Intelligent verification and workflow method for business expansion application materials for multi-role collaboration

Abstract

This invention discloses an intelligent verification and workflow method for business expansion application materials oriented towards multi-role collaboration. It relates to the field of intelligent material verification and workflow technology, and addresses the problems of inconsistent multi-channel data collection, structural extraction, and low efficiency in multi-role collaboration for business expansion application materials. The method includes voice and image path data collection, structural field generation, behavior offset recognition and rule-driven classification, field stability scoring, and a dynamic extraction adjustment mechanism. The materials undergo three-channel verification—field compliance, semantic consistency, and graph comparison—by an AI agent. Task identity matching and workflow distribution are achieved based on digital work badges. Paperless archiving and AI self-evolution training closed loops are realized through RPA cross-platform data entry and archive generation mechanisms, improving the stability, consistency, and multi-role collaboration efficiency of the materials.

Description

Technical Field

[0001] This invention relates to the field of intelligent data verification and workflow technology, and more specifically, to a method for intelligent verification and workflow of business expansion application data for multi-role collaboration. Background Technology

[0002] In existing business expansion and installation services, data is mainly collected through manual uploading, voice transcription, or paper scanning. Staff at each business hall then manually verify, transfer, and archive the data according to standardized procedures. Due to diverse user input methods and complex characteristics of voice behavior, such as non-standard pronunciation, jumps, and sentence breaks, the quality of generated structured fields is unstable, semantic consistency cannot be guaranteed, and the manual verification process is burdensome and inefficient. Existing solutions generally use fixed template extraction models and static verification rule bases, lacking multi-path semantic behavior differentiation and dynamic strategy adjustment mechanisms. They cannot adapt to scenarios such as field label drift, chaotic expression paths, or overlapping responsibilities of multiple roles. Especially during cross-platform data transfer, issues such as inconsistent interfaces, transcription errors, and response delays severely restrict the standardization of data processing and the controllability of the closed-loop flow. With the development of intelligent semantic parsing and graph-driven models, building an intelligent verification system based on semantic behavior recognition, dynamic rule generation, and multi-role scheduling and linkage mechanisms has become a key direction for ensuring the timeliness and accuracy of installation service processing.

[0003] To address the above problems, this invention proposes a solution. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent verification and transfer method for business expansion application materials oriented towards multi-role collaboration, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a preferred embodiment, it includes:

[0007] Establish multi-path data collection channels, complete the structured extraction of field data through speech transcription and image recognition, and construct a set of structured field objects;

[0008] Perform semantic behavior labeling and classification recognition, construct semantic sequence groups and field feature vectors, generate field stability scores, and drive the extraction strategy control channel;

[0009] Structural fields are extracted based on the field stability score ranking results, and slot locking is performed on fields with high scores first.

[0010] During the extraction process, path correction is performed based on the rule conflict mapping cluster structure, and path suppression processing is performed on fields that hit conflict trajectories;

[0011] When flexible matching is enabled, tags are allowed to be slightly misaligned within the context structure boundaries, and slot binding is completed in a tolerance-based matching manner;

[0012] Before writing the fields into the structure field object collection, append the sequence number, score value, and rule channel status identifier structure attributes;

[0013] Perform multi-channel verification on the structure field object collection, including field value compliance verification, semantic consistency verification and historical case similarity comparison, and generate field verification feedback information after the verification is completed;

[0014] Construct a task distribution chain based on employee badge recognition, and complete model training feedback through archived trigger paths.

[0015] In a preferred embodiment, during the data access phase, a voice input channel is established through a virtual digital human and the original voice content is collected. The voice stream is written into a cache and then voice transcription is performed to generate transcribed text and extract transcription auxiliary parameters.

[0016] In a preferred embodiment, the transcribed text is semantically classified and fields are extracted to generate structured field objects; and in the image input scenario, uploaded data images are acquired, and image preprocessing, field location, and field value extraction are performed to generate structured field items under the image channel.

[0017] In a preferred embodiment, channel parameters and auxiliary features are extracted from the recognition stream buffer to construct the context index information required for behavior recognition; behavior state analysis is performed on the entire voice input process to identify link breakage, prompt compensation, and label aggregation offset behaviors in field expression; when the triggering condition is met, the rule generation channel is entered to construct the behavior mapping structure and generate the corresponding rule set.

