An administrative law enforcement-oriented large model evidence element identification method and system

Abstract

The application discloses a large model evidence element identification method and system for administrative law enforcement, and relates to the technical field of computers. The application establishes an evidence object index of associated commodity links, order identifiers, logistics identifiers, sample identifiers and evidence collection session identifiers by taking a sample purchase event as an index unit, and generates a constrained retrieval condition according to a proof object set and the evidence object index, so that the candidate evidence object set is limited under the constraint of the corresponding index field. After the large model generates an evidence element record containing a sample flow relationship based on the candidate evidence object set, the materials between the commodity page, order payment, logistics flow, unpacking and inspection, sample sealing and identification opinions are matched in a fixed order hop by hop, so that the materials corresponding to different commodity links, order identifiers, logistics identifiers and sample identifiers respectively enter the corresponding sample flow relationship, and the field mismatch caused by the mixed use of cross-object materials is reduced.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for identifying large-scale evidence elements in administrative law enforcement. Background Technology

[0002] In the processing of administrative law enforcement case files, existing large-scale model evidence element identification methods typically first perform OCR recognition, ASR transcription, webpage parsing, or text parsing on the case file materials. Then, through prompt words, legal domain large-scale models, search enhancement generation, or structured extraction templates, evidence elements such as subjects, behaviors, goods, transactions, and conclusive opinions are identified from the material text, and structured records are generated.

[0003] In online sampling purchase cases for trademark administrative enforcement, the case files typically include registered trademark rights materials, platform product page materials, order payment materials, logistics flow materials, unpacking and inspection materials, sample sealing materials, identification opinion materials, and electronic data evidence materials. The evidence chain in such cases consists of a continuous sequence of product page, order payment, logistics flow, unpacking and inspection, sample sealing, and identification opinion. The evidentiary elements need to maintain a corresponding relationship through product links, order identifiers, logistics identifiers, sample identifiers, and evidence collection session identifiers.

[0004] Existing large-scale model evidence element identification methods mostly use document fragments, keywords, or semantically similar text blocks as recall targets, and then perform element extraction on the recalled content. When there are multiple similar product links, multiple orders, multiple logistics identifiers, or multiple evidence collection records in the same platform store, semantically similar product pages, transaction records, logistics records, and sample materials are easily recalled simultaneously, resulting in materials corresponding to different product links, order identifiers, logistics identifiers, or sample identifiers being mixed and used for the same identification result.

[0005] The above processing methods result in unclear correspondences between fields and evidence objects in the evidence element records, even though these records include fields such as subject, goods, transaction, logistics, sample, and identification conclusion. The continuity between the sample and the identification opinion material is difficult to confirm, making it challenging to establish the connection between the product link, order payment, logistics flow, unpacking and inspection, sample sealing, and the identification opinion material. Therefore, in the process of identifying evidence elements in large-scale models of online sampling purchase cases for trademark administrative enforcement, how to constrain the scope of evidence material recall and identify missing and conflicting nodes in the sample flow relationship becomes a technical problem that needs to be solved. Summary of the Invention

[0006] This application provides a method and system for identifying large-scale evidence elements in administrative law enforcement, which solves the problems of cross-object mixing of evidence materials and difficulty in closing the chain of sample identity in online sampling purchase case files.

[0007] In a first aspect, embodiments of the present invention provide a method for identifying large-scale model evidence elements for administrative law enforcement, comprising: Acquire multi-source case file materials and parse them into case file material records; Evidence objects are generated based on case file materials; Using sampled purchase events as the index unit, an index of evidence objects is established based on the evidence objects, including related product links, order identifiers, logistics identifiers, sample identifiers, and evidence collection session identifiers; Based on the set of proof objects and the index of evidence objects, constrained search conditions are generated and a set of candidate evidence objects is recalled. Input the candidate evidence object set and evidence element output structure into the large model to generate evidence element records containing sample flow relationships; A continuous association judgment is performed on the sample flow relationship. The continuous association judgment is performed by hopping match in the order of product link, order identifier, logistics identifier, unpacking and inspection materials, sample identifier, sample sealing materials and identification opinion materials. Each hop match is based on the index field that is common to adjacent nodes, and is verified by at least one of the following: material hash value reference, same evidence collection session identifier, original material location reference and evidence collection time sequence. When the set of target nodes obtained by each hop match is merged into a single target node after taking the same value from at least one of the common fields of order identifier, logistics identifier, and sample identifier, the sample flow relationship is recorded as a closed state; when no target node is matched in any hop, a missing node is output; when more than two target nodes are matched in the same hop and there are inconsistencies in at least one field of product information, logistics information, sealing status, and identification conclusion, a conflict node is output.

[0008] In some embodiments, the multi-source case file materials include materials on the basis of rights, materials on platform product pages, materials on order placement and payment, materials on logistics and circulation, materials on unpacking and inspection, materials on sample sealing, materials on identification opinions, and materials on electronic data evidence collection. The case file materials record includes the source of materials, type of materials, location of original materials, time of evidence collection, method of evidence collection, fields of evidence collection, and hash value of materials; When analyzing the multi-source case file materials, the following steps are taken: for the rights-based materials, the trademark registration number, rights holder, and rights status are analyzed; for the platform product page materials, the platform store identifier, product link, page display identifier, and page fixed time are analyzed; for the order payment materials, the order identifier, payment record, payee, and purchase account are analyzed; for the logistics flow materials, the logistics identifier, shipping entity, receiving information, and flow node are analyzed; for the unpacking and inspection materials and sample sealing materials, the sample identifier, unpacking process record, sealing status, and corresponding sealing materials are analyzed; for the identification opinion materials, the identification subject, identification object, identification conclusion, and corresponding sample identifier are analyzed; and for the electronic data forensics materials, the forensics session identifier, access path, file format, storage method, and integrity verification information are analyzed.

[0009] In some embodiments, generating an evidence object based on the case file materials includes: grouping a single case file material or several case file materials under the same evidence collection stage into an evidence object; Write the evidence object identifier, evidence type, original material location, evidence collection method, associated subject, associated product, associated order, associated sample, and integrity fields for the evidence object.

