefore, the target digital education service session logs and structured storage strategies included in the structured storage support decision-making results are used to achieve structured storage of the digital education service session logs to be managed, improving the accuracy and efficiency of structured storage of the digital education service session logs to be managed, thereby ensuring the management quality of the digital education service session logs.

[0005] However, existing data processing mechanisms in professional development generally suffer from problems such as heterogeneous data sources, fragmented field permissions, missing access records, and blurred role boundaries. This is particularly true in multi-organizational collaborative tasks involving schools, enterprises, teachers, and students, where different roles have significantly different purposes for accessing the same field, display structures, and permission requirements. This leads to unclear field sharing boundaries, unstable call responses, and consequently, risks of data misuse, silos of critical information, and low collaborative efficiency. Furthermore, current data permission allocation methods largely rely on static configuration or manual settings, lacking dynamic evaluation and derivation mechanisms based on behavioral data and task structures. This makes it difficult to address the frequently changing role relationships and task node requirements in multi-organizational collaborative tasks, hindering the deep integration and intelligent flow of data in professional development.

[0006] Therefore, there is an urgent need for a multi-source data integration and management platform for professional development. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a multi-source data integration and management platform for professional development. It solves the problems of difficulty in balancing permission granularity and data visibility in current vocational education management platforms, and the tendency for overly detailed permission allocation to lead to information silos and ineffective data sharing and flow when the platform is used by multiple types of users both inside and outside the school.

[0009] Technical solution

[0010] To achieve the above objectives, this invention provides the following technical solution: a multi-source data integration management platform for professional development, comprising a data acquisition module that collects structured data, behavioral data, and interactive response data during the professional development process, and preprocesses the collected structured data, behavioral data, and interactive response data to construct a multi-role access behavior dataset; a data structure-level desensitization module that analyzes the cross-role hierarchical display conditions of fields based on the multi-role access behavior dataset, and triggers field display structure reconstruction based on the analysis results; a collaboration scenario awareness module that evaluates the dynamic visual permissions of evaluation fields in a multi-role environment based on the multi-role access behavior dataset, and adjusts the hierarchical derivation of field permissions based on the evaluation results; a permission drift path identification module that comprehensively analyzes the cross-role sharing stability of fields using the display condition analysis results and display stability evaluation results as input, and dynamically adjusts the field sharing frequency control mechanism based on the analysis results; and a cross-organizational data flow monitoring module that dynamically monitors and marks cross-organizational access behaviors based on the multi-role data flow process, identifies data field sharing risks, and generates permission reconstruction suggestions.

[0011] Furthermore, the specific steps for collecting structured data, behavioral data, and interactive response data during the professional development process are as follows: Collect structured data during the professional development process, including: the number of fields subject to access control, the original content length of each field, the visible content length in each role's view, and record the basic field ratio and the average display length of each field in the task stage to which the field belongs; collect behavioral data through the teaching execution and resource operation process, including: the number of courses within the major, the number of teaching sub-tasks, the frequency of teaching resource uploads, and the frequency of course goal changes; collect interactive response data through platform behavior logs, including the number of users requesting access to a field, the number of field access requests, the number of nodes using the field, the number of visible views of the field, the number of tasks called by users, and the field call response latency, while also recording the average access frequency of each field in the task stage to which the field belongs and the number of role types of users simultaneously online on the page.

[0012] Furthermore, the specific steps for preprocessing the collected structured data, behavioral data, and interaction response data to construct a multi-role access behavior dataset are as follows: For the collected structured data, a unified structure mapping rule is used to convert the original content length and the visible content length for each role into a relative display ratio, and the basic field ratios are uniformly scaled to the same range; For the collected behavioral data, standard deviation standardization is performed to unify the fluctuation scale of each indicator across different professions and improve the horizontal comparability of behavioral activity analysis; For the collected interaction response data, logarithmic scaling is used to suppress extreme value interference, and distribution mean centering is used to adjust the response load offset under different interaction scenarios; After completing the standardization processing of structured data, behavioral data, and interaction response data, a normalization process is uniformly executed on all data dimensions, and the multi-role access behavior dataset is constructed.

[0013] Furthermore, the specific steps for analyzing the cross-role hierarchical display conditions of fields based on the multi-role access behavior dataset are as follows: Extract the original content length and the visible content length in the current role view of each field from the multi-role access behavior dataset; calculate the difference between the original content length and the courseware content length and divide it by the average display length of each field in the task stage to which the field belongs, to obtain the field display deviation; extract the number of access requests and the average access frequency of each field in the current period, and calculate the ratio of the number of access requests to the average access frequency to obtain the field's access request intensity; extract the number of users requesting access to the field, and calculate the ratio of the number of users requesting access to the field to the number of role types of users simultaneously online on the page where the field is located, to obtain the user distribution breadth of each field; add the field display deviation, access request intensity, and user distribution breadth of each field to obtain the normalized display value of each field; add the normalized display values ​​of all fields, divide by the number of fields included in permission control, and then subtract the field display adjustment factor to obtain the field display adaptation value.

[0014] Furthermore, the specific steps for triggering field display structure reconstruction based on analysis results are as follows: Real-time comparison of the current field display adaptation value with the field display adaptation threshold: When the field display adaptation value is less than or equal to the field display adaptation threshold, it is determined that the current field is in a low adaptation display stage, the display permission of the field in non-main role views is canceled, and it is only rendered in the main view as a brief information bar. The field freezing mechanism is enabled to block the shared link of the field in all collaborative tasks and prevent the field from being derived and inherited by other roles. When the field display adaptation value is greater than the field display adaptation threshold, it is determined that the current field has high adaptation display capability. A multi-role display version of the field is generated, the field writing mechanism is started, the role reference path of the field under different collaborative tasks is registered and dynamically expanded, and the export of differentiated content is automatically generated according to user identity.

[0015] Furthermore, the specific steps for evaluating the dynamic visual permissions of the evaluation field in a multi-role environment based on the multi-role access behavior dataset are as follows: Extract the number of teaching subtasks and the proportion of basic fields from the multi-role access behavior dataset; multiply the number of teaching subtasks by one, take the natural logarithm, and then multiply it by the square of the difference between one and the proportion of basic fields to obtain the teaching task intensity value; extract the number of user-invoked tasks and the number of nodes used by the field; multiply the number of user-invoked tasks and the number of nodes used by the field, add one, and then take the natural logarithm to obtain the field invoking weight value; extract the number of field visible views; divide the number of field visible views by the number of field visible views plus one, and then add it to the field invoking weight value to obtain the graph complexity; extract the field invoking response latency; use the teaching task intensity value as the numerator and the field invoking response latency and graph complexity as the denominator to obtain the field visual permission evaluation value.

