Data processing and verification system of front-end universal component library

Abstract

The invention relates to the technical field of computer software and Web front-end development, in particular to a data processing and verification system of a front-end universal component library, which comprises a metadata analysis module used for receiving configuration metadata, mapping the configuration metadata into a data dependency topological graph, analyzing a logic expression by using a hierarchical symbol table and processing dependency conflicts; the event-driven propagation module is used for monitoring a node change signal, calculating a propagation path and only generating an update trigger signal for a downstream node; the dynamic strategy execution module is used for calling a strategy operator to carry out calculation verification in response to the trigger signal, and generating a push priority state evaluation result; the state feedback rendering module is used for comparing new and old states by using a difference comparison algorithm, and triggering a redrawing instruction only when a substantial change is detected; according to the invention, accurate updating independent of the view layer is realized, and the maintainability of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of computer software and Web front-end development technology, specifically to a data processing and verification system for a general front-end component library. Background Technology

[0002] In existing instant messaging (IM) systems, users are prone to missing important messages in group chats or high-load scenarios. General push strategies cannot distinguish the individual importance of messages, resulting in both information overload and the omission of key information.

[0003] Traditional real-time messaging front-end component data processing design currently relies mainly on the following methods: directly embedding data processing logic code within view components, relying on the framework's native two-way binding listener mechanism, and distributed asynchronous callback processing. However, embedding data processing logic code, relying on two-way binding listeners, and distributed asynchronous processing all have certain drawbacks. For example, embedding data processing logic code leads to high coupling between data and view, making it difficult to achieve cross-component reuse of rules; relying on two-way binding listeners lacks a global dependency topology perspective, which can easily trigger redundant chain updates or even lead to computational deadlocks in complex dependency scenarios; and distributed asynchronous processing lacks global traffic management, which can easily generate a large number of redundant requests and data race conditions during high-concurrency interactions, making it difficult to adapt to dynamically changing data processing rules. Summary of the Invention

[0004] The purpose of this invention is to provide a data processing and verification system for a front-end general component library. This system is applied to instant messaging systems to handle messages in group chats or high-load scenarios. It avoids the problems of high coupling between data processing logic and views, redundant updates and computational deadlocks caused by two-way binding mechanisms, and network congestion and data race conditions under high-concurrency interaction, which are inherent in traditional development models. Furthermore, it enables precise updates independent of the view layer, complex dynamic logic verification, and adaptive traffic scheduling, effectively distinguishing the individual importance of messages to avoid missing key information, thereby improving system maintainability and execution efficiency. Specifically, the technical solution of this invention includes: The metadata parsing module is used to receive configuration metadata from the instant messaging front-end component and map the configuration metadata into a data dependency topology graph in memory. The data dependency topology graph consists of data nodes and directed edges that define the data flow direction. The data nodes include value nodes and derived nodes and / or verification nodes connected to the value nodes. The event-driven propagation module is connected in communication with the metadata parsing module. It is used to listen for change signals of the value nodes, calculate the affected propagation paths based on the adjacency relationship of the data dependency topology graph, and generate update trigger signals only for downstream nodes located on the propagation path. The dynamic strategy execution module is communicatively connected to the event-driven propagation module. It is used to respond to the update trigger signal, call the preset strategy operator to calculate and verify the affected derived nodes and / or verification nodes, and generate push priority status evaluation results. The status feedback rendering module is communicatively connected to the dynamic strategy execution module. It is used to receive the push priority status evaluation result and update the interaction status or display content of the instant messaging front-end component based on the push priority status evaluation result.

[0005] Preferably, the data nodes generated by the metadata parsing module include: The value node is used to store the raw input data of the instant messaging front-end component; The derived node is used to store intermediate state data after the original input data has been processed by the transformation function; The verification node, connected to the value node or the derived node, is used to store Boolean states for logically judging the validity of data. The data node also includes a source node, which is used to define the request interface configuration information of the external data source.

[0006] Preferably, the event-driven propagation module includes: An adjacency matrix maintenance unit is used to store the node dependency matrix of the data dependency topology graph in memory; The change detection queue management unit is used to query the adjacency matrix maintenance unit when the change signal is received, identify the direct successor node and the indirect successor node, and add the direct successor node and the indirect successor node to the queue to be updated. The topology sorting and scheduling unit is used to perform topology sorting on the nodes in the queue to be updated, and send the update trigger signal to the dynamic strategy execution module in the order of dependency to prevent computational deadlock caused by circular dependencies.

[0007] Preferably, the system further includes: an asynchronous request aggregation bus module; the asynchronous request aggregation bus module is communicatively connected to the dynamic strategy execution module and is used to intercept external data acquisition requests issued by multiple nodes in the data dependency topology graph; The asynchronous request aggregation bus module is also used to generate a unique request identifier based on the request parameters, and to merge multiple duplicate requests with the same unique request identifier into a single network request instruction within a preset time window. The asynchronous request aggregation bus module is also used to distribute the data to all nodes that initiated the external data acquisition request through a broadcast mechanism after obtaining the external data response.

[0008] Preferably, the asynchronous request aggregation bus module is configured with a global request merging mechanism: When the number of requests received within the time window is lower than a preset threshold, the network request instruction is immediately initiated. When the number of requests received within the time window is higher than or equal to the preset threshold, the network request instruction is delayed until the time window ends, so as to perform batch request merging.