[0018] In a preferred embodiment, the labeled fields are classified by behavior type, category labels are generated and written into the field attributes; semantic sequence groups are constructed based on category labels, source paths and time order; statistical features are extracted from the fields in the sequence groups, a set of field feature vectors is generated, and field stability scores are calculated.

[0019] In a preferred embodiment, the processing task is bound to the job role and identity is confirmed based on the digital work badge, the data is distributed to the next processing node according to the process rules, and the expediting process is triggered; at the same time, the structure field content is filled in to the external business and the distribution status information is recorded.

[0020] In a preferred embodiment, the structure fields and processing records are encapsulated and archived, and signature and timestamp processing is completed. The archive is written to the archive library and an index is created. After archiving is completed, the historical case update path is called, and the field features are written into the model training set to drive subsequent rule set enhancement.

[0021] The technical effects and advantages of this invention's intelligent verification and workflow method for business expansion application materials in a multi-role collaborative manner are as follows:

[0022] This invention effectively improves the structural consistency of raw data by acquiring voice and image data through dual-channel acquisition and unified encapsulation of structural fields. Through semantic behavior recognition and offset classification mechanisms, it accurately distinguishes between link breaks, redundant prompts, and abnormal tag aggregation. Combined with a rule-mapping cluster structure, it achieves dynamic suppression and flexible matching of field extraction paths, improving stability during the extraction stage. A three-channel AI verification engine integrates rule verification, semantic comparison, and graph case recommendation capabilities, significantly improving the coverage depth and accuracy of verification. Task scheduling, based on digital work badges and process weight calculations, completes multi-role identity mapping, supporting automatic reminders and cross-platform field entry, improving workflow efficiency. Finally, through archive package generation and field feedback to the model training set, it achieves model self-evolution, constructing a highly closed-loop, fault-tolerant, and fully deployed intelligent data processing system. Attached Figure Description

[0023] Figure 1 This is a sequence diagram of the intelligent verification and workflow method for business expansion application materials oriented towards multi-role collaboration according to the present invention.

[0024] Figure 2 This is a flowchart illustrating the implementation of the intelligent verification and transfer method for business expansion application materials for multi-role collaboration according to the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example

[0027] This invention discloses an intelligent verification and workflow method for business expansion application materials oriented towards multi-role collaboration, such as... Figure 2 As shown, it includes:

[0028] Establish multi-path data collection channels, complete the structured extraction of field data through speech transcription and image recognition, and construct a set of structured field objects.

[0029] Perform semantic behavior labeling and classification recognition, construct semantic sequence groups and field feature vectors, generate field stability scores, and drive the extraction strategy control channel.

[0030] A two-way linkage path for field extraction and rule generation is established, and the field extraction path is dynamically adjusted through tag locking, flexible matching and conflict compression mechanisms.

[0031] A three-channel field verification and data transfer mechanism is initiated, a task distribution chain based on employee badge recognition is constructed, and model training feedback is completed through the archive trigger path.

[0032] like Figure 1 As shown, during the data access phase, the virtual digital human component deployed in the self-service terminals and online platform of the business hall first calls the locally configured audio acquisition module to open the voice input channel, and binds the user-initiated application input behavior to the channel based on the session identifier established with the user interaction. Next, a dual-channel acoustic wave buffer architecture is adopted to record the audio amplitude frames, sound source features and background noise contours in the voice stream in real time, and writes the voice stream into the audio buffer queue in a time-series structure for the recognition engine to call.

[0033] Subsequently, the speech recognition engine integrated into the speech channel processing chain is invoked to perform a transcription operation on the original speech content in the aforementioned audio buffer queue. This recognition engine employs a speech transcription model jointly constructed based on a CTC connected temporal classifier and a Transformer decoder. It performs time alignment on the audio frame sequence and identifies semantic boundaries through a built-in sentence segmentation detection mechanism, generating sentence-segmented transcribed text. Each transcription result simultaneously outputs auxiliary data such as a character-level timestamp, confidence score, and inter-frame pause duration, which is written to the recognition stream buffer as input for subsequent semantic extraction and behavior labeling.

[0034] After the speech-to-text transcription is completed, the AI ​​semantic parser is immediately invoked to perform structured extraction on the transcribed text, which includes three operations: field value recognition, tag binding, and slot structure generation.