[0016] Furthermore, the specific steps for adjusting the field permission hierarchy based on the evaluation results are as follows: All fields are sorted in descending order based on the calculated field visual permission evaluation values ​​to generate a field permission scoring sequence. Based on the relative position of the field visual permission evaluation value in the field permission scoring sequence, different intervals are divided and corresponding permission hierarchy derivation strategies are executed: When the field visual permission evaluation value is in the top 30% of the field permission scoring sequence distribution, the field is automatically included in the shared candidate list of the current collaborative task, triggering a field multi-role view derivation mechanism. Field display templates are dynamically generated according to the collaborative task roles, allowing temporary cross-department visibility; When the field visual permission evaluation value is in the middle 40% of the field permission scoring sequence, the shared derivation operation is not executed temporarily. The field is only set to an intermediate state where it is called but requires approval. When a task node is detected to need the field during collaboration, the current task initiator confirms whether to open a copy of the field view; When the field visual permission evaluation value is in the bottom 30% of the field permission scoring sequence, the current view of the field is locked, only the original static visible role list is retained, and it does not participate in this round of collaborative permission reconstruction process.

[0017] Furthermore, the specific steps for comprehensively analyzing the cross-role sharing stability of fields using the display condition analysis results and display stability assessment results as input are as follows: obtain the field display adaptation value and the field visual permission assessment value; multiply the field display adaptation value, the field visual permission assessment value, and the number of courses within the major to obtain the field call matching degree; extract the teaching resource upload frequency and the course goal change frequency; calculate the absolute value of the difference between the teaching resource upload frequency and the course goal change frequency, add one, take the logarithm, and add one again to obtain the teaching resource dynamic adjustment degree; divide the field call matching degree by the teaching resource dynamic adjustment degree to obtain the field sharing stability value.

[0018] Furthermore, the specific steps of the dynamic adjustment of field sharing frequency control mechanism based on analysis results are as follows: Real-time comparison of the current field sharing stability adaptation value with the field sharing adjustment level threshold, which includes a first sharing threshold and a second sharing threshold: When the field sharing stability adaptation value is less than or equal to the second sharing threshold, it is determined to be a field sharing restricted stage, suspending cross-role synchronization and derived calls of the field, loading the original field version only in the current primary / secondary role task, and displaying it as a placeholder reference in other role tasks, while simultaneously freezing the reference interface authorization; when the field sharing stability adaptation value is greater than the second sharing threshold and less than or equal to... When the first sharing threshold is reached, it is determined to be in the field sharing adjustment transition phase. The usage frequency, number of jumps and interaction paths of derived fields are tracked in real time, and the field usage annotation mode is activated at the interface layer. When the field sharing stability adaptation value is greater than the first sharing threshold, it is determined to be in the field sharing enhancement phase. The multi-role view access and asynchronous synchronization mechanism of the field are fully opened: the field's view call permission in all collaborative modules is unlocked, and it is marked as a recommended shared field. The field is allowed to appear in resource co-construction and task review processes other than the main task chain. At the same time, the field is added to the real-time evolution queue, and customized output versions of field content are automatically generated for different user identities.

[0019] Furthermore, the specific steps for dynamically monitoring and marking cross-organizational access behavior based on multi-role data flow processes, identifying data field sharing risks, and generating permission reconstruction suggestions are as follows: Based on the call records of each data field in the platform and the cross-organizational role access trajectory, a multi-role data flow process is constructed; the actual data flow path and interaction nodes between schools, enterprises, teachers, and students are identified; high-frequency data access requests in the flow path are dynamically marked; organizational boundary markers are set and abnormal access events of role overstepping are recorded; the access frequency of high-frequency abnormal users who continuously trigger abnormal access events to key fields within a unit of time is restricted, and a field-level sharing risk warning log is generated; combined with the multi-role data flow process and field sharing stability adaptation value, permission boundary reconstruction suggestions are generated to optimize the cross-organizational call path of fields, improve the elasticity of data flow and the efficiency of collaborative sharing, and break down information silos caused by overly detailed permissions.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) This multi-source data integration management platform for professional construction dynamically quantifies the sharing adaptability of fields in multi-role collaborative tasks by constructing field visual permission evaluation values ​​and introducing a field call path identification mechanism, effectively solving the problems of static permission division and lack of task awareness in the existing technology.

[0023] (2) This multi-source data integration management platform for professional construction, by constructing stable values ​​for field sharing and integrating field access frequency, user type and view change trend, realizes the stability modeling of field calls in multi-organization collaboration process, which significantly improves the elasticity and security of data sharing process in professional construction.

[0024] (3) This multi-source data integration management platform for professional construction, by introducing role behavior tracking and field cross-organizational flow process, supports dynamic field derivation display and sharing path optimization, can effectively identify access beyond permission boundaries, risk nodes in sharing and abnormal access links, and ensure the access compliance of key fields in the collaboration process.

[0025] (4) This multi-source data integration management platform for professional construction, by constructing a partition sorting mechanism based on field visibility permission evaluation value and combining field sharing stable value, supports a multi-level permission control strategy of approval intermediate state - temporary visibility - role archiving, effectively alleviates the conflict between permission granularity and data visibility, and solves the problem of information silos caused by excessively fine permissions.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] Figure 1 This is a structural diagram of a multi-source data integration and management platform for professional construction according to the present invention;

[0028] Figure 2 This is a line graph showing the stable value of field sharing involved in this invention;

[0029] Figure 3 This is the multi-role data permission mapping diagram involved in this invention;

[0030] Figure 4 This is a flowchart illustrating the multi-role data flow involved in this invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figures 1-4This invention provides a technical solution: a multi-source data integration management platform for professional development, comprising a data acquisition module that collects structured data, behavioral data, and interactive response data during the professional development process, and preprocesses the collected structured data, behavioral data, and interactive response data to construct a multi-role access behavior dataset; a data structure-level desensitization module that analyzes the cross-role hierarchical display conditions of fields based on the multi-role access behavior dataset, and triggers field display structure reconstruction based on the analysis results; a collaboration scenario awareness module that evaluates the dynamic visual permissions of evaluation fields in a multi-role environment based on the multi-role access behavior dataset, and adjusts the hierarchical derivation of field permissions based on the evaluation results; a permission drift path identification module that comprehensively analyzes the cross-role sharing stability of fields using the display condition analysis results and display stability evaluation results as input, and dynamically adjusts the field sharing frequency control mechanism based on the analysis results; and a cross-organizational data flow supervision module that dynamically monitors and marks cross-organizational access behavior based on the multi-role data flow process, identifies data field sharing risks, and generates permission reconstruction suggestions.

