Page data analysis method and system based on informatization business

Abstract

The application provides a page data analysis method and system based on informatization business, relates to the field of informatization business, first collects a page interaction behavior data set generated in the running of an informatization business system, covers user operation records and other information, generates a page interaction intention chain with a semantic correlation weight based on the page interaction behavior data set, maps the intention chain to a business semantic conduction path after calling a business semantic model, calculates a conduction matching degree, compares an intention unit sequence and a semantic node sequence to locate a deviation position and trace a cause, generates a page data optimization adjustment instruction, sends the instruction to a page configuration module to trigger an interaction logic and semantic mapping relationship adjustment, optimizes the business page, and improves user experience and system running efficiency.

Description

Technical Field

[0001] This invention relates to the field of information technology, and more specifically, to a method and system for analyzing page data based on information technology. Background Technology

[0002] In the context of the rapid development of information technology services, various information technology systems are widely used in different industries and fields to provide users with diversified business services. These systems typically interact with users through well-designed business pages, where users trigger corresponding business functions by manipulating page elements.

[0003] However, existing IT business systems have many shortcomings in page data analysis. On the one hand, most of the interactive behavior data generated by users when using business pages is simply stored and statistically analyzed, lacking in-depth semantic analysis. For example, it only records which button a user clicked, without analyzing the user's true intention behind the click or the semantic connection between the click and the page state or the user's business identity.

[0004] On the other hand, in terms of business semantic understanding, existing systems struggle to effectively correlate user page interactions with the overall semantic logic of the business system. The business semantic models within the business system are relatively independent and cannot be dynamically adjusted and optimized based on actual user interactions. This makes it difficult to quickly and accurately pinpoint the root cause when there is a discrepancy between user interaction intent and business semantics, hindering effective optimization of the business page and impacting user experience and the operational efficiency of the business system. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a page data analysis method based on information-based business processes, the method comprising: Collect a set of page interaction behavior data generated during the operation of the information business system. The set of page interaction behavior data includes the operation records performed by the user on the business page elements, the business function call information triggered by the operation, the page status data when the operation occurs, and the user business identity information associated with the operation. Based on the operation records, page status data and user business identity information in the page interaction behavior data set, a page interaction intent chain with semantic association weight is generated. The page interaction intent chain with semantic association weight consists of multiple interaction intent units arranged in the order of operation time. Each interaction intent unit includes an operation intent description, intent association elements and intent semantic weight. The business semantic model of the information business system is retrieved, and the page interaction intent chain with semantic association weight is mapped to the business semantic transmission path in the business semantic model. The transmission matching degree between intent semantic and business semantic is calculated, and the business semantic transmission path mapping result and transmission matching degree value are generated. The business semantic model includes business semantic nodes, semantic transmission relationship between nodes and semantic transmission attenuation coefficient. Based on the transmission matching degree value, the sequence of interactive intent units in the page interaction intent chain with semantic association weight is compared with the sequence of semantic nodes in the business semantic transmission path mapping result to locate the deviation position between the interactive intent and the business semantics, trace the cause of the interaction behavior and the cause of semantic transmission caused by the deviation, and form the source information of the deviation between the interactive intent and the business semantics. Based on the interaction intent and business semantic deviation tracing information, a page data optimization and adjustment instruction is generated, which includes deviation correction parameters, page element adjustment rules, and business semantic mapping optimization scheme. The page data optimization and adjustment instruction is sent to the page configuration module of the information business system to trigger the synchronous adjustment operation of the interaction logic and semantic mapping relationship of the business page.

[0006] Furthermore, embodiments of the present invention also provide a page data analysis system based on information-based business processes, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned page data analysis method based on information-based services by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described page data analysis method based on information-based business.

[0008] Based on the above, by comprehensively collecting a set of page interaction behavior data generated during the operation of the information business system, including user operation records, business function call information, page status data, and user business identity information, a page interaction intent chain with semantic association weights is generated based on the page interaction behavior data set. This can accurately depict the user's interaction intent at different operation time points, as well as the semantic association between the intent and the page status and user identity, making the understanding of user interaction behavior deeper and more accurate. The page interaction intent chain with semantic association weights is mapped to the business semantic transmission path in the business semantic model, and the transmission matching degree is calculated, achieving effective docking and quantitative evaluation of user interaction intent and business system semantic logic. By comparing the sequence of interactive intent units with the sequence of semantic nodes, the deviation between interactive intent and business semantics can be quickly located, and the causes of the deviation in interactive behavior and semantic transmission can be traced. Finally, based on the deviation source information, page data optimization and adjustment instructions are generated, which include deviation correction parameters, page element adjustment rules, and business semantic mapping optimization schemes. This triggers the synchronous adjustment of the interactive logic and semantic mapping relationship of the business page, which can optimize the business page in a timely and effective manner, improve user experience and the operating efficiency of the business system, and enable the information business system to better adapt to user needs and business changes, thereby enhancing the system's flexibility and adaptability. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the page data analysis method based on information-based business provided in the embodiments of the present invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of the page data analysis system based on information-based business provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a page data analysis method based on information-based business provided in one embodiment of the present invention. The following is a detailed description of this page data analysis method based on information-based business.

[0012] Step S110: Collect a set of page interaction behavior data generated during the operation of the information business system. The set of page interaction behavior data includes user operation records for business page elements, business function call information triggered by the operation, page status data when the operation occurs, and user business identity information associated with the operation.

[0013] In this embodiment, a general enterprise information business system is used as the application scenario. This system encompasses multiple business modules for users with different business roles to perform daily business operations. The collection of page interaction behavior data is achieved through a data acquisition layer deployed in the system architecture. This data acquisition layer consists of a front-end data acquisition component and a back-end log recording module. The front-end data acquisition component is embedded in the code of each business page to capture various user operation events on the page. The back-end log recording module is responsible for receiving and storing the operation data sent by the front-end and the business function call logs generated by the system itself.

[0014] The collection of operation records specifically includes: when a user performs any operation on a business page, the front-end data collection component records the timestamp of the operation, the URL of the page where the operation occurred, and the identifier of the corresponding DOM element. Business function call information triggered by the operation is collected by the back-end logging module. When a front-end operation triggers a back-end API call, the back-end module records the API name, call parameters, call time, and return status code. Page state data at the time of the operation is also collected by the front-end data collection component, including the current scroll position of the page, the expanded / collapsed state of each module, the population status of form fields, and the completion rate of page resource loading. The user's business identity information associated with the operation is obtained through the system's user authentication mechanism. When a user logs into the system, their business identity information is stored in the session. When the user performs an operation, the system associates the user's business identity information in the session with the operation record. The user's business identity information includes the user's unique user ID in the system, their department code, the assigned business role name, and the corresponding permission level.

[0015] During the data collection process, privacy-sensitive data, such as user business identity information, was handled using privacy protection technologies. Specifically, sensitive fields in user business identity information were encrypted during storage; for example, user IDs and permission levels were encrypted using the AES encryption algorithm during transmission and storage. Simultaneously, data anonymization rules were set in the data collection component. For content that might contain personally identifiable information, certain characters were replaced with asterisks to ensure that privacy-sensitive data was not leaked during data collection and subsequent processing.

[0016] Step S120: Based on the operation records, page state data and user business identity information in the page interaction behavior data set, generate a page interaction intent chain with semantic association weights. The page interaction intent chain with semantic association weights consists of multiple interaction intent units arranged in the order of operation time. Each interaction intent unit includes an operation intent description, intent association elements and intent semantic weights.

[0017] After obtaining the set of page interaction behavior data, it is necessary to extract data related to generating the page interaction intent chain from the set of data, and perform a series of processing and calculations to finally form a page interaction intent chain with semantic association weights.

[0018] Step S121: Extract the operation records from the page interaction behavior data set, and separate the operation type, operation object identifier, operation trigger parameters and operation execution duration corresponding to each operation record. The operation type includes click operation, input operation, selection operation, jump operation and refresh operation.

[0019] When extracting operation records from the page interaction behavior data set, the original operation logs need to be parsed. The original operation logs are stored in a structured data format, with each record containing multiple fields. Through field parsing, the operation type field is separated; its value directly indicates the type of operation. The operation object identifier field stores the unique identifier of the operated element on the page. Operation trigger parameters vary depending on the operation type. The operation execution duration is obtained by calculating the time difference between the operation start time and the operation end time.

[0020] Step S122: Extract the page state data from the page interaction behavior data set, and separate the page element layout information, page loading progress information, activation status information of related business functions, and page data cache status information when each operation record occurs.

[0021] Page state data extraction is also based on raw page state information collected by the front-end data collection component. Page element layout information includes the position coordinates, width and height dimensions, and show / hide status of each major module on the page. Page loading progress information is obtained by monitoring the loading status of various resources on the page. Activation status information of associated business functions refers to whether the system functions associated with the current page are available. Page data cache status information includes whether the page is currently using cached data, the validity period of the cached data, and the source of the cached data.

[0022] Step S123: Extract user business identity information from the page interaction behavior data set, and separate the business role identifier, business permission scope and historical business operation preference record corresponding to each user.

[0023] The extraction of user business identity information is based on the user information database stored in the system. Each user has a corresponding user record in the system, which is linked to the user record through the user ID in the user's business identity information. The business role identifier is a field in the user record, storing the name or code of the role assigned to the user. The scope of business permissions is obtained by querying the role-permission association table. Historical business operation preference records are generated by analyzing the user's operation records over a period of time.

[0024] Step S124: Classify the operation records executed within a continuous time range under the same user business identity information into operation sequence groups. Each operation sequence group contains at least three operation records and corresponding page status data.

