Intelligent big data resource service method and system for cross-domain collaboration

Abstract

The present application relates to the technical field of resource service, in particular to a kind of intelligent big data resource service method and system for cross-domain collaboration, obtain cross-domain resource data frame sequence, record field position, change rule and identifier generation behavior fragment set, compare the order of different source fragments and the difference of identifier form corresponding relationship set, accordingly arrange position sequence and link arrangement chain to construct arrangement framework set, extract hierarchical features and generate label classification set, associate matching resource request and identity, generate cross-domain unified resource service result.The present application, by in-depth analysis of data frame field order and dynamic change, accurately realizes the automatic integration matching of cross-domain data, effectively identifies multi-source differences using field rules and optimizes the processing flow, ensures that the integration framework adapts to complex data exchange requirements, significantly improves the response speed and data accuracy of resource service, and provides a flexible and accurate resource sharing solution for multi-agent collaboration.

Description

Technical Field

[0001] This invention relates to the field of resource service technology, and in particular to an intelligent big data resource service method and system for cross-domain collaboration. Background Technology

[0002] The field of resource service technology encompasses methods and workflows related to data acquisition, management, organization, and allocation for different application scenarios. The core of this field lies in constructing a data resource service framework that can be used across entities, regions, and systems, focusing on the coordinated access, unified integration, security governance, and shared provision of multi-source data. Its overall technical system typically consists of data acquisition rule formulation, data source connection method determination, unified data structure conversion strategy establishment, data quality verification process establishment, and data access permission definition. Based on this, a data resource service system supporting multi-departmental collaboration is formed to ensure standardized data resource organization and service capabilities in complex environments.

[0003] One type of intelligent big data resource service method for cross-domain collaboration refers to addressing the data resource sharing needs among multiple independent users. This involves establishing cross-domain data exchange criteria, developing cross-domain data access verification methods, defining cross-domain data association identifiers, and employing rule-based cross-domain data matching, filtering, and sorting methods to organize and complete the unified integration service of cross-regional data resources. The technical aspects covered include establishing classification criteria for cross-domain data sources, clearly defining cross-domain data association methods, and specifying the formulation and execution criteria for cross-domain data service rules. Simultaneously, it completes the access, integration, association, filtering, and service processing of cross-domain data through a predetermined sequence of steps, thereby forming a big data resource service method applicable to multiple entities.

[0004] Existing technologies fail to adequately consider the dynamic nature of field changes when integrating cross-domain data, making it difficult to accurately capture differences between different data sources and affecting the accuracy of data sharing. Existing methods rely on fixed rules and processes, lacking intelligent and flexible processing mechanisms, and cannot be adjusted in real time to adapt to complex cross-domain data exchange needs. These limitations make the data integration process relatively rigid and inefficient. Especially when multiple entities collaborate and share data, the lack of an immediate response mechanism to data changes results in insufficient accurate service capabilities for cross-domain resources, and the accuracy and timeliness of data processing cannot be effectively guaranteed. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for providing intelligent big data resource services for cross-domain collaboration. The technical solution is as follows: A method for providing intelligent big data resource services for cross-domain collaboration includes the following steps: S1: Obtain the data frame sequence of adjacent time periods of the access channel, record the field position, compare the continuous content to extract the field change pattern, identify the field name, label or symbol as an identifier, record the repeated text combination to form the field identifier, and integrate to generate a cross-domain resource behavior fragment set; S2: Call each behavior fragment in the cross-domain resource behavior fragment set, compare the field positions to extract the order difference, compare the field change rules to extract the change difference, identify the field identifier to extract the identifier difference, integrate the three types of differences to form the field correspondence content, and generate the cross-domain resource correspondence set; S3: Call the differences in each field in the cross-domain resource correspondence set, organize the differences in field order into a field position sequence, compare the changes in the differences with the text combinations of the differences in the identifiers, group the fields with the same changes and the same text combinations into field combinations, and connect the field combinations in the field position sequence to form a field arrangement chain, thereby generating a cross-domain resource arrangement framework set. S4: Extract the hierarchical relationship of the fields in the arrangement chain of the cross-domain resource arrangement framework, and concatenate them with the field change differences and identifier differences to form field feature fragments. Compare and group the same features as the marked content, connect them according to position to form a mark association chain, and generate a cross-domain resource mark classification set.

[0006] As a further aspect of the present invention, the cross-domain resource behavior fragment set includes field position, field change pattern, field identifier, field order difference, field change difference, and field identification difference; the cross-domain resource correspondence set includes field order difference, field change difference, field identification difference, field position sequence, field arrangement chain, and field combination; the cross-domain resource arrangement framework set includes field arrangement chain, field position sequence, field arrangement order, and field combination analysis; and the cross-domain resource tag classification set includes tag content, tag association chain, field feature fragment, and tag combination.

[0007] As a further aspect of the present invention, the step of obtaining the cross-domain resource behavior fragment set is as follows: S101: Obtain the data frame sequence transmitted by the cross-domain resource access channel in adjacent time periods, record the arrangement order of fields in each data frame, perform comparison based on the adjacent positions of fields in the data frame, organize them into a sequence according to the comparison order, and obtain the field position. S102: Call the field position, and for the continuous content that appears successively in the data frame sequence, perform difference processing based on the adjacent positions of each field in different data frames and the character combination length of the field in the frame. Organize the differences into a continuous trajectory according to the directional changes that appear as the data frames progress, and obtain the field change pattern. S103: Based on the field change pattern, for the names, tags or symbols of the field presented in continuous content, perform difference processing on the text combinations that appear in different data frames with the same field identifier, merge the text combinations with zero difference by the number of occurrences, and associate them with the field position to generate a cross-domain resource behavior fragment set.

[0008] As a further aspect of the present invention, the step of obtaining the cross-domain resource correspondence set is as follows: S201: Call each behavior fragment in the cross-domain resource behavior fragment set, compare the field positions in different behavior fragments according to the order of the fields in the data frame, organize the differences between adjacent positions in the form of the differences corresponding to the fields in the same order, identify the relative offset of the fields under different sources, and obtain the field order difference. S202: Call the field order difference, compare the field change pattern in different behavior segments with the increase or decrease direction of the field trajectory in the continuous content under different frame sequences, organize the change segment sequence according to the directional difference of the field in the same position in the trajectory advancement, and concatenate the change segment sequence into dynamic difference according to the trajectory advancement order to obtain the field change difference; S203: Call the field change difference, perform difference comparison on the field identifier in the behavior fragment with the text combination of the same field identifier in different data frames, count the content with completely consistent character combinations in the form of the number of occurrences, and integrate it with the field order difference and field change difference to obtain the field correspondence content and generate a cross-domain resource correspondence set.