[0035] Specifically, the parser first uses an embedded semantic intent recognition network, combined with a pre-loaded application business keyword dictionary, to classify the intent of the text and identify the semantic category of the field to which the current statement belongs.

[0036] Specifically, the semantic category classification operation uses a bidirectional GRU network to extract text context features and uses the maximum class probability output by softmax as the initial label candidate for the field.

[0037] Next, a slot mapping rule table built based on the domain corpus is used to extract field values ​​from the text. The parsed text drives the entity recognition module through the configured rules, executing a position-encoded regularization matching algorithm to extract the corresponding field values ​​from the sentence and assign them to the specified slots. The binding relationship between field values ​​and tags is confirmed by the rule fields defined in the slot template, generating a temporary structured field object containing a four-tuple of field content, tag, confidence score, and original position.

[0038] The generated temporary structure field object is then written to the structure field object collection, and data such as the timestamp, confidence level, and semantic intent path of the transcribed statement containing the field are appended as the basis for context fusion verification.

[0039] Furthermore, for customers uploading paper application documents via scanning or photography, the OCR image recognition engine is invoked in parallel to perform image and text parsing operations on the original image. The image recognition process first uses a convolutional neural network to perform image preprocessing and layout segmentation, breaking down the form structure area and certificate / license area. Then, field localization is called, and the text lines are structurally recognized through character connectivity detection and comparison with the layout template. Based on path template rules, field values ​​such as certificate / license number, issuing authority, and validity period are extracted. After extraction, a structured binder constructs field label mappings and writes them into the original data stream, simultaneously marking the field source path as the image channel.

[0040] It should be noted that in the voice interaction scenario of business expansion application materials, when customers input application materials with virtual digital humans through mobile terminals, window devices or remote channels, due to differences in voice source paths, speaker speed, terminology structure and contextual completeness, uniformly incorporating statements from different sources into the standard slot model during the semantic extraction stage will lead to a decrease in the label stability and contextual relevance of some key fields during the semantic mapping process, thereby affecting the quality of data structuring and the accuracy of subsequent field consistency recognition.

[0041] Existing technologies employ a unified semantic recognizer to perform full entity extraction on all speech text and directly construct field label sets based on fixed template slots. However, they fail to classify the semantic path based on the speech source type or contextual continuity, leading to issues such as cross-mapping, slot overlap, or label drift under complex speech behaviors like non-standard pronunciation, sentence breaks, and jumpy supplementary input. Furthermore, due to the lack of explicit source markings in the recognition output, it is difficult to determine whether semantically coupled fields such as usage category, capacity type, and location nature were actively expressed by the customer, responded to system prompts, or mis-recognized and transcribed. This results in distorted semantic confidence scores and biased field reliability assessments, affecting the agent's critical decisions during the field verification stage. Therefore, in this embodiment,

[0042] First, after the original speech content is recognized and transcribed, speech channel parameter information is extracted synchronously based on the recognition stream cache structure, and a multi-dimensional semantic behavior feature set is constructed to support the behavior labeling process before semantic extraction.

[0043] Specifically, the speech recognition interface is called to write the character timestamp sequence and inter-frame recognition rhythm data cached in the stream. The pause rhythm parameters of each speech segment are calculated by the time interval difference between consecutive character frames. At the same time, the speech source path information and the current session number are extracted. The source path is obtained from the access channel connection record, while the session number and user identity are provided by the interaction log binding record. The recognition confidence score is returned by the transcription engine sentence by sentence. All of the above parameter fields together constitute the recognition context index unit.

[0044] Subsequently, behavioral offset recognition is performed on the identified text and its corresponding parameters to establish a set of field expression behavior labels. Specifically, throughout the entire voice input cycle, changes in field expression behavior states are continuously monitored to identify three types of expression offset features that only appear in the scenario of business expansion data application, and structured classification and labeling are performed, as follows:

[0045] The first category is the behavior of expressing link breakage. Based on the slot utilization rate calculation module, the sudden drop signal of the field slot occupancy rate is identified, and the subsequent slot hit rate is compared with the field structure integrity check function. If the subsequent field with a combination dependency relationship with the expression start field is missing, it is determined to be the behavior of link breakage, and the identified frame is marked as an unclosed structure.

[0046] The second category is prompt compensation feedback behavior, which calls the prompt timeline sequence in the voice guidance logic log and performs overlapping segment extraction in combination with field timestamps; if the current field is repeatedly expressed after the system prompt frame and forms a field label time sequence overlap area, it is judged as a redundant feedback behavior triggered by system guidance, and a redundant field trajectory set of this behavior is constructed.

