Product whole life cycle traceability after-sales service system based on multi-source heterogeneous data

Abstract

The application discloses a product whole life cycle traceability after-sales service system based on multi-source heterogeneous data, relates to the technical field of product traceability after-sales management, and accesses manufacturing history, work orders, spare parts warehouse in and out, and equipment logs, establishes product instances, component and software primary key mapping, is normalized as configuration change events containing effective time and warehouse in time double time axis, and carries difference operation, evidence package and idempotent event identification to write into an immutable event library. A conflict set is constructed for the same configuration target, uniqueness, causality and compatibility constraint verification is executed, an effective event is determined based on evidence package arbitration, arbitration records and confidence are output, a configuration version chain is derived according to the effective event, a snapshot is generated, late or error correction events are incrementally played back, and an impact propagation list is generated. When querying any point in time configuration, evidence chain and confidence are played back and output, and active check backflow error correction events are triggered when confidence is insufficient, so that the accuracy of quality assurance determination and spare part matching is improved.

Description

Technical Field

[0001] This invention relates to the field of product traceability and after-sales management technology, specifically a product lifecycle traceability and after-sales service system based on multi-source heterogeneous data. Background Technology

[0002] In the after-sales service of durable products such as automobiles, home appliances, construction machinery, and industrial equipment, there are often many installation, commissioning, maintenance, repair, component replacement, and software upgrade activities from the time the product is delivered by the manufacturer to its decommissioning and recycling. These activities are carried out on-site by manufacturers, service providers, distributors, and customers. Information is collected by systems such as manufacturing history, service work orders, spare parts warehouses, upgrade platforms, and on-site records, and is directly linked to business decisions such as spare parts matching, warranty assessment, and recall identification. To support asset management and after-sales collaboration, technologies such as "installation base / configuration management" are now available. For example, some systems can maintain the AS-built and AS-ad structures of products, and can also query the instance attribute status at a certain point in time by timestamp or version identifier, thereby obtaining the configuration diagram at that point in time.

[0003] However, the reality of after-sales configuration in operation is distributed: service engineers make notes on their mobile devices before replacing parts; spare parts entry and exit are independently recorded on the warehouse side; and device logs / telemetry also report versions or fingerprints at different granularities. Differences in offline and cross-organizational data transmission, return parts turnover, and manual entry lead to issues such as delayed and supplementary entries ("inconsistency between occurrence time and entry time"), inaccurate identifier mappings, inconsistent tag number definitions, multiple configurations of the same model running concurrently, or missing document associations, resulting in contradictory phenomena. Previous installation base / configuration management practices primarily relied on timely and consistent input records to provide a historical view. However, when multiple source records are out of order or conflict, it is difficult to manually verify, backfill, or rely on a single source, making it difficult to establish an actual configuration baseline. While general software architecture approaches involve adding events and then reconstructing the state through playback, applying this to after-sales configuration still faces problems such as scattered on-site evidence, conflict determination, and inconsistent temporal semantics. If the actual assembly / version status of a product cannot be accurately grasped in the long term, at the time of failure, before and after maintenance, or during the warranty period, it will not only cause incorrect spare parts ordering, warranty compliance disputes, maintenance plan deviations and reduced asset availability, but also make it difficult to accurately grasp the scope of batch defects, thereby increasing recall and service expenses and amplifying safety and compliance risks.

[0004] Therefore, the current technical problem is that after-sales maintenance and upgrades lead to continuous evolution of configurations, and there are late entries and contradictions in the multi-source records, which makes it impossible for existing technology to restore the actual assembly configuration of a single product instance at any point in time and ensure traceability. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a product lifecycle traceability after-sales service system based on multi-source heterogeneous data. It constructs a conflict set for the same configuration target, performs uniqueness, causality, and compatibility constraint checks, and determines effective events based on evidence package arbitration, outputting arbitration records and confidence levels. A configuration version chain is derived from the effective events, and snapshots are generated. Late or corrective events are incrementally replayed, generating an impact propagation list. When querying configurations at any point in time, replays and restores the system, outputting the evidence chain and confidence level. Insufficient confidence triggers proactive verification and backflow of corrective events, improving the accuracy of warranty assessment and spare parts matching, and solving the technical problems described in the background art.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] The product lifecycle traceability after-sales service system based on multi-source heterogeneous data includes: accessing manufacturing history, after-sales records, and equipment logs; establishing a primary key mapping between product instances, components, and software; normalizing records into configuration change events and writing them into an immutable event library; events including effective time, entry time, differential operation, evidence package, and idempotent event identifier; aggregating configuration change events for product instances according to configuration goals and time windows to construct conflict sets; performing hard constraint checks on uniqueness, causality, and compatibility for candidate events; determining effective events based on weighted arbitration of evidence packages; outputting arbitration records and confidence levels; deriving configuration version chains by dividing effective intervals according to effective time based on effective events; storing differentials and generating snapshots according to predetermined conditions; when late or error correction events occur, locating the affected version segment, performing incremental replay, and generating an impact propagation list of dependent configuration version chains; when responding to queries at any point in time, loading the nearest snapshot and replaying the differentials to the query time to obtain the configuration set, outputting the evidence chain and confidence level; when the confidence level is below a threshold or the conflict is not resolved, triggering active verification and forming error correction event backflow.

[0010] Furthermore, the primary key mapping establishes a unified primary key for product instances, components, and software respectively, and the records from each source are unified into configuration change events according to the configuration target and written into the immutable event library. The evidence package includes evidence item identifiers, collection time, and content summary, and generates an evidence package integrity summary.

[0011] Furthermore, the idempotent event identifier is generated by encoding the unified primary key, configuration target, effective time, entry time, differential operation, and evidence package integrity summary of the product instance in a predetermined order. The immutable event library performs deduplication based on the idempotent event identifier and retains the source record identifier.

[0012] Furthermore, conflict sets are constructed by aggregating configuration change events using product instances, configuration targets, and time windows obtained by discretizing the effective time as keys; when the same configuration change event corresponds to multiple configuration targets, it is first split into single-target events according to the configuration target before entering the conflict set.

[0013] Furthermore, hard constraint verification includes: verifying that components are not simultaneously occupied by different product instances within the same validity period based on the component occupancy index; verifying that differential operations satisfy the causal order of installation, disassembly, and replacement; and verifying that components and software match the configuration target based on the compatibility matrix. Those that fail are written into the arbitration record.