[0010] In some embodiments, the sampling purchase event is generated based on the evidence collection time, evidence collection personnel, evidence collection terminal, network access path, page access order, order payment time, and material generation order, including: The evidence collection session identifier is a data identifier used to distinguish an electronic evidence collection process. The evidence collection session identifier is generated by the electronic evidence collection system and is bound to at least one of the following: evidence collection time, evidence collection personnel, evidence collection terminal, network access path, screen recording file identifier, screenshot file identifier, and page access order. The sampling purchase event starts with entering the platform's product page and ends with the completion of sample sealing. If identification opinion materials exist, the event extends to the completion of receiving the identification opinions. When there are multiple product links or multiple order identifiers, the sampling purchase event is split according to the correspondence between product links and order identifiers. When one order identifier corresponds to multiple logistics identifiers, the corresponding sample flow relationship is generated according to the correspondence between the logistics identifier and the unpacking and inspection materials and sample identifiers; The sampling purchase event saves the event identifier, start and end time, product link, order identifier, logistics identifier, sample identifier, evidence collection session identifier, and associated evidence object identifier, and establishes a mapping relationship between the sampling purchase event and the corresponding evidence object.

[0011] In some embodiments, the evidence object index includes a rights-based index, an index of the object being investigated, an index of the source of the transaction, an index of the identity of the sample, and an index of the electronic evidence collection procedure; The rights-based index records the trademark registration number, rights holder, approved scope of use, rights status, and identification entity; The index records of the searched object include the platform store identifier, business entity, product link, product name, page display identifier, and fixed page time. The transaction source index records the order identifier, payment record, payee, purchase account, and product transaction information; The sample identity index records the correspondence between product links and order identifiers, the correspondence between order identifiers and logistics identifiers, the correspondence between logistics identifiers and unpacking and inspection materials, the correspondence between unpacking and inspection materials and sample identifiers, the correspondence between sample identifiers and sample sealing materials, and the correspondence between sample identifiers and identification opinion materials. The electronic forensics program index records the forensics session identifier, access path, file format, storage method, original material location, and integrity verification information.

[0012] In some embodiments, the set of evidence includes the trademark rights status, the business entity under investigation, the source of the goods involved, the transaction formation process, the sample circulation process, the source of the identification conclusion, and the electronic data fixation process; When generating the constrained search conditions, candidate evidence objects under the same proof object are limited to evidence objects with the same or related trademark registration number, product link, order identifier, logistics identifier, sample identifier, or evidence collection session identifier; When searching for subject relationships, the trademark owner, platform store entity, operating entity, shipping entity, payment recipient, and identification entity are respectively used as subject role fields for matching.

[0013] In some embodiments, inputting the candidate evidence object set and evidence element output structure into the large model includes: Input the field names, field value sources, and excluded output fields into the large model; The values ​​for the fields are derived from case file records, evidence object index fields, and the location of the original materials in the candidate evidence object set. The excluded output fields include the subject of the unassociated evidence object identifier, the transaction fact of the unassociated order identifier, the identification conclusion of the unassociated sample identifier, and the electronic data fixation process of the unassociated evidence collection session identifier; When the field value generated by the large model cannot be associated with the source of the field value, the corresponding field is written into the field to be verified.

[0014] In some embodiments, at least one of the output missing nodes and conflicting nodes includes: A missing node is generated when at least one of the corresponding evidence objects in the trademark registration number and the evidence collection session identifier is not recalled; a missing node is generated when the continuous association judgment of at least one of the corresponding product link, order identifier, logistics identifier, and sample identifier fails. A conflict node is generated when inconsistencies are found in the rights status, business entity, product information, payment records, logistics information, sealing status, identification conclusion, or integrity verification information under the same index field. Write at least one of the missing nodes and the conflicting nodes, along with the corresponding evidence object identifier, original material location, node field, node type, and trigger index field, into the evidence chain node table, and generate a prompt for materials to be supplemented based on the evidence chain node table.

[0015] Secondly, embodiments of the present invention provide a large-scale model evidence element recognition system for administrative law enforcement, including a case file material parsing unit, an evidence object generation unit, an index construction unit, a constrained retrieval unit, a large-scale model recognition unit, and an evidence chain output unit. The case file material parsing unit is used to generate case file material records; The evidence object generation unit is used to generate evidence objects based on the case file materials and to generate sampling purchase events; The index building unit is used to establish an index of evidence objects using sampled purchase events as index units; The constrained retrieval unit is used to generate constrained retrieval conditions based on the set of proof objects and recall the set of candidate evidence objects. The large model recognition unit is used to generate a record of evidence elements containing sample flow relationships within the material scope defined by the candidate evidence object set. The evidence chain output unit is used to perform continuous association judgment on the sample flow relationship corresponding to the evidence element record, and output at least one of missing nodes and conflict nodes according to the continuous association judgment result.

[0016] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the large-scale evidence element identification method for administrative law enforcement as described in the first aspect of the present invention.

[0017] Through the above technical solution, the present invention can achieve at least the following beneficial effects: This invention establishes an evidence object index using sample purchase events as index units, linking related product links, order identifiers, logistics identifiers, sample identifiers, and evidence collection session identifiers. It then generates constrained search conditions based on the set of evidence objects and the evidence object index, limiting the candidate evidence object set to the constraints of the corresponding index fields. After the large model generates evidence element records containing sample flow relationships based on the candidate evidence object set, product pages, order payments, logistics flows, unpacking and inspection, sample sealing, and identification opinion materials are matched sequentially in a fixed order. This ensures that materials corresponding to different product links, order identifiers, logistics identifiers, and sample identifiers are entered into their respective sample flow relationships, reducing field mismatches caused by mixing materials across objects.

[0018] By using the index field shared by adjacent nodes as the direct basis, and verifying it with at least one of the following: material hash value reference, same evidence collection session identifier, original material location reference, and evidence collection time sequence, each hop match in the sample flow relationship has corresponding field basis and material location basis, so that the goods, orders, logistics, samples and identification conclusions in the evidence element record can be associated with the corresponding evidence objects.

[0019] By performing a merging judgment on the target node set after each hop match, and outputting missing nodes when no target node is matched, and outputting conflict nodes when there are more than two target nodes in the same hop and their field contents are inconsistent, the material gaps and object mixing positions in the sample flow relationship can be written into the structured node results, so that the location of evidence that needs to be supplemented or verified during the case file review process is clearly recorded.

[0020] By parsing the rights-based materials, platform product page materials, order payment materials, logistics and circulation materials, unpacking and inspection materials, sample sealing materials, identification opinion materials, and electronic data evidence materials into case file material records, and writing the evidence object identifier, original material location, related subject, related product, related order, related sample, and integrity fields into the evidence object, the evidence element records can correspond to the original material location and integrity fields.

[0021] By using trademark holders, platform store owners, operating entities, shipping entities, payment recipients, and identifying entities as separate subject role fields for matching, the identification of subject relationships is performed based on the role fields in the corresponding materials, reducing the possibility of platform store owners, operating entities, shipping entities, and identifying entities being merged in the same identification result. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation on the scope of this application.