[0009] As a further aspect of the present invention, the step of obtaining the cross-domain resource arrangement framework set is as follows: S301: Call the field difference content in the cross-domain resource correspondence set, obtain the field order difference content recorded in the field order difference content in the data frame, organize the fields according to the order in which they appear in each frame, arrange the corresponding fields continuously according to the order of appearance to form a field order chain, and obtain the field position sequence. S302: Call the field position sequence, and based on the field change difference content and the field identification difference content, compare the change performance of the same field in the change difference content with the text combination in the identification difference content, and group the fields with the same change performance and the same text combination into the same group to obtain the field combination; S303: Call the field combination, connect each field group in the field position sequence to obtain the field arrangement chain, and connect it with the sequential position of the cross-domain resource access entry to form a content arrangement framework, thus obtaining a cross-domain resource arrangement framework set.

[0010] As a further aspect of the present invention, the step of obtaining the cross-domain resource tag classification set is as follows: S401: Call the field arrangement chain in the cross-domain resource arrangement framework set, extract the hierarchical relationship of each field content according to the presentation order of the fields in the chain, use the hierarchical order as the connection benchmark, connect the field change difference content and the field identifier difference content one by one according to the field name order, so that the corresponding information of the field in the two difference contents is integrated into a continuous record along the arrangement chain order to obtain the field feature fragment. S402: Call the field feature fragments, and compare the order of appearance and text content of multiple fragments with the order of appearance and the corresponding text combination in the fragment sequence as the comparison basis. Organize the fragments with the same order and the same text combination into the same set, and summarize the overall performance of the set according to the continuity of the field appearance within the set to obtain the marked content. S403: Call the marked content, connect multiple marked contents sequentially according to their corresponding positions in the field arrangement chain to obtain a marked link, establish the association relationship between fields based on the link, and obtain a cross-domain resource marked classification set.

[0011] As a further aspect of the present invention, the method further includes: S5: Call the tag association chain in the cross-domain resource tag classification set, compare the target resource content to form a tag matching record, associate the tag matching record with the field position relationship to obtain the content mapping record, and merge it with the access identity content to form a service mapping set, and generate a cross-domain unified resource service result; The cross-domain unified resource service results include tag matching records, content mapping records, service mapping sets, and access identity content.

[0012] As a further aspect of the present invention, the step of obtaining the cross-domain unified resource service result is as follows: S501: Call the tag association chain in the cross-domain resource tag classification set, match the target resource content proposed by the service scheduling entry with the tag content in the chain in the order of appearance, compare the consistency relationship between the target resource content and the tag content in the sequence, and organize them into matching records according to the sequence position after comparison to obtain the tag matching record; S502: Call the marked matching record, and according to the field position relationship in the cross-domain resource arrangement framework set, match the position of the matching record in the sequence with the field order position one by one, connect them in the corresponding order to form the association link from field to content, and organize the link to form the corresponding result to obtain the content mapping record; S503: Call the field content in the content mapping record and the access identity content of the service scheduling entry, merge them in the order of field appearance, organize the merged content into a service access set, and obtain the cross-domain unified resource service result.

[0013] A smart big data resource service system for cross-domain collaboration, the system comprising: The data frame analysis module acquires the data frame sequence of adjacent time periods of the access channel, records the field position, compares continuous content to extract the field change pattern, identifies field names, tags or symbols as identifiers, records repeated text combinations to form field identifiers, and integrates them to generate a set of cross-domain resource behavior fragments. The behavior fragment comparison module calls each behavior fragment in the cross-domain resource behavior fragment set, compares the field position to extract the order difference, compares the field change pattern to extract the change difference, identifies the field identifier to extract the identifier difference, integrates the three types of differences to form the field correspondence content, and generates the cross-domain resource correspondence set. The field difference integration module calls the field difference content of each field in the cross-domain resource correspondence set, organizes the field order differences into field position sequences, compares the changes in the differences with the identification differences in the text combinations and classifies them into field combinations, analyzes the position of the grouped content to form a permutation chain, and generates a cross-domain resource permutation framework set. The field feature classification module extracts the hierarchical relationship of fields in the arrangement chain of the cross-domain resource arrangement framework, and concatenates them with field change differences and identifier differences to form field feature fragments. It compares and classifies the same features as marked content, connects them into a marked association chain according to position, and generates a cross-domain resource marked classification set. The service mapping generation module calls the tag association chain in the cross-domain resource tag classification set, compares the target resource content to form a tag matching record, associates the tag matching record with the field position relationship to obtain the content mapping record, and merges it with the access identity content to form a service mapping set, generating a cross-domain unified resource service result.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, through in-depth analysis of the order and changes of fields in data frames, accurate integration and automated matching of cross-domain data can be achieved, ensuring the consistency and accuracy of data fields in multi-source data. Based on the arrangement order, change patterns, and multiple occurrences of identifiers of fields, the resulting field correspondence can effectively identify differences between different data sources and accurately extract dynamic change information, thereby optimizing the data integration and matching process. The efficient processing capability of cross-domain data ensures that the integration framework of different data sources can adapt to complex data exchange needs, improve the response speed and accuracy of data resource services, and provide a more flexible and accurate resource sharing solution for multi-entity collaboration. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the cross-domain resource behavior fragment set according to the present invention. Figure 3 This is a flowchart illustrating the process of obtaining the cross-domain resource correspondence set in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the cross-domain resource arrangement framework set of the present invention. Figure 5 This is a flowchart illustrating the process of obtaining the cross-domain resource tagging and classification set in this invention. Figure 6 This is a flowchart illustrating the process of obtaining the cross-domain unified resource service results of this invention. Detailed Implementation