[0033] Specifically, the steps for collecting structured data, behavioral data, and interactive response data during the professional development process are as follows: Collect structured data during the professional development process. This structured data includes: the number of fields subject to access control, the original content length of each field, the visible content length in each role's view, and record the basic field ratio and the average display length of each field in the task stage to which the field belongs. Specifically, the number of fields subject to access control is extracted from the field identifier in the professional access configuration table; the original content length of the fields is batch-counted from the field data in the initial imported template; the visible content length is generated by matching the role view configuration with field occlusion rules; the basic field ratio is compared and filtered based on the field source and field layering definition; and the average display length of fields in the task stage is calculated by aggregating the display stage logs and field rendering results.

[0034] Behavioral data is collected through the teaching execution and resource operation process. The behavioral data includes: the number of courses within the major, the number of teaching sub-tasks, the frequency of teaching resource uploads, and the frequency of course goal changes. Among them, the number of courses and the number of teaching sub-tasks are extracted from the course schedule list and teaching task configuration record, respectively. The frequency of teaching resource uploads is aggregated by time period through timestamps and resource type fields in the resource upload log. The frequency of course goal changes is extracted and statistically analyzed by version sequence and change time point fields in the course goal change record.

[0035] Interaction response data is collected through platform behavior logs. This data includes the number of users requesting access to a field, the number of field access requests, the number of nodes using the field, the number of visible views of the field, the number of tasks called by users, and the field call response latency. It also records the average access frequency of each field within its task phase and the number of user roles simultaneously online on the page. Specifically, the number of users requesting access to a field and the number of field access requests are generated by aggregating user and field identifiers from the logs. The number of nodes using a field is extracted from the call chain relationship logs between functional modules. The number of visible views is statistically recorded based on the field's display configuration on different interfaces. The number of tasks called by users is obtained by summarizing task trigger records from user behavior logs. The field call response latency is calculated based on the time difference between the access request and the return record. The field access frequency and the number of role types are dynamically statistically analyzed by combining task phase tags and user online behavior time windows.

[0036] This implementation plan constructs a multi-dimensional, standardized data infrastructure framework to provide precise support for subsequent hierarchical display of permissions, behavioral characteristic assessment, and interactive response analysis. Through refined collection and classification statistics of structured data, behavioral data, and interactive response data, it not only comprehensively reconstructs the visibility and access characteristics of various fields in different roles and task stages during the professional development process, but also quantifies the frequency and change trends of behaviors during teaching resource and task operation processes. This further reveals field access popularity, response efficiency, and user distribution characteristics, providing traceable and computable data support for key processing flows such as subsequent field display structure reconstruction, dynamic permission adjustment, and data sharing risk control.

[0037] Specifically, the collected structured data, behavioral data, and interaction response data are preprocessed to construct a multi-role access behavior dataset. The specific steps are as follows: For the collected structured data, a unified structure mapping rule is used to convert the original content length and the visible content length of each role into a relative display ratio, and the basic field ratio is uniformly scaled to the same range. The relative display ratio is obtained by calculating the ratio between the visible length of each field in different role views and its original content length, and the result is converted to a percentage. The basic field ratio is based on the type label of the field, and the proportion of commonly used fields and extended fields in the total field set is normalized to between 0 and 1 to support the standard discrimination of field hierarchy.

[0038] The collected behavioral data is standardized by standard deviation to unify the fluctuation scale of each indicator across different majors and improve the horizontal comparability of behavioral activity analysis. In the specific processing, each behavioral indicator is subtracted from its mean within its major and then divided by its standard deviation to generate a standard distribution result with zero mean and unit variance. This allows the frequency of resource use, the pace of teaching task execution, and the density of goal adjustment across majors to be compared and clustered on a unified scale.

[0039] Logarithmic scaling is applied to the collected interactive response data to suppress extreme value interference. At the same time, distribution mean centering is used to adjust the response load offset under different interaction scenarios. Specifically, logarithmic scaling is performed by adding 1 to the access frequency, number of calls and response latency and then taking the natural logarithm to compress the fluctuation of large value ranges. Distribution mean centering is performed by shifting the mean of the response data of each type of interactive field to zero point under its task stage to reduce the basic load offset error caused by the difference in task characteristics.

[0040] After standardizing the structured data, behavioral data, and interactive response data, a normalization process is uniformly executed on all data dimensions, and a multi-role access behavior dataset is constructed. The normalization process uses a maximum-minimum linear transformation to uniformly map all processed feature values ​​to the range of 0 to 1, and finally integrates them into a multi-role access behavior dataset that can be used for permission path identification and behavior distribution evaluation.

[0041] This implementation plan achieves standardized and unified representation of various heterogeneous data, constructing a multi-role access behavior dataset with horizontal comparability and vertical consistency, providing high-quality data support for subsequent field display optimization, permission layering and discrimination, and behavior pattern analysis. By performing proportional mapping and interval scaling on structured data, the visibility differences of fields across different roles are quantified; standard deviation standardization of behavioral data helps eliminate scale interference from fluctuations in indicator values ​​under different professional backgrounds; logarithmic scaling and centralization of interactive response data effectively suppress the influence of extreme values ​​and unify the interaction load baseline. Finally, normalization operations achieve fusion representation of various features at the same scale, ensuring the dataset's stability, comparability, and operability in subsequent path identification, visual permission reconstruction, and shared risk assessment.

[0042] Specifically, based on the multi-role access behavior dataset, the specific steps for analyzing the cross-role hierarchical display conditions of fields are as follows: Extract the original content length and the visible content length in the current role's view for each field from the multi-role access behavior dataset; calculate the difference between the original content length and the courseware content length and divide it by the average display length of each field in the task stage to which the field belongs, to obtain the field display deviation; extract the number of access requests and the average access frequency for each field in the current period, and calculate the ratio of the number of access requests to the average access frequency to obtain the field's access request intensity; extract the number of users requesting access to the field, and calculate the ratio of the number of users requesting access to the field to the number of role types of users simultaneously online on the page where the field is located, to obtain the user distribution breadth of each field; add the field display deviation, access request intensity, and user distribution breadth of each field to obtain the normalized display value of each field; add the normalized display values ​​of all fields, divide by the number of fields included in permission control, and then subtract the field display adjustment factor to obtain the field display adaptation value.