[0025] First, all operation records in the page interaction behavior dataset are grouped according to the user ID in the user's business identity information, resulting in a subset of operation records corresponding to each user ID. Then, for each user ID's subset of operation records, they are sorted in ascending order by timestamp, resulting in a chronologically ordered user operation sequence. Next, a threshold for judging continuous time range is set. Starting from the first operation record in the sorted user operation sequence, the difference between the timestamp of subsequent operation records and the timestamp of the starting operation record of the current sequence is checked to see if it is within a continuous time range. When an operation record's timestamp difference exceeds the threshold, all operation records preceding that operation record are considered a candidate operation sequence group. Then, it is checked whether the number of operation records in the candidate operation sequence group is at least three. If it is, it is confirmed as an operation sequence group.

[0026] Step S125: For each operation record in each operation sequence group, combine the operation type, operation object identifier, operation trigger parameters, operation execution duration, page status data, and business role identifier and business permission scope in the user business identity information corresponding to the operation record to extract an operation behavior feature set. The operation behavior feature set includes operation attribute features, page environment features, and user identity association features.

[0027] For each operation record in the operation sequence group, relevant features are extracted from the operation record, page state data, and user business identity information, and combined to form an operation behavior feature set. Operation attribute features are extracted based on the operation record. Page environment features are extracted based on page state data. User identity association features are extracted based on user business identity information.

[0028] Step S126: Associate and match the set of operation behavior features with a preset intent feature library. The preset intent feature library contains a variety of operation behavior feature combinations, corresponding interaction intent descriptors, intent association elements, and intent semantic weight calculation rules.

[0029] The pre-built intent feature library is a database that stores the correspondence between various combinations of operational behavior features and interactive intents. During the association matching process, a feature matching algorithm is used to compare each feature in the operational behavior feature set with the feature combination conditions of each record in the intent feature library. When the operational behavior feature set satisfies all the feature combination conditions of a record, the association matching is completed, and the corresponding interactive intent descriptor, intent association elements, and intent semantic weight calculation rules are obtained.

[0030] Step S127: Based on the matching results, assign a corresponding interaction intent descriptor and intent association element to each operation record. Based on the intent semantic weight calculation rules, combined with the operation execution time and the user's historical business operation preference records, calculate the intent semantic weight value corresponding to each operation record to form an interaction intent unit corresponding to each operation record.

[0031] Step S1271: Read the preset intent semantic weight calculation rules, which include operation attribute weight coefficients, page environment weight coefficients, user identity weight coefficients, and the calculation priority of each coefficient.

[0032] The preset intent semantic weight calculation rules are stored in the system's configuration file or database. When calculating the intent semantic weight value, the rules are read from the corresponding storage location.

[0033] Step S1272: For each operation record, extract the operation type and operation execution duration from the operation attribute features. Based on the preset intent semantic weight calculation rules, assign the corresponding basic weight value of the operation attribute to the operation type. According to the relationship between the operation execution duration and the preset duration range, calculate the duration correction weight value through the preset duration-weight mapping function. The duration correction weight value is a dimensionless value.

[0034] The base weight values ​​for operation attributes are allocated based on the operation type. The operation execution time is compared with a preset time range, and a time-adjusted weight value is calculated using a time-weight mapping function.

[0035] Step S1273: Extract page loading progress information and page data cache status information from the page environment features. Based on the relationship between the page loading progress and the preset completion ratio and the page data cache status, calculate the basic weight value of the page environment. According to the degree of page loading progress not reaching the preset completion ratio or cache status abnormality, calculate the environment abnormality correction weight value through the preset environment abnormality-weight mapping function. The environment abnormality correction weight value is a dimensionless value.

[0036] The basic weight value of the page environment is calculated based on a combination of page loading progress and page data caching status. Depending on the degree to which page loading progress falls short of the preset completion percentage or the extent of cache anomalies, an environment anomaly-weight mapping function is used to calculate a corrected weight value for environment anomalies.

[0037] Step S1274: Extract the business role identifier and business permission range from the user identity association features, query the historical operation frequency under the business role identifier that is consistent with the current operation type in the user's historical business operation preference record, calculate the user identity basic weight value based on the relationship between the historical operation frequency and the preset frequency range, and calculate the frequency correction weight value through the preset frequency-weight mapping function according to the degree to which the historical operation frequency deviates from the preset frequency range. The frequency correction weight value is a dimensionless value.

[0038] The basic weight value for user identity is determined based on the business role identifier and business permission scope. The basic weight value for user identity is calculated based on the relationship between historical operation frequency and a preset frequency range. A frequency correction weight value is then calculated based on the degree to which historical operation frequency deviates from the preset frequency range.

[0039] Step S1275: According to the calculation priority in the preset intent semantic weight calculation rules, the operation attribute weight, page environment weight, and user identity weight are weighted and summed. The operation attribute weight is the algebraic sum of the basic operation attribute weight value and the duration correction weight value. The page environment weight is the algebraic sum of the basic page environment weight value and the environment anomaly correction weight value. The user identity weight is the algebraic sum of the basic user identity weight value and the frequency correction weight value. All basic weight values ​​and correction weight values ​​are dimensionless values.

[0040] Based on the calculation priority, the operation attribute weight, page environment weight, and user identity weight are processed in sequence. The operation attribute weight is multiplied by its corresponding weight coefficient, the page environment weight is multiplied by its weight coefficient, and the user identity weight is multiplied by its weight coefficient. Then, these three products are added together to obtain an intermediate weighted sum.

[0041] Step S1276: Calculate the ratio of the weighted summation result to the preset weight benchmark value to obtain the intention semantic weight value corresponding to each operation record. The range of the intention semantic weight value is within the preset weight value range. If the calculated intention semantic weight value exceeds the preset weight value range, it is corrected based on the boundary value of the preset weight value range. The corrected value is used as the final intention semantic weight value.

[0042] Divide the weighted sum obtained in step S1275 by the preset weight baseline value to obtain the preliminary intent semantic weight value. If the preliminary calculated value is within the preset weight value range, it is directly used as the intent semantic weight value; if it is less than the lower limit of the range, it is corrected to the lower limit value of the range; if it is greater than the upper limit of the range, it is corrected to the upper limit value of the range.

[0043] Step S128: Connect the interactive intent units corresponding to each operation sequence group in sequence according to the time order of the operation records in the operation sequence group, and add an intent semantic transmission relationship identifier between adjacent interactive intent units. The intent semantic transmission relationship identifier is determined based on the overlap of intent association elements of two interactive intent units.

[0044] The operation records in the operation sequence group are arranged in chronological order, with each operation record corresponding to an interactive intent unit. Therefore, the interactive intent units are also arranged sequentially according to the chronological order of the operation records. The semantic transmission relationship identifier between adjacent interactive intent units is used to indicate how the previous intent unit guides or influences the subsequent intent unit. The overlap of intent association elements is obtained by comparing the intent association elements of two adjacent interactive intent units.

[0045] Step S129: Calculate the semantic association weight value between adjacent interactive intent units based on the semantic weight value of the intent association elements and the semantic transmission relationship identifier. The semantic association weight value is positively correlated with the overlap of intent association elements and the semantic weight value of the intent association elements.

[0046] The calculation of semantic association weight values ​​comprehensively considers the semantic weight values ​​of adjacent interactive intent units and the semantic transmission relationship identifiers between them. First, corresponding relationship weight coefficients are assigned to different semantic transmission relationship identifiers. Then, the semantic weight value of the previous interactive intent unit is multiplied by the semantic weight value of the next interactive intent unit, and then multiplied by the relationship weight coefficient to obtain the semantic association weight value.

[0047] Step S1210: Add an operation sequence group identifier, chain generation time, total number of interactive intent units, and semantic association weight distribution table to the chain corresponding to each operation sequence group to form a page interactive intent chain with semantic association weight.

[0048] The operation sequence group identifier is a unique identifier for that operation sequence group within the system. The chain generation time is the timestamp of the currently generated page interaction intent chain. The total number of interaction intent units included is the number of interaction intent units in the chain. The semantic association weight distribution table records the semantic association weight values ​​between all adjacent interaction intent units in the chain. Combining the above information with the chronologically arranged sequence of interaction intent units forms a complete page interaction intent chain with semantic association weights.

[0049] Step S130: Retrieve the business semantic model of the information business system. The business semantic model includes business semantic nodes, semantic transmission relationships between nodes, and semantic transmission attenuation coefficients. Map the page interaction intent chain with semantic association weights to the business semantic transmission path in the business semantic model. Calculate the transmission matching degree between intent semantics and business semantics. Generate the business semantic transmission path mapping result and the transmission matching degree value.

[0050] The business semantic model of an information technology business system is a pre-built model used to describe the system's business logic and semantic relationships, stored in the model repository of the information technology business system. By calling the API interface provided by the model repository and passing in the model identifier parameter, the required business semantic model can be retrieved.

[0051] Step S131: Retrieve the pre-built business semantic model through the model interface of the information business system. The pre-built business semantic model includes multiple business semantic nodes, semantic transmission relationships between nodes, business function descriptions corresponding to each semantic node, business scenario tags, and semantic transmission attenuation coefficients between nodes.

[0052] The model interface of an information-based business system is a RESTful API or other type of interface deployed in the system's service layer. When calling this interface, authentication information is required to ensure secure access. The interface's request parameters include the version number or model name of the business semantic model to specify which model to retrieve.

[0053] Step S132: Parse the pre-built business semantic model, extract all business semantic nodes, semantic transmission relationships between nodes, semantic transmission attenuation coefficients, and business scenario labels, and construct a business semantic node network. The business semantic node network has business semantic nodes as vertices and semantic transmission relationships as edges. The attributes of the edges include semantic transmission attenuation coefficients and associated business scenario labels.

[0054] To parse the pre-built business semantic model, the first step is to read the model's metadata to understand its overall structure and version information. Then, the list of business semantic nodes is extracted, obtaining attributes such as ID, name, business function description, and business scenario tag for each node. Next, the semantic transmission relationships between nodes are extracted. When constructing the business semantic node network, each business semantic node is treated as a vertex in the network, and the semantic transmission relationships between nodes are treated as edges connecting the vertices.