[0016] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0017] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0018] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0019] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0020] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0021] Please see Figure 1 This invention provides a technical solution: a method for providing intelligent big data resource services for cross-domain collaboration, comprising the following steps: S1: Obtain the data frame sequence transmitted by the cross-domain resource access channel in adjacent time periods, record the arrangement order of each field in the data frame within the data frame to form the field position, select the data frames that appear successively in the data frame sequence as continuous content, compare the performance of each field in the continuous content, extract the change trajectory and form the field change pattern, identify the name, label or symbol of the field presented in the data frame as the field identifier, obtain the text composition when the field identifier appears multiple times in the continuous content, record the repeated text combinations presented in different data frames to form the field identifier, integrate the field position, field change pattern and field identifier into a behavior fragment, and generate a cross-domain resource behavior fragment set; S2: Call each behavior fragment in the cross-domain resource behavior fragment set, compare the field positions of behavior fragments from different sources, extract the order difference based on the actual arrangement position of the field in the data frame, compare the field change pattern in each behavior fragment, extract the change difference based on the dynamic performance of the field in continuous content, identify the field identifier in the behavior fragment, extract the identifier difference for the text combination contained in the field in different data frames, integrate the field order difference, field change difference and field identifier difference to form the field correspondence content, and generate the cross-domain resource correspondence set; S3: Call the field difference content of each field in the cross-domain resource correspondence set, organize the positions of each field in the data frame in the field order difference content into a field position sequence, compare the changes contained in the field change difference content between multiple fields with the text combination contained in the field identifier difference content, extract the field content with the same change content and consistent text combination into the same field group, analyze the corresponding position of the grouped field content in the field position sequence, and connect them in the order of the field position sequence to form a field arrangement chain, connect the field arrangement chain in the cross-domain resource access entry in order to form a content arrangement framework, and generate a cross-domain resource arrangement framework set; S4: Call the field arrangement chain in the cross-domain resource arrangement framework set, extract the hierarchical relationship of each field content in the arrangement chain, and concatenate it with the field change difference content and field identifier difference content according to the field name order to obtain field feature fragments. Compare the differences of multiple field feature fragments in terms of order and text combination, extract field feature fragments with the same order and the same text combination, and collect them into tag content. Connect the tag content according to the positional relationship in the field arrangement chain to form a tag association chain, and generate a cross-domain resource tag classification set. S5: Call the tag association chain in the cross-domain resource tag classification set, match and analyze the target resource content proposed by the service scheduling entry with the tag content recorded in the tag association chain to form a tag matching record, associate and match the tag matching record with the field position relationship contained in the cross-domain resource arrangement framework set to obtain the content mapping record, merge the access identity content of the content mapping record and the service scheduling entry record into a service mapping set, and generate the cross-domain unified resource service result.

[0022] The cross-domain resource behavior fragment set includes field position, field change pattern, field identifier, field order difference, field change difference, and field identification difference. The cross-domain resource correspondence set includes field order difference, field change difference, field identification difference, field position sequence, field arrangement chain, and field combination. The cross-domain resource arrangement framework set includes field arrangement chain, field position sequence, field arrangement order, and field combination analysis. The cross-domain resource tag classification set includes tag content, tag association chain, field feature fragment, and tag combination. The cross-domain unified resource service result includes tag matching record, content mapping record, service mapping set, and access identity content.

[0023] Please see Figure 2 The steps for obtaining the cross-domain resource behavior fragment set are as follows: S101: Obtain the data frame sequence transmitted by the cross-domain resource access channel in adjacent time periods, record the arrangement order of fields in each data frame, perform comparison based on the adjacent positions of fields in the data frame, organize them into a sequence according to the comparison order, and obtain the field position. The system captures data frame sequences transmitted through the cross-domain resource access channel in adjacent time periods using a network interface controller (NIC) or application layer socket interface deployed on the gateway node. For each independent data frame in the data frame sequence, a pre-built protocol parsing library is invoked to start a binary stream parsing program to read the header identifier and trailer checksum of the data frame. The payload area located between the header identifier and trailer checksum is extracted, and the payload area is divided into several minimum storage units. For each minimum storage unit, its corresponding physical address index value is extracted. Predefined delimiters existing in the payload area are identified through byte feature matching. A delimiter is defined to divide the payload area into several independent field fields. For each independent field field, the first address index value corresponding to its start position and the second address index value corresponding to its end position are obtained. The arithmetic mean of the first and second address index values ​​is calculated as the center coordinate value of the field field. For example, for a data frame with a length of 64 bytes, the first field field occupies bytes 0 to 7, and its center coordinate value is 3.5. The second field field occupies bytes 8 to 15, and its center coordinate value is 11.5. Any two field fields within the data frame are selected as the reference field and the comparison field, respectively. The center coordinate value of the reference field is extracted. Center coordinates of the comparison field Perform numerical subtraction operation If the result of the calculation If the value is positive, the comparison field is determined to be after the baseline field. If the calculation result is positive... If the value is negative, the comparison field is determined to be before the reference field. The above comparison operation is performed on all fields in the data frame pairwise to generate an adjacency matrix containing the order of all fields. The adjacency matrix is ​​traversed, and all fields are rearranged in ascending order of their center coordinate values. If the absolute value of the difference between the center coordinate values ​​of two fields is less than the preset minimum field interval threshold, and the minimum field interval threshold is set to 1 byte, then the two fields are determined to be closely adjacent. The identifiers of the rearranged fields that are closely adjacent are written into a one-dimensional array in sequence to form a field arrangement linked list that reflects the internal structure of the data frame, and the field position is obtained.

[0024] S102: Call the field position, for the continuous content that appears successively in the data frame sequence, perform difference processing based on the adjacent positions of each field in different data frames and the length of the character combination of the field in the frame, and organize the directional changes of the differences as the data frames progress into a continuous trajectory to obtain the field change pattern. The field position is used to select the current moment from consecutive content appearing in a data frame sequence. The data frame is used as the first reference frame to select the next time step. The data frame is used as the second reference frame, and the first starting byte offset of the specified field is extracted from the first reference frame. With the length of the first content byte Extract the second starting byte offset of the same specified field in the second reference frame. With the second content byte length For example, setting the first starting byte offset The length of the first content byte is 128. Set the second starting byte offset to 16. The length of the second content byte is 132. It is 20; Calculate the position offset difference respectively and length expansion / contraction difference The position offset difference Difference between length and expansion Substitute into the formula for calculating the characteristic value of change: ; in For location weighting coefficients, Set the position weighting coefficient as the length weighting coefficient. The length weighting coefficient is 0.6. It is 0.4; Based on the example values ​​above, the change characteristic values ​​are calculated as follows: For continuous The above calculation process is repeated for each data frame to obtain the specified field within a continuous time period. Each feature value is a different value. These feature values ​​are stored in a feature vector set in chronological order. The distribution of positive and negative signs of the values ​​in the feature vector set is then examined. If the positional offset difference is significant... If the value remains consistently positive for multiple consecutive periods, the judgment field exhibits a backward drift trajectory. If the length scaling difference... If the value remains positive for multiple consecutive periods, the field is determined to exhibit an expansion trajectory. If the changing characteristic value oscillates between positive and negative or remains constant at zero, it is determined to be either an irregular fluctuation state or a static constant state, respectively. The data structure containing the drift trajectory direction, expansion trajectory direction, fluctuation state, and set of changing characteristic values ​​is encapsulated to obtain the field change pattern.