[0043] The formula for calculating the field display adaptation value is:

[0044]

[0045] In the formula: N represents the number of fields included in the permission control, which is used to normalize the mean of the adaptation calculation of each field. It is a structural parameter that measures the complexity of the current task permission management and comes from the field permission list and role field mapping table automatically generated during the initialization phase of the collaborative task. This represents the original content length of field i, used to depict the theoretical total amount of content that the field can display without permission intervention. It is a basic indicator for measuring the original information capacity of a field and is derived from the field data record in the field design structure document. This indicates the length of the visible content of field i in the current role view. It is used to evaluate the degree of display compression of the field under specific permissions and is an important indicator for determining the view clipping strength and display integrity. It is derived from the field role clipping strategy execution log and the actual front-end page load length record. This represents the number of access requests for field i within the current period, reflecting the periodic usage activity of this field. It is a behavioral characteristic variable for identifying frequently shared fields and adjusting display priority, and it originates from the field-level access logs during task execution. This represents the number of users requesting access to field i. It measures the breadth of field usage in a multi-user environment and is a user coverage metric for judging the strength of cross-role sharing needs for the field. It is derived from the deduplicated summary table of user IDs and field call records in the field access tracking module; τ lThis represents the average display length of all fields within the task phase to which the field belongs. It is used to normalize the difference in display length and serves as a baseline for calculating the field display compression rate. It is derived from the average display configuration of all fields in each role view within the same task phase, and is statistically analyzed from task phase data and actual front-end display data; τ q This represents the average access frequency of each field within the task phase to which it belongs. It is used to standardize individual fluctuations in access popularity and serves as a calibration parameter for identifying the relative intensity of field access frequency. It is derived from the statistical mean of the access frequency of the field dimension in the task phase field logs; τ t λ represents the number of user roles simultaneously online on the page containing the field. It is used to assess the diversity of the target audience when displaying the field and is an important contextual parameter for measuring the cross-role adaptation requirements of the field. It is derived from the role access concurrency analysis results based on timestamps from front-end session tracking. λ represents the field display adjustment factor, which ranges from 0.1 to 1.0. It is used to adjust the amplification effect of abnormal display compression or extreme access frequency on the overall evaluation results in the field adaptation value calculation, and to prevent the adaptation value from deviating from the overall display capability of the field due to local extreme values. It is derived from the relative deviation between the stability fluctuation range of the current field's display configuration in different role views and the display load threshold of the task stage. In the specific calculation process, firstly, the display length sequence of the field in each role view within the current task cycle is extracted, and the variance of the display compression rate, role distribution density, and cropping ratio range are calculated to measure the display fluctuation of the field under multi-view configuration. Then, the display configuration of the field in the standard view template is extracted, and a historical configuration benchmark table of the field is constructed to record the average display length, typical cropping ratio, and maximum compression tolerance. The current multi-view display characteristics are compared with the benchmark model in multiple dimensions to obtain the deviation score of the display configuration, including configuration consistency score, abnormal cropping rate, and role imbalance index. Finally, the response time distribution and display load contribution value of the field during the front-end page loading process are combined to comprehensively assign weights to form the final display adjustment factor λ. If the current field exhibits significant clipping anomalies under certain roles or the page display load is excessively concentrated, the λ value will increase accordingly, and anomalies in the adaptation value will be weighted to suppress them. If the field display configuration is stable, the clipping differences are small, and the load distribution is uniform, the λ value will tend to be lower, thereby improving the field's contribution to the visibility and completeness of the collaborative task, and enhancing the stability of the adaptation value evaluation and the robustness of the actual display control.

[0046] This implementation plan comprehensively considers the difference between the original content length of a field and its visible length in the actual view, the frequency of access requests and the number of requesting users for the field within the current period, and combines this with the average display length, access frequency, and complexity of online user roles in the task flow. Finally, a display adjustment factor is subtracted to control abnormal display fluctuations. The field display adaptation value calculated by this formula serves as an important reference indicator for whether a field is suitable for sharing and displaying in derived views in the current collaborative task, supporting field view configuration optimization, permission adjustments, and cross-role display strategy reconstruction.

[0047] Specifically, the steps for triggering field display structure reconstruction based on the analysis results are as follows: Real-time comparison of the current field display adaptation value with the field display adaptation threshold.

[0048] When a field's display adaptation value is less than or equal to its display adaptation threshold, the field is determined to be in a low-adaptation display stage. Its display permission in non-primary role views is revoked, and it is rendered only as a brief information bar in the primary view. A field freezing mechanism is enabled, blocking the field's shared link in all collaborative tasks and preventing the field from being derived or inherited by other roles. Specifically, the field freezing mechanism is based on a joint judgment of the field's visibility identifier and role call records. The field is marked as frozen in the field call management table, prohibiting it from being invoked in data calls initiated by non-primary roles. Simultaneously, the field's shared reference channel in the cross-task data chain is disconnected, and field access bridging records between the field and other role task scenarios are cleared. This ensures that the field no longer appears in multi-role task flows such as collaborative data entry, interactive feedback, and approval pushes. A static mask flag is set for the field in the data export interface, maintaining its non-rendered state in all derived views.

[0049] When a field's display adaptation value exceeds its threshold, the field is deemed to have high adaptability. A multi-role display version is then generated, and the field writing mechanism is initiated. This registers and dynamically expands the field's role reference paths across different collaborative tasks, supporting automatic generation of differentiated content based on user identity. The multi-role display version is achieved by creating role branch templates for the field. Each template defines the field's display format, summary level, and interactive functionality scope within the target role's view. The field writing mechanism identifies the field's reference path in each role's task stage based on the task call chain logs, dynamically adding a three-level relationship record (role-task-field) to the field extension index table. It also matches the field's display content's visual template based on user identity tags, enabling targeted rendering and customized output across multiple roles. Simultaneously, the export module adaptively assembles field content according to user permissions, ensuring the differentiation and integrity of the exported data.

[0050] In this implementation plan, the display scope and sharing status of fields across roles are dynamically adjusted based on field adaptability, thereby achieving fine-grained management of field-level permissions and differentiated configuration of display strategies. By comparing field display adaptability values ​​and adaptability thresholds in real time, the usability and collaborative value of fields in multi-role task scenarios can be identified. When field adaptability is low, the field freezing mechanism can effectively block its rendering and sharing in non-primary role views, avoiding the spread of invalid information and data redundancy. When field adaptability is high, multi-role display versions are automatically generated, and reference paths and display templates between roles are established through the field writing mechanism, supporting precise differentiated presentation of field content based on user identity, thereby improving the targeting of data visualization, the coordination of role linkage, and the flexibility of data export.