[0014] Furthermore, the weighted arbitration of the evidence package extracts the consistency of work orders and inbound / outbound documents, on-site barcode scanning, upgrade signatures, and equipment log fingerprints as evidentiary features for each candidate event. The arbitration score is calculated according to predetermined weights, and the event with the highest score is selected as the effective event. At the same time, the confidence level and the reason for rejection are written into the arbitration record.

[0015] Furthermore, the version chain is configured to divide the effective interval by the effective time, the differential operation of the effective event is written to the differential storage, and a snapshot is generated when the cumulative number of changes reaches a threshold and when the effective interval span reaches a threshold; the snapshot records the location information of the starting point of the corresponding version segment and the differential storage.

[0016] Furthermore, when a late or error correction event occurs, the insertion point is located based on the effective time, and incremental replay is performed on the version segment between the insertion point and the recalculation watermark, and the snapshot and differential storage are updated.

[0017] It also generates an impact propagation list, which includes the affected warranty determination identifier, recall scope identifier, spare parts recommendation conclusion identifier, and corresponding time interval.

[0018] Furthermore, when querying at any point in time, the base state is loaded from the nearest snapshot and the differential operation is replayed to the query point in time to obtain the configuration set, output the evidence chain, and the evidence chain is associated with the effective event, the evidence package integrity summary and the arbitration record;

[0019] The confidence level is obtained by combining the confidence level recorded in the arbitration record with the evidence integrity summary represented by the evidence package integrity, and is output along with the query results.

[0020] Furthermore, an active verification task is generated when the confidence level is below the threshold, and when the conflict set is not resolved. When there is a component conflict, the active verification task specifies the configuration target to perform on-site scanning and return the serial number. When there is a software inconsistency, the device is triggered to return the firmware signature digest. When there is a document gap, the warehouse is triggered to review the document index. The returned results are encapsulated as error correction events and fed back.

[0021] (III) Beneficial Effects

[0022] This invention provides a product lifecycle traceability and after-sales service system based on multi-source heterogeneous data, which has the following beneficial effects:

[0023] By aligning manufacturing history, after-sales work orders, spare parts in / out records, upgrade records, and equipment logs into configuration change events, and writing differential operations, evidence packages, and idempotent event identifiers into an immutable event database along a timeline of effective time and inbound time, scattered records are transformed into a traceable and auditable sequence of facts, avoiding state bifurcation caused by repeated reporting and supplementary entries. A conflict set is constructed based on the same configuration target and time window, and uniqueness, causality, and compatibility hard constraint checks are performed sequentially. Arbitration is then performed based on the evidence package to output effective events, arbitration records, and confidence levels. Multi-source contradictions can be resolved, and the basis for rejection can be traced and verified. Based on the effective event configuration version chain and combined with differential storage and snapshot management, when a late or error correction event occurs, the affected version segment is located for incremental replay and an impact propagation list is generated. Downstream conclusions within the historical repair control range that depend on the configuration version chain can be recalculated synchronously according to the list, avoiding both drift in citation criteria and lag in conclusions.

[0024] By querying the most recent snapshot at any time point and loading the base state replay differential to obtain the configuration set, the evidence chain and the confidence level are output. This enables quality assurance judgment, recall identification and spare parts matching to be auditable under a unified configuration. Dispute resolution directly locates the corresponding events, evidence items and arbitration records. When the confidence level is lower than the threshold or the conflict is not resolved, an active verification task is issued. The feedback results such as QR code verification, firmware signature feedback, and document review are encapsulated as error correction events and fed back to the first step. This allows the dual time axis configuration change events and the immutable event library, conflict set arbitration, incremental replay and impact propagation to work together to form an interpretable, auditable and self-healing full life cycle traceability and after-sales support capability. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the product lifecycle traceability and after-sales service system based on multi-source heterogeneous data according to the present invention;

[0026] Figure 2 This is a diagram showing the overall architecture of the system of the present invention;

[0027] Figure 3 This is a schematic diagram illustrating the configuration change event generation process and identifier construction of the present invention;

[0028] Figure 4 This is a schematic diagram illustrating the structure of the evidence package and the generation of the root of the evidence set in this invention;

[0029] Figure 5 This is a schematic diagram illustrating the conflict arbitration and effective event selection based on time windows according to the present invention.

[0030] Figure 6This is a schematic diagram illustrating the version chain replay and late / supplementary event insertion of the present invention;

[0031] Figure 7 This is a flowchart of the closed-loop process for calculating confidence at the query time point in this invention and actively verifying the results.

[0032] Figure 8 This is a schematic diagram of the data structure relationship entity structure of the present invention. Detailed Implementation

[0033] 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.

[0034] Please see Figures 1-8 This invention provides a product lifecycle traceability after-sales service system based on multi-source heterogeneous data, including: Step 1, converting multi-source heterogeneous records into configuration change events with a unified primary key, dual timelines and evidence packages, so that they can be directly referenced by subsequent steps without relying on manual traceability.

[0035] On-site maintenance, parts replacement, and upgrades are recorded in the work order system, warehouse system, and equipment logs. If these records are not standardized in terms of object references and field definitions before being used in subsequent calculations, the same configuration fact may be split, merged, or mismatched, making subsequent conflict arbitration impossible under the same product primary key. Aggregate evidence on (PUID). Step one therefore first converges on four elements: who the object is, what happened, when it took effect, and when it was entered into the database. Then, the uncertainty is retained as a structured tag for processing in step two.

[0036] First, merge different identifiers for the same object from various sources into a unified primary key. Then, register the mapping process as a traceable record to prevent subsequent instances where events are unified but object references drift. During processing, bind each original record to the product primary key before it is entered into the database. Component primary key (CID) or software primary key (SWID) ensures that subsequent calculations revolve around a unique mapping.

[0037] Manufacturing history, after-sales work orders, warehouse inbound and outbound operations, and upgrade platforms are naturally separated in terms of identification systems, and different entities may reuse the same field names to represent different objects. Relying solely on string matching can misjudge occasional similarities as the same object, and can also split different identifiers of the same object into multiple objects.

[0038] The original records undergo an identifier extraction, intra-domain validation, and inter-domain mapping process. Identifier extraction parses the local identifier from the payload and determines its domain affiliation. Intra-domain validation checks that the identifier structure segment meets a preset format. Inter-domain mapping generates a candidate primary key set in the primary key mapping table and performs a consistency check using a closed association number: candidate product primary key. The mapping must be closed on at least one common association number, such as the same shipping order number or the same service order number, thus limiting the mapping to an interpretable link consistency. If the closure check fails, the record is marked as pending mapping and a candidate set is retained.