[0023] Figure 1 This is a flowchart of the large-scale evidence element identification method for administrative law enforcement in the embodiment. Figure 2 This is a framework diagram of the large-scale evidence element recognition system for administrative law enforcement in the embodiment. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] All terms used in this application (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0026] To facilitate understanding, the relevant terms and concepts involved in the embodiments of this application will be introduced below: Multi-source case files refer to a collection of materials from different sources, formats, or objects of proof in online sampling purchases of case files for trademark administrative enforcement.

[0027] Case file material records refer to structured data records obtained from the analysis of multi-source case file materials. Evidence objects refer to data objects used to carry a single case file material or several case file materials in the same evidence collection stage. A sampling purchase event refers to the process object formed during an online sampling purchase, including fixing the product page, placing an order and making payment, logistics flow, unpacking and inspection, sample sealing, and receiving identification opinions.

[0028] An evidence object index refers to an evidence object query structure built according to index fields. A set of evidence objects refers to the set of matters that need to be proven by evidence materials during the review of trademark administrative enforcement case files. Constrained search conditions refer to search conditions that simultaneously include constraints on evidence objects and index fields. A candidate set of evidence objects refers to the set of evidence objects recalled according to constrained search conditions.

[0029] The output structure of evidence elements refers to the data structure that defines the output fields of the large model and the sources of those fields. Evidence element records refer to the structured recognition results generated by the large model based on a set of candidate evidence objects. Missing nodes refer to location records in sample transfer relationships or electronic forensics procedures where no corresponding evidence object is matched. Conflicting nodes refer to location records where inconsistent content exists under the same index field.

[0030] Product link refers to the page address, page identifier, or platform product number on the platform's product page that can locate the product being searched. Order identifier refers to the order number or platform order record identifier in the order payment materials that can locate a single transaction record. Logistics identifier refers to the waybill number or logistics record identifier in the logistics flow materials that can locate a single delivery record. Sample identifier refers to the seal number, sample label number, or sample record number used to locate the sample being searched in the unpacking and inspection materials, sample sealing materials, or identification opinion materials. Original material location reference refers to the record of the evidence object referencing at least one of the following location fields: file identifier, page number, screenshot sequence number, video time period, webpage node path, and electronic file storage path. Material hash value reference refers to the record of the evidence object referencing the material hash value in the case file material record, used to establish a correspondence between structured fields and corresponding electronic files, screenshot files, audio and video files, or parsed text records.

[0031] A target node refers to the next type of node that is adjacent to and matched with the current node in a continuous association judgment. A target node set refers to the data set formed by all target nodes obtained from the same hop match. The same target node refers to a target node with the same evidence object identifier, or a target node with at least one field value in its order identifier, logistics identifier, and sample identifier having the same value, and a correspondence between the original material location references and the same case file material record. When merging target nodes, common field values ​​are retained, and the original material location references, material hash value references, and evidence object identifiers from different target nodes are merged and saved. If target nodes have inconsistent content in product information, logistics information, sealing status, or identification conclusions, they are treated as conflicting nodes.

[0032] Example 1: like Figure 1 As shown, this embodiment employs a large-scale model evidence element identification method for administrative law enforcement, applied to online sampling purchase of case files for trademark administrative law enforcement, including: Step S1: Obtain multi-source case file materials and parse the multi-source case file materials into case file material records; Step S2: Generate evidence objects based on the case file materials. Step S3: Using the sampled purchase event as the index unit, establish an evidence object index based on the evidence object. The evidence object index is associated with the product link, order identifier, logistics identifier, sample identifier, and evidence collection session identifier. Step S4: Generate constrained search conditions based on the set of proof objects and the index of evidence objects, and recall the set of candidate evidence objects according to the constrained search conditions; Step S5: Input the candidate evidence object set and evidence element output structure into the large model to generate evidence element records containing sample flow relationships; Step S6: Perform continuous association judgment on the sample circulation relationship corresponding to the evidence element record. The continuous association judgment starts with the product link as the starting node and performs jump matching in the order of product link, order identifier, logistics identifier, unpacking and inspection materials, sample identifier, sample sealing materials and identification opinion materials. Each jump matching uses the index field common to adjacent nodes as the direct basis and is verified by at least one of the following: material hash value reference, same evidence collection session identifier, original material location reference and evidence collection time sequence. Step S7: When the set of target nodes obtained by each hop matching is a single target node after merging the values ​​of at least one of the same fields among order identifier, logistics identifier, and sample identifier, the sample flow relationship is recorded as a closed state; when no target node is matched in any hop, a missing node is output; when two or more target nodes are matched in the same hop, and the two or more target nodes have inconsistent content in at least one field among product information, logistics information, sealing status, and identification conclusion, a conflict node is output.

[0033] In one implementation, the multi-source case file materials include rights-based materials, platform product page materials, order payment materials, logistics flow materials, unpacking and inspection materials, sample sealing materials, identification opinion materials, and electronic data evidence materials. The case file material records include the material source, material type, original material location, evidence collection time, evidence collection method, evidence collection fields, and material hash value. When parsing the multi-source case file materials, the rights-based materials are analyzed to include the trademark registration number, rights holder, and rights status; the platform product page materials are analyzed to include the platform store identifier, product link, page display identifier, and page fixed time; the order payment materials are analyzed to include the order identifier, payment record, payee, and purchase account; the logistics flow materials are analyzed to include the logistics identifier, shipping entity, receiving information, and flow node; the unpacking and inspection materials and sample sealing materials are analyzed to include the sample identifier, unpacking process record, sealing status, and corresponding sealed materials; the identification opinion materials are analyzed to include the identification subject, identification object, identification conclusion, and corresponding sample identifier; and the electronic data evidence materials are analyzed to include the evidence collection session identifier, access path, file format, storage method, and integrity verification information.

[0034] The parsing of multi-source case files is completed through at least one of the following processes: layout recognition, speech-to-text transcription, webpage element parsing, and text field extraction. Material sources include documents fixed by administrative law enforcement personnel, e-commerce platform pages, payment platform records, logistics platform records, unpacking and inspection videos, sample sealing records, identification opinions issued by trademark holders, and electronic data evidence materials. Evidence fields are saved synchronously with the original material location, which includes at least one of the following: file identifier, page number, screenshot sequence number, video time period, webpage node path, and electronic file storage path. The material hash value is calculated from the corresponding electronic file, screenshot file, audio / video file, or parsed text record and is used to record the integrity status of the material content.