[0009] Preferably, the dynamic strategy execution module includes: a context-aware injection unit; the context-aware injection unit is used to monitor the changes in the values ​​of the predecessor nodes in the data dependency topology graph in real time; The context-aware injection unit is also used to dynamically insert a specific policy operator into the verification chain of the current node or remove an existing policy operator from the verification chain when the value of the preceding node meets a preset specific condition, so as to realize hot-plugging of verification logic.

[0010] Preferably, the system further includes: a background verification and prediction module; the background verification and prediction module runs in a background thread or WebWorker, and is used to pre-calculate the potential legitimacy status of other related nodes based on the data dependency topology graph while the user performs input operations on the current node; The status feedback rendering module is also used to disable options that would lead to subsequent verification failure or to display early warning information based on the potential legality status.

[0011] Preferably, the derived node includes a date formatting node; the date formatting node is connected as middleware between the value node and the status feedback rendering module, and is used to convert the timestamp data output by the value node into a date string in a preset format, and pass the date string to the status feedback rendering module for display.

[0012] Preferably, the verification node includes a gated node; the gated node is associated with the form submission action and is used to aggregate the Boolean states of all verification nodes in the data dependency topology graph in real time. The state feedback rendering module is further configured to activate the clickable state of the submit button when the aggregation result of the gate node is true, and freeze the clickable state of the submit button when the aggregation result is false.

[0013] Preferably, the strategy operators include: regular expression matching operators, numerical range verification operators, mandatory content verification operators, and user-defined function operators; The dynamic policy execution module is also used to support the dynamic loading of new policy operators into the operator pool through a configuration interface during system runtime.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This system constructs a data dependency topology graph through a metadata parsing module and combines it with a topology sorting and scheduling unit to achieve efficient decoupling between data processing logic and the view layer. By defining data flow direction using directed edges and calculating affected paths based on the adjacency matrix, the system can generate update trigger signals only for downstream nodes on the propagation path, thereby avoiding full checks and redundant updates in traditional two-way binding mechanisms. At the same time, by performing topology sorting on nodes, computational deadlocks caused by circular dependencies are physically blocked, ensuring the stability and determinism of complex form logic execution. 2. This system effectively solves the network congestion problem in high-frequency concurrency scenarios through the asynchronous request aggregation bus module and global request merging mechanism; by using the unique identifier of the request generated by the request parameters, the system can automatically merge duplicate requests with the same identifier within a preset time window, and intelligently select the immediate or delayed batch merging strategy according to the request quantity threshold, and distribute response data through the broadcast mechanism; this not only greatly reduces server load and network bandwidth consumption, but also ensures that all nodes that initiate requests receive consistent response data; 3. This system achieves dynamic hot-plugging of verification logic and real-time expansion of the operator pool through context-aware injection unit and dynamic policy execution module; the system can monitor changes of preceding nodes in real time and dynamically insert or remove policy operators into the verification chain of the current node when specific conditions are met. Combined with the pre-calculation mechanism of the background verification prediction module, the component library can flexibly adapt to changing data processing rules and can identify potential legality states in advance to optimize the interactive experience, which greatly improves the logic reusability and maintainability of the system. 4. This system effectively solves the problem of information overload and omission of key information in existing instant messaging systems under group chat or high-load scenarios. By mapping the metadata of the instant messaging front-end components into a data dependency topology graph containing value nodes and derived nodes and / or verification nodes connected to the value nodes, and generating push priority status evaluation results based on the above nodes, the system achieves accurate message distribution and reminders, ensuring that users can obtain key information first under high load conditions. Attached Figure Description

[0015] Figure 1 This is a structural diagram of the system of the present invention; Figure 2 This is a flowchart of the event-driven propagation module; Figure 3 This is a flowchart of the data processing of the asynchronous request aggregation bus module. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0017] Example 1: Please see Figure 1 A data processing and validation system for a general-purpose front-end component library, comprising: The metadata parsing module is used to receive configuration metadata from the instant messaging front-end component and map the configuration metadata into a data dependency topology graph in memory. The data dependency topology graph consists of data nodes and directed edges that define the data flow direction. Data nodes include value nodes and derived nodes and / or verification nodes connected to the value nodes. The event-driven propagation module communicates with the metadata parsing module to listen for change signals of value nodes, calculate the affected propagation path based on the adjacency relationship of the data dependency topology graph, and generate update trigger signals only for downstream nodes located on the propagation path. The dynamic strategy execution module communicates with the event-driven propagation module. It responds to update trigger signals, calls preset strategy operators to calculate and verify the affected derived nodes and / or verification nodes, and generates push priority status evaluation results. The status feedback rendering module communicates with the dynamic strategy execution module to receive the push priority status evaluation results and update the interaction status or display content of the instant messaging front-end component based on the push priority status evaluation results.