[0047] The third category is label aggregation imbalance behavior. The system retrieves multi-path hit trajectories of the same field label in the current recognition period based on the label binding record table, and calculates the context boundary similarity of each hit path using the semantic path distribution map. If the context boundaries between hit trajectories are inconsistent or the label mutual exclusion relationship conflicts, and the field is a semantically coupled field, it is marked as label aggregation imbalance behavior, and the field is added to the label conflict tensor structure.

[0048] When any two of the above-mentioned behaviors occur simultaneously within a single recognition period, or when any one of the behaviors occurs consecutively for more than a set period threshold, the rule generation channel is triggered. This rule generation channel uses the current behavior state combination as the driving input, skips the traditional behavior template path based on field name or type division, and directly calls the expression chain break region index, prompt feedback frame segment information, and label conflict tensor results to construct a three-dimensional behavior mapping structure.

[0049] Next, the rule builder is invoked to perform feature combination generation on the structure, outputting the corresponding rule set;

[0050] This set of rules includes, but is not limited to:

[0051] The structure of the link interruption field is marked with priority for compensation.

[0052] Behavioral determination based on the intra-frame location features of redundant fields and the confidence fluctuation curve;

[0053] Slot coverage priority definition and redundant locking rules in tag mutual exclusion mapping.

[0054] Furthermore, the behavior classifier is invoked to perform behavior type classification operations on the labeled fields. This classifier relies on a behavior label definition table derived from the rule set, and performs conditional matching based on four features: the timeline of the corresponding prompt frame, the length of the pause between frames, the contextual dependency, and the number of label paths. It outputs the corresponding classification labels in order of priority rules. Specifically, if the field's timestamp is adjacent to the system prompt response frame, and its corresponding label path has not jumped or been interrupted, it is marked as a guided response type; if the field's label path jumps to other slots, and there is no guiding field in the preceding context, it is marked as a jump supplement type; if the silence interval in the speech frame containing the field exceeds the system-defined silence threshold, and the field label fluctuates significantly before and after the pause, it is marked as a sentence break type; if the field is repeatedly expressed after the prompt frame, and there is no continuous dependency structure in the context, it is marked as redundant. The classification labels are finally written into the field attribute structure and used as input dimensions to construct sequence groups.

[0055] Furthermore, the semantic sequence builder is activated to cluster all fields in the structured field object set according to source path, behavior type, and timeline order. This clustering process is based on the semantic path consistency principle, requiring fields within the same sequence group to have the same speech channel source, the same behavior label type, and their recognition frame indices to be continuously distributed along the timeline. The clustering algorithm employs a sliding window adjacency matching strategy and calls a path consistency discriminant function to verify the contextual dependency integrity of fields within a group, ultimately forming several semantic sequence groups. Each group serves as a field set block under an independent semantic path, possessing contextual structure stability and semantic distribution consistency.

[0056] Subsequently, structural feature extraction is performed on each set of fields. Specifically, this extraction process calls a field statistician to calculate the frequency of occurrence within a group, average confidence score, label concentration, and mean distribution of expression location for each field, and constructs a set of field feature vectors based on clustering feature vector generation rules. All field feature vector sets are then input into the stability score calculation channel, and the field stability score is calculated using a configured multi-factor weighting function. The weighting function uses field frequency as the primary weighting factor, supplemented by the mean confidence score, label concentration coefficient, and location balance factor to construct the scoring expression, outputting the stability score value and writing it into the field scoring table.

[0057] The field stability scoring function η(s) is expressed as follows:

[0058] η(s)=α1·f1(s)+α2·f2(s)+α3·f3(s);

[0059] Where s represents the current structure field object; f1(s) is the tag path hit frequency, derived from the field tag trajectory record table; f2(s) is the semantic consistency score, derived from the similarity results of the semantic embedding comparison module; f3(s) is the frequency ratio of the field entering the rule adjustment channel, derived from the historical access log of the rule mapping cluster; α1~α3 are static weighting parameters, set by the deployment strategy and configurable. The scoring results will serve as a priority reference factor for field extraction path suppression.

[0060] Subsequently, the fields are sorted from largest to smallest according to their corresponding field stability scores. Fields with higher ranking scores are given priority for slot locking and context fusion verification during the structured extraction process.