[0039] First, set the product as the primary key in the primary key mapping table. Set up a primary identifier index and a secondary identifier index to separate the confirmed hit from the candidate retrieval; then record the local identifier-unified primary key-closed association number in the mapping register, and save the record source and registration time to support reverse tracing of the mapping basis.

[0040] When using it, ensure that the object references from the source system are consistently assigned to the product primary key. Component primary key Software primary key Subsequent event generation no longer depends on the naming conventions of each system. The unmapped markers and candidate sets explicitly expose ambiguities, providing an entry point for subsequent proactive verification or manual review. The mapping register makes the object binding process traceable and interpretable.

[0041] After the objects are bound, align the installation tag number, component category, software object, and action semantics into a set of computable fields to ensure the differential operation set. It can be generated under a unified standard. The key is to map natural language actions, material codes, and log fields to the same semantic space, avoiding the misinterpretation of changes as different operations from different sources. Specifically, the work order text might only state "control panel replacement," the warehouse system might record it as material code issuance and recycling, and the equipment log might record version or fingerprint changes. If misaligned, it will be impossible to create matching "before" and "after" entries on the same installation tag.

[0042] Using the primary key mapping result as an anchor, alignment is performed for installation tag numbers, component categories, software objects, and action semantics. Installation tag number alignment maps the unit number text, the Bill of Materials (BOM) tag number code, and the equipment location identifier to a unified installation tag number, while retaining the original tag number as a bypass field. Component category alignment maps warehouse material codes to unified component category codes. Software object alignment maps the upgrade platform's software package name to a unified software object code. Action semantic alignment maps the source action to a differential operation set. The operation type allows replacement to be expressed as an ordered pair of REMOVE and ADD, bound to before and after objects. If multiple matches occur, an alignment candidate list is generated and the matching criteria are saved.

[0043] First, maintain the installation tag dictionary and component category code table, and save the source system identifier in the dictionary. - Field value - Mapping of Unicode so that it can evolve independently by source; then generate an aligned signature, which is formed by concatenating the Unicode installation tag number, Unicode component category code, Unicode software object code and action semantic code, and is used to identify whether the same record has been processed repeatedly.

[0044] When used, this makes the set of difference operations... Certain fields are stored. The alignment candidate list structures uncertainty, allowing for the definition of ambiguity sources in subsequent conflict arbitration. Alignment signatures provide a basis for idempotent processing, avoiding misjudgments caused by multiple sources; the alignment results are combined into a single configuration change event, clearly defining the effective time. and warehousing time Separating semantics to structure late arrivals and supplementary entries; and setting up a set of difference operations. Transform it into a deterministic serialized payload, generate stable event identifiers and idempotent deduplication.

[0045] In after-sales scenarios, the actual occurrence time and the system receiving time are often separate. If only one time field is retained, it is impossible to determine whether the data was added late or a new change occurred, nor is it possible to determine a valid split point when deriving the version chain.

[0046] Extract the effective time from the work order completion receipt, upgrade completion receipt, or device confirmation record. The receiving time for writing to the immutable event library will be fixed as the entry time. Subsequently, the pre-change state and post-change state are paired according to the unified installation tag number to generate a differential operation set. .

[0047] To ensure that the same event identifier is generated repeatedly upon receiving data. Define the differential serialization function Sort by installation tag number, then by operation type. Write each differential operation into a concatenated byte string containing the operation type, target tag number, before primary key, and after primary key. Finally, add the product primary key. Effective date Differential serialization results, root of evidence set With source system identifier After concatenation, input into the hash function Get event identifier :

[0048]

[0049] Where: Event identifier : Idempotent deduplication digest, a string of 128 to 512 bits, providing a unique key for the same configuration fact; hash function Deterministic cryptographic hash algorithm, implemented with 256 or 512-bit output; Product primary key The product instance uses a unified primary key, with a globally unique string value; effective time. Configure the effective timestamp of the fact, with the value being a sortable time identifier;

[0050] Differential serialization function : Set of difference operations The determined serialized byte string, whose value is a byte string; the root of the evidence set : Evidence packet digest, with a value being a fixed-length bit string; Source system identifier : Source domain identifier, a finite set of strings, used to distinguish serialization boundaries from different sources.

[0051] Differential serialization function : Set of difference operations First, sort according to fixed rules, then convert each difference operation into a byte string according to a fixed field order and concatenate them sequentially to obtain the serialized result, where 1) is the set of difference operations. It consists of several differential operations. Each differential operation includes: operation type, configuration target identifier, object primary key before change, and object primary key after change; 2) The sorting rule is: first sort by configuration target identifier, then sort by operation type, and if they are still the same, sort by object primary key before change and object primary key after change; 3) The serialization of each differential operation adopts the method of "length prefix + field content", and is encoded and concatenated in the order of "operation type → configuration target identifier → object primary key before change → object primary key after change". Empty fields are represented by a length of zero; 4) This is the sequential concatenation of the serialized results of each difference operation after sorting. The byte string is used to generate idempotent event identifiers and for consistency verification.

[0052] First, write the effective time in the configuration change event header. With the time of entry into the warehouse Regarding the missing effective time Source priority is used to select and write the source marker; then the differential serialization function is solidified. The sort key and field order make the event identifier To maintain consistency in scenarios involving duplicate and out-of-order reception, you can first receive records from each source via message queues such as Kafka or RabbitMQ and retain the source system identifier. Then, using an append-only transaction table or a distributed log storage as an immutable event library, the event identifier... It can be quickly verified as a unique index.

[0053] When in use, the effective time and recording time are separated, and late entries have an identifiable structure. Serialization of differential payloads allows event identifiers to be deduplicated using idempotent methods to avoid duplicate reporting and event bifurcation; the source marker of the effective time provides an interpretable basis for handling missing data, facilitating the definition of the source of uncertainty in subsequent arbitration.

[0054] The on-site evidence was packaged into an evidence package, and the evidence set root was used. Binding the evidence package to the configuration change event enables subsequent replay of the differential operation set. It also allows for the tracing of the source and integrity of evidence. Its implementation emphasizes summarization and sequential procedures, ensuring that evidence remains verifiable during cross-system transfers.

[0055] Disputes over configuration changes often boil down to whether to replace the part, when to replace it, and whether to authorize spare parts. If only differential operation sets exist... Subsequent investigations may require reviewing the original system or relying on verbal statements, which can easily lead to a break in the connection.