[0055] Step S133: In the business semantic node network, classify according to business scenario labels, identify the path containing continuous semantic transmission relationship under each business scenario, each path consists of at least four business semantic nodes arranged in semantic transmission order, the path attributes include the total path length, the total semantic transmission attenuation of the path and the business scenario label corresponding to the path, forming a set of business semantic transmission paths classified by business scenario.

[0056] Step S1331: Parse the business scenario tags in the business semantic model and divide them into multiple business scenario categories according to the type of the business scenario tags. Each business scenario category contains at least one business scenario tag.

[0057] Business scenario tags in a business semantic model may be of various types; some are high-level category tags, while others are low-level specific scenario tags. During parsing, all business scenario tags are first identified, and then they are classified into different business scenario categories based on the hierarchical relationship or business logic relationship between the tags.

[0058] Step S1332: For each business scenario category, select business semantic nodes with business scenario tags under that business scenario category from the business semantic node network to form a subset of semantic nodes for that business scenario category.

[0059] For each business scenario category, traverse all business semantic nodes in the business semantic node network and check whether the business scenario label of a node belongs to the label under that business scenario category. If a node has a business scenario label in the list of business scenario labels included in that category, then select that node into the semantic node subset of that business scenario category.

[0060] Step S1333: In the semantic node subset, identify all paths containing continuous semantic transmission relationships. The starting semantic node of the path must have the business trigger node attribute, and the ending semantic node of the path must have the business completion node attribute.

[0061] Within the semantic node subset, a graph traversal algorithm is used to identify paths of continuous semantic transmission relationships. The business trigger node attribute is an attribute identifier for a business semantic node, indicating that the node is the starting point of a business process. The business completion node attribute indicates that the node is the ending point of the business process. During traversal, starting from a node with the business trigger node attribute, the algorithm traverses downstream nodes along the semantic transmission relationship edges between nodes until it encounters a node with the business completion node attribute, forming a path from the starting node to the ending node.

[0062] Step S1334: Extract the number of semantic nodes in each path as the total path length, and calculate the sum of the semantic transmission attenuation coefficients between all adjacent semantic nodes in each path as the total semantic transmission attenuation of the path.

[0063] The total path length is the number of business semantic nodes contained in the path. The total semantic transmission attenuation of the path is obtained by adding the semantic transmission attenuation coefficients between all adjacent nodes in the path.

[0064] Step S1335: Analyze the business function descriptions of semantic nodes in each path, verify the completeness of the business logic of the path. If the business function descriptions of the path from the starting node to the ending node conform to the core business process of the business scenario category, then mark it as a valid business semantic transmission path; if there are logical gaps in the business function descriptions of the path, then mark it as an invalid business semantic transmission path and remove it from the path list.

[0065] The core business process is a predefined standard business processing flow for each business scenario category. The business function description of each semantic node in the analysis path is then linked together in node order to form a description of the business process represented by that path. This description is then compared with the core business process of the business scenario category to check for logical consistency and completeness of steps.

[0066] Step S1336: Add a path identifier, business scenario classification identifier, total path length, total path semantic transmission attenuation, sequence of semantic nodes included in the path, and business function description of each semantic node to each valid business semantic transmission path.

[0067] The path identifier is a unique identifier assigned to a valid business semantic transmission path. The business scenario classification identifier indicates the business scenario category to which the path belongs. The total path length and the total semantic transmission attenuation of the path have been calculated in previous steps. The sequence of semantic nodes contained in the path is a list of business semantic node IDs arranged in semantic transmission order.

[0068] Step S1337: Classify all valid business semantic transmission paths according to business scenario classification identifiers to form a set of business semantic transmission paths classified by business scenario; query the core business process list of the business scenario classification, compare the correspondence between the core business process list and the valid business semantic transmission paths, and if there are core business processes that do not correspond to valid business semantic transmission paths, supplement and construct the corresponding valid business semantic transmission paths to improve the set of business semantic transmission paths classified by business scenario.

[0069] All valid business semantic transmission paths are grouped according to their business scenario classification identifiers. Each business scenario classification identifier corresponds to a path list, and all these groups together constitute a set of business semantic transmission paths categorized by business scenario. Each business scenario category has a core business process list, which lists the core business processes that must be covered under that category. Each process in the core business process list is compared with the valid business semantic transmission paths under that business scenario category to check if there is a valid path corresponding to that process.

[0070] Step S134: For each page interaction intent chain with semantic association weight, extract the business scenario requirement tag corresponding to the user business identity information in the page interaction intent chain with semantic association weight, and select the subset of business semantic transmission paths corresponding to the same business scenario tag from the set of business semantic transmission paths classified by business scenario based on the business scenario requirement tag.

[0071] The user business identity information in the page interaction intent chain with semantic association weights includes the user's business scenario requirement tag, which indicates the business scenario involved in the user's current operation. From the set of business semantic transmission paths categorized by business scenario, all business semantic transmission paths whose business scenario category identifier or path corresponds to the same business scenario requirement tag are found. These paths together form a subset of business semantic transmission paths.

[0072] Step S135: Extract the sequence of interactive intent units in the page interactive intent chain with semantic association weights, separate the intent association elements and semantic weight values ​​in each interactive intent unit, and extract the core intent keywords of each interactive intent unit. The core intent keywords are obtained by filtering from the intent association elements.

[0073] The interaction intent unit sequence is a list of interaction intent units arranged chronologically within the page's interaction intent chain. For each interaction intent unit, intent-related elements and semantic weight values ​​are extracted. Core intent keywords are extracted from the intent-related elements, selecting the words or phrases that most accurately represent the core meaning of the interaction intent unit.

[0074] Step S136: For the core intent keywords of each interactive intent unit, perform semantic similarity analysis with the business function descriptions corresponding to the semantic nodes of each business semantic transmission path in the business semantic transmission path subset, and obtain the semantic similarity value between each interactive intent unit and each semantic node.

[0075] Step S1361: Extract the core intent keywords of each interactive intent unit, perform semantic segmentation on the core intent keywords to obtain multiple semantic segmentation units, and assign a semantic contribution value to each semantic segmentation unit based on the lexical semantic library. The semantic contribution value reflects the degree to which the segmentation unit represents the core intent.

[0076] Core intent keywords may be phrases composed of multiple words. Semantic segmentation is performed on these phrases, breaking them down into individual words or morphemes, i.e., semantic segmentation units. A lexical semantic database is a large database containing a vast amount of vocabulary and its semantic information, where a semantic contribution value is assigned to each word.

[0077] Step S1362: Extract the business function descriptions corresponding to each semantic node in the business semantic transmission path subset, perform semantic word segmentation on the business function descriptions to obtain multiple business semantic word segmentation units, and assign a business semantic contribution value to each business semantic word segmentation unit based on the lexical semantic library. The business semantic contribution value reflects the degree to which the word segmentation unit represents the business function.

[0078] The business function description is a text that describes the functions of business semantic nodes. It undergoes semantic word segmentation, breaking it down into multiple business semantic word segmentation units. Also based on a lexical semantic database, a business semantic contribution value is assigned to each business semantic word segmentation unit.

[0079] Step S1363: Construct a cross-intent-business semantic word segmentation mapping table. The cross-intent-business semantic word segmentation mapping table includes semantic word segmentation units, business semantic word segmentation units, and semantic association coefficients between the two. The semantic association coefficients are determined based on the association relationships of synonyms and near-synonyms in the lexical semantic database.

[0080] The cross-intent-business semantic word segmentation mapping table is used to establish the association between the semantic word segmentation units of the interactive intent unit and the business semantic word segmentation units of the business semantic node. By querying the lexical semantic database, synonyms and near-synonyms of the semantic word segmentation units are found, and these synonyms and near-synonyms may appear in the business semantic word segmentation units.

[0081] Step S1364: For each semantic segmentation unit of each interactive intent unit, find the corresponding business semantic segmentation unit in the cross-intent-business semantic segmentation mapping table, extract the corresponding semantic association coefficient, and calculate the association score between the semantic segmentation unit and the corresponding business semantic segmentation unit; calculate the sum of the association scores between all semantic segmentation units of each interactive intent unit and all business semantic segmentation units of any semantic node, and use it as the initial similarity sum between the interactive intent unit and the semantic node.

[0082] For each semantic segmentation unit of the interactive intent unit, a search is performed in the cross-intent-business semantic segmentation mapping table to find all corresponding business semantic segmentation units. For each pair of matching segmentation units, a semantic association coefficient is extracted, and then the semantic contribution value of the semantic segmentation unit is multiplied by the semantic association coefficient to obtain the association score between the semantic segmentation unit and the corresponding business semantic segmentation unit.

[0083] Step S1365: Calculate the minimum value between the total number of semantic segmentation units of the interactive intent unit and the total number of business semantic segmentation units of the semantic node, and use it as the similarity calculation benchmark; divide the initial sum of similarities by the similarity calculation benchmark to obtain the semantic similarity value between the interactive intent unit and the semantic node; if there is no corresponding mapping relationship between the semantic segmentation units of the interactive intent unit and the business semantic segmentation units of the semantic node, the semantic similarity value is assigned to the preset minimum similarity benchmark value.

[0084] The similarity calculation baseline is the smaller of the total number of semantic segmentation units in the interactive intent unit and the total number of business semantic segmentation units in the semantic node. The initial sum of similarities is divided by this baseline to obtain the semantic similarity value. If there is no corresponding mapping relationship between all semantic segmentation units of the interactive intent unit and the business semantic segmentation units of a certain semantic node, the semantic similarity value is assigned to the preset minimum similarity baseline value.

[0085] Step S137: Combine the intention semantic weight value and semantic similarity value of the interactive intention unit to calculate the intention-semantic matching score between each interactive intention unit and each semantic node. The calculation method is to multiply the intention semantic weight value and the semantic similarity value.