[0051] Specifically, based on the multi-role access behavior dataset, the specific steps for evaluating the dynamic visual permissions of the evaluation field in a multi-role environment are as follows: Extract the number of teaching subtasks and the proportion of basic fields from the multi-role access behavior dataset. Add one to the number of teaching subtasks, take the natural logarithm, and multiply this by the square of the difference between one and the proportion of basic fields to obtain the teaching task intensity value. Extract the number of user-invoked tasks and the number of nodes used by the field. Multiply this by the number of user-invoked tasks and the number of nodes used by the field, add one, and take the natural logarithm to obtain the field invocation weight value. Extract the number of visible views of the field. Divide the number of visible views of the field by the number of visible views of the field plus one, and add this to the field invocation weight value to obtain the graph complexity. Extract the field invocation response latency. Use the teaching task intensity value as the numerator and the field invocation response latency and graph complexity as the denominator to obtain the field visual permission evaluation value.

[0052] The formula for calculating the field's visual permission evaluation value is as follows:

[0053]

[0054] In the formula: M represents the number of teaching subtasks, used to characterize the complexity of the teaching tasks associated with the field, and is the core parameter for calculating the background influence factor of field access. It is derived from the statistical results of the number of subtask nodes in the teaching process of each major during the structured data collection phase; Z represents the proportion of basic fields, used to reflect the proportion of the current field in all fields of basic information fields, and is an important basis for identifying general attribute fields and specific business fields. It is derived from the field structure classification label analysis results; D represents the field call response latency, used to evaluate the average response time after the field is called and to complete the data return. It is the core parameter for measuring the timeliness of the field during the access process, derived from the field response time records in the interactive response data, after extreme value removal and averaging. Values ​​are normalized; Y represents the number of user-invoked tasks, reflecting the breadth of the field's use by users in different tasks. It is a key variable for evaluating the adaptability of the field to multiple scenarios and is derived from the list of teaching sub-tasks and user access mapping records in behavioral data; S represents the number of nodes in which the field is used, characterizing the frequency of nodes in which the field is called in the task flow. It is a key factor in calculating the field's access popularity and is derived from the statistical analysis of the frequency of field calls at each task node in the teaching process structure; X represents the number of visible views of the field, describing the types of display views generated by the field from multiple role perspectives. It is a basic indicator reflecting the complexity of field permission configuration and the flexibility of sharing strategies and is derived from the statistics of the types of field-role view mapping tables in structured data.

[0055] In this implementation plan, the formula is used to evaluate the visibility permission adaptation level of a field in a multi-role, multi-task context, aiming to quantify the degree of coordination between visibility control and shared access for the current field. By performing logarithmic compression and nonlinear adjustment on various factors, the calculated field visibility permission evaluation value is used to dynamically classify field levels and drive the sharing authorization, temporary visibility, and permission freezing operations of the field in different collaborative scenarios. This provides a calculation basis for the refinement and dynamism of permission control in the process of multi-source data integration management.

[0056] Specifically, the steps for adjusting the field permission hierarchy based on the evaluation results are as follows: Based on the calculated visual permission evaluation values ​​of all fields, all fields are sorted in descending order to generate a field permission score sequence. The fields are then divided into three hierarchical intervals according to their relative position in the score sequence, corresponding to high-adaptability, medium-adaptability, and low-adaptability fields, respectively. High-adaptability fields, typically appearing in multiple role views and exhibiting high access frequency and collaborative call intensity, are automatically included in the shared candidate list for the current collaborative task. A multi-role view derivation mechanism is initiated for these fields. Based on the task role composition, differentiated field display templates are dynamically generated. These templates include display scope, summary level, and interactive function settings, supporting temporary cross-departmental visibility of fields within a specific collaboration period, and are tracked and recorded in conjunction with access behavior logs. Fields in the middle 40% of the score set are not automatically shared and are designated as intermediate-state fields. They are only accessed when a task node requests a field during collaboration. The task initiator then reviews whether to open a copy of the corresponding field view. If approved, a one-time field view is generated, usable only in this round of collaboration. This view has role binding and access time limits to prevent permission drift. Fields in the bottom 30% of the score set are locked in read-only mode, retaining only the original static visible role list. They do not participate in this round of permission refactoring, and their content cannot be accessed or requested by unauthorized roles during collaboration.

[0057] Combination such as Figure 3 The multi-role data permission mapping diagram shown further clarifies the attribution relationships of various fields within the role system: The course participation index, as the overall field set, is divided into three perspectives: course co-construction, teaching diagnosis, and achievement reporting. Each perspective is controlled by a specific role, which manages the field attribution boundaries. Course co-construction-related fields belong to teaching administrators and corporate mentors; teaching diagnosis fields are used by teachers and students; and achievement reporting fields belong to department heads. This multi-role data permission mapping diagram illustrates the permission boundaries and attribution structure of fields under each role, providing a role distribution reference for the implementation of field derivation strategies.

[0058] This implementation plan, based on the field visibility permission assessment results, achieves hierarchical and categorized management of field display permissions and dynamic visibility strategy adjustment in multi-role scenarios, thereby improving the accuracy of field display, the controllability of sharing, and the security of data access. By constructing a field permission scoring sequence and dividing it into high, medium, and low adaptation ranges, the display priority and sharing boundaries of each field in collaborative tasks can be clearly defined. High-adaptation fields enhance collaborative efficiency by deriving multi-role display templates, medium-adaptation fields set an approval intermediate state to avoid field abuse, and low-adaptation fields maintain the original role visibility to ensure that data boundaries are not breached. At the same time, the multi-role permission mapping diagram is combined for attribution determination to ensure that field derivation and display strategies strictly correspond to the actual role permission structure, realizing the computability, verifiability, and traceability of permission division, effectively supporting data display management and cross-role field flow control in complex collaborative scenarios.

[0059] Specifically, using the results of the display condition analysis and the display stability assessment as input, the comprehensive analysis of the cross-role sharing stability of fields is conducted through the following steps: First, obtain the field display adaptation value and the field visual permission assessment value. Then, multiply the field display adaptation value, the field visual permission assessment value, and the number of courses within the major to obtain the field call matching degree. Next, extract the teaching resource upload frequency and the course objective change frequency. Calculate the absolute value of the difference between the teaching resource upload frequency and the course objective change frequency, add one, take the logarithm, and add one again to obtain the teaching resource dynamic adjustment degree. Finally, divide the field call matching degree by the teaching resource dynamic adjustment degree to obtain the field sharing stability value.