[0018] This embodiment details the specific operating mechanism of the data processing and verification system of the front-end general component library. This system is logically independent of the view layer and runs as a pure logic engine. The metadata parsing module serves as the system's entry point, and its core function is graph construction. This module receives JSON-formatted configuration metadata from the instant messaging front-end components. This metadata includes not only UI attributes of fields but also definitions of data dependencies. The metadata parsing module maps the configuration metadata into a data dependency topology graph in memory using a recursive traversal algorithm. During this mapping process, the module uses a lexical analyzer to parse logical expressions in the metadata, such as formulas or explicit / implicit conditions, generating a lexical unit stream, and performing variable extraction by constructing an abstract syntax tree. In this process, to clarify the composition and conflict handling of the symbol table, the system constructs a hierarchical symbol table: it sequentially scans the local variables of the current component, such as v-for instruction iterators, internal states or computed properties, and external properties, and stores these identifiers in the symbol table, while assigning priority weights, that is, local variables have higher priority than internal states, and internal states have higher priority than external properties; specifically, the system adopts a scope chain resolution strategy to handle nodes of identifier types: for simple identifiers, its name is directly extracted; for member expressions, such as user.age (user age), only its leftmost primary identifier user (user) is extracted as a candidate dependency key; The system performs precise string matching between the extracted candidate keys and the hierarchical symbol table. If a duplicate identifier is found during the matching process, the system performs masking processing based on preset priority weights, establishing a dependency association only with the highest priority identifier to resolve the naming conflict. If the match is successful, a directed dependency edge is established in the topology graph from the referenced node identifier (ID) to the current node ID. If no match is found, such as Math in Math.abs (absolute value function) or system ENV (environment variable), the system further queries the preset global reserved word whitelist. If the value is in the whitelist, it is treated as a static constant and no dependency relationship is established. If neither the node ID is matched nor in the whitelist, it is marked as an orphan reference and a warning log is output. In this graph, data nodes represent instant messaging message fields, form fields, or logical units, and directed edges represent data flow, meaning that a change in the value of one node will directly drive the calculation or verification of another node. The event-driven propagation module serves as the system's power engine, aiming to achieve precise updates. This module communicates with the metadata parsing module and uses a publish-subscribe pattern to listen for change signals from all data nodes. When the value of any node undergoes a sudden change, this module does not perform a full check but instead calculates the affected propagation path based on the adjacency relationship of the data dependency topology graph. The system only generates update trigger signals for downstream nodes located on the propagation path. The dynamic strategy execution module, as the core of the system's operation, responds to update trigger signals and calls preset strategy operators for affected nodes. These operators include, but are not limited to, data formatting, type conversion, or validity verification. After the calculation is completed, the module generates a push priority status evaluation result. The status feedback rendering module receives the push priority status evaluation result and uses a difference comparison algorithm to update the interaction status or display content of the instant messaging front-end component. The difference comparison algorithm executes the following logic: receiving the old and new state objects. and Compare the memory reference addresses of the two; if they are the same, skip them; if they are different, iterate through them. All attribute keys For basic type properties, strict equality checks are performed. For special data objects in reference types, the values ​​returned by their getTime() methods are directly compared to see if they are strictly equal. For regular expression objects, the strings returned by their toString() methods are compared to see if they are strictly equal. For ordinary objects or arrays, a recursive comparison with a maximum depth of 3 levels is performed on reference type properties. To ensure data consistency, when the recursion depth reaches level 3 and the attribute value is still a reference type, the algorithm automatically downgrades to reference address comparison; if the addresses are different, it is conservatively judged as a change; only when a substantial change in the attribute value is detected or a downgrade judgment is triggered, the corresponding UI component is marked as dirty and a redraw instruction is triggered.

[0019] Example 2: The data nodes generated by the metadata parsing module include: Value nodes are used to store the raw input data of instant messaging front-end components; Derived nodes are used to store intermediate data based on the original input data after processing by the transformation function; Verification nodes, connected to value nodes or derived nodes, are used to store Boolean states that perform logical judgments on the validity of data. The data node also includes the source node, which defines the request interface configuration information for external data sources.

[0020] This embodiment further defines the specific types of data nodes generated by the metadata parsing module to support complex data processing scenarios. In this embodiment, the data dependency topology graph includes the following four core nodes: Value nodes are used to store the original input data of the instant messaging front-end components, such as strings entered by the user or selected option IDs. They are the source of the topology graph and usually have an in-degree of 0. Derived nodes connect to value nodes and are used to store intermediate data processed by transformation functions based on the original data, such as the total price node automatically calculated based on the unit price and quantity value nodes. Next, validation nodes connect to value nodes or derived nodes and are used to store Boolean states for logically judging the validity of data. This node outputs a structure containing Boolean values ​​and error information. Source nodes are used to define the request interface configuration information for external data sources. They do not directly store user input but rather store configurations for how to obtain dropdown menu options, such as API addresses and parameter mappings. By categorizing nodes, the system can clearly distinguish data storage, logical transformation, rule validation, and external dependencies, enabling complex form logic to be structured as a combination and connection between different types of nodes. This greatly improves the maintainability and reusability of the system. Especially when dealing with large and complex forms, this classification management mechanism ensures clear and controllable data flow. To meet the requirements for code-level reproduction, this embodiment further clarifies the memory data structure definitions for each of the above nodes: Value nodes are defined as structures: {Identifier (id): String, Type (type): Value type (VALUE), Number: Any type (Any), Default value (defaultValue): Any} The value field supports two-way binding with view layer components; Derivative nodes are defined as: {id:String,type:DERIVED,expression:String,dependencies:Array <string>}, where DERIVED is the derived type; expression is the expression; expression stores a template string such as '{dep1(dependency1)}{dep2(dependency2)}', and dependencies stores a list of IDs of the dependency nodes; Verification node, defined as: {id:String, type:VALIDATION, targetId:String, rule set:Array<RuleConfig>}, used to attach the verification chain; The source node is defined as: {id:String,type:SOURCE,apiConfig:{url:String,method:'GET method'|'POST method',paramsMapping:Record<String,String>}} Here, SOURCE represents the source type; apiConfig represents the interface configuration; url represents the Uniform Resource Locator; method represents the request method; and paramsMapping defines the parameter name sent to the backend API as its key and the dependency node ID from which the parameter value originates as its value. Before initiating a request, the system iterates through this mapping table and extracts the real-time value of the corresponding node from the memory graph based on the ID. This constructs the final request payload, thereby achieving a declarative binding between interface parameters and form data; This standardized data structure design enables the metadata parsing module to directly instantiate various node objects through the factory pattern, ensuring the determinism of memory graph construction.