[0035] Furthermore, when the same case file contains multiple electronic files, screenshots, or audio / video files, a hash value is generated for each file, and these hash values ​​are saved corresponding to the original file locations. When parsed text records are used in the hash value calculation, the text content after character encoding, line breaks, and whitespace are normalized as the calculation object; when electronic files, screenshots, and audio / video files are used, the file byte content is used as the calculation object. If the hash value is empty, duplicated, or does not correspond to the original file location, the corresponding case file record is written to the verification pending record.

[0036] In one implementation, the evidence object index includes a rights-based index, an index of the object under investigation, an index of transaction sources, an index of sample identity, and an electronic evidence collection procedure index. The rights-based index records the trademark registration number, rights holder, approved scope of use, rights status, and identification subject. The index of the object under investigation records the platform store identifier, operating entity, product link, product name, page display identifier, and page fixed time. The transaction source index records the order identifier, payment record, payee, purchase account, and product transaction information. The sample identity index records the correspondence between product links and order identifiers, the correspondence between order identifiers and logistics identifiers, the correspondence between logistics identifiers and unpacking and inspection materials, the correspondence between unpacking and inspection materials and sample identifiers, the correspondence between sample identifiers and sample sealing materials, and the correspondence between sample identifiers and identification opinion materials. The electronic evidence collection procedure index records the evidence collection session identifier, access path, file format, storage method, original material location, and integrity verification information.

[0037] In one implementation, the set of evidence objects includes the trademark rights status, the investigated business entity, the source of the goods involved, the transaction formation process, the sample circulation process, the source of the identification conclusion, and the electronic data fixation process; when generating constrained search conditions, candidate evidence objects under the same evidence object are limited to evidence objects with the same or related trademark registration number, product link, order identifier, logistics identifier, sample identifier, or evidence collection session identifier; when searching for subject relationships, the trademark owner, platform store entity, business entity, shipping entity, payment entity, and identification entity are respectively used as subject role fields for matching.

[0038] The "Subject Role" field stores the subject's name, unified social credit code or identity identifier, source of materials, location of original materials, and the role source field. When matching subject relationships, candidate subject records are first generated based on the "Subject Role" field, and then the relationships between roles are established using authorization documents, business licenses, platform operator information, payment account information, shipping information, and identification opinion signature information. Missing or conflicting nodes are generated when the platform store subject differs from the operating subject, the shipping subject differs from the platform store subject, the payment subject differs from the operating subject, or the authorization document does not cover the corresponding goods.

[0039] In one implementation, the set of candidate evidence objects and the output structure of evidence elements are input into a large model, including: Input field names, field value sources, and excluded output fields into the large model; The field values ​​are derived from case file records in the candidate evidence object set, evidence object index fields, and the location of the original materials; Excluded output fields include subjects without associated evidence object identifiers, transaction facts without associated order identifiers, identification conclusions without associated sample identifiers, and electronic data fixation processes without associated evidence collection session identifiers; When the field values ​​generated by the large model cannot be associated with the source of the field values, the corresponding field will be written into the field to be verified.

[0040] Fields pending verification refer to records in the output of the large model that fail to be associated with the evidence object identifier, the location of the original material, or the source of the field value. Fields pending verification store the field name, field value, candidate evidence object identifier, candidate original material location, and the reason for pending verification. Records pending verification refer to abnormal records formed during the attribution, matching, or verification process of case file records, evidence objects, sampling purchase events, or sample circulation chains. Records pending verification store the record type, triggering field, triggering reason, associated evidence object identifier, and original material location. Fields pending verification correspond to the output process of the large model fields, while records pending verification correspond to the event attribution, continuous association judgment, and node writing processes.

[0041] The large-scale model is deployed using one of the following methods: a pre-trained language model, a legal domain language model, or a multimodal language model. Deployment methods include local deployment, private cloud deployment, and API call deployment. Training data sources include administrative law enforcement case files, evidence material field samples, electronic data evidence collection record samples, fixed samples from product pages, and manually annotated evidence element samples. Annotation methods include subject role annotation, index field annotation, sample flow node annotation, original material location annotation, missing node annotation, and conflict node annotation. Training objectives include field extraction, field attribution, material range constraints, node status recognition, and output structure alignment. Inference inputs include a set of candidate evidence objects, evidence object indexes, and evidence element output structures. Inference outputs include evidence element records, fields to be verified, missing nodes, and conflict nodes. Model updates are performed based on newly annotated samples, reviewed fields to be verified, and confirmed node records.

[0042] In one implementation, outputting at least one of missing nodes and conflicting nodes includes: Missing nodes are generated when the continuous association judgment corresponding to trademark registration number, product link, order identifier, logistics identifier, sample identifier, or evidence collection session identifier fails; A conflict node is generated when inconsistencies are found in the rights status, business entity, product information, payment records, logistics information, sealing status, identification conclusion, or integrity verification information under the same index field. Write at least one of the missing nodes and conflicting nodes, along with the corresponding evidence object identifier, original material location, node field, node type, and trigger index field, into the evidence chain node table, and generate a prompt for materials to be supplemented based on the evidence chain node table.

[0043] The evidence chain node table includes node identifier, node type, proof object, trigger index field, evidence object identifier, original material location, node field, node status, associated entity, associated product, associated order, associated sample, material hash value, and supplementary material prompts. Supplementary material prompts are generated based on the node type. For missing nodes, the supplementary material prompt record includes the missing material category, missing index field, and the missing sample's transfer location. For conflicting nodes, the supplementary material prompt record includes the conflict field, conflict value, conflicting material source, and conflicting evidence object identifier. The evidence chain node table and evidence element records establish a correspondence through evidence object identifiers and original material locations.

[0044] In one embodiment of Example 1, the sample flow chain is a node-based representation of the sample flow relationship. The continuous association judgment takes the product link as the starting node and generates the sample flow chain in the order of product link, order identifier, logistics identifier, unpacking and inspection materials, sample identifier, sample sealing materials and identification opinion materials. Then, the adjacent nodes in the sample flow chain are matched one step at a time.

[0045] The sample transfer chain is represented as follows: , in, For the first Sample transfer chain; Number the sample transfer chain; For the first Product link nodes in a sample transfer chain; For the first Order identification node in the sample transfer chain; For the first Logistics identification nodes in a sample transfer chain; For the first The unpacking and inspection material nodes in the sample transfer chain; For the first Sample identification nodes in a sample transfer chain; For the first Sample sealing material nodes in a sample transfer chain; For the first Identification opinion material nodes in the sample transfer chain.

[0046] When performing hop-by-hop matching on two adjacent nodes, the index field, evidence session identifier, and original material location reference of the previous node are read from the evidence object index, and candidate target nodes are searched within the evidence object range corresponding to the next type of node.