[0086] The semantic weight value reflects the importance of the interaction intent unit itself, while the semantic similarity value reflects the degree of matching between the interaction intent unit and the business semantic node. The product of the two is the intent-semantic matching score.

[0087] Step S138: According to the time order of the interactive intent units in the page interactive intent chain with semantic association weights, assign a matching sequence score to each business semantic transmission path. The calculation method is to sum the intent-semantic matching scores of each interactive intent unit and the corresponding semantic node, and then subtract the total path semantic transmission attenuation.

[0088] For each business semantic transmission path in the subset of business semantic transmission paths, its matching sequence score with the page interaction intent chain needs to be calculated. First, the sequence of interaction intent units in the page interaction intent chain is matched sequentially with the sequence of semantic nodes in the business semantic transmission path. Then, the intent-semantic matching scores of each interaction intent unit and its corresponding semantic node are added together to obtain the total matching score. Finally, the total path semantic transmission attenuation of the business semantic transmission path is subtracted to obtain the matching sequence score.

[0089] Step S139: Select the business semantic transmission path with the highest matching sequence score as the target business semantic transmission path corresponding to the page interaction intent chain with semantic association weight.

[0090] The matching sequence scores of all paths in the subset of business semantic transmission paths are compared, and the path with the highest score is identified as the target business semantic transmission path.

[0091] Step S1310: Calculate the ratio of the matching sequence score of the target business semantic transmission path to the total path length of the target business semantic transmission path to obtain the transmission matching degree value between intent semantics and business semantics.

[0092] The conduction matching degree is used to standardize the matching sequence score, taking into account the influence of the total path length. The conduction matching degree is obtained by dividing the matching sequence score by the total path length.

[0093] Step S1311: Record the correspondence between the page interaction intent chain with semantic association weight and the target business semantic transmission path, the correspondence between each interaction intent unit and the matching semantic node, the intent-semantic matching score and the transmission matching degree value, to form the business semantic transmission path mapping result and the transmission matching degree value.

[0094] The business semantic transmission path mapping result is a detailed record of the mapping process and results, including the correspondence between the identifiers of the page interaction intent chain and the identifiers of the target business semantic transmission path; a list of correspondences between the ID of each interaction intent unit and the ID of the matched semantic node; the intent-semantic matching score for each correspondence; and the final transmission matching degree value.

[0095] Step S140: Based on the transmission matching degree value, compare the sequence of interactive intent units in the page interaction intent chain with semantic association weight with the sequence of semantic nodes in the business semantic transmission path mapping result, locate the deviation position between the interactive intent and the business semantics, trace the cause of the interaction behavior and the cause of the semantic transmission of the deviation, and form the source information of the deviation between the interactive intent and the business semantics.

[0096] Step S141: Set the transmission matching degree threshold range. If the transmission matching degree value is within the transmission matching degree threshold range, it is determined that the page interaction intent chain with semantic association weight matches the business semantic transmission path normally and there is no deviation position. If the transmission matching degree value is lower than the lower limit of the transmission matching degree threshold range, it is determined that there is a deviation position and the deviation positioning process is started.

[0097] The transmission matching degree threshold range is set based on business needs and historical data. If the transmission matching degree value is within this range, it indicates that the overall matching is good and there are no significant deviations. If the value is below the lower limit of the range, it indicates that the overall matching degree is insufficient and there are deviations that need to be located and analyzed. In this case, the deviation location process is initiated.

[0098] Step S142: Extract the sequence of interactive intent units in the page interactive intent chain with semantic association weights, assign consecutive sequence numbers to each interactive intent unit in chronological order, and record the semantic weight value and intent association elements of each interactive intent unit.

[0099] The interactive intent units are numbered sequentially from front to back according to time. For each numbered interactive intent unit, its semantic weight value and associated intent elements are recorded.

[0100] Step S143: Extract the semantic node sequence from the business semantic transmission path mapping result, assign consecutive sequence numbers to each semantic node according to the semantic transmission order, and record the business function description, associated semantic transmission attenuation coefficient, and business scenario label of each semantic node.

[0101] The semantic node sequence is numbered from front to back according to the semantic transmission order in the target business semantic transmission path. Each numbered semantic node records its business function description, the semantic transmission attenuation coefficient between that semantic node and the previous semantic node, and the business scenario label of the semantic node.

[0102] Step S144: Establish the number correspondence between the interactive intent unit sequence and the semantic node sequence to form a sequence correspondence matrix. Each element in the sequence correspondence matrix contains the interactive intent unit number, the semantic node number, and the intent-semantic matching score of the two.

[0103] The sequence correspondence matrix is ​​a two-dimensional table structure. The rows represent the sequence numbers of interactive intent units, the columns represent the sequence numbers of semantic nodes, and the matrix elements record the correspondence between the two and the intent-semantic matching score.

[0104] Step S145: Traverse each element in the sequence correspondence matrix. If the intent-semantic matching score of any element is lower than the preset matching score threshold, mark the interaction intent unit number and semantic node number corresponding to that element as potential deviation positions.

[0105] A preset matching score threshold is the standard for determining whether a single interactive intent unit matches a semantic node. Each valid element in the sequence correspondence matrix is ​​traversed, and its intent-semantic matching score is checked against the threshold. If the score is below the threshold, the interactive intent unit number and semantic node number are marked as potential deviation positions.

[0106] Step S146: For each potential deviation location, extract the operation record, page status data and user business identity information of the corresponding interaction intent unit, and analyze whether the operation type corresponding to the interaction intent unit conforms to the business permission range in the user business identity information. If the operation type exceeds the business permission range, mark it as an interaction behavior permission trigger.

[0107] For the interaction intent unit corresponding to the potential deviation location, trace back to the original operation record, page state data, and user business identity information corresponding to that interaction intent unit in the page interaction behavior data set. Compare the operation type corresponding to the interaction intent unit with the business permission range in the user business identity information. If the operation type is not within the permission range, mark it as an interaction behavior permission trigger.

[0108] Step S147: Analyze the page loading progress information in the page state data corresponding to the interactive intent unit. If the page loading progress does not reach the minimum progress requirement required for the operation, mark it as an environmental trigger for the interactive behavior.

[0109] The page loading progress information in the page status data indicates the loading status of page resources when an operation occurs. If the page loading progress information shows that the current loading progress is lower than the minimum progress required for the operation type to execute normally, it is marked as an environmental trigger for the interaction behavior.

[0110] Step S148: Extract the semantic transmission attenuation coefficient of the semantic node corresponding to the potential deviation position. If the semantic transmission attenuation coefficient of the semantic node and the preceding semantic node is higher than the preset attenuation coefficient threshold, it is marked as a semantic transmission attenuation cause.

[0111] The semantic transmission attenuation coefficient of a semantic node refers to the attenuation coefficient between that node and its preceding semantic nodes. If the semantic transmission attenuation coefficient is higher than a preset attenuation coefficient threshold, it is marked as a semantic transmission attenuation cause.

[0112] Step S149: Analyze the semantic coherence between the business function description of the semantic node and the business function description of the preceding semantic node. If the semantic coherence is lower than the preset coherence threshold, mark it as a semantic transmission coherence inducement.

[0113] Semantic coherence is evaluated by comparing the logical consistency and relevance of the business function descriptions of the current semantic node with those of the preceding semantic nodes. If the calculated semantic coherence value is lower than a preset coherence threshold, it is marked as a semantic transmission coherence inducement.

[0114] Step S1410: Summarize the identifiers, interaction behavior trigger types, and semantic transmission trigger types of all potential deviation locations. The interaction behavior trigger types include permission triggers and environmental triggers, and the semantic transmission trigger types include attenuation triggers and continuity triggers. Verify the authenticity of the trigger for each potential deviation location. Confirm the correlation between the trigger and the deviation location by querying the operation logs and semantic transmission records of the information business system.

[0115] Step S14101: Retrieve the operation log of the information business system. The operation log of the information business system includes the operation time, operation initiator user identifier, operation permission verification result, page environment parameters at the time of operation execution, and operation execution result for each operation record; retrieve the semantic transmission record of the information business system. The semantic transmission record of the information business system includes the semantic transmission time of each semantic node, the identifier of the preceding semantic node, the calculation process of the semantic transmission attenuation coefficient, the semantic coherence evaluation result, and the semantic transmission status.

[0116] Operation logs of information-based business systems are typically stored in databases or log files and can be retrieved through log query interfaces or tools. Semantic transmission records are also stored in the information-based business systems, documenting the transmission between business semantic nodes.

[0117] Step S14102: For each potential deviation location corresponding to the interactive behavior permission trigger, search the operation permission verification result of the operation record corresponding to the deviation location in the operation log of the information business system. If the operation permission verification result is insufficient permission, the interactive behavior permission trigger is confirmed to be real; if the operation permission verification result is permission passed, the trigger type is excluded.

[0118] Based on the interaction intent unit number at the potential deviation location, locate the corresponding operation record and query the operation permission verification result for that operation record in the operation log. If the operation permission verification result indicates insufficient permissions, confirm that the interaction behavior permission trigger is genuine; if the permissions are granted, exclude that trigger type.

[0119] Step S14103: For the interactive behavior environment trigger, search the operation log of the information business system for the page environment parameters of the operation record corresponding to the deviation position. If the loading progress value in the page environment parameters is consistent with the loading progress information in the page status data, and the loading progress value is lower than the minimum progress requirement required for the operation, then the interactive behavior environment trigger is confirmed to be real; if the loading progress value is higher than the minimum progress requirement, then the trigger type is excluded.

[0120] Locate the page environment parameters of the operation record corresponding to the deviation position in the operation log, extract the loading progress value, and compare it with the loading progress information in the previously extracted page status data. If the two are consistent and lower than the minimum progress requirement for the operation, the environmental trigger for the interaction behavior is confirmed to be real; if it is higher than the minimum progress requirement, the environmental trigger is ruled out.