[0021] Example 3: Please see Figure 2 The event-driven propagation module includes: an adjacency matrix maintenance unit, used to store the node dependency matrix of the data dependency topology graph in memory; The change detection queue management unit is used to query the adjacency matrix maintenance unit when a change signal is received, identify the direct successor node and the indirect successor node, and add the direct successor node and the indirect successor node to the queue to be updated. The topology sorting scheduling unit is used to perform topology sorting on the nodes in the queue to be updated, and sends update trigger signals to the dynamic policy execution module in the order of dependency to prevent computational deadlock caused by circular dependencies.

[0022] This embodiment details the internal implementation mechanism of the event-driven propagation module, particularly how to efficiently manage dependencies and prevent computational deadlocks. To quickly query dependencies, the adjacency matrix maintenance unit stores a node dependency matrix in memory, mathematically denoted as a matrix. Let the total number of data nodes in the data dependency topology graph be . Then the matrix For one A Boolean matrix; the rows of the matrix represent predecessor nodes and the columns represent successor nodes. If the nodes... Changes directly lead to nodes If an update is needed, then ,otherwise ; When a change signal is received, such as a node When changes occur, the change detection queue management unit queries the node dependency matrix. The Line, identify all that meet the requirements The direct successor node, in the data dependency topology graph, is a node that succeeds another node. The node directly pointed to by the originating directed edge is the next-level node into which the data flow directly enters; and a breadth-first search algorithm is used to traverse the node dependency matrix. To obtain all reachable indirect successor nodes; indirect successor nodes are those that are not nodes in the data dependency topology graph. Directly connected, but through at least one intermediate node to the node. Nodes with a connected path are downstream nodes located deep within the dependent link; In practice, the system maintains a first-in-first-out breadth-first search queue. and the visited set used for deduplication Initially, the direct successor node is pushed into the fold. Pop the head node of the queue in the loop. Read the matrix The Okay, satisfy all and nodes join in and Add all affected nodes to the update queue. ; To prevent infinite recursion caused by circular dependencies, the topology sorting scheduling unit... The nodes in the system undergo topological sorting; specifically, the system statistics... The middle node is based only on Local in-degree of internal dependencies; that is, for the queue to be updated Any unsorted node Calculate its local in-degree The calculation formula is as follows: in, Represents a node Considering only queues The in-degree value for internal dependencies; The set of node indices to be updated output by the change detection queue management unit; The index of the target node whose in-degree is currently being calculated; For queue The traversal node index in the table represents a potential predecessor node; Node dependency matrix The Middle Line number The column's values ​​can be either 0 or 1, where... Represents a node It is a node The direct predecessor, i.e. Change-driven The update; the physical meaning of this formula is that it only accumulates data from the queue to be updated. Internal predecessor dependencies; assume For nodes The real-time local in-degree value, the initial value is equal to The scheduler follows The update trigger signals are sent to the dynamic policy execution module in sequence. In practice, to ensure the completeness of the sorting algorithm, the system initializes an in-degree mapping table InDegreeMap that only records... The scheduler establishes the pointer relationships between nodes; nodes with an in-degree of 0 in the InDegreeMap are placed into the execution buffer. Whenever a node completes computation, the system decrements the in-degree of all its successor nodes in the InDegreeMap by 1; if the in-degree becomes 0 after decrementing, the successor node is added to the execution buffer; if the final number of nodes in the execution buffer is less than... If the total length is not specified, a circular dependency is determined, and the system will immediately terminate the computation and throw a deadlock exception, thus physically blocking computational deadlock at the algorithm level.

[0023] Example 4: Please see Figure 3 The system also includes: an asynchronous request aggregation bus module; the asynchronous request aggregation bus module is connected to the dynamic policy execution module and is used to intercept external data acquisition requests issued by multiple nodes in the data dependency topology graph; The asynchronous request aggregation bus module is also used to generate a unique request identifier based on the request parameters, and to merge multiple duplicate requests with the same unique request identifier into a single network request instruction within a preset time window. The asynchronous request aggregation bus module is also used to distribute the data to all nodes that initiated the external data acquisition request through a broadcast mechanism after receiving the external data response.