[0056] The evidence package is divided into a set of evidence items and an evidence index. Evidence items include barcode scanning records, outbound order numbers, return order numbers, upgrade signatures, and log fingerprint digests. The set of evidence items generates an evidence digest. The evidence issuer's identifier; the evidence index stores the location results of the original evidence system. To obtain the same result according to different arrival orders, first sort by evidence type - evidence issuer - evidence time to obtain the root of the evidence item set. Then, the root of the evidence set is calculated for the sequence of evidence summaries.

[0057] Root function of evidence set It's a binary aggregation, where two evidence summaries are superimposed and then input into a hash function. Obtain the upper-level summary, and then repeat the above process until the root of the evidence set is obtained:

[0058]

[0059] Where: the root of the evidence set : Evidence packet integrity digest, a fixed-length bit string; Evidence digest : No. Each piece of evidence is a summary, a fixed-length bit string; the number of pieces of evidence... : Size of the set of evidence items, a positive integer;

[0060] Define evidence serialization function Generate a byte string for each piece of evidence, specifying the following fields: evidence type, issuing party identifier, evidence time, location index, and content digest; UTC milliseconds; UTF-8 string; null values ​​are empty bytes. Define the evidence digest: evidence item. .

[0061] Root function of evidence set : Evidence summary aggregation function, implemented as a deterministic algorithm, for sorting... Perform binary aggregation to output the root value. Its implementation is as follows: if the number of evidence items... If the number is odd, copy the last evidence summary. Participate in pairing; calculate the upper-level summary for each layer. Obtain the upper-level summary sequence until the root value is obtained. Hash function : A deterministic cryptographic hash algorithm, implemented with 256 or 512-bit output, for aggregation compression and consistency verification.

[0062] First, perform summary generation, location index registration, and issuer identification record for each evidence item, and write it as an evidence package into the configuration change event; then, when writing to the immutable event database, perform a verification-before-append procedure: first verify the event identifier. If the evidence exists, complete the missing evidence index and keep the event from being appended; otherwise, append the event and solidify the entry time into the database. Evidence can be collected via QR code scanning or RFID reading; log fingerprints can be generated using firmware signature digests or security chip message digests; and hash functions can be used. SHA-256 or SM3 can be used, and the root function of the evidence set can be employed. It can be a binary aggregation or a single hashing after sorting and concatenation.

[0063] Make the evidence set root It is recalculated, allowing subsequent verification of the integrity of event evidence without relying on the availability of the original system. The procedure of verifying before appending maintains the append semantics of the immutable event library, while allowing the completion of index information for the same event without introducing forks. The evidence issuer's identifier provides a basis for the source credibility input in subsequent conflict arbitration.

[0064] Step 2: For each product's primary key Each configuration target identifier In the time window width Within the defined scope, from the mutual exclusion event identifier The unique effective event identifier is determined in the middle. And solidify arbitration records and credibility. As a basis for subsequent citations.

[0065] Although step one has unified the multi-source records into a configuration change event, after-sales sites often experience supplementary entries and backfilling, affecting the entry time. Effective date The order is not stable; at the same time, the same configuration target identifier It may be described separately by work orders, outbound orders, return orders, and equipment logs, forming a set of mutually exclusive differential operations. If we directly proceed to step three to derive a version, the mutual exclusion event will first be fixed as a configuration state, then backfilled and negated, triggering a version rollback, and the selection criteria will be difficult to interpret. Therefore, step two first fixes the conflict boundaries and selection criteria, so that step three only processes the effective event identifiers of the decisions already made. .

[0066] As a supplement: when the set of difference operations If the result is empty, the event is marked as an "evidence completion event," and its processing rules are as follows: Step two still allows it to participate in arbitration, but only as a response to the same... The evidence enhancement items are not considered effective configuration changes; step three does not segment the version based on this, but only links its evidence index to the root of the evidence set. Bind to the corresponding effective event identifier In the extended fields of the arbitration record.

[0067] Based on product primary key As anchor, based on the set of difference operations Extract configuration target identifier Then, based on the width of the time window Effective date Discretization yields the time window index; subsequently, the product primary key is... Configure target identifier Concatenate the time window index in a predetermined order and then process it using a hash function. Conflicting keys , where hash function This uses a deterministic hashing algorithm and outputs a fixed-length digest for indexing. (Collision keys) No source system identifier is introduced This ensures that the same conflict will not be split into multiple groups due to different sources.

[0068] Define time window index function :

[0069]

[0070] Time window index function : Values ​​are non-negative integers; function is to set the effective time. Mapped to a fixed-width time window; effective time : Values ​​are sortable timestamps; function as the effective timeline position of the event; time window width : Value is a positive number (duration in seconds or milliseconds); starting reference time The value is taken as the timestamp of the unified epoch starting point; and it is stipulated that... The time unit is unified by the system configuration, and all sources are converted to the same time base when entering the database to avoid time zone differences.

[0071] Conflict keys on the immutable event library side Create an inverted index, with each index entry recording an event identifier. The sequence, and by effective time. Main sequence, entry time The order of events ensures that the added events remain observable within the sequence. Time window width. Can be preset by machine model or adjusted by inbound delay level, conflict key The splicing order must be kept consistent.

[0072] To avoid the set of difference operations for the same event Simultaneously including multiple installation tag numbers and mixing multiple conflicts into the same candidate set, the system adopts a single-event, single-objective procedure: when the differential operation set... Memory in multiple configuration target identifiers When this happens, the event is split into multiple sub-events, and the sub-events inherit the original event identifier. The reference relationships are defined and conflict keys are generated for each. This limits the mutual exclusion relationship to a single installation tag or a single software object.

[0073] Configure target identifier Defined as a triple and the field source is fixed:

[0074]

[0075] Among them: configuration category The value can be one of hardware, software, parameter, or calibration; it is determined by the action semantic code and object type in step one; installation tag number. : Normalization function based on place value Output: Objects without a physical tag number will be returned null, but the object dimension must be provided; object category code. Hardware uses the component category code, software uses the software object code, and parameters use the parameter set identifier.

[0076] First, based on the width of the time window Solidified conflict keys The generation procedure is then used to decompose the differential operation set into a single-event, single-objective procedure. Multi-target actions, and write conflict keys. The inverted index is used to obtain the candidate event sequence.

[0077] When used, it ensures that the same conflict has a unique candidate boundary, and subsequent arbitrations will not produce differences in the candidate set due to different calling paths; it splits multiple aliasing into single-target candidates, making the referenced objects of the arbitration record clear and easy to review.