[0060] The formula for calculating the stable value of a field sharing is:

[0061]

[0062] In the formula: R represents the field display adaptation value, which measures the degree of coordination in the display of field content from the perspective of different roles. It is a key indicator for evaluating the readability and information matching degree of a field in a multi-user scenario; E represents the field visibility permission assessment value, which describes the rationality of access authorization and the flexibility of invocation of a field in multiple types of roles. It is an important reference for evaluating the potential of a field to be shared across permissions; P represents the number of courses within the major, which reflects the completeness of the teaching system and the scale of the course structure of the major to which the field belongs. It is an important dimension for measuring whether a field can be reused and invoked in multiple course scenarios, and is derived from the summary statistics of the number of courses published for the corresponding major in the academic affairs scheduling database; Δ π This indicates the frequency of teaching resource uploads, used to measure the update activity of the teaching content associated with the field on the platform. It is a dynamic parameter used to determine whether the field is in a high-frequency update environment, derived from the aggregated value of the number of updates within a unit period in the teaching resource upload records associated with the field; Δ θThis indicates the frequency of changes in course objectives, representing the intensity of changes to the course objectives supported by the field in instructional design. It is an important indicator for measuring the stability of the field and the fluctuations in its correlation with the teaching objectives, derived from the frequency and content differences of related objective field change events in the course standard version update record.

[0063] In this implementation example, the field display adaptation value of field F1 is set to 0.85, the field visibility permission evaluation value is 0.72, the number of courses within the major is 14, the teaching resource upload frequency is 0.12, and the course goal change frequency is 0.08.

[0064] The field display adaptation value for field F2 is set to 0.90, the field visibility permission evaluation value is 0.68, the number of courses within the major is 11, the teaching resource upload frequency is 0.15, and the course objective change frequency is 0.09.

[0065] The field display adaptation value for field F3 is set to 0.75, the field visibility permission evaluation value is 0.81, the number of courses within the major is 13, the teaching resource upload frequency is 0.05, and the course objective change frequency is 0.14.

[0066] The field display adaptation value for field F4 is set to 0.60, the field visibility permission evaluation value is 0.77, the number of courses within the major is 10, the teaching resource upload frequency is 0.18, and the course objective change frequency is 0.07.

[0067] The field display adaptation value for field F5 is set to 0.88, the field visibility permission evaluation value is 0.65, the number of courses within the major is 15, the teaching resource upload frequency is 0.10, and the course objective change frequency is 0.11.

[0068] The field display adaptation value for field F6 is set to 0.92, the field visibility permission evaluation value is 0.91, the number of courses within the major is 12, the teaching resource upload frequency is 0.16, and the course objective change frequency is 0.04.

[0069] Field F7 has a field display adaptation value of 0.79, a field visibility permission evaluation value of 0.80, a number of courses within the major of 13, a teaching resource upload frequency of 0.11, and a course goal change frequency of 0.09. The shared stable values ​​for each field are calculated, as shown in Table 1.

[0070] Table 1: Field Shared Stable Value Data Table

[0071]

[0072] like Figure 2 As shown, this is a line chart of the stable values ​​of field sharing provided in this application example. (See Table 1 and...) Figure 2As can be seen, field F6 has the highest stability value in field sharing, indicating that it has a high display adaptability value, excellent visual permission evaluation value, a moderate number of courses within the major, and relatively low fluctuations in the upload frequency of associated teaching resources and the frequency of changes in course objectives. This comprehensively reflects that this field has good display stability and cross-task sharing reliability in the current professional construction scenario, and can be given priority as a key field for joint scheduling and resource view integration among multiple roles. On the other hand, field F4 has the lowest stability value in field sharing. Although its display adaptability value and visual permission value are at a medium-to-high level, it is limited by the small number of professional courses, frequent updates of teaching resources, and unstable changes in objectives, resulting in low overall sharing stability. It will be automatically downgraded to a secondary shared field to reduce its presentation priority in multi-user access paths and avoid causing permission redundancy and display jitter. The line graph of field sharing stability value clearly depicts the stability distribution pattern of each field in the current data integration process. The higher the value, the better it has the ability to support consistent display and strategy control in dynamic task scenarios, and the more suitable it is as a core field to be included in the shared scheduling strategy configuration.

[0073] Specifically, the steps for dynamically adjusting the field sharing frequency control mechanism based on the analysis results are as follows:

[0074] The system compares the current field sharing stability adaptation value with the field sharing adjustment level threshold in real time. The field sharing adjustment level threshold includes a first sharing threshold and a second sharing threshold, which are used to dynamically classify the sharing response level of fields in a collaborative environment.

[0075] When the field sharing stability adaptation value is less than or equal to the second sharing threshold, it is determined to be in the field sharing restricted stage. This stage indicates that there is a risk of access conflict between different role tasks, low call stability, or insufficient role usage dependency between the fields. Cross-role synchronization and derived call operations of the fields are suspended. The fields are only loaded with the original field version for rendering in the current primary role task. In other non-primary role tasks, the fields will be displayed as a weak hint in the form of an uneditable reference placeholder, without loading the actual content. At the same time, the interface call permissions related to the field reference are frozen, and the inheritance, forwarding, and transmission of the fields in collaborative task nodes are forcibly prohibited to ensure that the data boundaries of low-stability fields are not destroyed under multi-role operations.

[0076] When the field sharing stability adaptation value is greater than the second sharing threshold and less than or equal to the first sharing threshold, it is determined to be in the field sharing adjustment transition stage. This indicates that the field has initial sharing potential among some roles, but there are still usage deviations or scenario limitations. In this stage, the field is in a dynamic evaluation state. The frequency of use of derived fields, the number of task jumps and interaction paths will be tracked and recorded in real time. At the same time, the "purpose annotation mode" will be activated in the field display interface to add contextual prompts, role adaptation suggestions and historical access summary information to the field. This will help task participants understand the meaning, scope of limitations and precautions for calling the field, and enhance the controllable usability and transparent visibility of the field during the sharing transition period.

[0077] When the stability adaptation value of a field sharing exceeds the first sharing threshold, it is determined to be in the field sharing enhancement stage. The field has passed the stable access verification between roles and the consistency detection of task calls, and has high-frequency collaboration value. The multi-role view access and asynchronous synchronization mechanism of the field are fully opened, and the view call restrictions in all collaborative modules are removed. At the same time, the field is marked as a recommended shared field. Such fields can be actively called into resource co-construction processes, task review interfaces, and auxiliary filling scenarios. They can be authorized to access and reference content in non-primary task chains. The field is added to the real-time evolution queue. Based on user identity, role permissions, and task context, a customized output version of the field content is automatically generated to achieve differentiated information display granularity, dynamic matching of field summary methods, and adjustment of interaction methods according to roles, thereby enhancing the role adaptability of field content dissemination and the semantic consistency of shared expression.