[0025] S103: Based on the field change pattern, for the name, label or symbol of the field presented in continuous content, perform difference processing on the text combination that appears in different data frames with the same field identifier, merge the text combination with zero difference by the number of occurrences, and associate it with the field position to generate a cross-domain resource behavior fragment set; Based on the pattern of field changes, the binary data carried at a specified field position in each data frame of the data frame sequence is extracted. The encoding format is determined by detecting byte distribution characteristics or the header BOM bit. After confirming that it conforms to text characteristics, the character encoding sequence is converted into a standard ASCII or Unicode numerical stream. The field content at the same position in two consecutive data frames is selected as the preceding character numerical stream. With subsequent character numeric stream Compare the lengths of the two sets of numerical streams. If the lengths are the same, perform an XOR operation bit by bit, and count the total number of bits where the XOR result is zero. If this total number equals the total length of the numerical streams, the difference in this comparison is determined to be zero, and the content of this field is recorded as a repeat occurrence. A dynamic counter is set up with an initial value of 0. During the traversal of the entire data frame sequence, whenever the difference in the content of the same field in adjacent frames is detected to be zero, the dynamic counter is incremented by 1, and the character combination corresponding to the repeating content is recorded. For example, if the content of a field is "UserType", and in 100 consecutive data frames, the comparison results show that its content remains unchanged in 95 times, that is, the difference is zero, then the value of the dynamic counter is 95. A frequency baseline value is set. This benchmark value is calculated by determining the total number of frames in the data frame sequence. With preset ratio coefficient The product is obtained by setting the total number of data frame sequences to 1000 and the preset scaling factor. It is 0.05; Then frequency reference value The current value of the dynamic counter is compared with the frequency reference value. If the counter value is greater than the frequency benchmark value, the text combination is confirmed as a high-frequency stable identifier. The specific text string "UserType" corresponding to the high-frequency stable identifier is extracted, and the field position index determined in S101 is obtained. The field name, field position index, high-frequency text combination and the change trajectory features obtained in S102 are packaged into an independent data object to generate a cross-domain resource behavior fragment set.

[0026] Please see Figure 3 The steps to obtain the cross-domain resource mapping set are as follows: S201: Call each behavior fragment in the cross-domain resource behavior fragment set, compare the field positions in different behavior fragments according to the order of the fields in the data frame, organize the differences between adjacent positions in the form of the differences corresponding to the fields in the same order, identify the relative offset of the fields under different sources, and obtain the field order difference. Call each behavior fragment in the cross-domain resource behavior fragment set, initialize a doubly linked list to store the comparison results, and read the first and second behavior fragments from different access channels respectively. Extract the first field position array from the first behavior fragment. Extract the second field position array from the second line fragment. ,in and These represent the center coordinates of the same logical field in different data frame structures, and a traversal pointer is set. Increasing from 1 to Calculate the absolute positional deviation for each pair of corresponding fields. For example, the center coordinates of the "Header" field in the first line of the fragment. The second line contains the center coordinates of this field in the fragment. Then the absolute position deviation Further calculate the relative spacing between adjacent fields to obtain the first segment. The first field and the second Spacing between fields And the spacing of the corresponding fields in the second segment. Perform the relative ranking difference calculation formula ,like If the value is not zero, it indicates that there is a shift in the order of fields between the two data frames. A threshold for determining this shift is set. The threshold value is the total length of the data frame. 1%; like byte, then ,when When this difference in ranking is determined to be a significant architectural change, all calculated absolute positional deviations are considered. Relative ranking differences Write the data into a doubly linked list in the order of field indexes. Mark the field nodes with significant architectural changes as “sequence offset points” and mark the nodes that do not exceed the threshold as “sequence matching points”. Complete the sequence logic verification of all fields and obtain the field sequence differences.

[0027] S202: Call the field order difference, compare the field change pattern in different behavior segments with the increase or decrease direction of the field trajectory in the continuous content under different frame sequences, organize the change segment sequence according to the directional difference of the field in the same position in the trajectory advancement, and concatenate the change segment sequence into dynamic difference according to the trajectory advancement order to obtain the field change difference; The field order difference is invoked, and the field is divided into several comparison groups based on the classification results of the order offset point and the order coincidence point. The change pattern vector of the first field is retrieved from the behavior fragment set. Vector of the change pattern of the second field ; in The elements in the vector represent the number of frames in a continuous observation period. To represent the feature value of the position or length change of the field at that moment, a sliding window with a window size of 5 frames is set, and the values ​​are calculated within the sliding window. and trend slope and ,Compare and The direction of the sign, if and The time period is determined to be a "reverse change segment". and The signs are the same, but the absolute value of the numerical difference is greater than the preset change intensity threshold. The time period was determined to be an "intensity difference zone," and a threshold for the change in intensity was set. It is 20% of the mean of the eigenvalues, for example If the mean is 5.0, then If at a certain moment , A difference of 2.0 is greater than 1.0 and is classified as an intensity difference segment. The entire time sequence is scanned, and time points that are continuously identified as reverse changes or intensity differences are connected into segments. The starting frame number of each difference segment is recorded. With end frame number For time periods where no difference occurred, they were marked as "synchronous change segments". These segments were then concatenated in chronological order to form a complete description of the differences in the dynamic behavior of the field, thus obtaining the field change differences.