[0078] conflicting keys Aggregated event identifiers Sequence execution constraint gating first eliminates candidates that are physically unlikely to be valid, then adds soft constraint flags to the remaining candidates to affect the subsequent arbitration score. The input has an interpretable structure. Hard-constraint gating includes uniqueness, difference closure, and compatibility.

[0079] Uniqueness constraints based on component primary keys For objects, system maintenance component primary keys -Product Key Uses index, uses index record component primary key Recently confirmed product primary key Effective date If the same component primary key appears within the same time window Pointed to different product primary keys If the installation action is not specified, the relevant candidate events are marked as unique suspicious points and transferred to pending review. Differential closure constraints are defined by a set of differential operations. For objects, replacement must be performed on the same configuration target identifier. The following conditions must be met for both delete and add actions. Candidate events with missing actions are marked as having open differences and restricted to weak candidates. Compatibility constraints are based on the product primary key. Model information and configuration target identifier For an object, the system queries the compatibility matrix to obtain the allowed set; if the newly added component's primary key Or software key If an event is not in the allowed set, it is marked as a compatibility risk and the evidence package is required to contain at least one of the outbound order index or the upgrade signature index; otherwise, it will not participate in the selection of effective events.

[0080] After completing the hard constraint gating, the system writes a soft constraint tag to each retained candidate. The soft constraint tag includes at least the entry delay level and the source system identifier. Credibility level, evidence set root The recalculation status. The inbound delay level is determined by the inbound time. Effective date The difference interval is obtained by mapping, the confidence level is preset by the organizational process and can be adjusted according to the audit conclusion, and the evidence set is based on... The computable state is used to distinguish between evidence indexes that are complete and those that only contain description fields.

[0081] First, events are filtered out or downgraded using index occupancy checks, differential closure checks, and compatibility matrix verification. Then, soft constraint tags are written into the candidate summaries to generate a comparable candidate set. And its explanatory fields. Block obviously invalid candidate events without generating a chain of versions, reduce noise consumption in subsequent arbitration, make uncertainty explicit with soft constraints, and enable arbitration records to indicate the type of missing evidence and the cause of conflict;

[0082] Candidate set The soft constraint markers and evidence package content are transformed into evidence feature functions. And calculate the arbitration score. The system first uses the evidence set as the root. The evidence package undergoes recalculation and verification. Only candidates that pass the recalculation are included in feature construction to avoid evidence with missing indexes or that has been replaced from being compared. Subsequently, three types of feature functions are constructed: association closure features, physical closure features, and time consistency features. The association closure feature describes whether the work order number, outbound order number, and return order number are in the same product primary key. The above forms a closed loop; the physical closed loop characteristic describes whether the outbound and return occur simultaneously and point to the same component primary key. The time consistency feature will determine the entry time. Relative effective time The degree of supplementary recording is mapped to a monotonically decreasing value.

[0083] The three types of features are uniformly restricted to... to Within the range, and by feature weights Configure the system. The arbitration score is calculated using logarithmic summation and exponential aggregation, minus the penalty function derived from the soft constraint flag. :

[0084]

[0085] Where: Arbitration score Scoring function; range is real numbers; candidate ranking; candidate event identifier. : Unique key, ranging from 128 to 512 bits, serving as index evidence and marker; Number of features : Number of features, in positive integers, representing the limited number of aggregation terms; Feature index : Indexed variable; range is Up to the number of features The integers are the enumerated feature terms; feature weights : Weight parameter; ranges from non-negative real numbers, used to configure the importance of evidence;

[0086] Evidence feature function Feature mapping; ranging from 0 to 1, for quantization closure, closed-loop, and time consistency; associated closed features. If the set of event association numbers is in the product primary key The above satisfies If true, set the value to 1; otherwise, set the value to 0.

[0087] Physical closed-loop characteristics If the evidence package contains both an outbound order index and a return order index, and both point to the same configured target identifier. If the primary key is a before / after component, then the value is 1; otherwise, it is 0.

[0088] Time consistency feature :make ,Pick:

[0089]

[0090] time constant Positive number; its function is to control the rate at which the delay reduces the feature value. Inbound delay. Non-negative time difference; its function is to reflect the degree of supplementary recording. Penalty coefficient. : Scaling parameter; range is to To adjust the intensity of punishment;

[0091] penalty function : Penalty mapping; the range is non-negative real numbers, used to convert compatibility risks, difference non-closure, and uniqueness doubts into deductions; the penalty function is defined as a weighted sum of constraint doubt indicators:

[0092]

[0093] in: : Uniqueness indicator function; returns 1 if there is a violation or conflict, otherwise returns 0. : Indicator function for non-closed difference; : Compatibility risk indicator function; : Evidence gap indicator function (such as the root of the evidence set) (Unrecalculated or missing key indexes). Weight For non-negative configuration parameters, the version number is written into the arbitration record for recalculation.

[0094] First, recalculate the root of the evidence set. Lock the evidence package, then generate the evidence feature function. And calculate the arbitration score. In engineering implementation, numerically stable logarithmic summation exponential calculation or vectorized operator calculation can be used. The barcode index can be replaced with the RFID index, and the log fingerprint digest can be replaced with the secure chip signature digest; both are derived from the evidence set root. Bind to event identifier When in use, the evidence package and soft constraint markers are incorporated into a unified scoring framework, allowing arbitration results to be recalculated using the same inputs; the impact of supplementary records and missing evidence is explicitly injected into the penalty function. The feature function makes it more difficult for weak evidence candidates to obtain high scores.

[0095] In the same conflict key candidate set Internal selection of arbitration score The largest event identifier is used as the effective event identifier. and output the confidence level. The arbitration record will be cited in step three. Confidence level. The temperature coefficient is derived from the score distribution of the candidate set. Control the degree of certainty; when confidence level Below the confidence threshold At that time, the arbitration record will include the conflict key. Mark as pending review and retain candidate event identifiers. Evidence indexes facilitate triggering supplementary sampling or manual review in step four:

[0096]

[0097] Where: confidence level Normalized confidence level; range is This serves as the basis for verification and triggering; temperature coefficient Sensitivity parameter; ranging from 0.1 to 20, representing the moderated dominance distribution; arbitration score. The scoring function, in the range of real numbers, is a normalized input.