[0078] This implementation plan dynamically adjusts the sharing scope, referencing method, and display strategy of fields in multi-role collaboration scenarios based on the level of field sharing stability adaptation value, achieving hierarchical control of field sharing behavior and refined output under role awareness. Through a first and second sharing threshold, three stages—restricted sharing, regulated sharing, and enhanced sharing—are defined, allowing fields to automatically enter the corresponding sharing state in different collaboration environments based on actual access stability and role usage consistency. In the restricted sharing stage, placeholder references and interface freezing mechanisms effectively block the cross-role propagation of low-stability fields, ensuring data boundaries. In the regulated sharing stage, usage annotations and behavior tracking mechanisms guide the safe transition of fields, avoiding misuse and information ambiguity. In the enhanced sharing stage, field access permissions are fully opened and content customization generation is supported, enhancing the collaborative value of high-stability fields in resource co-construction and task linkage processes. This mechanism ensures that the field sharing process is flexible, controllable, and targeted, providing stable guarantees and dynamic adaptability for field display scheduling in multi-role parallel collaboration.

[0079] Specifically, based on the dynamic monitoring and marking of cross-organizational access behavior in a multi-role data flow process, the risk of data field sharing is identified, and permission refactoring suggestions are generated. The specific steps are as follows: Based on the call records of each data field in the platform and the cross-organizational role access trajectory, a multi-role data flow process is constructed to identify the actual data flow paths and interaction nodes between schools, enterprises, teachers, and students, thereby understanding the circulation mechanism and usage rhythm of various fields in teaching feedback, job matching, and data analysis tasks. For example... Figure 4 The multi-role data flow flowchart shows that data flow originates from the school administrator, synchronizing data to the teacher's end through teaching records. The teacher then provides feedback on classroom activities and task assignments to students. After students complete their assignments and create project logs, their behavioral data is synchronized into the data buffer for anonymization and summary extraction. The flowchart also shows that students and corporate mentors directly connect through assignment feedback and job co-creation. Corporate mentors generate job suggestions based on this feedback and input them into the user behavior analysis stage. Corporate data analysts then conduct in-depth analysis of student behavioral characteristics based on this feedback, forming structured analysis results, which are ultimately fed back to the school to complete the closed loop of teaching adjustments.

[0080] During this process construction, high-frequency data access requests in the workflow are dynamically marked, sensitive nodes of fields under multiple role transitions are identified, and organizational boundary markers are set to determine whether a field exceeds the authorized view scope. When a role is detected to trigger field access operations in an unauthorized path, the platform automatically records the abnormal access event and restricts the frequency of access to key fields per unit time for high-frequency abnormal users who repeatedly exhibit out-of-bounds access behavior. Simultaneously, this stage also generates field-level shared risk warning logs for administrators to assess whether there are risks of misconfiguration or data leakage in fields.

[0081] By combining the aforementioned multi-role data flow paths and stable values ​​for field sharing, the platform can further generate permission boundary reconstruction suggestions, identify redundant jump nodes and frequent blocking points, optimize the cross-role field call path structure, reduce access bottlenecks, and improve the efficiency of data flow between multiple organizational roles. This mechanism effectively overcomes information gaps between roles caused by overly granular field permission divisions, improving the flexibility of field sharing and the consistency of task response in the overall collaboration chain.

[0082] This implementation plan constructs a data flow structure among multiple roles based on field call trajectories and role access behaviors. This structure enables risk identification of field sharing, interception of abnormal access, and generation of permission boundary reconstruction suggestions, thereby improving the security, fluidity, and structural resilience of data collaboration. By dynamically marking high-frequency access paths and setting organizational boundaries, the actual call chains of fields in cross-organizational scenarios between schools and enterprises can be accurately identified, role overstepping behaviors can be detected in a timely manner, and the access frequency of abnormal users can be restricted, preventing the unauthorized dissemination of sensitive fields. Simultaneously, combined with field sharing stability adaptation values, permission reconstruction suggestions are generated for fields with blocked or inefficient flow, optimizing cross-role field path configurations, eliminating information silos caused by permission fragmentation, and ultimately achieving stable data sharing and efficient flow in a multi-role collaborative environment.

[0083] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0084] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-source data integration management platform for professional construction, characterized in that: include: The data acquisition module collects structured data, behavioral data, and interactive response data during the professional development process, and preprocesses the collected structured data, behavioral data, and interactive response data to construct a multi-role access behavior dataset. The data structure-level desensitization module analyzes the cross-role hierarchical display conditions of fields based on a multi-role access behavior dataset, and triggers the reconstruction of the field display structure based on the analysis results. The collaboration scenario awareness module evaluates the dynamic visual permissions of the evaluation field in a multi-role environment based on a multi-role access behavior dataset, and adjusts the hierarchical derivation of field permissions based on the evaluation results. The specific steps for evaluating the dynamic visual permissions of the evaluation field in a multi-role environment based on the multi-role access behavior dataset are as follows: Extract the number of teaching subtasks and the proportion of basic fields from the multi-role access behavior dataset. Add one to the number of teaching subtasks, take the natural logarithm, and multiply it by the square of the difference between one and the proportion of basic fields to obtain the teaching task intensity value. Extract the number of user-invoked tasks and the number of nodes used by the field. Multiply the number of user-invoked tasks and the number of nodes used by the field, add one, and take the natural logarithm to obtain the field call weight value. Extract the number of visible views for a field, divide the number of visible views by the number of visible views plus one, and then add the result to the field call weight value to obtain the graphical complexity. Extract the field call response latency, use the teaching task intensity value as the numerator, and the field call response latency and the diagram complexity as the denominator to obtain the field visibility permission evaluation value. The permission drift path identification module takes the display condition analysis results and display stability assessment results as inputs, performs a comprehensive analysis of the cross-role sharing stability of fields, and dynamically adjusts the field sharing frequency control mechanism based on the analysis results; The specific steps of the field sharing frequency control mechanism that dynamically adjusts the analysis results are as follows: Real-time comparison of the current field-sharing stability adaptation value with the field-sharing adjustment level threshold, which includes a first sharing threshold and a second sharing threshold: When the stability adaptation value of field sharing is less than or equal to the second sharing threshold, it is determined to be the field sharing restricted stage. Cross-role synchronization and derived calls of the field are suspended. The original field version is only loaded in the current primary role task and displayed as a reference placeholder in other role tasks. At the same time, the authorization of the reference interface is frozen. When the stability adaptation value of field sharing is greater than the second sharing threshold and less than or equal to the first sharing threshold, it is determined to be the field sharing adjustment transition stage. The usage frequency, number of jumps and interaction paths of derived fields are tracked in real time, and the field usage annotation mode is activated in the interface layer. When the stability adaptation value of field sharing is greater than the first sharing threshold, it is determined to be the field sharing enhancement stage, and the multi-role view access and asynchronous synchronization mechanism of the field are fully opened: unlock the view call permission of the field in all collaborative modules, mark it as a recommended shared field, allow the field to appear in the resource co-construction and task review process other than the main task chain, and add the field to the real-time evolution queue to automatically generate customized output versions of field content for different user identities; The cross-organizational data flow monitoring module dynamically monitors and marks cross-organizational access behavior based on multi-role data flow processes, identifies data field sharing risks, and generates permission restructuring suggestions.