[0028] S203: Call the field change difference, perform difference comparison on the field identifier in the behavior fragment with the text combination of the same field identifier in different data frames, count the content with completely consistent character combinations in the form of the number of occurrences, and integrate it with the field order difference and field change difference to obtain the field correspondence content and generate the cross-domain resource correspondence set; The function calls the field change difference function. For each field marked as sequentially matching and changing synchronously, it extracts the corresponding first field identifier string from the behavior fragment set. With the second field identifier string The string is converted into an ASCII code sequence, and the numerical values ​​are compared bit by bit to calculate the Levenstein distance between the two strings. ,like If the identifiers are completely identical, the frequency of each identifier in its respective source is read. and Execution frequency weighted merging formula The weight All are set to 1.0, and the merged frequency is associated with a unified identifier text; if If a discrepancy in the identifier is determined, it is recorded. and As a pair of fields that are aliases to each other; For example ="UserID", =“UID”, record the mapping relationship of “UserID<->UID”, and mark the difference type as “name conflict”. Create a structured data object, which contains three core attribute fields: the order offset value field obtained by S201, the dynamic change segment field obtained by S202, and the identifier mapping field generated in this step. Aggregate the structured data objects of all fields into the hash mapping table, use the unified index of the field as the key value, and the corresponding difference integration information as the value value to form the field correspondence content and generate the cross-domain resource correspondence set.

[0029] Please see Figure 4 The steps to obtain the cross-domain resource arrangement framework set are as follows: S301: Call the field difference content in the cross-domain resource correspondence set, obtain the field order difference content recorded in the data frame, organize the fields according to the order in which they appear in each frame, arrange the corresponding fields continuously according to the order of appearance to form a field order chain, and obtain the field position sequence. The system retrieves the differences between each field in the cross-domain resource mapping set, initializes a linear buffer array to store the sorting results, and reads the starting byte offset of each field unit within the original data frame from the field difference content. offset from end byte ; Calculate the span value of this field cell. Set a cursor variable The initial value is 1. Iterate through all read field elements, using the quicksort algorithm, and select the field element at the middle position of the array as the pivot element. Record its starting byte offset as... The starting byte offset of the remaining field units and Perform a numerical comparison; if Move the field cell to the left storage area of ​​the base element. Move the field cell to the right storage area of ​​the pivot element, recursively execute this partitioning operation until all field cells are strictly arranged in ascending order of their starting byte offsets. Traverse the sorted buffer array, instantiate a singly linked list object, encapsulate the first field cell in the array as the head node of the linked list, and read subsequent field cells sequentially, targeting the current node. With the next node ,extract End byte offset and Starting byte offset Execution gap calculation formula If the calculation result If the value is zero, establish a direct link pointer. If the value is greater than zero, insert a virtual node marked "blank padding" between the two nodes and set the gap value accordingly. The attributes of the records are set as virtual nodes until the last field unit in the array is encapsulated as the tail node of the linked list, thus completing the reconstruction of the physical order of all field units and obtaining the field position sequence.

[0030] S302: Call the field position sequence, and based on the field change difference content and the field identification difference content, compare the change performance of the same field in the change difference content with the text combination in the identification difference content, and group the fields with the same change performance and the same text combination into the same group to obtain the field combination; The system calls the field position sequence, creates an empty hash map to store the grouping results, iterates through each field node in the field position sequence, and extracts the feature vector of the field's change over the continuous sampling period from the field change difference content. ,in To determine the number of samples, extract the character encoding sequence of the field from the differences in the field identifier content. Set a traversal pointer The current field node is taken as the object to be matched, and all subsequent field nodes in the sequence are scanned to obtain the feature vectors of the subsequent nodes. With character encoding sequence Perform character consistency verification and calculate and Byte matching rate ,like If the value is 100%, continue with the change performance comparison and calculate the feature vector. and cosine similarity Set a similarity threshold The threshold Set to 0.95, if the calculated value is... If two fields are found to be highly consistent in both dynamic change patterns and static text identifiers, these two field nodes are grouped into the same source field set and assigned a unique set index number. If neither of the above conditions is met, the current field node is divided into an independent set. This process is repeated until all field nodes are assigned the corresponding set index number. All field nodes under the same set index number are then packaged to obtain the field combination.

[0031] S303: Call the field combination, connect the fields according to their position order in the field position sequence to obtain the field arrangement chain, and connect it with the order position of the cross-domain resource access entry to form a content arrangement framework, thus obtaining the cross-domain resource arrangement framework set; The process involves calling field combinations to initialize an empty topology graph object, reading the original order index of each node in the field position sequence, mapping this index to the corresponding field combination, establishing the predecessor and successor relationships between each field combination, extracting the definition parameters of the protocol header from the cross-domain resource access entry configuration, identifying the bootstrap field set contained in the protocol header, setting the bootstrap field set as the root node of the topology graph, and sequentially mounting subsequent field combinations starting from the root node according to the physical arrangement order in the field position sequence. For each connection point, the gap value calculated in S301 is read. ,like If the value is greater than zero, an edge with a delay weight is generated in the topological graph, and the weight value is equal to... ,like If the value is zero, a direct edge with no delay is generated. For cases where multiple field combinations are mapped to the same physical location index, i.e., multiplexing scenarios, a branch node is created in the topology graph. Parallel field combinations are connected to the branch node as downstream branches. The generated topology path is scanned to detect whether there is a closed loop path. If a closed loop exists, the loop edge is cut to ensure the directed acyclic property of the path. The organized node connection relationship and edge weight information are solidified into a standardized description file to form a content arrangement framework and generate a cross-domain resource arrangement framework set.

[0032] Please see Figure 5 The steps to obtain the cross-domain resource tag classification set are as follows: S401: Call the field arrangement chain in the cross-domain resource arrangement framework set, extract the hierarchical relationship of each field content according to the presentation order of the fields in the chain, use the hierarchical order as the connection benchmark, connect the field change difference content and the field identifier difference content one by one according to the field name order, so that the corresponding information of the field in the two difference contents is integrated into a continuous record along the arrangement chain order to obtain the field feature fragment. The cross-domain resource arrangement framework is invoked to set up a field arrangement chain, which initiates a list traversal pointer. Starting from the first address of the list, the metadata contained in each node is read one by one, and the depth index value of the current node in the list topology is extracted. With breadth index value The depth index value determines the level of the field in the overall resource tree. For example, the root node has a depth of 0, the first-level child node has a depth of 1, and so on, thus constructing a level coordinate pair. Then, the storage area for field change differences is accessed, and the corresponding dynamic change feature vector is retrieved using the field name as a key index. This vector records the field in consecutive... The system analyzes the numerical fluctuations within each sampling period and accesses the field identifier difference storage area to extract the text identifier string presented by the field in different sampling periods. Perform a string hash operation to generate a unique identifier fingerprint. The hierarchical coordinate pairs, dynamically changing feature vectors, and identifier fingerprints are concatenated according to a preset byte alignment format to construct a heuristic weighting formula for representing the comprehensive features of the fields: ; in For hierarchical weights, Assign weights to the magnitude of change, and set... , Assuming a field has a depth of 2 and a change vector magnitude of 0.8, then The calculated weight values ​​are appended to the end of the concatenated data to form a comprehensive data package containing three-dimensional attributes of hierarchical positioning, dynamic behavior, and static identification. This data package is written to a temporary buffer, the traversal pointer is moved to the next node, and the above extraction and concatenation operations are repeated until the tail node of the linked list is processed to obtain the field feature fragment.