[0047] Material hash values ​​are used to verify the integrity fields of materials corresponding to adjacent nodes; the association strength between adjacent nodes is calculated based on common index fields, common evidence session identifiers, and original material location references. , in, For the first The first sample transfer chain Each node and candidate target nodes The strength of the correlation between them; This represents the adjacent jump number in the sample transfer chain, with a value ranging from 1 to 6; For the first Candidate target nodes in the class node; Weights for common index items; For the first The first sample transfer chain Each node corresponds to a set of index entries, and each index entry consists of an index field name and an index field value. Candidate target nodes The corresponding set of index items; Weighting of shared evidence collection session identifiers; This is an indicator function that takes the value 1 when the condition inside the parentheses is true and takes the value 0 when the condition inside the parentheses is false. For the first The first sample transfer chain The evidence collection session identifier corresponding to each node; Candidate target nodes The corresponding evidence collection session identifier; Weight the original material location reference; For the first The first sample transfer chain The set of original material location references corresponding to each node; Candidate target nodes The corresponding set of original material location references. , and The values ​​of all three are greater than or equal to 0 and less than or equal to 1, and the sum of the three is 1; when When the set is empty, the value of the sub-item corresponding to the common index item is 0. The index item set includes at least one of the following: evidence object identifier, product link, order identifier, logistics identifier, sample identifier, evidence collection session identifier, and original material location reference.

[0048] , and The offline settings are based on a manually verified complete sample transfer chain and remain unchanged during the processing of the same batch of case files. When the number of complete sample transfer chains is lower than the preset associated weight settings, the weight values ​​corresponding to the previous batch of case files are used. If the weight values ​​of the previous batch do not exist, the preset initial associated weights are used as a conservative judgment standard. The preset initial associated weights are a combination of non-negative weights that sum to 1.

[0049] For each hop association, filter to reach the first... The target nodes that meet the corresponding consecutive association thresholds and satisfy the direct anchoring relationship: , in, For the first The first sample transfer chain Jump to the first The set of target nodes that meet the corresponding consecutive association threshold and satisfy the direct anchoring relationship; For the first The first sample transfer chain Jump to the corresponding set of candidate target nodes; For the first The corresponding consecutive association threshold for jumping; The index field name in the index item; To retrieve the value of the index field in the index entry; For the first The set of directly anchored field names corresponding to each jump. The set of directly anchored field names corresponding to the first jump includes the product link and order identifier; the set of directly anchored field names corresponding to the second jump includes the order identifier and logistics identifier; the set of directly anchored field names corresponding to the third jump includes the logistics identifier and the evidence object identifier corresponding to the unpacking and inspection materials; the set of directly anchored field names corresponding to the fourth jump includes the evidence object identifier corresponding to the unpacking and inspection materials and the sample identifier; the set of directly anchored field names corresponding to the fifth jump includes the sample identifier and the evidence object identifier corresponding to the sample sealing materials; and the set of directly anchored field names corresponding to the sixth jump includes the sample identifier and the evidence object identifier corresponding to the identification opinion materials.

[0050] The consecutive association threshold is calibrated according to the adjacent hop numbers, and the calibration method is as follows: , in, For the first The corresponding consecutive association threshold for jumping; This represents the upper limit of the continuous association threshold. This is the lower limit of the continuous association threshold; For the first part of a complete sample transfer chain that has been manually verified The mean of the association strength between adjacent nodes; This is the threshold adjustment coefficient for continuous correlation; For the first part of a complete sample transfer chain that has been manually verified The standard deviation of the association strength between adjacent nodes. , and All are preset values. and The values ​​of are all greater than or equal to 0 and less than or equal to 1, and Greater than or equal to , Greater than or equal to 0; when the first When the number of complete sample transfer chains corresponding to the jump is less than the preset continuous correlation calibration number, the previous batch of case file materials will be used. The corresponding continuous association threshold for the jump; the first in the previous batch of case files If the corresponding consecutive association threshold for the jump does not exist, use a threshold greater than or equal to. and less than or equal to The preset initial continuous association threshold is used as the first Conservative criteria for judging jumps.

[0051] The complete sample circulation chain used to calibrate the continuous association threshold comes from the sample circulation chain that has been manually reviewed in the historical case files, or from the sample circulation chain that has been independently manually reviewed in the current batch of case files; the output results of the large model that has not been reviewed, the records to be verified, the chains corresponding to missing nodes and the chains corresponding to conflicting nodes do not participate in the continuous association threshold calibration.

[0052] when When it is an empty set, determine the first... Jumping consecutive associations failed, and with the first Class nodes are written as missing nodes in the evidence chain node table; when If there are two or more target nodes, and the order identifier, logistics identifier, sample identifier, sealing status, or identification conclusion corresponding to the two or more target nodes are inconsistent, then the first node is determined to be the target node. If a conflicting node exists, the corresponding target node will be jointly written into the evidence chain node table; when If there are two or more target nodes that point to the same order identifier, the same logistics identifier, or the same sample identifier, and the original material location references can complement each other, the two or more target nodes are merged into the same chain node and the process continues. When any hop generates a missing node or a conflicting node, the sample flow chain is not output as a closed state result, and the missing node or conflicting node, along with the corresponding evidence object identifier, original material location, node field, node type, and trigger index field, are written into the evidence chain node table; when all six hop associations between the seven types of nodes are successful, the sample flow chain is recorded as a closed state.

[0053] Example 2: Based on Embodiment 1, this embodiment provides a specific method for generating sampled purchase events in the large-scale model evidence element identification method for administrative law enforcement, which is based on evidence collection time, evidence collection personnel, evidence collection terminal, network access path, page access order, order payment time, and material generation order. Evidence objects are generated based on case file materials, including: grouping a single case file material or several case file materials under the same evidence collection stage into one evidence object; and writing evidence object identifier, evidence type, original material location, evidence collection method, associated subject, associated goods, associated order, associated sample and integrity fields for the evidence object.

[0054] In one implementation, a sampling purchase event is generated based on the evidence collection time, evidence collection personnel, evidence collection terminal, network access path, page access order, order payment time, and material generation order. This includes: an evidence collection session identifier, a data identifier used to distinguish each electronic evidence collection process, generated by the electronic evidence collection system and bound to at least one of the evidence collection time, evidence collection personnel, evidence collection terminal, network access path, screen recording file identifier, screenshot file identifier, and page access order; the sampling purchase event starts with entering the platform's product page and ends with the completion of sample sealing, extending to the completion of receiving identification opinions if such materials exist; when multiple product links or multiple order identifiers exist, the sampling purchase event is split according to the correspondence between product links and order identifiers; when one order identifier corresponds to multiple logistics identifiers, a corresponding sample flow relationship is generated according to the correspondence between logistics identifiers and unpacking and inspection materials and sample identifiers; the sampling purchase event saves the event identifier, start and end time, product link, order identifier, logistics identifier, sample identifier, evidence collection session identifier, and associated evidence object identifier, and establishes a mapping relationship between the sampling purchase event and the corresponding evidence object.