[0121] Step S14104: For semantic transmission attenuation causes, search the semantic transmission records of the information business system for the semantic node corresponding to the deviation position and the semantic transmission attenuation coefficient calculation process of the preceding semantic node. If the parameters used in the semantic transmission attenuation coefficient calculation process are consistent with the preset parameters in the business semantic model, and the calculated attenuation coefficient value is consistent with the marked attenuation coefficient value and is higher than the attenuation coefficient threshold, then the semantic transmission attenuation cause is confirmed to be real; if the calculated parameters are abnormal or the value is not higher than the attenuation coefficient threshold, then this cause type is excluded.

[0122] Locate the record of the semantic node corresponding to the deviation position in the semantic transmission record, and examine the calculation process of its semantic transmission attenuation coefficient with the preceding semantic node. If the parameters used in the calculation process are consistent with the preset parameters in the business semantic model, and the calculation result is the same as the marked attenuation coefficient value and higher than the threshold, the semantic transmission attenuation cause is confirmed to be real; otherwise, the attenuation cause is excluded.

[0123] Step S14105: For semantic transmission coherence causes, search the semantic transmission records of the information business system for the semantic coherence assessment results of the semantic node corresponding to the deviation position and the preceding semantic node. If the coherence value in the semantic coherence assessment result is consistent with the marked coherence value and is lower than the coherence threshold, then the semantic transmission coherence cause is confirmed to be real; if it is not lower than the coherence threshold, then the cause type is excluded.

[0124] The semantic communication record contains semantic coherence assessment results. The coherence values ​​in this record are compared with the coherence values ​​marked during the previous analysis. If the two values ​​are consistent and lower than the preset coherence threshold, the semantic communication coherence trigger is confirmed to be genuine; if the values ​​are higher than the coherence threshold, the coherence trigger is excluded.

[0125] Step S14106: Count the number of real causes for each potential deviation location. If there is at least one real cause, the potential deviation location is determined to be an actual deviation location. If all causes are eliminated, the potential deviation location is determined to be a mislabeled location and removed from the deviation location list. Record the cause verification result, operation log query segment, and semantic transmission record query segment for each actual deviation location.

[0126] For each potential deviation location, count the number of verified true causes. If at least one true cause exists, it is determined to be an actual deviation location. If all marked causes are excluded, it is removed from the deviation location list. For actual deviation locations, record their cause verification results, operation log query fragments, and semantic transmission record query fragments.

[0127] Step S1411: Integrate the verified deviation location identifier, cause type, specific information of the cause, and associated operation records and semantic node information to form the source information of the deviation between interaction intent and business semantics.

[0128] Interaction intent and business semantic deviation tracing information is a comprehensive record of the actual deviation location and its cause, including the identifier of each actual deviation location, the type of cause that passed verification, the specific information of the cause, the details of the associated operation record, and the associated semantic node information.

[0129] Step S150: Based on the interaction intent and business semantic deviation tracing information, generate a page data optimization and adjustment instruction that includes deviation correction parameters, page element adjustment rules, and business semantic mapping optimization scheme.

[0130] Step S151: Analyze the source information of the deviation between the interaction intent and the business semantics, and extract the identifier of each actual deviation location, the type of interaction behavior trigger, the type of semantic transmission trigger, the associated operation record and semantic node information.

[0131] In this embodiment, the source information of the deviation between interaction intent and business semantics is stored in a structured data format, containing records of multiple actual deviation locations. The parsing process is completed through a dedicated source information parsing module. This module first reads the metadata of the source information to determine the data structure and field definitions. Then, for each record of actual deviation location, its unique identifier is extracted. This identifier consists of a business scenario prefix, deviation location sequence number, etc. The interaction behavior trigger type is extracted from the "Trigger Type" field of the record, and possible values ​​include "Permission Trigger," "Environment Trigger," etc. The semantic transmission trigger type is also extracted from this field, and possible values ​​include "Attenuation Trigger," "Continuous Trigger," etc. The associated operation record is linked to the specific operation record in the original page interaction behavior data set through the "Operation Record ID" field, containing key information such as operation type and operation object identifier. The associated semantic node information is obtained through the "Semantic Node ID" field, containing content such as node name and business function description.

[0132] Step S152: For the actual deviation position corresponding to the interaction behavior permission trigger, based on the business role identifier in the user's business identity information, generate permission adaptation correction parameters. The permission adaptation correction parameters include a list of operation types that the business role identifier can allow, the permission verification threshold corresponding to the operation type, and permission application process configuration parameters.

[0133] For actual deviations in user interaction permission settings, the cause stems from insufficient permissions for the user's business role. First, the business role identifier is extracted from the user's business identity information. Then, a list of permitted operation types for that role is generated, adding specific operation types outside the original permission scope to the list while retaining existing operation types. The permission verification threshold for each operation type is set according to the operation's risk level. Basic operations have a lower permission verification threshold, allowing for some error tolerance; high-risk operations have a higher threshold, requiring strict verification of user permission credentials. The permission application process configuration parameters define the application path when a user needs to perform an operation beyond their current permissions, including the approval role sequence, application form fields, and approval timeout handling strategies.

[0134] Step S153: Based on the actual deviation position corresponding to the environmental trigger of the interactive behavior, and the loading progress requirement in the page status data, generate page environment correction parameters. The page environment correction parameters include the minimum loading progress value required for the operation, page loading acceleration strategy parameters, and loading status prompt configuration parameters.

[0135] For actual deviation locations where interactive behavior is triggered, the cause stems from insufficient page loading progress. Page status data shows that when the operation corresponding to this deviation location occurred, the loading progress of critical page resources had not met the operation requirements. Based on this, the minimum loading progress value required for the operation is set as the proportion that ensures all critical resources are loaded. Page loading acceleration strategy parameters include resource preloading configuration and loading priority adjustment rules, such as advancing the timing of critical interface calls to the page initialization stage and setting them as high-priority requests; at the same time, enabling browser caching mechanisms to cache infrequently changing data locally. Loading status prompt configuration parameters specify the user prompt methods for different loading stages, such as displaying a dynamic progress bar and prompt text next to the operation button when the loading progress is below the minimum requirement; and displaying an interactive retry prompt box when loading times out.

[0136] Step S154: For the actual deviation position corresponding to the semantic transmission attenuation cause, generate attenuation correction parameters based on the semantic transmission attenuation coefficient. The attenuation correction parameters include a new semantic transmission attenuation coefficient value, dynamic adjustment rules for the attenuation coefficient, and a monitoring threshold for the attenuation coefficient.

[0137] For actual deviation locations where semantic transmission attenuation is a contributing factor, the cause is an excessively high semantic transmission attenuation coefficient between semantic nodes, leading to weakened semantic transmission strength. The new semantic transmission attenuation coefficient value has been adjusted to within the standard range. The dynamic adjustment rule for the attenuation coefficient stipulates that when the semantic transmission success rate exceeds a certain percentage for multiple consecutive statistical periods, the attenuation coefficient is automatically reduced; if the success rate falls below a certain percentage, the attenuation coefficient is increased. Attenuation coefficient monitoring thresholds are set as a lower and upper limit. When the adjusted attenuation coefficient exceeds this range, the system automatically sends an alarm notification to the business administrator, including the current attenuation coefficient value, historical trends, and suggested handling measures.

[0138] Step S155: Based on the semantic coherence requirements, generate coherence correction parameters for the actual deviation position corresponding to the semantic transmission coherence cause. The coherence correction parameters include supplementary content of the semantic node business function description, semantic association words of the preceding and following semantic nodes, and semantic coherence evaluation cycle parameters.

[0139] For actual deviations where semantic coherence is compromised, the cause manifests as a logical gap in the business function descriptions between semantic nodes. The coherence correction parameter first adds supplementary content to the semantic nodes to ensure logical connection between preceding and subsequent nodes. Semantic association words between preceding and subsequent semantic nodes are added to the description of the transmission relationship between nodes. The semantic coherence assessment cycle parameter is set to a fixed period; the system will periodically and automatically assess the coherence between all semantic nodes and generate an assessment report. The report includes a coherence score, node pairs requiring optimization, and optimization suggestions.

[0140] Step S156: Summarize all deviation correction parameters, which include permission adaptation correction parameters, page environment correction parameters, attenuation correction parameters, and continuity correction parameters. Sort them according to the business impact range of the deviation location to form a set of deviation correction parameters.

[0141] The various deviation correction parameters generated in the above steps are summarized. Each deviation correction parameter includes metadata such as parameter ID, corresponding deviation location identifier, and parameter effective time. After summarization, they are sorted according to the business impact range of the deviation location. The business impact range is determined through calculations in subsequent steps, and correction parameters corresponding to deviation locations with larger business impact ranges are prioritized. The sorted parameters form a deviation correction parameter set, which is used to guide subsequent page element adjustments and semantic mapping optimization.

[0142] For example, step S1561: Extract the business semantic node associated with the actual deviation position corresponding to each deviation correction parameter, and query the business function association list of the business semantic node in the information business system. The business function association list includes the downstream business function modules associated with the business semantic node, the size of the associated user group, and the frequency of business operations.

[0143] For each deviation correction parameter, the corresponding business semantic node associated with the actual deviation position is extracted. A list of business function associations for that node is obtained by querying the business function association database of the information system. Downstream business function modules include multiple functional modules that directly depend on the output of this semantic node. The associated user group size covers multiple roles using these downstream modules. Regarding business operation frequency, it includes the average number of operations triggered on this node per day.

[0144] Step S1562: Count the number of downstream business function modules associated with each deviation correction parameter, as the number of business impact modules.

[0145] For the business function association list of a business semantic node, the number of downstream business function modules is counted as the number of modules affected by the business. Different semantic nodes may be associated with different numbers of downstream modules, thus having different numbers of modules affected by the business.