[0033] S402: Call the field feature fragments, and compare the order of appearance and text content of multiple fragments with the order of appearance and the corresponding text combination in the fragment sequence. The fragments with the same order and the same text combination are organized into the same set, and the overall performance of the set is summarized according to the continuity of the field appearance within the set to obtain the marked content. The system calls the field feature fragments, initializes a multidimensional clustering container, imports all field feature fragments from the buffer into the container, and sets the primary key to the physical index of the field in the permutation chain. The secondary key is for fingerprint identification. The quicksort algorithm is executed to preprocess the fragments, ensuring that fragments with the same physical index and fingerprint are stored contiguously in memory. A sliding window scanning program is then started, with a window size of 2, to compare adjacent fragments within the window. and Dynamically changing feature vectors and ; Calculate Euclidean distance Set a similarity threshold This threshold is obtained by calculating 5% of the average modulus of all segment feature vectors. Assuming the average modulus is 10.0, then... ,like If two segments are determined to have the same behavior, they are grouped into the same candidate set, and the number of segments in that set is counted. It also checks whether the original sampling period indices corresponding to these segments are continuous and calculates the continuity coefficient. ; in To satisfy the index increment relationship The number of adjacent segment pairs, setting a continuity benchmark value. ,like To confirm that the set possesses stable tagging features, the most frequent text identifier string in the set is extracted as the standard tag name for that group. For example, if the set contains 100 segments, and 95 of them are sequentially consecutive, the calculation... If the value is greater than 0.9, the set is valid. The standard tag name is then bound to the corresponding physical sequence number to obtain the tag content.

[0034] S403: Call the tag content, connect multiple tag contents in the order of the chain according to the corresponding position of the tag content in the field arrangement chain to obtain the tag chain, establish the association relationship between fields based on the chain, and obtain the cross-domain resource tag classification set; The process involves retrieving the labeled content, creating an empty directed acyclic graph (DAG) to construct the association network, reading the topological skeleton of the field permutation chain, mapping each valid labeled content back to the corresponding node position in the permutation chain, marking a node as an active node if it has labeled content attached to it, and traversing all paths in the permutation chain to find any two active nodes. and Check if there is a reachable path in the original permutation chain. If so, calculate the path span. This refers to the number of inactive nodes between two nodes, setting a threshold for the association radius. The threshold is set to 5. In a directed acyclic graph, establish a path from... point to The associated edges, and the path span As the weight value of the edge, if This indicates that two nodes are adjacent, with a weight of 1. For each activated node, its in-degree is calculated. With out The system identifies nodes with an out-degree of 0 as leaf tags and nodes with an in-degree of 0 as root tags. Starting from the root tag, it traverses down along the associated edges to all connected leaf tags. It then connects all tag names on the traversal path into a string sequence in chronological order, for example, the path is "Header->Auth->Token", forming a complete tag link. All generated tag links are aggregated, and duplicate paths are removed to obtain a cross-domain resource tag classification set.

[0035] Please see Figure 6 The steps to obtain the results of cross-domain unified resource services are as follows: S501: Call the tag association chain in the cross-domain resource tag classification set, match the target resource content proposed by the service scheduling entry with the tag content in the chain in the order of appearance, compare the consistency relationship between the target resource content and the tag content in the sequence, and organize them into matching records according to the sequence position after comparison to obtain the tag matching record; The system invokes the tag association chain in the cross-domain resource tag classification set, initializes a temporary storage queue to hold the comparison results, reads the target resource content string to be requested from the service scheduling entry point, and uses a standard delimiter to split the string into independent target keyword arrays. ,in The number of requests representing the target resource is used to iterate through the tag classification set and extract the tag node sequence from each tag association chain. Set up a nested loop, with the outer loop iterating through the target keyword array. The inner loop iterates through the sequence of marked nodes. For each pair of elements and Character similarity is calculated using the Jaccard similarity coefficient formula. The intersection and union operations are based on the types and quantities of characters, and a similarity threshold is set. If the threshold is set to 0.95, and the calculated value is... If the target keyword is determined to match the current marker node, the current marker node is recorded in the sequence. index position in and tuple Write to a temporary matching list. After comparing all keywords, update the index positions in the temporary matching list. Perform a monotonicity check and calculate the index difference between adjacent matches. If all If all values ​​are greater than zero, it indicates that the request order of the target resource is consistent with the actual arrangement order of the underlying data, and the matching coverage is calculated. ,in Set a coverage baseline value based on the number of successfully matched keywords. ,like For example, if a request asks for 5 fields, and all 5 fields successfully match and are verified in order, resulting in 100% coverage, then the matching list is considered a valid consistency description. Information containing the target keyword, corresponding tag name, and index position in the chain is encapsulated into standardized data units and stored sequentially in a queue. If the target resource request does not match the current tag association chain, discard the current temporary matching list and continue to traverse the next tag association chain, and finally obtain the tag matching record.

[0036] S502: Call the marker matching record, and according to the field position relationship in the cross-domain resource arrangement framework set, match the position of the matching record in the sequence with the field order position one by one, connect them in the corresponding order to form the association link from field to content, and organize the link to form the corresponding result to obtain the content mapping record. The system retrieves the tag matching record, reads each tag name and its corresponding index information, accesses the field location lookup table in the cross-domain resource arrangement framework set, and extracts the physical starting address of the underlying field corresponding to the tag in the data frame using the tag name as the retrieval key. With physical end address Calculate the length of bytes occupied by the field. For example, if a certain marker corresponds to a field with a start address of 0x0040 and an end address of 0x0048, then the length is 8 bytes. This applies to two adjacent fields arranged in sequence in the matching record. and Obtain their physical coordinate ranges respectively. and Execute the interval calculation formula ,like An equality of zero indicates that the two fields are physically contiguous, and a direct read pointer is established. A value greater than zero indicates the presence of non-target data segments; the jump step size is recorded. Generate a read path containing jump instructions, if If the value is less than zero, a data overlap conflict is determined, triggering an exception flag and initiating a conflict resolution mechanism. Priority is given to retaining the content of the fields with the earlier sequence numbers, while discarding the subsequent content of the overlapping parts to maintain data integrity. The entire matching record sequence is traversed, and all physical address ranges, byte lengths, and jump step sizes between ranges are assembled according to a chain topology to form a logical execution link describing the extraction of target content from the data source. A unique sequence number is assigned to each node in this link, and the mapping relationship between the sequence number and the physical address is written into the configuration table to ensure that subsequent extraction operations can accurately correspond to specific positions in the original bitstream to obtain content mapping records.