[0055] In the implementation of Example 2, when generating sampling purchase events based on evidence collection time, evidence collection personnel, evidence collection terminal, network access path, page access order, order payment time, and material generation order, the evidence collection session identifier is used as the basic aggregation field of the candidate sampling purchase event. Case file material records with the same evidence collection session identifier and consistent relationships in evidence collection personnel, evidence collection terminal, or network access path are grouped into the same candidate sampling purchase event record set, and candidate sampling purchase events are generated based on the candidate sampling purchase event record set.

[0056] For case file records within the same candidate sampling purchase event record set, the records are arranged chronologically according to the evidence collection time, page access order, order payment time, and material generation order. This ensures that platform product page materials precede order payment materials, order payment materials precede logistics materials, logistics materials precede unpacking and inspection materials, unpacking and inspection materials precede sample sealing materials, and sample sealing materials precede identification opinion materials. The order payment time is used to determine the chronological position of the order payment materials in the candidate sampling purchase event record set and to verify the binding relationship between product links and order identifiers.

[0057] When calculating the attribution relationship between case file records and candidate sampling purchase events, the merging results are jointly limited by the identity consistency coefficient and the temporal fit. The identity consistency coefficient is: , in, Recording case files Compared to candidate sampling purchase events The identity consistency coefficient; Records of case files awaiting attribution; Purchase event for candidate sampling; This is an indicator function that takes the value 1 when the condition inside the parentheses is true and takes the value 0 when the condition inside the parentheses is false. Recording case files The evidence collection session identifier in the middle; Purchase of candidate samples The evidence collection session identifier in the middle; Recording case files The evidence collection personnel; Purchase of candidate samples The corresponding group of evidence collectors; Recording case files The evidence collection terminal in the middle; Purchase of candidate samples The corresponding set of evidence collection terminals; Recording case files The network access path in the middle; Purchase of candidate samples The corresponding set of network access paths; Recording case files Order identifier in the middle; Purchase of candidate samples The corresponding set of order identifiers; Recording case files Logistics identification in the middle; Purchase of candidate samples The corresponding set of logistics identifiers; Recording case files Sample identification in the sample; Purchase of candidate samples The corresponding set of sample identifiers; Recording case files With candidate sampling purchase event The number of fields that can be compared. The indicator functions in the formula numerator are only recorded in the case file materials. With candidate sampling purchase event All fields must have corresponding values ​​to participate in the summation; Count according to the number of fields involved in the summation, when When it is 0, The value is 0.

[0058] The attribution score is calculated based on the consistency coefficient of identity, the time of evidence collection, the order of page access, and the order of material generation. , in, Recording case files Compared to candidate sampling purchase events Attribution score; The weight of the identity consistency coefficient; Weighting for the timeliness of evidence collection; Weighting of page access order fit; Weights for the fit of the material generation sequence; It is a natural exponential function; Recording case files The time of evidence collection; Purchase of candidate samples The central value of the evidence collection time that has been included in the case file records; The time decay scale for evidence collection; Recording case files Page access order value; Purchase of candidate samples The central value of the page access order that has been included in the case file records; The decay scale for page access order; Recording case files The material generation order value; Purchase of candidate samples The central value of the material generation sequence that has been included in the case file records; The decay scale represents the material generation sequence. , , and The values ​​of all four are greater than or equal to 0 and less than or equal to 1, and the sum of the four values ​​is 1. , and All are preset scale values ​​greater than 0.

[0059] Candidate sampling purchase event Before a record of materials included in the case file is formed, , and The candidate sampling purchase events were determined based on the median of the evidence collection time of the case file materials within the candidate sampling purchase event record set, the median of the page access order value obtained according to the page access order value selection rule described later, and the median of the material generation order value obtained according to the material generation order value selection rule described later; After the materials have been included in the case file, , and After all candidate sampling purchase events have been traversed and merged once, the data is refreshed and updated according to the median value of the records already included in the case file. Case file records written to the pending verification record do not participate in the above median value update; when the evidence collection time, page access order value, or material generation order value is in the opposite order to the aforementioned material types, and cannot be verified through the original material location, the corresponding case file record is written to the pending verification record.

[0060] , , , , , and The data is manually verified and correctly assigned to case files offline, and remains unchanged during the processing of the same batch of case files. When the number of manually verified correctly assigned case files is lower than the preset assignment parameter set number, the parameter values ​​corresponding to the previous batch of case files are used. If the parameter values ​​for the previous batch do not exist, the preset initial assignment parameter values ​​are used as a conservative merging standard. The preset initial assignment parameter values ​​include non-negative initial weights that sum to 1, and an initial decay scale greater than 0.

[0061] When the case file materials record When preparing materials for the platform's product pages, Values ​​are retrieved according to the page access order; when case file materials are recorded. When preparing payment materials, logistics materials, unpacking and inspection materials, sample sealing materials, or identification opinion materials, The values ​​are mapped according to the order of the materials recorded in the case file, including materials on the platform's product page, order payment materials, logistics materials, unpacking and inspection materials, sample sealing materials, and identification opinion materials. The order mapping values ​​are set in ascending order of the aforementioned material types.

[0062] Case file materials record Lacking the material generation sequence but possessing the original material positional arrangement sequence Values ​​are taken in the order of their original location; case file records. If the order of material generation and the original material location are missing, the case file material record should be written into the verification record.

[0063] when When the event attribution threshold is reached, the case file materials will be recorded. Classified as a candidate sampling purchase event When the case file records When multiple candidate sampling purchase events all reach the event attribution threshold, the candidate sampling purchase event with the highest attribution score is selected. When the difference between the highest and second-highest attribution scores is lower than a preset difference threshold, the case file material record is written into the pending verification record. The preset difference threshold is determined based on the 10th percentile of the difference between the highest and second-highest attribution scores in the case file material records that have been manually confirmed as correctly attributed, and its value is greater than or equal to 0 and less than or equal to 1. When the number of case file material records that have been manually confirmed as correctly attributed is less than the number specified by the preset difference threshold, the preset initial difference threshold with a value greater than or equal to 0 and less than or equal to 1 is used as a conservative judgment standard.