[0098] Effective event identifier : Effective event key; ranges from 128 to 512 bits; candidate set : Candidate set, which is a finite set and represents the range of the normalized denominator; Candidate event identifier : Set elements, ranging from 128 to 512 bit strings, are normalized denominator terms; candidate indices : Index variable, with a range of candidate sets The element index is used to identify the enumerated candidate events; the confidence threshold is... Threshold parameter, ranging from 0.5 to 0.95, is the condition to be verified.

[0099] Arbitration records are written to an immutable audit table, containing at least conflict keys. Effective event identifier Candidate set Arbitration scores for each candidate Hard constraint check results, soft constraint markers, and evidence set root. The rejection reason field. Step 3: Based on the effective event identifier. Call its set of difference operations Effective date Version splitting is performed, and conflict keys are no longer traversed. The full pool of candidates is determined. Candidate selection can be implemented using sorting; when there are many compatibility constraints, a constraint programming solver can be used to solve for the unique feasible event. First, the identifier of the effective event is determined. And calculate the confidence level. Then change the conflict key The candidate evidence index and reasons for rejection are fixed into arbitration records and provided with a read-only traceability interface. This allows step three to process only a single effective event identifier. To prevent mutually exclusive events from directly entering the version chain and causing repeated configuration switching, thus increasing confidence... The reproducibility and traceability of arbitration records provide a basis for identifying potential sources for supplementary data collection and dispute interpretation.

[0100] Step 3: Identify the effective events determined in Step 2. Transform it into a traceable configuration version chain, making the product primary key At any effective time It has a unique, replayable As-Maintained configuration state.

[0101] Step two has already targeted the product primary key. With configuration target identifier The event identifier is given. However, in after-sales service, the effective date... Often earlier than the time of entry into the warehouse And it may take a long time before it is added to the immutable event library. If the configuration is derived directly based on the arrival order of events before the version chain rules are established, it will result in the same product primary key The configuration status showed different results during playback and reconstruction, which prevented downstream warranty assessments and recall targeting from using the same configuration benchmark. Therefore, step three uses the effective time... Use the unique dividing axis and set the difference operations. The application order and snapshot generation conditions are written into a deterministic procedure, thereby ensuring that the same event sequence obtains the same As-Maintained configuration result when replayed repeatedly, while limiting the impact of delays and error corrections to a controllable range.

[0102] First, use the effective event identifier. The driver will set up differential operations. Write the version segment index table and differential records, and generate snapshot pointers according to milestone conditions, then at the time of data entry. When introducing lateness or error correction, the effective time should be considered. The insertion point is located to generate a replay queue, and an impact propagation list is output after replay to drive the recalculation of downstream conclusions.

[0103] When used, this makes the product primary key The version chain has a defined replay order and segmentation boundaries. Late fixes are confined to local replay intervals, and the downstream conclusions are consistent with the version chain's reference relationships.

[0104] First, define the version segment as the product primary key. In two adjacent effective times The valid range between these points is defined, and any configuration change can only be identified by the effective event identifier output from step two. The driver enters the main chain. To avoid instability in version segment order caused by multiple sources updating simultaneously, the system manages the primary key of the same product. Effective event identifier Deterministic sorting: by effective time As the primary key, based on the entry time Used as the sequence key, and then as the effective event identifier. The byte order is used as the final decision key.

[0105] During the write operation, the system uses the product primary key. Maintain a version segment index table. Each record in the index table must contain at least the version segment start time, the version segment end time, and the activation event identifier. and by confidence level With confidence threshold Derived version segment trust tags. Version segment trust tags do not replace confidence levels. Instead of specifying the numerical meaning, the direct reference benchmark for quality assurance and recall is solidified into a readable field, so that subsequent step four can determine whether to trigger supplementary sampling according to the same rules when performing configuration restoration.

[0106] First, identify the effective events based on deterministic sorting. Insert into the corresponding effective time The location is determined, and the termination time of the version segment is updated; then, the version segment trust flag is written to increase the confidence level. With confidence threshold The comparison results can be reused at the version level.

[0107] When using, make the same product primary key The version segments have consistent segmentation boundaries and write order during repeated playback, thereby reducing state differences caused by the order of event arrival; the confidence level The reference boundaries are fixed to the version segment, which makes it easier to decide whether to enter the supplementary collection process without recalculating the arbitration.

[0108] Set of difference operations As the sole change payload for the version segment, its disk write granularity must be consistent with the configuration target identifier. Consistency means that each differential record only covers one configuration target identifier. The `before` and `after` statements are used to avoid writing across different bit positions, which could make it difficult to locate the affected area during subsequent playback. Differential records must contain at least the product primary key. Effective event identifier Effective date Configure target identifier and the set of difference operations Serialize the payload and preserve the root of the evidence set. This reference allows for the binding of configuration changes to evidence packages during auditing. Differential record writes follow an append-only procedure; any modifications are acknowledged via a new effective event identifier. Therefore, differential records naturally possess a traceable version evolution path.

[0109] To reduce the link length of step four, which involves replaying at specific points in time, snapshot generation conditions are defined. Snapshots are not generated for every version segment, but rather triggered by milestones: when the primary key of the same product... The number of differential records exceeds a preset threshold, or the effective time span between adjacent snapshots exceeds a preset period, or the differential records involve configuration target identifiers corresponding to critical component category codes. At that time, the system generates a snapshot at that version segment. The snapshot content is a serialized view of the complete As-Maintained configuration at that point in time. The view is obtained by replaying the previous snapshot and the subsequent differential records in sequence. The serialized view is written to a document or relation table, and the snapshot pointer is fixed to the version segment index table during the writing process.

[0110] First, configure the target identifier. The granularity of the set of difference operations Write the difference record and match it with the root of the evidence set. Establish reference relationships; then, when milestone conditions are met, generate a snapshot and update the snapshot pointer in the version segment index table.

[0111] When used, the influence of the differential record is limited to a single configured target identifier. This allows for precise identification of the affected interval by bit number when a late insertion occurs; by using milestone snapshots, the number of steps in long chain replay is reduced, so that restoration at any point in time in step four does not require traversing all historical difference records.