[0037] S503: The field content in the content mapping record and the access identity content of the service scheduling entry are merged in the order of field appearance. The merged content is then organized into a service access set to obtain the cross-domain unified resource service result. The system retrieves the field descriptions from the content mapping record, initiates a data extraction engine based on Direct Memory Access (DMA) technology, and extracts binary fragments from the original data frame stream using pointer offset addressing instructions according to the physical start address and length parameters defined in the logical execution link. The extracted fragments are then written to the output buffer in the link order. Finally, the system retrieves the access identity information of the currently requesting user from the service scheduling entry point; this information includes the user's permission code. With session token Convert the permission code to a fixed-length binary prefix, convert the session token to a fixed-length binary suffix, and count the total number of bytes of extracted field content in the output buffer. Perform packet assembly operations, insert a binary prefix at the beginning of the buffer, append a binary suffix to the end of the buffer, and calculate the overall length of the merged packet: ; For example, if the permission code is 4 bytes long, the extracted content is 1024 bytes, and the token is 16 bytes long, the total length is 1044 bytes. Based on the assembled data packet content, a cyclic redundancy check is performed to generate a 32-bit checksum. The verification code is appended to the end of the data packet to construct a final service response data block with authentication information and integrity verification functions. This data block is then identified as a standardized response object for this cross-domain request, resulting in a cross-domain unified resource service.

[0038] A smart big data resource service system for cross-domain collaboration, the system comprising: The data frame analysis module acquires the data frame sequence of adjacent time periods of the access channel, records the field position, compares continuous content to extract the field change pattern, identifies field names, tags or symbols as identifiers, records repeated text combinations to form field identifiers, and integrates them to generate a set of cross-domain resource behavior fragments. The behavior fragment comparison module calls each behavior fragment in the cross-domain resource behavior fragment set, compares the field position to extract the order difference, compares the field change pattern to extract the change difference, identifies the field identifier to extract the identifier difference, integrates the three types of differences to form the field correspondence content, and generates the cross-domain resource correspondence set. The field difference integration module calls the field difference content of each field in the cross-domain resource correspondence set, organizes the field order differences into a field position sequence, compares the change difference performance with the identification difference text combination, and groups fields with consistent change performance and consistent text combination into field combinations. Based on the position order of the field combinations in the field position sequence, a field arrangement chain is formed to generate a cross-domain resource arrangement framework set. The field feature classification module extracts the hierarchical relationship of fields in the arrangement chain in the cross-domain resource arrangement framework, and concatenates them with field change differences and identifier differences to form field feature fragments. It compares and collects the same features as the marked content, connects them into a mark association chain according to position, and generates a cross-domain resource mark classification set. The service mapping generation module calls the tag association chain in the cross-domain resource tag classification set, compares the target resource content to form a tag matching record, associates the tag matching record with the field position relationship to obtain the content mapping record, and merges it with the access identity content to form a service mapping set, generating a cross-domain unified resource service result.

[0039] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for providing intelligent big data resource services for cross-domain collaboration, characterized in that, Includes the following steps: S1: Obtain the data frame sequence of adjacent time periods of the access channel, record the field position, compare the continuous content to extract the field change pattern, identify the field name, label or symbol as an identifier, record the repeated text combination to form the field identifier, and integrate to generate a cross-domain resource behavior fragment set; S2: Call each behavior fragment in the cross-domain resource behavior fragment set, compare the field positions to extract the order difference, compare the field change rules to extract the change difference, identify the field identifier to extract the identifier difference, integrate the three types of differences to form the field correspondence content, and generate the cross-domain resource correspondence set; S3: Call the differences in each field in the cross-domain resource correspondence set, organize the differences in field order into a field position sequence, compare the changes in the differences with the text combinations of the differences in the identifiers, group the fields with the same changes and the same text combinations into field combinations, and connect the field combinations in the field position sequence to form a field arrangement chain, thereby generating a cross-domain resource arrangement framework set. The steps for obtaining the cross-domain resource arrangement framework set include: S301: Call the field difference content in the cross-domain resource correspondence set, obtain the field order difference content recorded in the field order difference content in the data frame, organize the fields according to the order in which they appear in each frame, arrange the corresponding fields continuously according to the order of appearance to form a field order chain, and obtain the field position sequence. S302: Call the field position sequence, and based on the field change difference content and the field identification difference content, compare the change performance of the same field in the change difference content with the text combination in the identification difference content, and group the fields with the same change performance and the same text combination into the same group to obtain the field combination; S303: Call the field combination, connect the fields according to their position order in the field position sequence to obtain the field arrangement chain, and connect it with the order position of the cross-domain resource access entry to form a content arrangement framework, thus obtaining a cross-domain resource arrangement framework set. S4: Extract the hierarchical relationship of the fields in the arrangement chain of the cross-domain resource arrangement framework, and concatenate them with the field change differences and identifier differences to form field feature fragments. Compare and collect the same features as the marked content, connect them according to position to form a mark association chain, and generate a cross-domain resource mark classification set. S5: Call the tag association chain in the cross-domain resource tag classification set, compare the target resource content to form a tag matching record, associate the tag matching record with the field position relationship to obtain the content mapping record, and merge it with the access identity content to form a service mapping set, and generate a cross-domain unified resource service result; The cross-domain unified resource service results include tag matching records, content mapping records, service mapping sets, and access identity content.

2. The intelligent big data resource service method for cross-domain collaboration according to claim 1, characterized in that: The cross-domain resource behavior fragment set includes field position, field change pattern, field identifier, field order difference, field change difference, and field identification difference. The cross-domain resource correspondence set includes field order difference, field change difference, field identification difference, field position sequence, field arrangement chain, and field combination. The cross-domain resource arrangement framework set includes field arrangement chain, field position sequence, field arrangement order, and field combination analysis. The cross-domain resource tag classification set includes tag content, tag association chain, field feature fragment, and tag combination.