2. The multi-source data integration management platform for professional construction according to claim 1, characterized in that: The specific steps for collecting structured data, behavioral data, and interactive response data during the professional development process are as follows: Collect structured data during the professional development process. The structured data includes: the number of fields subject to access control, the original content length of each field, the visible content length in each role's view, and record the basic field ratio and the average display length of each field in the task stage to which the field belongs. Behavioral data is collected through the teaching execution and resource operation process. The behavioral data includes: the number of courses within the major, the number of teaching sub-tasks, the frequency of uploading teaching resources, and the frequency of changes in course objectives. Interaction response data is collected through platform behavior logs. This data includes the number of users requesting access to a field, the number of field access requests, the number of nodes used by the field, the number of visible views of the field, the number of tasks called by users, and the response latency of field calls. It also records the average access frequency of each field in the task stage to which the field belongs, as well as the number of role types of users online on the page at the same time.

3. The multi-source data integration management platform for professional construction according to claim 1, characterized in that: The specific steps for preprocessing the collected structured data, behavioral data, and interaction response data to construct a multi-role access behavior dataset are as follows: For the collected structured data, a unified structure mapping rule is adopted to convert the original content length and the visible content length of each role into a relative display ratio, and the basic field ratio is uniformly scaled to the same range. The collected behavioral data is standardized by standard deviation to unify the fluctuation scale of each indicator across different professions and improve the horizontal comparability of behavioral activity analysis. Logarithmic scaling is applied to the collected interactive response data to suppress extreme value interference, while distribution mean centering is used to adjust the response load offset under different interactive scenarios. After standardizing the structured data, behavioral data, and interactive response data, a normalization process is uniformly executed on all data dimensions, and a multi-role access behavior dataset is stored and constructed.

4. The multi-source data integration management platform for professional construction according to claim 1, characterized in that: The specific steps for analyzing the cross-role hierarchical display conditions of fields based on the multi-role access behavior dataset are as follows: Extract the original content length and the visible content length in the current role view of each field in the multi-role access behavior dataset. Calculate the difference between the original content length and the courseware content length and divide it by the average display length of each field in the task stage to which the field belongs to obtain the field display deviation. Extract the number of access requests and the average access frequency for each field within the current period, calculate the ratio of the number of access requests to the average access frequency, and obtain the access request intensity of the field. Extract the number of users requesting access to a field, calculate the ratio of the number of users requesting access to a field to the number of role types of users online at the same time on the page containing the field, and obtain the user distribution breadth of each field; The normalized display value of each field is obtained by adding the field display deviation, access request intensity, and user distribution breadth of each field. Add up the normalized display values ​​of all fields, divide by the number of fields included in access control, and then subtract the field display adjustment factor to obtain the field display adaptation value.

5. A multi-source data integration management platform for professional construction according to claim 1, characterized in that: The specific steps for reconstructing the field display structure based on the analysis results are as follows: Real-time comparison of the current field display adaptation value with the field display adaptation threshold: When the field display adaptation value is less than or equal to the field display adaptation threshold, the current field is determined to be in the low adaptation display stage. The field's display permission in non-main role views is canceled, and it is only rendered in the main view as a brief information bar. The field freezing mechanism is enabled to block the field's shared link in all collaborative tasks and prevent the field from being derived and inherited by other roles. When the field display adaptation value is greater than the field display adaptation threshold, it is determined that the current field has high adaptation display capability, multi-role display versions of the field are generated, the field writing mechanism is started, the role reference path of the field under different collaborative tasks is registered and dynamically expanded, and it supports automatic generation of differentiated content export based on user identity.

6. The multi-source data integration management platform for professional construction according to claim 1, characterized in that: The specific steps for adjusting the field permission hierarchical derivation based on the evaluation results are as follows: Based on the calculated visual permission assessment values ​​of all fields, sort all fields in descending order to generate a field permission score sequence. Based on the relative position of the field's visual permission assessment value in the field permission score sequence, divide the field into different intervals and execute the corresponding permission hierarchical derivation strategy: When the field's visibility permission assessment value is in the top 30% of the field permission scoring sequence distribution, the field is automatically included in the shared candidate list of the current collaborative task, triggering the field multi-role view derivation mechanism, dynamically generating field display templates according to the collaborative task roles, and allowing temporary visibility across departments; When the field's visible permission evaluation value is in the middle 40% of the field permission scoring sequence, the shared derivation operation will not be performed. Instead, the field will be set to an intermediate state where it is called but requires approval. When a task node is detected to need the field during the collaboration process, the current task initiator will confirm whether to open a copy of the field view. When the field's visible permission evaluation value is 30% of the field's permission scoring sequence, the current view of that field is locked, and only the original static visible role list is retained. It will not participate in this round of collaborative permission reconstruction process.

7. A multi-source data integration management platform for professional construction according to claim 1, characterized in that: The specific steps for comprehensively analyzing the cross-role sharing stability of fields, using the display condition analysis results and display stability assessment results as input, are as follows: Obtain the field display adaptation value and the field visual permission evaluation value. Multiply the field display adaptation value, the field visual permission evaluation value and the number of courses within the major to obtain the field call matching degree. Extract the frequency of teaching resource uploads and the frequency of course objective changes, calculate the absolute value of the difference between the frequency of teaching resource uploads and the frequency of course objective changes, add one, take the logarithm, and add one again to obtain the dynamic adjustment degree of teaching resources; Divide the field matching degree by the dynamic adjustment degree of teaching resources to obtain the stable value of field sharing.

8. A multi-source data integration management platform for professional construction according to claim 1, characterized in that: The specific steps for dynamically monitoring and marking cross-organizational access behavior based on multi-role data flow processes, identifying data field sharing risks, and generating permission refactoring recommendations are as follows: Based on the call records of each data field in the platform and the access trajectory of cross-organizational roles, a multi-role data flow process is constructed to identify the actual data flow path and interaction nodes between schools, enterprises, teachers and students. High-frequency data access requests in the flow path are dynamically marked, organizational boundary markers are set and abnormal access events of role overstepping are recorded, the access frequency of high-frequency abnormal users who continuously trigger abnormal access events is restricted to the number of times they can access key fields per unit time, and field-level shared risk warning logs are generated. By combining multi-role data flow processes with field sharing stability adaptation values, suggestions for reconstructing permission boundaries are generated, field cross-organization call paths are optimized, data flow flexibility and collaborative sharing efficiency are improved, and information silos caused by overly granular permissions are broken down.

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