[0112] For the time of entry In cases where the version is finalized later than the existing version, the effective time is guaranteed. Even if it falls into a historical range, a single main chain can still be restored. The system receives a new effective event identifier. Then, first determine its effective time. Is it earlier than the latest start time of the version segment index table? If it is earlier, it is judged as a late or error correction event, and the effective time is used. A binary search is performed in the version segment index table to find the version segment that covers the effective time as the insertion point. After the insertion point is determined, the system does not directly rewrite the original differential record. Instead, it generates a replay marker for the version segments after the insertion point and writes the replay marker to the replay queue. Each task in the replay queue must contain at least the product primary key. Insertion point effective time, affected configuration target identifier , the relevant effective event identifier The sequence, and the current value of the recalculated water level. The recalculated water level is configured and maintained by the system to limit the earliest effective time boundary of automatic playback; when the insertion point is earlier than the recalculated water level, the system still generates a playback queue, but marks the task as requiring manual review or batch processing, so that the online link does not bear the uncontrollable recalculation overhead.

[0113] During playback execution, the snapshot view is read from the nearest snapshot pointer before the insertion point, and then the differential operation set is applied sequentially according to the deterministic sorting of the version segment index table. This process continues until the replay reaches the latest version segment recorded in the version segment index table, updating the termination effective time and snapshot pointer in the version segment index table segment by segment. During replay, if the version segment's trust flag is found to be affected by the confidence level... With confidence threshold If the relationship changes, the flag is updated synchronously to ensure that the triggering basis for the subsequent step four (supplementary data collection) is consistent with the replay results. In engineering implementation, the replay queue can be written to a message queue and pulled for execution by the task scheduler. Replay storage read / write can utilize append tables and methods based on product primary keys. Partitioned object storage to reduce cross-product interference.

[0114] First, the insertion point is determined using binary positioning, and a product primary key is generated. With configuration target identifier The replay queue tasks; then, using the snapshot pointer as the ground state, the set of replay difference operations is deterministically ordered. And update the version segment index table.

[0115] When used, make the event identifier for late arrival or error correction effective. It can enter the main chain without overwriting the history, thus ensuring that the event append semantics and version consistency semantics are valid at the same time; it limits the replay boundary to the range allowed by the recalculation waterline, avoiding late supplementary recording from triggering a full history recalculation and crowding out online resources.

[0116] To address the risk that the version chain has been fixed but business conclusions still reference the old configuration, it is required to synchronously generate an impact propagation list after replay, and link it with the effective event identifier. Establish traceable relationships. When writing to the version segment index table and differential records, the system saves a reference anchor for each downstream conclusion record. The reference anchor must contain at least the product primary key. The version segment start time and effective event identifier referenced when the conclusion is generated. When partial playback causes a certain version segment to take effect, an event flag is displayed. When a sequence changes, the system retrieves the affected conclusion records using the reference anchor as an index and generates an impact propagation list. Each impact propagation list entry must contain at least the product primary key. Affected conclusion type, affected conclusion identifier, effective time interval requiring recalculation, and effective event identifier corresponding to the triggering cause. The system also monitors the current version's trusted status. Affected conclusion types include warranty assessment, recall identification, spare parts recommendation, and maintenance plan generation. The system employs different actions for different conclusion types: for warranty assessment and recall identification records, a recalculation task is generated, and the original conclusion is retained as read-only history; for spare parts recommendation records, a void mark is generated, and the system awaits regeneration in step four with the latest configuration; for maintenance plan records, they are marked as requiring manual confirmation and accompanied by arbitration record citations, allowing on-site personnel to view the evidence set. The evidence index that is bound to it.

[0117] After the impact propagation list is published, the system writes it to the append-only task table and simultaneously delivers it to the message queue, where business-side job processes retrieve and execute it according to the conclusion type. To avoid duplicate deliveries, impact propagation list entries are based on product primary keys. Affected conclusion identifier and triggered effective event identifier Forming idempotent keys. If the version segment's trust flag displays confidence level... Below the confidence threshold The items in the impact propagation list will carry a verification mark, enabling step four to directly trigger barcode scanning or log fingerprint re-collection when generating the configuration restoration result, without having to rescan the historical conflict keys.

[0118] First, downstream conclusions are recorded and referenced at anchor points. After playback, differences are retrieved from the anchor points to generate an impact propagation list. Then, the impact propagation list is written to the task table by idempotent keys and delivered to the message queue to drive recalculation and invalidation.

[0119] When used, the version chain repair and downstream conclusion updates form a single-link reference relationship, thereby reducing inconsistencies where configurations have changed but conclusions have not been updated; the confidence level is... With confidence threshold The relationship is incorporated into the impact propagation list, so that supplementary sampling triggers and recalculation triggers can be uniformly scheduled on the same entry.

[0120] Step 4: Focus on query time Output and Product Primary Key The unique corresponding actual assembly configuration As-Maintained view, query evidence chain, and query point-in-time confidence. It also transforms insufficient confidence or evidence gaps into actionable on-site verification actions, thereby closing the error correction loop.

[0121] Step 3: Mark the effective event After being solidified into version segments and differential records, the configuration state can be replayed. However, after-sales use cases typically require both reproducible point-in-time configurations and interpretable evidence to be output simultaneously. Outputting only the configuration view makes it difficult to locate the source of evidence in warranty disputes, recalls, and spare parts matching; outputting only the evidence index makes it impossible for the system to distinguish between directly referenced configurations and configurations that require supplementary verification. Therefore, step four involves configuration restoration, evidence chain assembly, and querying point-in-time confidence levels. The calculations are bound to the same playback path, and the confidence level at the query time is included. Using evidence gap types as gating conditions to generate proactive verification tasks, a closed loop is formed between query, verification, error correction, and replay.

[0122] First, the playback ground state is determined using the snapshot pointer, and then a set of differential operations is applied in a deterministic order of the differential records. Obtain the configuration view, and then use the evidence set root. Recalculate and verify the evidence, and generate the query evidence chain and the confidence level at the query time. Subsequently, the confidence level at the query time was used. Gating is performed based on the type of evidence gap, observable on-site actions are issued, and the returned results are encapsulated as event identifiers. The corresponding configuration change event is routed back to step one. When used, this allows for arbitrary query times. The configuration query has a recalculated replay path, a traceable chain of evidence, and an interpretable confidence scale, and allows uncertain configurations to be included in the version chain evolution as append events after verification.

[0123] First, ensure that the primary key is the same for the same product. At the same query time The playback path is consistent. Next, the set of difference operations... The order in which these operations are applied is fixed as consistency check, deletion, and addition.