3. The intelligent big data resource service method for cross-domain collaboration according to claim 1, characterized in that: The steps for obtaining the cross-domain resource behavior fragment set are as follows: S101: Obtain the data frame sequence transmitted by the cross-domain resource access channel in adjacent time periods, record the arrangement order of fields in each data frame, perform comparison based on the adjacent positions of fields in the data frame, organize them into a sequence according to the comparison order, and obtain the field position. S102: Call the field position, and for the continuous content that appears successively in the data frame sequence, perform difference processing based on the adjacent positions of each field in different data frames and the character combination length of the field in the frame. Organize the differences into a continuous trajectory according to the directional changes that appear as the data frames progress, and obtain the field change pattern. S103: Based on the field change pattern, for the names, tags or symbols of the field presented in continuous content, perform difference processing on the text combinations that appear in different data frames with the same field identifier, merge the text combinations with zero difference by the number of occurrences, and associate them with the field position to generate a cross-domain resource behavior fragment set.

4. The intelligent big data resource service method for cross-domain collaboration according to claim 1, characterized in that: The steps for obtaining the cross-domain resource correspondence set are as follows: S201: Call each behavior fragment in the cross-domain resource behavior fragment set, compare the field positions in different behavior fragments according to the order of the fields in the data frame, organize the differences between adjacent positions in the form of the differences corresponding to the fields in the same order, identify the relative offset of the fields under different sources, and obtain the field order difference. S202: Call the field order difference, compare the field change pattern in different behavior segments with the increase or decrease direction of the field trajectory in the continuous content under different frame sequences, organize the change segment sequence according to the directional difference of the field in the same position in the trajectory advancement, and concatenate the change segment sequence into dynamic difference according to the trajectory advancement order to obtain the field change difference; S203: Call the field change difference, perform difference comparison on the field identifier in the behavior fragment with the text combination of the same field identifier in different data frames, count the content with completely consistent character combinations in the form of the number of occurrences, and integrate it with the field order difference and field change difference to obtain the field correspondence content and generate a cross-domain resource correspondence set.

5. The intelligent big data resource service method for cross-domain collaboration according to claim 1, characterized in that: The steps for obtaining the cross-domain resource tag classification set are as follows: S401: Call the field arrangement chain in the cross-domain resource arrangement framework set, extract the hierarchical relationship of each field content according to the presentation order of the fields in the chain, use the hierarchical order as the connection benchmark, connect the field change difference content and the field identifier difference content one by one according to the field name order, so that the corresponding information of the field in the two difference contents is integrated into a continuous record along the arrangement chain order to obtain the field feature fragment. S402: Call the field feature fragments, and compare the order of appearance and text content of multiple fragments with the order of appearance and the corresponding text combination in the fragment sequence as the comparison basis. Organize the fragments with the same order and the same text combination into the same set, and summarize the overall performance of the set according to the continuity of the field appearance within the set to obtain the marked content. S403: Call the marked content, connect multiple marked contents sequentially according to their corresponding positions in the field arrangement chain to obtain a marked link, establish the association relationship between fields based on the link, and obtain a cross-domain resource marked classification set.

6. The intelligent big data resource service method for cross-domain collaboration according to claim 1, characterized in that: The steps for obtaining the results of the cross-domain unified resource service are as follows: S501: Call the tag association chain in the cross-domain resource tag classification set, match the target resource content proposed by the service scheduling entry with the tag content in the chain in the order of appearance, compare the consistency relationship between the target resource content and the tag content in the sequence, and organize them into matching records according to the sequence position after comparison to obtain the tag matching record; S502: Call the marked matching record, and according to the field position relationship in the cross-domain resource arrangement framework set, match the position of the matching record in the sequence with the field order position one by one, connect them in the corresponding order to form the association link from field to content, and organize the link to form the corresponding result to obtain the content mapping record; S503: Call the field content in the content mapping record and the access identity content of the service scheduling entry, merge them in the order of field appearance, organize the merged content into a service access set, and obtain the cross-domain unified resource service result.

7. An intelligent big data resource service system for cross-domain collaboration, characterized in that, The system is used in the intelligent big data resource service method for cross-domain collaboration as described in any one of claims 1-6, and the system comprises: The data frame analysis module acquires the data frame sequence of adjacent time periods of the access channel, records the field position, compares continuous content to extract the field change pattern, identifies field names, tags or symbols as identifiers, records repeated text combinations to form field identifiers, and integrates them to generate a set of cross-domain resource behavior fragments. The behavior fragment comparison module calls each behavior fragment in the cross-domain resource behavior fragment set, compares the field position to extract the order difference, compares the field change pattern to extract the change difference, identifies the field identifier to extract the identifier difference, integrates the three types of differences to form the field correspondence content, and generates the cross-domain resource correspondence set. The field difference integration module calls the field difference content of each field in the cross-domain resource correspondence set, organizes the field order differences into a field position sequence, compares the change difference performance with the identification difference text combination, and groups the fields with the same change performance and the same text combination into field combinations. Based on the position order of the field combinations in the field position sequence, a field arrangement chain is formed to generate a cross-domain resource arrangement framework set. The steps for obtaining the cross-domain resource arrangement framework set include: The field difference content in the cross-domain resource correspondence set is called to obtain the field order difference content and the field position in the data frame. The fields are organized according to the order in which they appear in each frame, and the corresponding fields are arranged continuously in the order of appearance to form a field order chain, thus obtaining the field position sequence. The field position sequence is called, and based on the field change difference content and the field identification difference content, the change performance of the same field in the change difference content is compared with the text combination in the identification difference content. Fields with the same change performance and the same text combination are grouped into the same group to obtain the field combination. The field combination is invoked, and the fields are concatenated based on their position order in the field position sequence to obtain a field arrangement chain. This chain is then connected with the sequential position of the cross-domain resource access entry point to form a content arrangement framework, resulting in a cross-domain resource arrangement framework set. The field feature classification module extracts the hierarchical relationship of fields in the arrangement chain of the cross-domain resource arrangement framework, and concatenates them with field change differences and identifier differences to form field feature fragments. It compares and classifies the same features as marked content, connects them into a marked association chain according to position, and generates a cross-domain resource marked classification set. The service mapping generation module calls the tag association chain in the cross-domain resource tag classification set, compares the target resource content to form a tag matching record, associates the tag matching record with the field position relationship to obtain the content mapping record, and merges it with the access identity content to form a service mapping set, generating a cross-domain unified resource service result.

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