[0064] The event attribution threshold is calibrated based on a manually verified complete sample transfer chain. The calibration method is as follows: , in, This is the threshold for event attribution. This represents the upper limit of the event attribution threshold. This is the lower limit of the event attribution threshold; The mean score of the attribution rating of case file materials in a complete sample transfer chain that has been manually verified; This is the threshold adjustment factor; The standard deviation of the attribution score for case file material records in a complete sample transfer chain that has been manually verified.

[0065] and The values ​​of are all greater than or equal to 0 and less than or equal to 1, and Greater than or equal to ; Greater than or equal to 0. A calibration is performed after each batch of case files is imported. If the number of manually confirmed complete sample transfer chains in this batch of case files is lower than the preset event attribution calibration number, the event attribution threshold corresponding to the previous batch of case files is used. If the event attribution threshold for the previous batch does not exist, a threshold greater than or equal to is used. and less than or equal to The preset initial event attribution threshold is used as a conservative merging standard.

[0066] The complete sample circulation chain used to calibrate the event attribution threshold comes from the sample circulation chain that has been manually verified in the historical case files, or from the sample circulation chain that has been independently manually verified in the current batch of case files; the output results of the large model that has not been verified, the records to be verified, the chains corresponding to missing nodes and the chains corresponding to conflicting nodes do not participate in the event attribution threshold calibration.

[0067] The event attribution threshold, consecutive association threshold for each jump, preset difference threshold, attribution score weight, association strength weight, evidence collection time decay scale, page access order decay scale, material generation order decay scale, and stage order mapping value all correspond to a parameter version identifier. The parameter version identifier is a field used to record the version of the threshold, weight, scale, and mapping value. The same parameter version identifier is used in the merging, continuous association judgment, and evidence chain node table generation processes of the same batch of case materials. When a rollback occurs due to insufficient samples, the parameter version identifier corresponding to the rollback source is written into the evidence chain node table.

[0068] When multiple product links or order identifiers exist under the same evidence collection session identifier, candidate sampling purchase events are split according to the binding relationship between the product links and order identifiers. If there is a corresponding relationship between the product link and the order identifier through page redirection records, shopping cart records, order details, payment records, or the same access path in electronic data evidence materials, the product link and the order identifier are grouped into the same sampling purchase event. If multiple product links correspond to the same order identifier and the order details can distinguish each product link, sampling purchase events are generated for different product links under that order identifier. If the order details cannot distinguish each product link, the corresponding candidate sampling purchase event is marked as pending verification, and its associated case file material is recorded in the pending verification record.

[0069] When an order identifier corresponds to multiple logistics identifiers, the events are split or merged based on the correspondence between the logistics identifiers and the unpacking and inspection materials and sample identifiers. If different logistics identifiers correspond to different unpacking and inspection materials or different sample identifiers, sampling purchase events are generated separately according to the logistics identifiers. If multiple logistics identifiers correspond to the same unpacking and inspection material and the unpacking and inspection material records the same sample identifier, they are merged into a single sampling purchase event. If there is no correspondence between the logistics identifier and the unpacking and inspection material, or if the sample identifier cannot form a continuous correspondence among the unpacking and inspection material, sample sealing material, and identification opinion material, the corresponding logistics identifier, unpacking and inspection material, or sample identifier is written into the missing node or conflict node. After the above processing is completed, a mapping relationship is established between each sampling purchase event and the corresponding evidence object, and this mapping is written into the product link, order identifier, logistics identifier, sample identifier, and evidence collection session identifier.

[0070] Example 3: Based on Embodiments 1 and 2, this embodiment provides a large-scale model evidence element identification system for administrative law enforcement, applied to online sampling purchase of case files in trademark administrative law enforcement, such as... Figure 2 As shown, it includes a case file material analysis unit, an evidence object generation unit, an index construction unit, a constrained retrieval unit, a large model recognition unit, and an evidence chain output unit; The case file material parsing unit is used to generate case file material records; The evidence object generation unit is used to generate evidence objects based on case file materials and to generate sampling purchase events; The index building unit is used to create an index of evidence objects based on sampled purchase events; The constrained retrieval unit is used to generate constrained retrieval conditions based on the set of evidence objects and to recall the set of candidate evidence objects. The large model recognition unit is used to generate a record of evidence elements containing sample flow relationships within the material scope defined by the candidate evidence object set; The evidence chain output unit is used to perform continuous association judgment on the sample flow relationship corresponding to the evidence element record, and output at least one of missing nodes and conflict nodes based on the continuous association judgment result.

[0071] This embodiment also provides a computer device, including a processor and a memory, the memory storing a computer program, which, when executed by the processor, implements a method for identifying large-scale evidence elements for administrative law enforcement.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0073] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of this application and form different embodiments. For example, all the embodiments above can be used in any combination. The information disclosed in this background section is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

Claims

1. A method for identifying evidence elements in a large-scale model for administrative law enforcement, characterized in that, include: Acquire multi-source case file materials and parse them into case file material records; Evidence objects are generated based on case file materials; Using sampled purchase events as the index unit, an index of evidence objects is established based on the evidence objects, including related product links, order identifiers, logistics identifiers, sample identifiers, and evidence collection session identifiers; Based on the set of proof objects and the index of evidence objects, constrained search conditions are generated and a set of candidate evidence objects is recalled. Input the candidate evidence object set and evidence element output structure into the large model to generate evidence element records containing sample flow relationships; A continuous association judgment is performed on the sample flow relationship. The continuous association judgment is performed by hopping match in the order of product link, order identifier, logistics identifier, unpacking and inspection materials, sample identifier, sample sealing materials and identification opinion materials. Each hop match is based on the index field that is common to adjacent nodes, and is verified by at least one of the following: material hash value reference, same evidence collection session identifier, original material location reference and evidence collection time sequence. When the set of target nodes obtained by each hop matching is merged into a single target node after taking the value of at least one of the same fields of order identifier, logistics identifier and sample identifier, the sample flow relationship is recorded as a closed state; If no target node is matched in any hop, output the missing node; When a single hop matches two or more target nodes and there are inconsistencies in at least one field among the product information, logistics information, sealing status, and identification conclusion, the conflicting node is output.