[0146] Step S1563: Calculate the size of the associated user group related to the deviation correction parameter, as the number of users affected by the business.

[0147] The size of the user group associated with the statistical business semantic node is the sum of all users in the downstream modules associated with that node, which is used as the number of users affected by the business.

[0148] Step S1564: Count the frequency of business operations associated with the deviation correction parameter, and use it as the number of business-affected operations.

[0149] Based on the operation log statistics of the information business system, the average number of operations triggered on business semantic nodes per day is obtained as the number of operations affecting the business.

[0150] Step S1565: Set the business impact weights. The weight coefficients for the number of modules affected by the business, the number of users affected by the business, and the number of operations affected by the business are determined based on the business priority configuration of the information business system.

[0151] The business priority configuration file of the information system specifies the priority of different business objectives. Accordingly, the weight coefficients for the number of modules affected by the business, the number of users affected by the business, and the number of operations affected by the business are set according to the business priority, and the sum of the three is 1 to ensure the normalization of weight allocation.

[0152] Step S1566: Calculate the business impact score for each deviation correction parameter. The calculation method is to multiply the number of business impact modules by the weight coefficient of the number of business impact modules, multiply the number of business impact users by the weight coefficient of the number of business impact users, and multiply the number of business impact operations by the weight coefficient of the number of business impact operations. Then, sum the results of the three multiplication operations.

[0153] For each deviation correction parameter, the number of business-affected modules is multiplied by its weight coefficient, the number of business-affected users is multiplied by its weight coefficient, and the number of business-affected operations is multiplied by its weight coefficient. Then, these three products are summed to obtain the business impact score.

[0154] Step S1567: Sort all deviation correction parameters in descending order of business impact score, analyze the dependencies between the sorted deviation correction parameters, and if the execution of the next deviation correction parameter requires the correction result of the previous deviation correction parameter as a basis, adjust the sorting position of the next deviation correction parameter so that it is after the previous deviation correction parameter.

[0155] The deviation correction parameters are initially sorted according to their business impact scores, from largest to smallest. Further analysis of the dependencies between parameters reveals that if the execution of a parameter depends on the correction result of another parameter, the sorting position of that parameter is adjusted so that it follows the dependent parameters.

[0156] Step S1568: Add the execution conditions and expected results after execution for each parameter to form a set of deviation correction parameters.

[0157] Add execution conditions to each deviation correction parameter, specifying under what circumstances the parameter will be executed; also add the expected effect after execution, specifying the goal to be achieved after the parameter is executed. Integrate the sorted parameters, their execution conditions, and expected effects to form the final deviation correction parameter set.

[0158] Step S157: Based on the deviation correction parameter set, generate page element adjustment rules, formulate operation permission association rules for the operation object identifier corresponding to the permission trigger, and clarify the operation type and permission verification process that can be triggered by the operation object identifier.

[0159] Based on the deviation correction parameter set, and targeting the operation object identifier corresponding to the permission trigger, the operation permission association rules in the page element adjustment rules stipulate that: when a user's business role is a specific role, clicking the operation object identifier can trigger a specific operation type; the permission verification process is as follows: first, check whether the user is in the authorized list; if not, automatically redirect to the permission application page, and the application must be approved by the specific role before the operation can be executed. The rules also clarify the timeout handling mechanism for permission verification.

[0160] Step S158: Based on the page element layout information corresponding to the environmental factors, formulate page loading linkage rules and clarify the correspondence between the page element loading order and the timing of operation execution.

[0161] Regarding the layout information of page elements corresponding to environmental factors, the page loading linkage rules stipulate that during page initialization, basic forms are loaded first, followed by key tables and interface data in parallel. After these are loaded, dependent tags and action buttons are loaded. As for when actions are available, action buttons only switch from a "disabled" state to an "available" state after key data has been loaded and specific conditions are met. The rules also include a fallback strategy for loading failures.

[0162] Step S159: For the semantic nodes corresponding to the semantic transmission triggers, formulate a business semantic mapping optimization plan, including the business function description update rules of semantic nodes, the dynamic adjustment mechanism of semantic transmission attenuation coefficient, and the regular evaluation and optimization process of semantic coherence.

[0163] For semantic nodes corresponding to semantic transmission triggers, the semantic node business function description update rules in the business semantic mapping optimization scheme stipulate that supplementary content should be added to the description of the semantic node to ensure logical connection between the preceding and following nodes. The dynamic adjustment mechanism for the semantic transmission attenuation coefficient automatically adjusts the attenuation coefficient based on the semantic transmission success rate. The periodic evaluation and optimization process for semantic coherence specifies the evaluation cycle and optimization measures to ensure coherence between semantic nodes.

[0164] Step S1510: Integrate the deviation correction parameter set, page element adjustment rules, and business semantic mapping optimization scheme. The page element adjustment rules include operation permission association rules and loading linkage rules, which are converted into an instruction format that can be recognized by the page configuration module of the information business system.

[0165] The deviation correction parameter set, page element adjustment rules, and business semantic mapping optimization scheme are integrated to ensure logical consistency across all parts. Page element adjustment rules include operation permission association rules and loading linkage rules. The integrated content is then converted into a command format recognizable by the information business system's page configuration module, such as a specific XML or JSON format, including command headers, parameter lists, and rule details.

[0166] Step S1511: Add instruction execution identifier, instruction generation time, target page identifier, instruction execution priority, and verification indicators after instruction execution. The verification indicators include operation permission verification pass rate, page loading compliance rate, and semantic transmission matching degree. Generate page data optimization and adjustment instructions.

[0167] Add an execution identifier to the integrated content to uniquely identify the optimization and adjustment instruction. Add the instruction generation time, recording the timestamp of instruction creation. The target page identifier clearly identifies the business page to which the instruction applies. The instruction execution priority is set based on the scope and urgency of the business impact. Post-instruction verification metrics include operation permission verification pass rate, page load compliance rate, and semantic communication matching degree, used to evaluate the effectiveness of instruction execution. Add this information to the instruction to generate a complete page data optimization and adjustment instruction.

[0168] Based on the same inventive concept, please refer to Figure 2 The diagram shows a schematic block diagram of an information-based page data analysis system 100 provided in this application embodiment for executing the above-described information-based page data analysis method. The information-based page data analysis system 100 may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0169] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the page data analysis system 100 based on information-based services and are configured separately. However, it should be understood that the machine-readable storage medium 120 may also be independent of the page data analysis system 100 based on information-based services and may be accessed by the processor 130 via a bus interface. Alternatively, the machine-readable storage medium 120 may be integrated into the processor 130 and may communicate and interact with external systems through the communication unit 110.

[0170] The processor 130 is the control center of the information-based business page data analysis system 100. It connects various parts of the system via various interfaces and lines, and executes software programs and / or modules stored in the machine-readable storage medium 120, as well as calling data stored in the machine-readable storage medium 120. This allows for the execution of various functions and data processing by the information-based business page data analysis system 100, thereby providing overall monitoring of the system. Optionally, the processor 130 may include one or more processing cores; for example, it may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may not be integrated into the processor. The machine-readable storage medium 120 stores machine-executable instructions for implementing the scheme of this application, and the processor 130 executes the machine-executable instructions stored in the machine-readable storage medium 120 to implement the information-based business page data analysis method provided in the aforementioned method embodiments.

[0171] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for analyzing page data based on information-based business processes, characterized in that, The method includes: Collect a set of page interaction behavior data generated during the operation of the information business system. The set of page interaction behavior data includes the operation records performed by the user on the business page elements, the business function call information triggered by the operation, the page status data when the operation occurs, and the user business identity information associated with the operation. Based on the operation records, page status data and user business identity information in the page interaction behavior data set, a page interaction intent chain with semantic association weight is generated. The page interaction intent chain with semantic association weight consists of multiple interaction intent units arranged in the order of operation time. Each interaction intent unit includes an operation intent description, intent association elements and intent semantic weight. The business semantic model of the information business system is retrieved, and the page interaction intent chain with semantic association weight is mapped to the business semantic transmission path in the business semantic model. The transmission matching degree between intent semantic and business semantic is calculated, and the business semantic transmission path mapping result and transmission matching degree value are generated. The business semantic model includes business semantic nodes, semantic transmission relationship between nodes and semantic transmission attenuation coefficient. Based on the transmission matching degree value, the sequence of interactive intent units in the page interaction intent chain with semantic association weight is compared with the sequence of semantic nodes in the business semantic transmission path mapping result to locate the deviation position between the interactive intent and the business semantics, trace the cause of the interaction behavior and the cause of semantic transmission caused by the deviation, and form the source information of the deviation between the interactive intent and the business semantics. Based on the interaction intent and business semantic deviation tracing information, a page data optimization and adjustment instruction is generated, which includes deviation correction parameters, page element adjustment rules, and business semantic mapping optimization scheme. The page data optimization and adjustment instruction is sent to the page configuration module of the information business system to trigger the synchronous adjustment operation of the interaction logic and semantic mapping relationship of the business page.