[0061] Specifically, in the structural field extraction stage, the extraction strategy controller is invoked. Instead of directly using the scoring and ranking table for fixed extraction, a rule conflict mapping cluster structure derived from the rule set is introduced for path correction. First, it is determined whether the current field exists in the mapping cluster. If the score is high and the sequence structure is stable, and no label conflict path is found in the mapping cluster, a label locking strategy is activated. Slot binding is performed based on the label path and contextual semantic dependencies, and a forward contextual convolutional channel is constructed to expand the semantic context in a window-like manner and enhance the accuracy of continuity recognition. If the field score is low but there is no label drift history in the current mapping cluster, a flexible label matching logic is activated, allowing slight misalignment of labels within the contextual structure boundaries, and slot binding is completed using a tolerance-based matching method.

[0062] If the current path of a field hits any label trajectory in the conflict mapping cluster, and the trajectory overlap rate exceeds a set threshold, the strategy reverse compression logic is automatically triggered. The controller pauses the extraction and binding operation of the scoring and ranking results, and instead constructs a path suppression function based on the label mutual exclusion weights recorded in the mapping cluster. It performs label dilution operation on the sequence containing the hit field, and sets its context segment as a semantic gap protection zone, suspending the extraction process of the remaining fields in the path to avoid label redundancy or incorrect mapping interfering with the stability of the main structure.

[0063] In response to conflicts such as overlapping tags and slot sharing in the semantic path of structured field objects, the platform constructs a path suppression function Ψ(s) to dynamically adjust the tag hit priority. The function definition is as follows:

[0064] Ψ(s) = γ·Λ(s)·D(s);

[0065] Where Λ(s) represents the label trajectory overlap rate, which is derived from the field label historical path vector library; D(s) represents the semantic segment local density score, which is derived from the semantic embedding density evaluation function of the field context segment; γ is a fixed suppression coefficient, set by the policy configuration file. The higher the value of Ψ(s), the lower the label priority of the path in which the field is located will be during the label merging stage, thereby avoiding path selection interference caused by label drift.

[0066] After the field completes the extraction path evaluation and strategy control, three types of structural information are added before the field is finally written into the structure field object set: the first is the semantic sequence number to which it belongs, the second is the stability score, and the third is the status indicator of whether it has entered the rule adjustment channel. At the same time, the association index with the mapping cluster is recorded.

[0067] It should be noted that this encapsulation structure is retained as an extended attribute of the structure field object during writing. It is used to support the invocation of the context-based difference strategy in subsequent consistency verification. It also serves as a feedback vector in the self-evolution path of the behavior rule set, realizing a two-way control closed loop between the structure field extraction path and the rule generation path. This ensures that the structuring process still has stability, self-consistency, and fault tolerance even in the presence of semantic conflicts and path interference.

[0068] During the data review and processing stage, the system takes a set of structured field objects as input and calls a verification engine built by an AI agent to complete the full-dimensional consistency verification of the structured data. The verification process is executed through three channels, corresponding to field value compliance verification, semantic consistency verification, and heterogeneous semantic comparison and recommendation.

[0069] Specifically, the first channel is driven by a rule matcher, which performs parameter specification validation on the values ​​of each field in the structured field object. The rule matcher reads the field value range specification table configured in the power supply business hall service standard library, and calls the value range judgment function and cross-validation function according to the field label to complete the legality verification of key fields such as capacity, voltage level, and access point number, and the comparison of constraints between fields. At the same time, the validation logic performs group judgment based on the rule mapping relationship bound to the field label. If a field matches multiple sets of constraints, it automatically switches to a combined rule path to perform multiple cross-validations.

[0070] The second channel is driven by semantic comparison, which determines the consistency between the original natural language expression of the field and the structured field value. It also constructs a text alignment path based on a vectorized semantic embedding network, performs context deconstruction and vector alignment on the natural language statements corresponding to the structured field labels, and automatically marks fields with a matching degree lower than a set threshold as semantically conflicting fields, and outputs the start and end indexes of the contradictory fragments.