[0024] The asynchronous request aggregation bus module is configured with a global request merging mechanism: When the number of requests received within the time window is lower than a preset threshold, a network request command is immediately initiated. When the number of requests received within a time window is higher than or equal to a preset threshold, the network request instruction is delayed until the time window ends in order to perform batch request merging.

[0025] This embodiment involves an asynchronous request aggregation bus module, specifically designed to address the issue of high-frequency, repetitive external data requests from the front end. When multiple source nodes in the data dependency topology issue external data acquisition requests, the asynchronous request aggregation bus module intercepts these requests. The system generates a unique request identifier based on the request parameters. The calculation formula is as follows: in, For hash generation functions; For the requested address; For the request method; This is a set of request parameters; Hash generation function The specific calculation steps are as follows: Parameter normalization: To address the issue of uncertain key order in JSON serialization, the system employs a recursive key sorting algorithm to process the parameter set. The algorithm strictly defines the following processing rules to eliminate black-box states: Object sorting: Extract all keys of the object, sort them in ascending order by Unicode code point, and reconstruct the object in this order; Array processing: For array types, the index order of elements is kept unchanged and no sorting is performed, but each element in the array is recursively traversed to perform a normalization operation to prevent damage to the semantic structure of the parameters; Circular reference blocking: The algorithm maintains a set of weak references to record object references on the current traversal path. If a duplicate reference is detected, it is replaced with a circular reference placeholder "__CYCLE__" and the recursion of that branch is terminated to prevent an infinite loop. Termination conditions: When the traversed node is a basic data type string, number, boolean value, or null value, return the value directly to terminate the recursion; if the traversed node is a data object instance, call the getTime() method to convert it to a numeric timestamp and return it; if the traversed node is a RegExp regular expression object instance, call the toString() method to convert it to a string and return it; if the traversed node is undefined, return the string constant undefined placeholder "__UNDEFINED__". Through the above steps, not only are the keys of the top-level object sorted in ascending order of Unicode code points, but also... All nested child objects also undergo key sorting to ensure that... and The serialized string is completely identical. This ensures the consistency of hash values ​​under complex nested parameters; Serialization: Using a custom serializer that integrates the circular reference blocking mechanism described above, the sorted parameter object is converted into a standard JSON string. ; Concatenation and Summarization: Constructing Feature Strings Symbols are used here. The formula for string concatenation is as follows: And on The hexadecimal string generated by executing the MD5 or SHA-256 algorithm is the unique identifier. ; Based on this, in order to strictly adapt to the global request merging mechanism of the implementation, the system implements a time-slice-based adaptive traffic scheduler; this scheduler uniquely identifies each request in memory. Maintain a state machine The specific operating logic is as follows: Request arrival and counting: When a request is received with the identifier... When a request is received, the system increments a counter. ;like If not started, initialize the countdown window. ,like And trigger when the window ends. operate; Dynamic mode determination: Immediate penetration mode, i.e., meeting the following conditions: in, This is a preset threshold for the number of requests, and its value is a positive integer, for example... : System check Does it exist and is in a suspended state? If it exists, directly attach the Resolve / Reject handle of the current request to it. In the chained callbacks; if it does not exist, immediately construct the network request instruction and generate the... Assign to And mark it as in transit; this mode guarantees zero-latency response for low-frequency interactions; delayed merging queue mode, i.e. The system intercepts the current request, does not initiate a network call, and instead encapsulates its callback handle into a task object. Push in At the rear, it enters a suspended waiting state; Window termination and batch processing: When the time window End trigger Time: System check Is it empty? If not empty, the system checks again. The status; if there are active requests pending: directly... All task callbacks are subscribed to this To achieve a hitch-rider merging; if there are no active requests / empty values: the system extracts the first task in the queue, initiates a new physical network request, and generates a new... And bind the callbacks of all remaining tasks in the queue to this new commitment object; reset ,destroy And clear This completes one scheduling loop; When the network response returns data Then, the bus module broadcasts the information. Distributed to all registered users The nodes below complete data population; the system maintains the requested registry in memory, with the key being the identifier. The value is a callback array; when the response is returned, the array is traversed to execute the callback, ensuring data consistency.

[0026] Example 5: The dynamic policy execution module includes: a context-aware injection unit; the context-aware injection unit is used to monitor changes in the values ​​of predecessor nodes in the data dependency topology graph in real time; The context-aware injection unit is also used to dynamically insert specific policy operators into the verification chain of the current node or remove existing policy operators from the verification chain when the value of the current node meets the preset specific conditions, so as to realize the hot-plugging of verification logic.