2. The page data analysis method based on information-based business as described in claim 1, characterized in that, The step of generating a page interaction intent chain with semantic association weights based on the operation records, page state data, and user business identity information in the page interaction behavior data set includes: Extract operation records from the page interaction behavior data set, and separate the operation type, operation object identifier, operation trigger parameters and operation execution duration corresponding to each operation record. The operation type includes click operation, input operation, selection operation, jump operation and refresh operation. Extract page state data from the page interaction behavior data set, and separate the page element layout information, page loading progress information, activation status information of related business functions, and page data cache status information when each operation record occurs; Extract user business identity information from the page interaction behavior data set, and separate the business role identifier, business permission scope and historical business operation preference record for each user; Operation records executed within a continuous time range under the same user business identity information are classified into operation sequence groups. Each operation sequence group contains at least three operation records and corresponding page status data. For each operation record in each operation sequence group, an operation behavior feature set is extracted by combining the operation type, operation object identifier, operation triggering parameters, operation execution duration, page status data, and business role identifier and business permission scope in the user's business identity information. The operation behavior feature set includes operation attribute features, page environment features, and user identity association features. The set of operational behavior features is associated and matched with a preset intent feature library, which includes multiple combinations of operational behavior features, corresponding interactive intent descriptors, intent association elements, and intent semantic weight calculation rules. Based on the matching results, each operation record is assigned a corresponding interaction intent descriptor and intent association element. Based on the intent semantic weight calculation rules, combined with the operation execution time and the user's historical business operation preference records, the intent semantic weight value corresponding to each operation record is calculated to form the interaction intent unit corresponding to each operation record. According to the time order of the operation records in the operation sequence group, the interactive intent units corresponding to each operation sequence group are connected sequentially, and an intent semantic transmission relationship identifier is added between adjacent interactive intent units. The intent semantic transmission relationship identifier is determined based on the overlap of intent association elements of two interactive intent units. Based on the semantic weight values ​​of adjacent interactive intent units and the semantic transmission relationship identifier, the semantic association weight values ​​between adjacent interactive intent units are calculated. The semantic association weight values ​​are positively correlated with the overlap of intent association elements and the semantic weight values ​​of intent. Add an operation sequence group identifier, chain generation time, total number of interactive intent units, and semantic association weight distribution table to the chain corresponding to each operation sequence group to form a page interactive intent chain with semantic association weight.

3. The page data analysis method based on information-based business as described in claim 2, characterized in that, The intent semantic weight calculation rule, combined with the operation execution duration and the user's historical business operation preference records, calculates the intent semantic weight value corresponding to each operation record, including: Read the preset intent semantic weight calculation rules, which include operation attribute weight coefficients, page environment weight coefficients, user identity weight coefficients, and the calculation priority of each coefficient; For each operation record, the operation type and operation execution duration are extracted from the operation attribute features. Based on the preset intent semantic weight calculation rules, the operation type is assigned a corresponding basic weight value. According to the relationship between the operation execution duration and the preset duration range, the duration correction weight value is calculated through the preset duration-weight mapping function. The duration correction weight value is a dimensionless value. Extract page loading progress information and page data cache status information from page environment features. Based on the relationship between page loading progress and preset completion ratio and page data cache status, calculate the basic weight value of page environment. According to the degree of page loading progress not reaching the preset completion ratio or cache status abnormality, calculate the environment abnormality correction weight value through the preset environment abnormality-weight mapping function. The environment abnormality correction weight value is a dimensionless value. Extract the business role identifier and business permission scope from the user identity association features, query the historical operation frequency under the business role identifier that is consistent with the current operation type in the user's historical business operation preference record, calculate the user identity basic weight value based on the relationship between the historical operation frequency and the preset frequency range, and calculate the frequency correction weight value through the preset frequency-weight mapping function according to the degree of deviation of the historical operation frequency from the preset frequency range. The frequency correction weight value is a dimensionless value. According to the calculation priority in the preset intent semantic weight calculation rules, the operation attribute weight, page environment weight, and user identity weight are weighted and summed. The operation attribute weight is the algebraic sum of the basic operation attribute weight value and the duration correction weight value. The page environment weight is the algebraic sum of the basic page environment weight value and the environment anomaly correction weight value. The user identity weight is the algebraic sum of the basic user identity weight value and the frequency correction weight value. All basic weight values ​​and correction weight values ​​are dimensionless values. The weighted summation result is calculated as a ratio to the preset weight benchmark value to obtain the intent semantic weight value corresponding to each operation record. The range of the intent semantic weight value is within the preset weight value range. If the calculated intent semantic weight value exceeds the preset weight value range, it is corrected based on the boundary value of the preset weight value range, and the corrected value is used as the final intent semantic weight value.

4. The page data analysis method based on information-based business as described in claim 1, characterized in that, The process of retrieving the business semantic model of the information business system, mapping the page interaction intent chain with semantic association weights to the business semantic transmission path in the business semantic model, calculating the transmission matching degree between intent semantics and business semantics, and generating the business semantic transmission path mapping result and transmission matching degree value includes: Through the model interface of the information business system, a pre-built business semantic model is retrieved. The pre-built business semantic model includes multiple business semantic nodes, semantic transmission relationships between nodes, business function descriptions corresponding to each semantic node, business scenario tags, and semantic transmission attenuation coefficients between nodes. The pre-built business semantic model is parsed, and all business semantic nodes, semantic transmission relationships between nodes, semantic transmission attenuation coefficients, and business scenario labels are extracted. A business semantic node network is constructed, with business semantic nodes as vertices and semantic transmission relationships as edges. The attributes of the edges include semantic transmission attenuation coefficients and associated business scenario labels. In the business semantic node network, the business scenario labels are classified and the paths containing continuous semantic transmission relationships under each business scenario are identified. Each path consists of at least four business semantic nodes arranged in the semantic transmission order. The path attributes include the total path length, the total semantic transmission attenuation of the path, and the business scenario label corresponding to the path, forming a set of business semantic transmission paths classified by business scenario. For each page interaction intent chain with semantic association weight, extract the business scenario requirement tag corresponding to the user business identity information in the page interaction intent chain with semantic association weight, and based on the business scenario requirement tag, select a subset of business semantic transmission paths corresponding to the same business scenario tag from the set of business semantic transmission paths classified by business scenario. Extract the sequence of interactive intent units in the page interactive intent chain with semantic association weights, separate the intent association elements and semantic weight values ​​in each interactive intent unit, and extract the core intent keywords of each interactive intent unit. The core intent keywords are obtained by filtering from the intent association elements. For each interactive intent unit, the core intent keywords are analyzed for semantic similarity with the business function descriptions corresponding to the semantic nodes of each business semantic transmission path in the business semantic transmission path subset, so as to obtain the semantic similarity value between each interactive intent unit and each semantic node. By combining the intention semantic weight value and semantic similarity value of the interactive intention unit, the intention-semantic matching score between each interactive intention unit and each semantic node is calculated. The calculation method is to multiply the intention semantic weight value and the semantic similarity value. According to the time order of the interactive intent units in the page interactive intent chain with semantic association weight, a matching sequence score is assigned to each business semantic transmission path. The calculation method is to sum the intent-semantic matching scores of each interactive intent unit and the corresponding semantic node, and then subtract the total semantic transmission attenuation of the path. The business semantic transmission path with the highest matching sequence score is selected as the target business semantic transmission path corresponding to the page interaction intent chain with semantic association weight. The ratio of the matching sequence score of the target business semantic transmission path to the total path length of the target business semantic transmission path is calculated to obtain the transmission matching degree value between intent semantics and business semantics. Record the correspondence between the page interaction intent chain with semantic association weight and the target business semantic transmission path, the correspondence between each interaction intent unit and the matching semantic node, the intent-semantic matching score and the transmission matching degree value, to form the business semantic transmission path mapping result and the transmission matching degree value.

5. The page data analysis method based on information-based business as described in claim 4, characterized in that, The semantic similarity analysis is performed on the core intent keywords of each interactive intent unit and the business function descriptions corresponding to the semantic nodes of each business semantic transmission path in the business semantic transmission path subset to obtain the semantic similarity value between each interactive intent unit and each semantic node, including: Extract the core intent keywords of each interactive intent unit, perform semantic segmentation on the core intent keywords to obtain multiple semantic segmentation units, and assign a semantic contribution value to each semantic segmentation unit based on the lexical semantic library. The semantic contribution value reflects the degree to which the segmentation unit represents the core intent. Extract the business function descriptions corresponding to each semantic node in the business semantic transmission path subset, perform semantic word segmentation on the business function descriptions to obtain multiple business semantic word segmentation units, and assign a business semantic contribution value to each business semantic word segmentation unit based on the lexical semantic library. The business semantic contribution value reflects the degree of representation of the business function by the word segmentation unit. Construct a cross-intent-business semantic word segmentation mapping table, which includes semantic word segmentation units, business semantic word segmentation units, and semantic association coefficients between the two. The semantic association coefficients are determined based on the association relationships of synonyms and near-synonyms in the lexical semantic database. For each semantic segmentation unit of each interactive intent unit, the corresponding business semantic segmentation unit is found in the cross-intent-business semantic segmentation mapping table, the corresponding semantic association coefficient is extracted, and the product of the semantic contribution value of the semantic segmentation unit and the semantic association coefficient is calculated to obtain the association score between the semantic segmentation unit and the corresponding business semantic segmentation unit. The sum of the association scores between all semantic segmentation units of each interactive intent unit and all business semantic segmentation units of any semantic node is calculated and used as the initial sum of similarity between the interactive intent unit and the semantic node. The minimum value between the total number of semantic segmentation units of the interactive intent unit and the total number of business semantic segmentation units of the semantic node is used as the benchmark for similarity calculation. Divide the initial sum of similarities by the similarity calculation benchmark to obtain the semantic similarity value between the interactive intent unit and the semantic node; If there is no corresponding mapping relationship between the semantic segmentation unit of the interactive intent unit and the business semantic segmentation unit of the semantic node, the semantic similarity value is assigned to the preset minimum similarity benchmark value.