[0071] The consistency matching score is calculated using the following vector matching function, Score(s,f):

[0072] Score(s,f) = cos(⟨V_s, V_f>);

[0073] In the formula, V_s is the context embedding vector of the original semantic text s of the field, and V_f is the label vector of the structural field f, which is calculated by a semantic embedding network based on the Transformer architecture. The threshold is set to 0.85, and fields below this value are marked as semantically conflicting fields. The matching path adopts a dynamic vector alignment algorithm, which performs the alignment index location of conflicting segments through the local shortest path selection method, and supports the semantic segment adversarial enhancement detection.

[0074] Semantic comparison employs a dual-channel encoder architecture to achieve semantic vectorization processing, constructing feature representation spaces based on the CTC speech-to-text structure and the Transformer self-attention path, respectively. The semantic embedding model undergoes comparative learning using a triplet training set. The training data comes from over 50,000 manually annotated semantic slot expression instances in the application business corpus. The model training uses the TripletLoss function to optimize semantic distance convergence performance, ensuring that fields with consistent semantic expressions have an Euclidean distance in the vector space below a set threshold, thereby improving the fault tolerance and accuracy of field semantic matching.

[0075] The third channel is driven by a historical data graph model, which calls structured case comparison. For fields with ambiguous expressions or conflicting labels in the data, feature comparison is performed according to customer historical trajectory, site type, and business scenario category. At the same time, the system queries a set of structured field objects with similar semantic features in the historical application graph, and executes the shortest path algorithm based on similarity vector distance to output a case recommendation path and corresponding conflict warning, which is attached to the field verification structure.

[0076] The similarity metric is performed using the following Euclidean distance function:

[0077] Dist_sim(f_i, f_j) = ||V_i - V_j||2;

[0078] Where V_i and V_j are the semantic embedding vectors of the structural field object f_i and the current field f_j in the graph, respectively, and the vectors are generated by the shared pre-trained semantic model; the shortest path selection is based on the label co-occurrence probability weights established in the graph index, and the optimal recommended field path is matched with the minimum similarity distance.

[0079] After the three-channel verification is completed, the verification engine integrates the verification status identifier, contradiction prompts and recommended paths, generates a field verification feedback table, and writes it into the current data verification status structure.

[0080] During the data flow process, a digital employee badge system is used to map responsibilities between the current processing task and the roles within the service hall. This mapping generates a processing binding table based on a combination of permission identifiers and task categories. Each piece of data is automatically bound to a responsible position code after verification, and the employee badge recognition interface is called to confirm the identity of the currently accessing processing personnel. Simultaneously, the AI ​​scheduling unit allocates the data to be processed to the responsible person's processing view based on the employee badge identification code.

[0081] When data flows to the next role node, the process dispatch rule table is invoked. Based on the current processing status of the data, the risk level of field verification, and the task backlog status, weight calculations are performed to generate the decision for the next flow node. If there is a priority conflict between nodes, the status evaluation function is invoked to calculate the dynamic weight of the task, and a reminder priority identifier is bound to the current data, driving the task dispatcher to trigger the reminder process.

[0082] The state evaluation function R(t,s) is used to comprehensively calculate the urgency of processing data under the current state, and its expression is:

[0083] R(t,s)=δ1·U(t)+δ2·Q(s)+δ3·L(s);

[0084] Wherein, U(t) represents the task backlog index under the current role, derived from the ratio of the length of the pending data queue to the role's processing capacity per unit time; Q(s) is the total risk score for field verification, derived from the risk level of all fields in the structured field object set; L(s) is the percentage of waiting time for data at the current node, derived from the process control log; δ1~δ3 are task weighting parameters. The R(t,s) value is used to determine the expediting level and scheduling priority.

[0085] Meanwhile, the platform is embedded into heterogeneous platforms such as marketing business systems and construction progress systems through RPA control channels, and calls the field synchronizer module to perform structure field content filling operations. This filling operation completes the field value transmission based on the mapping table between field tags and target platform field interfaces. All transmission results are written to the dispatch status log structure, achieving logical decoupling between structure field objects and external business systems, avoiding errors from manual transcription and communication delays between platforms.

[0086] During the document archiving phase, the archiving scheduler is automatically triggered upon confirmation of the closed-loop document flow process, initiating the paperless archiving mechanism. Specifically, the archiving process is completed by the document encapsulator. The system encapsulates structured field objects, flow records, and processing logs into an archive package, and calls the electronic signature service module to perform digital signatures on the encapsulated data. Simultaneously, it calls the trusted timestamp service to generate a timestamp structure and append it to the archive package. The archive package is stored in the document archive repository, and an indexable field structure is established according to four dimensions: field tags, processing time, responsible role, and document type, generating an archive index table to support fast retrieval.