[0027] This embodiment describes the context-aware injection unit in the dynamic policy execution module, which realizes dynamic hot-plugging of verification logic; the context-aware injection unit monitors the changes in the values ​​of the predecessor nodes in the data dependency topology graph in real time; let the current node be... The predecessor node is This unit maintains a policy mapping table. The table uses a key-value structure for storage, where the key is the previous node. A unique identifier, where Value is the list of triggering rules. Each rule Defined as a quintuple: in, Point to the current node The logo, As the trigger threshold, For comparison logical operators, it supports equality, containment, and regular expression matching. The operation type is either insertion or removal. Configure the policy operator to be operated on, containing a unique operator identifier (operatorId) or signature; To ensure the determinism and robustness of hot-plugging of the verification logic, this embodiment strictly defines the topology and mutation algorithm of the verification chain: Chain structure definition: current node verification chain It is implemented as an ordered priority queue based on array storage, where each element has the following structure: in, For identifiers; ( The field is an integer value used to determine the execution order of operators, following a min-heap logic, meaning smaller values ​​have higher priority. For example... Priority over For example, required field validation is usually set to 0, and format validation is set to 10. To execute the function; For configuration items; Differential injection algorithm: responding to detection To become a specific value, that is When the calculation result is True, the system triggers an operation; when an INSERT operation is executed, the system receives... It includes operatorId and target priority; the system iterates based on operatorId. Perform fingerprint deduplication; if it already exists, compare it with the configuration fingerprint and update it only when the configuration changes; if it does not exist, use binary search based on targetPriority to determine the insertion position and perform insertion to ensure the chain's order; when performing a REMOVE operation, the system... operatorId in In the process Find and remove target operators; Atomicity Commit and Deadlock Prevention: The mutation operation of the above chain is completed atomically on the shadow node in memory. After completion, the system does not immediately run verification, but sends a virtual change event to the event-driven propagation module. This event carries a logical mutation flag (isLogicMutation = true). After recognizing this flag, the propagation module forcibly commits the current node. Add to the dirty check queue and perform a recalculation according to the topology sorting mechanism of Example 3; This mechanism ensures data consistency after logical changes and uses topological sorting to physically prevent system loops caused by logical deadlocks, such as A's insertion rule depending on B, and B's insertion rule depending on A.

[0028] Example 6: The system also includes: a background verification and prediction module; running in a background thread or WebWorker, used to pre-calculate the potential legitimacy status of other related nodes based on the data dependency topology graph while the user is inputting on the current node; The status feedback rendering module is also used to disable options that could lead to subsequent verification failures or to display early warning information based on potential validity status.

[0029] This embodiment introduces a background verification and prediction module to improve user experience. The background verification and prediction module is configured to run in a background thread or utilize the main thread's idle time, and is logically isolated from the main UI thread. When the user interacts with the current node... While performing input operations, this module clones the current data dependency topology graph and, based on... Given the current input value, pre-calculate the set of potential legitimate states for other associated nodes. The pre-computation process employs an exploratory execution mechanism and is strictly limited to associated nodes with discrete value domains, i.e., nodes configured with options or enumeration constraints, to avoid infinite computation over continuous value domains. Specifically, the potential legitimacy state refers to the set of invalid option values ​​pre-calculated for the above nodes, which would cause the verification to fail if selected. This is used to guide the front-end rendering process to disable invalid options in advance; The specific algorithm flow is as follows: Identify other related nodes: Other related nodes refer to the set of downstream nodes in the data dependency topology graph that depend on the current node, have enumerable options (isEnumerable=true), and are currently visible (isVisible=true). ); Constructing a shadow state: Creating a deep copy of the current state tree data snapshot. During this process, DOM references and non-data objects are removed. For non-data objects, function objects retain their reference addresses in the main thread mode and are serialized into operator IDs in WebWorker mode. To address the differences in runtime environments, the system adopts differentiated strategies to synchronize verification logic: if the module runs in the idle time slice of the main thread, the original verification rule function references are directly reused; if the module runs in WebWorker, the system adopts an operator registry synchronization mechanism, that is, the standard operator library code is preloaded during the WebWorker thread initialization phase, and when building the shadow tree, only the strategy operator ID and configuration parameters associated with the node are serialized and transmitted to the WebWorker. After receiving the data, the WebWorker matches the corresponding function implementation from the local registry based on the ID, thereby rebuilding a logically consistent verification chain in physically isolated memory spaces, effectively avoiding the technical limitation that function objects cannot be cloned across threads. State synchronization and propagation: This is a crucial step in ensuring prediction accuracy; the system will pass in the main thread's... Latest input value Injected into The corresponding node is identified, and the target subgraph activation algorithm is immediately executed in the shadow tree to complete the local dependency propagation; the algorithm steps are as follows: (a) in In the adjacency matrix, with Perform a depth-first search starting from the root, retaining only the paths leading to it. For any node in the graph, construct an active subgraph; (b) Perform a topological sort on the set of nodes within the active subgraph; (c) Trigger the calculation logic sequentially based on the sorting results, only updating those between... and The values ​​of intermediate derived nodes between them ensure that subsequent verifications are based on the latest context. Iterative exploration: for Each node in Obtain its set of alternative values. ; Simulated verification: In In the middle, maintain The new value remains unchanged, and then... Assign to Note that this is only a logical assignment and does not trigger UI rendering or execution. All validation rules; State recording: If assigned a value If the verification fails, then... Add node The corresponding set of illegal option values The sandbox environment is a shadow state tree built by performing a deep copy of the current component's state tree. A memory object with the same state tree structure as the main thread; all simulated assignment and verification logic is executed on this shadow state tree. This technique ensures that temporary dirty data and intermediate states generated during the background exploratory calculations do not contaminate the main thread's actual data processing data, nor do they trigger meaningless UI redraw events, until the final potential valid state is calculated. That is, the above After the data is collected, the results are synchronized back to the main thread via a message passing mechanism; the status feedback rendering module disables options that may cause subsequent verification failures or displays warning information in advance based on potential legality states. Regarding the communication mechanism between the main thread and the background WebWorker thread described above, this embodiment defines a strict message protocol to ensure the accuracy of data synchronization: Request protocol: When the main thread triggers prediction, the message payload is sent as follows: Response protocol: After completing the computation, the WebWorker returns a message payload: in, The key is the associated node ID, and the value is the set of option values ​​that are judged to be invalid under this node; Rendering mapping logic: The state feedback rendering module listens for the onmessage event and receives... It locates the target component using document.getElementById or a virtual DOM lookup algorithm, iterates through the component's options list, and if the value of an option exists in the invalidMap[targetNodeId] array, it immediately sets the DOM disabled attribute of that option to true and adds the style class attribute to predict invalid style class, thus visually blocking illegal paths on the user interface.