2. The method for identifying large-scale evidence elements for administrative law enforcement according to claim 1, characterized in that, The multi-source case file materials include materials on the basis of rights, materials on platform product pages, materials on order placement and payment, materials on logistics and circulation, materials on unpacking and inspection, materials on sample sealing, materials on identification opinions, and materials on electronic data evidence collection. The case file materials record includes the source of materials, type of materials, location of original materials, time of evidence collection, method of evidence collection, fields of evidence collection, and hash value of materials; When analyzing the multi-source case file materials, the following steps are taken: for the rights-based materials, the trademark registration number, rights holder, and rights status are analyzed; for the platform product page materials, the platform store identifier, product link, page display identifier, and page fixed time are analyzed; for the order payment materials, the order identifier, payment record, payee, and purchase account are analyzed; for the logistics flow materials, the logistics identifier, shipping entity, receiving information, and flow node are analyzed; for the unpacking and inspection materials and sample sealing materials, the sample identifier, unpacking process record, sealing status, and corresponding sealing materials are analyzed; for the identification opinion materials, the identification subject, identification object, identification conclusion, and corresponding sample identifier are analyzed; and for the electronic data forensics materials, the forensics session identifier, access path, file format, storage method, and integrity verification information are analyzed.

3. The method for identifying large-scale evidence elements for administrative law enforcement according to claim 2, characterized in that, The process of generating evidence objects based on the case file materials includes: grouping a single case file material or several case file materials from the same evidence collection stage into one evidence object. Write the evidence object identifier, evidence type, original material location, evidence collection method, associated subject, associated product, associated order, associated sample, and integrity fields for the evidence object.

4. The method for identifying large-scale evidence elements for administrative law enforcement according to claim 3, characterized in that, The sampled purchase event is generated based on the evidence collection time, evidence collection personnel, evidence collection terminal, network access path, page access order, order payment time, and material generation order, including: The evidence collection session identifier is a data identifier used to distinguish an electronic evidence collection process. The evidence collection session identifier is generated by the electronic evidence collection system and is bound to at least one of the following: evidence collection time, evidence collection personnel, evidence collection terminal, network access path, screen recording file identifier, screenshot file identifier, and page access order. The sampling purchase event starts with entering the platform's product page and ends with the completion of sample sealing. If identification opinion materials exist, the event extends to the completion of receiving the identification opinions. When there are multiple product links or multiple order identifiers, the sampling purchase event is split according to the correspondence between product links and order identifiers. When one order identifier corresponds to multiple logistics identifiers, the corresponding sample flow relationship is generated according to the correspondence between the logistics identifier and the unpacking and inspection materials and sample identifiers; The sampling purchase event saves the event identifier, start and end time, product link, order identifier, logistics identifier, sample identifier, evidence collection session identifier, and associated evidence object identifier, and establishes a mapping relationship between the sampling purchase event and the corresponding evidence object.

5. The method for identifying large-scale evidence elements for administrative law enforcement according to claim 1, characterized in that, The evidence object index includes a rights-based index, an index of the investigated object, an index of the source of the transaction, an index of sample identity, and an index of electronic evidence collection procedures; The rights-based index records the trademark registration number, rights holder, approved scope of use, rights status, and identification entity; The index records of the searched object include the platform store identifier, business entity, product link, product name, page display identifier, and fixed page time. The transaction source index records the order identifier, payment record, payee, purchase account, and product transaction information; The sample identity index records the correspondence between product links and order identifiers, the correspondence between order identifiers and logistics identifiers, the correspondence between logistics identifiers and unpacking and inspection materials, the correspondence between unpacking and inspection materials and sample identifiers, the correspondence between sample identifiers and sample sealing materials, and the correspondence between sample identifiers and identification opinion materials. The electronic forensics program index records the forensics session identifier, access path, file format, storage method, original material location, and integrity verification information.

6. The method for identifying large-scale evidence elements for administrative law enforcement according to claim 1, characterized in that, The set of evidence includes the status of trademark rights, the business entity under investigation, the source of the goods involved, the transaction formation process, the sample circulation process, the source of the identification conclusion, and the electronic data fixation process; When generating the constrained search conditions, candidate evidence objects under the same proof object are limited to evidence objects with the same or related trademark registration number, product link, order identifier, logistics identifier, sample identifier, or evidence collection session identifier; When searching for subject relationships, the trademark owner, platform store entity, operating entity, shipping entity, payment recipient, and identification entity are respectively used as subject role fields for matching.

7. The method for identifying large-scale evidence elements for administrative law enforcement according to claim 1, characterized in that, The candidate evidence object set and evidence element output structure are input into the large model, including: Input the field names, field value sources, and excluded output fields into the large model; The values ​​for the fields are derived from case file records, evidence object index fields, and the location of the original materials in the candidate evidence object set. The excluded output fields include the subject of the unassociated evidence object identifier, the transaction fact of the unassociated order identifier, the identification conclusion of the unassociated sample identifier, and the electronic data fixation process of the unassociated evidence collection session identifier; When the field value generated by the large model cannot be associated with the source of the field value, the corresponding field is written into the field to be verified.

8. The method for identifying large-scale evidence elements for administrative law enforcement according to claim 1, characterized in that, The output includes at least one of missing nodes and conflicting nodes, including: A missing node is generated when at least one of the corresponding evidence objects in the trademark registration number and the evidence collection session identifier is not recalled; a missing node is generated when the continuous association judgment of at least one of the corresponding product link, order identifier, logistics identifier, and sample identifier fails. A conflict node is generated when inconsistencies are found in the rights status, business entity, product information, payment records, logistics information, sealing status, identification conclusion, or integrity verification information under the same index field. Write at least one of the missing nodes and the conflicting nodes, along with the corresponding evidence object identifier, original material location, node field, node type, and trigger index field, into the evidence chain node table, and generate a prompt for materials to be supplemented based on the evidence chain node table.

9. A large-scale model evidence element recognition system for administrative law enforcement, based on the large-scale model evidence element recognition method for administrative law enforcement as described in any one of claims 1 to 8, characterized in that, include: The unit includes a case file material analysis unit, an evidence object generation unit, an index construction unit, a constrained retrieval unit, a large model recognition unit, and an evidence chain output unit. The case file material parsing unit is used to generate case file material records; The evidence object generation unit is used to generate evidence objects based on the case file materials and to generate sampling purchase events; The index building unit is used to establish an index of evidence objects using sampled purchase events as index units; The constrained retrieval unit is used to generate constrained retrieval conditions based on the set of proof objects and recall the set of candidate evidence objects. The large model recognition unit is used to generate a record of evidence elements containing sample flow relationships within the material scope defined by the candidate evidence object set. The evidence chain output unit is used to perform continuous association judgment on the sample flow relationship corresponding to the evidence element record, and output at least one of missing nodes and conflict nodes according to the continuous association judgment result.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the large-scale evidence element identification method for administrative law enforcement as described in any one of claims 1 to 8.

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