[0087] After archiving is completed, historical case updates are invoked based on the archiving trigger. The extracted feature vectors from the set of structured field objects are written into the training set of the AI ​​verification model for subsequent training to perform incremental learning and rule set enhancement. This ensures that the verification engine has the ability to adaptively evolve based on archiving experience in subsequent tasks, and builds a self-evolving closed-loop mechanism for data verification and rule maintenance.

[0088] The platform defines an archived feedback vector ζ(f) = [η(s), Ψ(s), ρ(f)], where η(s) is the field stability score, Ψ(s) is the path suppression function score, and ρ(f) is the field hit anomaly frequency, derived from the ratio of the number of field markings to the number of rule hit failures in the verification feedback log. This feedback vector will be written into the incremental training dataset and used as input to the rule weight path in the AI ​​verification model, driving the dynamic update of the rule set structure. This achieves adaptive enhancement of the structured field object under different semantic scenarios and a closed loop of rule evolution.

[0089] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0090] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0091] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0092] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0094] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent checking and transferring of business expansion application materials for multi-role collaboration, Characterized in that; comprising: Establish a multi-path data collection channel, complete field data structure extraction through voice transcription and image recognition, and build a structured field object set; Perform semantic behavior tagging and classification identification, build semantic sequence groups and field feature vectors, generate field stability scores and drive extraction strategy control channels; According to the field stability score sorting result, perform structure field extraction, and give priority to fields with high scores for slot locking; In the extraction process, based on the rule conflict mapping cluster structure, correct the path, and perform path suppression processing on the fields that hit the conflict track; When flexible matching is enabled, allow the label to be slightly misaligned within the context structure boundary, and complete slot binding in a tolerance matching manner; Before writing the field to the structured field object set, append sequence number, score value and rule channel state identification structure attributes; Perform multi-channel review on the structured field object set, including field value compliance verification, semantic consistency review and historical case similarity comparison, and generate field review feedback information after the review is completed; Build a task distribution chain based on badge recognition, and complete model training feedback through archiving trigger path.

2. The intelligent verification and transfer method for business expansion application materials oriented towards multi-role collaboration as described in claim 1, characterized in that: In the data access stage, establish a voice input channel through a virtual digital human and collect the original voice content, write the voice stream to the cache, then perform voice transcription, generate the transcribed text and extract the transcription auxiliary parameters. 3.The method of claim 2, wherein the method further comprises: receiving the application data from the first role; and determining whether the application data is valid according to the first role. Perform semantic classification and field extraction on the transcribed text to generate structured field objects; and in the image input scenario, collect and upload the data images, perform image preprocessing, field positioning and field value extraction to generate structured field items under the image channel.

4. The intelligent verification and transfer method for business expansion application materials oriented towards multi-role collaboration as described in claim 1, characterized in that; Extract the channel parameters and auxiliary features in the recognition stream buffer to build the context index information required for behavior identification; analyze the behavior state throughout the voice input process, identify the link breakage, prompt compensation and label aggregation offset behavior in the field expression; when the trigger condition is met, enter the rule generation channel, build the behavior mapping structure and generate the corresponding rule set. 5.The method of claim 4, wherein the method further comprises: receiving the application data from the first role; and determining whether the application data is valid according to the first role. Perform behavior type classification on the marked fields, generate classification labels and write them into the field attributes; and according to the classification labels, source path and time sequence, build semantic sequence groups; extract statistical features from the fields in the sequence groups to generate field feature vector sets, and calculate the field stability scores. 6.The method of claim 5, wherein the method further comprises: receiving the application data from the first role; and determining whether the application data is valid according to the first role. Complete the binding and identity confirmation of processing tasks and post roles based on digital badges, distribute the data to the next processing node according to the process rules, and trigger the follow-up process; at the same time, fill in the structured field content to the external business and record the distribution state information.

7. The multi-role collaboration oriented industry expansion application data intelligent checking and circulation method according to claim 6, characterized in that: Encapsulate the structured fields and processing records for archiving and complete the signature and timestamp processing, write them into the archive library and establish the index; after archiving is completed, call the historical case update path, write the field features into the model training set to drive subsequent rule set enhancement.

Images