[0030] Example 7: Derived nodes include date formatting nodes, which act as middleware between value nodes and the status feedback rendering module. They are used to convert the timestamp data output by the value nodes into date strings in a preset format and then pass the date strings to the status feedback rendering module for display.

[0031] This embodiment specifically describes the application of the date formatting node as a derived node; date formatting node As middleware, it is connected in series with the value node. Between this node and the status feedback rendering module, this node is used to convert the timestamp data output by the value node into a date string in a preset format, and then pass the date string to the status feedback rendering module for display; this node is configured with a conversion function. The formula is as follows: in, This is the original timestamp data; Use a preset date formatting template string, such as "year-month-day format (YYYY-MM-DD)"; This is the output date string; the design separates the data storage format from the display format. The backend API usually requires a timestamp, while the frontend display requires a user-friendly format. Through middleware nodes, the system automatically handles bidirectional conversion, ensuring the purity of the data source while meeting the diverse display needs of the UI. Specifically, the transformation function A lightweight implementation based on regular expression replacement is adopted to avoid introducing a heavy-duty time library. The algorithm steps are as follows: Time component extraction: parsing timestamps Extract the set of key-value pairs for time components : Pattern Matching and Replacement: Creating Regular Expressions for Matching Template Placeholders : String generation: Perform string replacement operations: This algorithm ensures performance in date formatting when processing large amounts of list data, while also guaranteeing strict consistency in alignment format through header completion.

[0032] Example 8: Verification nodes include gated nodes; The gate node is associated with the form submission action and is used to aggregate the Boolean state of all validation nodes in the data dependency topology graph in real time. The status feedback rendering module is also used to activate the clickable state of the submit button when the aggregation result of the gate node is true, and to freeze the clickable state of the submit button when the aggregation result is false.

[0033] This embodiment specifically describes the application of gated nodes in form submission control; gated nodes Associated with form submission actions, it is used to aggregate the boolean states of all validation nodes in the data dependency topology graph in real time; its output state It is determined by the following logic: in, This represents the logical AND and aggregate operations; The first data dependency in the topology graph represents the first data dependency. One verification node; Represents a node Output a boolean value indicating validity; The total number of nodes to be validated in the data dependency topology graph; when the aggregation result of the gated node is true, the state feedback rendering module activates the clickable state of the submit button; when the aggregation result is false, it freezes the clickable state of the submit button and can selectively display a summary of the list of nodes that failed validation; Gated nodes provide a real-time view of the global state. Instead of triggering a full check when the submit button is clicked, they reflect the health of the form in real time. This allows users to intuitively perceive whether the form is ready to submit, providing a smooth interactive feedback.

[0034] Example 9: The strategy operators include: regular expression matching operators, numerical range validation operators, required field validation operators, and user-defined function operators; The dynamic policy execution module is also used to support the dynamic loading of new policy operators into the operator pool through the configuration interface during system runtime.

[0035] This embodiment describes the types of policy operators and their dynamic expansion capabilities; policy operators include: regular expression matching operators, numerical range verification operators, mandatory content verification operators, and user-defined function operators; the dynamic policy execution module also supports dynamically loading new policy operators into the operator pool through the configuration interface during system runtime; Specifically, this module maintains an operator pool with built-in standard operators, such as those for validating string format based on regular expressions, validating whether a value is within a range, and validating whether a value is non-empty. Simultaneously, the system provides a configuration interface that allows developers to pass in custom function operators, thereby dynamically loading new strategy operators into the operator pool at runtime. To ensure the executability and type safety of dynamically loaded operators, the configuration interface adheres to a unified interface contract: typeStrategyOperator=(value:Any,config:Object,context:GraphContext)=>{valid:Boolean,message:String} The system exposes a global registration method: registerOperator(name:String,impl:StrategyOperator) Wherein, typeStrategyOperator is the strategy operator type definition; GraphContext is the graph context; registerOperator is the operator registration method; impl is the implementation function; when this method is called, the system will inject the impl function into the operator hash table in memory and perform runtime verification of the function signature; here, the pre-set strategy operators in Example 1 should be understood as the set of operators that have been registered and are available in the operator pool at the time of the call; this set includes both the standard operators loaded during system initialization and the custom operators dynamically injected during runtime; the dynamic loading mechanism is essentially a real-time expansion of the pre-set operator library, ensuring that the dynamic strategy execution module always performs operations within the known and controlled range of operators, eliminating the apparent contradiction in timing between pre-set and dynamic loading; Through pooling management of strategy operators and dynamic interfaces, the system achieves a unified standardization and scalability of validation logic. Developers can reuse standard operators to quickly build forms and can also seamlessly extend the system for special needs without modifying the core code.