6. The page data analysis method based on information-based business as described in claim 4, characterized in that, The formation of a set of business semantic transmission paths categorized by business scenario includes: Parse the business scenario tags in the business semantic model and divide them into multiple business scenario categories according to the type of the business scenario tags. Each business scenario category contains at least one business scenario tag. For each business scenario category, select business semantic nodes with business scenario tags under that business scenario category from the business semantic node network to form a subset of semantic nodes for that business scenario category. In the semantic node subset, identify all paths containing continuous semantic transmission relationships. The starting semantic node of the path must have the business trigger node attribute, and the ending semantic node of the path must have the business completion node attribute. Extract the number of semantic nodes in each path as the total path length, and calculate the sum of the semantic transmission attenuation coefficients between all adjacent semantic nodes in each path as the total semantic transmission attenuation of the path; Analyze the business function descriptions of semantic nodes in each path to verify the completeness of the business logic of the path. If the business function descriptions of the path from the start node to the end node match the core business process of the business scenario category, then mark it as a valid business semantic transmission path. If there is a logical gap in the business function description of a path, it will be marked as an invalid business semantic transmission path and removed from the path list; Add a path identifier, business scenario classification identifier, total path length, total semantic transmission attenuation of the path, sequence of semantic nodes contained in the path, and business function description of each semantic node to each valid business semantic transmission path; All valid business semantic transmission paths are categorized and classified according to business scenarios to form a set of business semantic transmission paths classified by business scenarios. Query the core business process list for this business scenario category, compare the correspondence between the core business process list and the effective business semantic transmission path, and if there are core business processes that do not correspond to effective business semantic transmission paths, supplement and construct the corresponding effective business semantic transmission paths to improve the set of business semantic transmission paths categorized by business scenario.

7. The page data analysis method based on information-based business as described in claim 1, characterized in that, Based on the transmission matching degree value, the sequence of interactive intent units in the page interaction intent chain with semantic association weights is compared with the sequence of semantic nodes in the business semantic transmission path mapping result to locate the deviation position between the interactive intent and the business semantics, trace the cause of the deviation in interactive behavior and the cause of semantic transmission, and form the source information of the deviation between interactive intent and business semantics, including: Set a transmission matching degree threshold range. If the transmission matching degree value is within the transmission matching degree threshold range, it is determined that the page interaction intent chain with semantic association weight matches the business semantic transmission path normally and there is no deviation position. If the transmission matching degree value is lower than the lower limit of the transmission matching degree threshold range, it is determined that there is a deviation position and the deviation positioning process is started. Extract the sequence of interactive intent units from the page interactive intent chain with semantic association weights, assign consecutive sequence numbers to each interactive intent unit in chronological order, and record the semantic weight value and intent association elements of each interactive intent unit. Extract the semantic node sequence from the business semantic transmission path mapping result, assign consecutive sequence numbers to each semantic node according to the semantic transmission order, and record the business function description, associated semantic transmission attenuation coefficient and business scenario label of each semantic node; Establish a number correspondence between the interactive intent unit sequence and the semantic node sequence to form a sequence correspondence matrix. Each element in the sequence correspondence matrix contains the interactive intent unit number, the semantic node number, and the intent-semantic matching score between the two. Traverse each element in the sequence-corresponding matrix. If the intent-semantic matching score of any element is lower than the preset matching score threshold, mark the interaction intent unit number and semantic node number corresponding to that element as potential deviation positions. For each potential deviation location, extract the operation record, page status data and user business identity information of the corresponding interaction intent unit, and analyze whether the operation type corresponding to the interaction intent unit conforms to the business permission range in the user business identity information. If the operation type exceeds the business permission range, mark it as an interaction behavior permission trigger. Analyze the page loading progress information in the page state data corresponding to the interactive intent unit. If the page loading progress does not reach the minimum progress requirement required for the operation, mark it as an environmental trigger for the interactive behavior. Extract the semantic transmission attenuation coefficient of the semantic node corresponding to the potential deviation position. If the semantic transmission attenuation coefficient of the semantic node and the preceding semantic node is higher than the preset attenuation coefficient threshold, it is marked as a semantic transmission attenuation cause. Analyze the semantic coherence between the business function description of the semantic node and the business function description of the preceding semantic node. If the semantic coherence is lower than the preset coherence threshold, it is marked as a semantic transmission coherence inducement. Summarize the identifiers, interaction behavior trigger types, and semantic transmission trigger types of all potential deviation locations. The interaction behavior trigger types include permission triggers and environmental triggers, while the semantic transmission trigger types include attenuation triggers and continuity triggers. Verify the authenticity of the trigger for each potential deviation location by querying the operation logs and semantic transmission records of the information business system to confirm the correlation between the trigger and the deviation location. The verified deviation location identifier, cause type, specific cause information, and associated operation records and semantic node information are integrated to form deviation tracing information of interaction intent and business semantics.

8. The page data analysis method based on information-based business as described in claim 7, characterized in that, The verification of the authenticity of the trigger for each potential deviation location involves querying the operation logs and semantic transmission records of the information business system to confirm the correlation between the trigger and the deviation location, including: Retrieve the operation logs of the information technology business system. The operation logs of the information technology business system include the operation time, operation initiator user ID, operation permission verification result, page environment parameters at the time of operation execution, and operation execution result for each operation record. Retrieve the semantic transmission records of the information business system. The semantic transmission records of the information business system include the semantic transmission time of each semantic node, the identifier of the preceding semantic node, the calculation process of the semantic transmission attenuation coefficient, the semantic coherence evaluation result, and the semantic transmission status. For each potential deviation location corresponding to the interactive behavior permission trigger, search the operation permission verification result of the operation record corresponding to the deviation location in the operation log of the information business system. If the operation permission verification result is insufficient permission, the interactive behavior permission trigger is confirmed to be real; if the operation permission verification result is permission passed, the trigger type is excluded. For interactive behavior environment triggers, search the operation log of the information business system for the page environment parameters of the operation record corresponding to the deviation position. If the loading progress value in the page environment parameters is consistent with the loading progress information in the page status data, and the loading progress value is lower than the minimum progress requirement required for the operation, then the interactive behavior environment trigger is confirmed to be real; if the loading progress value is higher than the minimum progress requirement, then this trigger type is excluded. To identify the cause of semantic transmission attenuation, the semantic transmission attenuation coefficient calculation process of the semantic node corresponding to the deviation position and the preceding semantic node is searched in the semantic transmission record of the information business system. If the parameters used in the semantic transmission attenuation coefficient calculation process are consistent with the preset parameters in the business semantic model, and the calculated attenuation coefficient value is consistent with the marked attenuation coefficient value and is higher than the attenuation coefficient threshold, then the semantic transmission attenuation cause is confirmed to be real; if the calculated parameters are abnormal or the value is not higher than the attenuation coefficient threshold, then this cause type is excluded. For semantic transmission coherence causes, the semantic coherence assessment results of the semantic node corresponding to the deviation position and the preceding semantic node are searched in the semantic transmission records of the information business system. If the coherence value in the semantic coherence assessment result is consistent with the marked coherence value and is lower than the coherence threshold, the semantic transmission coherence cause is confirmed to be real; if it is not lower than the coherence threshold, the cause type is excluded. Count the number of actual causes for each potential deviation location. If there is at least one actual cause, the potential deviation location is determined to be an actual deviation location. If all causes are eliminated, the potential deviation location is determined to be a mislabeled location and removed from the deviation location list. Record the verification results of the causes of each actual deviation location, operation log query segments, and semantic transmission record query segments.

9. The page data analysis method based on information-based business as described in claim 1, characterized in that, The step of generating page data optimization and adjustment instructions based on the deviation tracing information between the interaction intent and business semantics, including deviation correction parameters, page element adjustment rules, and business semantic mapping optimization schemes, includes: The source information of the deviation between the interaction intent and the business semantics is analyzed, and the identifier, interaction behavior trigger type, semantic transmission trigger type, associated operation record and semantic node information of each actual deviation location are extracted; For the actual deviation location corresponding to the cause of the interactive behavior permission, based on the business role identifier in the user's business identity information, a permission adaptation correction parameter is generated. The permission adaptation correction parameter includes a list of operation types that the business role identifier can allow, the permission verification threshold corresponding to the operation type, and the permission application process configuration parameters. Based on the actual deviation location corresponding to the environmental factors of interactive behavior, and the loading progress requirements in the page status data, page environment correction parameters are generated. The page environment correction parameters include the minimum loading progress value required for the operation, page loading acceleration strategy parameters, and loading status prompt configuration parameters. For the actual deviation location corresponding to the semantic transmission attenuation cause, an attenuation correction parameter is generated based on the semantic transmission attenuation coefficient. The attenuation correction parameter includes a new semantic transmission attenuation coefficient value, a dynamic adjustment rule for the attenuation coefficient, and a monitoring threshold for the attenuation coefficient. Based on the semantic coherence requirement, a coherence correction parameter is generated for the actual deviation position corresponding to the semantic transmission coherence cause. The coherence correction parameter includes supplementary content of the semantic node business function description, semantic association words of the preceding and following semantic nodes, and semantic coherence evaluation cycle parameters. All deviation correction parameters are summarized, including permission adaptation correction parameters, page environment correction parameters, attenuation correction parameters, and continuity correction parameters. They are sorted by the business impact range of the deviation location to form a set of deviation correction parameters. Based on the deviation correction parameter set, page element adjustment rules are generated. For the operation object identifier corresponding to the permission trigger, operation permission association rules are formulated to clarify the operation type and permission verification process that can be triggered by the operation object identifier. Based on the page element layout information corresponding to environmental factors, formulate page loading linkage rules to clarify the correspondence between the page element loading order and the timing of operation execution; For semantic nodes corresponding to semantic transmission triggers, formulate a business semantic mapping optimization scheme, including business function description update rules for semantic nodes, dynamic adjustment mechanism for semantic transmission attenuation coefficient, and regular evaluation and optimization process for semantic coherence; The deviation correction parameter set, page element adjustment rules, and business semantic mapping optimization scheme are integrated. The page element adjustment rules include operation permission association rules and loading linkage rules, which are converted into an instruction format that can be recognized by the page configuration module of the information business system. Add command execution identifier, command generation time, target page identifier, command execution priority, and verification metrics after command execution. The verification metrics include operation permission verification pass rate, page loading compliance rate, and semantic transmission matching degree, and generate page data optimization and adjustment commands.

10. A page data analysis system based on information-based business processes, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the page data analysis method based on information-based services as described in any one of claims 1 to 8 by executing the machine-executable instructions.

Images