[0036] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.< / string>

Claims

1. A data processing and verification system for a front-end general component library, the system being applied in an instant messaging system to process messages in group chats or high-load scenarios, characterized in that, include: The metadata parsing module is used to receive configuration metadata from the instant messaging front-end component and map the configuration metadata into a data dependency topology graph in memory. The data dependency topology graph consists of data nodes and directed edges that define the data flow direction. The data nodes include value nodes and derived nodes and / or verification nodes connected to the value nodes. The event-driven propagation module is connected in communication with the metadata parsing module. It is used to listen for change signals of the value nodes, calculate the affected propagation paths based on the adjacency relationship of the data dependency topology graph, and generate update trigger signals only for downstream nodes located on the propagation path. The dynamic strategy execution module is communicatively connected to the event-driven propagation module. It is used to respond to the update trigger signal, call the preset strategy operator to calculate and verify the affected derived nodes and / or verification nodes, and generate push priority status evaluation results. The status feedback rendering module is communicatively connected to the dynamic strategy execution module. It is used to receive the push priority status evaluation result and update the interaction status or display content of the instant messaging front-end component based on the push priority status evaluation result.

2. The data processing and verification system for a front-end general component library according to claim 1, characterized in that, The data nodes generated by the metadata parsing module include: The value node is used to store the raw input data of the instant messaging front-end component; The derived node is used to store intermediate state data after the original input data has been processed by the transformation function; The verification node, connected to the value node or the derived node, is used to store Boolean states for logically judging the validity of data. The data node also includes a source node, which is used to define the request interface configuration information of the external data source.

3. The data processing and verification system for a front-end general component library according to claim 1, characterized in that, The event-driven propagation module includes: An adjacency matrix maintenance unit is used to store the node dependency matrix of the data dependency topology graph in memory; The change detection queue management unit is used to query the adjacency matrix maintenance unit when the change signal is received, identify the direct successor node and the indirect successor node, and add the direct successor node and the indirect successor node to the queue to be updated. The topology sorting and scheduling unit is used to perform topology sorting on the nodes in the queue to be updated, and send the update trigger signal to the dynamic strategy execution module in the order of dependency to prevent computational deadlock caused by circular dependencies.

4. The data processing and verification system for a front-end general component library according to claim 1, characterized in that, The system further includes: an asynchronous request aggregation bus module; the asynchronous request aggregation bus module is communicatively connected to the dynamic strategy execution module and is used to intercept external data acquisition requests issued by multiple nodes in the data dependency topology graph; The asynchronous request aggregation bus module is also used to generate a unique request identifier based on the request parameters, and to merge multiple duplicate requests with the same unique request identifier into a single network request instruction within a preset time window. The asynchronous request aggregation bus module is also used to distribute the external data to all nodes that initiated the external data acquisition request through a broadcast mechanism after obtaining the external data response.

5. The data processing and verification system for a front-end general component library according to claim 4, characterized in that, The asynchronous request aggregation bus module is configured with a global request merging mechanism: When the number of requests received within the time window is lower than a preset threshold, the network request instruction is immediately initiated. When the number of requests received within the time window is higher than or equal to the preset threshold, the network request instruction is delayed until the time window ends, so as to perform batch request merging.

6. The data processing and verification system for a front-end general component library according to claim 1, characterized in that, The dynamic strategy execution module includes: a context-aware injection unit; the context-aware injection unit is used to monitor the changes in the values ​​of the preceding nodes in the data dependency topology graph in real time; The context-aware injection unit is also used to dynamically insert a specific policy operator into the verification chain of the current node or remove an existing policy operator from the verification chain when the value of the preceding node meets a preset specific condition, so as to realize hot-plugging of verification logic.

7. The data processing and verification system for a front-end general component library according to claim 1, characterized in that, Also includes: Backend verification and prediction module; The background verification and prediction module runs in a background thread or WebWorker and is used to pre-calculate the potential legitimacy status of other related nodes based on the data dependency topology graph while the user is inputting on the current node. The status feedback rendering module is also used to disable options that would lead to subsequent verification failure or to display early warning information based on the potential legality status.

8. The data processing and verification system for a front-end general component library according to claim 2, characterized in that, The derived node includes a date formatting node; the date formatting node is connected as middleware between the value node and the status feedback rendering module, and is used to convert the timestamp data output by the value node into a date string in a preset format, and pass the date string to the status feedback rendering module for display.

9. The data processing and verification system for a front-end general component library according to claim 2, characterized in that, The verification node includes a gated node; the gated node is associated with the form submission action and is used to aggregate the Boolean state of all verification nodes in the data dependency topology graph in real time. The state feedback rendering module is further configured to activate the clickable state of the submit button when the aggregation result of the gate node is true, and freeze the clickable state of the submit button when the aggregation result is false.

10. The data processing and verification system for a front-end general component library according to claim 1, characterized in that, The strategy operators include: regular expression matching operator, numerical range check operator, mandatory field check operator, and user-defined function operator; The dynamic policy execution module is also used to support the dynamic loading of new policy operators into the operator pool through a configuration interface during system runtime.

Images