[0124] Based on product primary key Search coverage query time The target version segment is determined, and the snapshot pointer is traced back to the snapshot closest to the target version segment; the snapshot view pointed to by the snapshot pointer is used as the playback base state, which is used to configure the target identifier. The system stores the occupancy relationship between component primary keys and software primary keys. Then, starting from the version segment following this snapshot view, the system sequentially reads the effective event identifiers according to the deterministic sorting of the version segment index table. The corresponding difference records, and the set of difference operations. Apply to the playback ground state. Before differential application, a consistency check is performed on the deletion action: if a target identifier is configured... If the current occupancy is inconsistent with the before reference of the differential record, then the differential record is marked as a playback discrepancy, and the differential record and the root of the evidence set are retained. The reference is used for subsequent verification; deletion and addition are only performed after the consistency check passes, ensuring the replacement action remains closed during replay. For software primary key updates, the system uses the same configuration target identifier. The data is written in the order of old version releasing occupancy and new version creating occupancy, thereby reducing query time. The hardware and software usage semantics are consistent. During project implementation, the version segment index table can be placed in a relational index, the snapshot view can be placed in object storage, and the differential records can be placed in an append table, using the product primary key. Partitioning reduces cross-product read / write interference.

[0125] First, locate the playback ground state using the snapshot pointer and configure the target identifier. Create an occupancy view, then read the differential records in deterministic sorting and apply the differential operation set in the order of consistency check-deletion-addition. The replay of doubts and the collection of evidence will be the root cause. The reference is written into the exception segment of the query evidence chain.

[0126] When using, make the same product primary key The configuration restoration has a unique replay starting point and a fixed application order, thereby reducing replay discrepancies caused by differences in the order of event arrival. This allows replay discrepancies to be exposed simultaneously in the configuration view and the query evidence chain, providing clear location fields for subsequent proactive verification.

[0127] Aggregate the evidence and confidence scores from the replay path into a reusable query output. This requires configuring views, querying the chain of evidence, and setting confidence scores at the query point in time. The same replay event set must be referenced. .

[0128] After completing differential playback, the system collects the effective event identifiers of the events involved in the playback. Form a replay event set And read the single-event confidence level in each of its arbitration records one by one. With the root of the evidence set The system performs a root test on each set of evidence. Recalculation and verification: events that pass the recalculation will have their evidence indexed by their effective time. -Configure target identifier - Evidence types are sequentially written into the query evidence chain; events that fail recalculation are written into the evidence gap type and trigger the evidence integrity factor. The reduction. Confidence level at the query time. By employing an aggregation method that compresses confidence loss index, individual low-confidence events can form traceable reduction sources along the playback path, and avoids simply averaging to mask weak links.

[0129]

[0130] Query point-in-time confidence : Value This serves as a basis for verifying the gating system and the conclusions cited; the evidence completeness factor. : Value , for the root of the evidence set Reduction factors that are unrecalculated or lack evidence indexes; replay event set : Values ​​are taken from a finite set, limiting the scope of aggregation; replay event elements : Value is the effective event identifier, which is an enumerated aggregation item; confidence decay coefficient Values ​​range from 0.1 to 10, used to adjust the cumulative strength of the confidence deficit; single-event confidence. The range of values ​​is This refers to the arbitration confidence level cited in step two;

[0131] First, follow the replay event set Recalculation of the root of the evidence set Then, a chain of evidence is woven to determine the confidence level at the time of the query, and the above formula is used to calculate the confidence level at the time of the query. And with configuration view Figure 1 And output; the engineering implementation can use a hash verification library or a signature verification library to recalculate the root of the evidence set. The evidence index is stored using a full-text index.

[0132] When using it, configure the view, query evidence chain, and query point-in-time confidence. From the same set of replay events This avoids separating the interpretation link from the computation link; and increases the confidence level at the query time. The source of the reduction can be located to the specific effective event identifier. With the root of the evidence set This makes it easier to allocate verification resources to weak links in the future.

[0133] Furthermore, the uncertainty is transformed into actionable on-site actions, and the triggering conditions are solidified into the confidence level at the query time. Confidence threshold Combined gating with evidence gap types.

[0134] After outputting the configuration view, compare the confidence levels at the query time. With confidence threshold The system checks for evidence gaps in the query evidence chain and replays suspicious segments. When the gating conditions are met, the system generates an active verification task and embeds the product primary key into the task. Query time Configure target identifier The primary key of the object to be verified and the triggering reason are used to ensure that the verification result can be written back to the same object and the same key.

[0135] Verification tasks are mapped to observable actions: if the trigger cause corresponds to a primary key conflict in the component, then the target identifier needs to be configured on-site. The installation unit performs QR code scanning or RFID reading and returns the serial number. If the triggering cause corresponds to an inconsistent software primary key, the device gateway is required to initiate firmware signature readback or software package hash readback and return the digest. If the triggering cause corresponds to an incomplete outbound and return process, the warehouse side is required to verify the outbound and return order numbers and supplement the return item serial number. The system includes an evidence collection format when issuing tasks, so that the scanned photo digest, signature digest, and document number are all included in the evidence package as evidence items. Task issuance generates to-do lists through the workflow engine, the mobile terminal uses the scanning component, and the device side calls the gateway interface through a message queue.

[0136] First, use the confidence level at the time of query. Confidence threshold Gating based on evidence gap types, then generating a database containing product primary keys. With configuration target identifier The active verification task is mapped to an action sequence of QR code scanning, RFID reading, firmware signature rereading, or document verification.

[0137] When in use, the verification trigger has definite conditions and field-based reasons, allowing the content returned from the site to be directly included in the evidence package and used for subsequent event generation. This establishes a one-to-one correspondence between verification actions and evidence gap types, thereby reducing irrelevant data collection and improving the field completeness of error correction events.

[0138] Furthermore, the verification results are converted into arbitrable and replayable configuration change events, avoiding the overwriting of modification history and using the impact propagation list as the sole trigger for downstream conclusion reconciliation. This binds configuration repair and conclusion updates to the same link. After receiving the verification feedback, the feedback content is written into the evidence package and the root of the evidence set is calculated. Then, generate a differential operation set based on the verification type. When the scanned serial number displays an inconsistent actual installed component with the configuration view, differential operation set Write and delete the primary key of the old component and replace the primary key of the newly added component; when the firmware signature readback shows that the software primary key is inconsistent, perform differential operation set. Write software update or rollback operations; when document reconciliation only completes the serial number of the recovered item, differential operation set. The event is left blank, and only evidence completion is retained. The system generates an event identifier for this event. It is also written to the immutable event library, and the effective time is written to the event header. With the time of entry into the warehouse This allows the error correction event to proceed to step two (arbitration) and trigger partial replay in step three. After the new impact propagation list output in step three enters step four, the system retrieves the affected conclusion records using the reference anchors of the impact propagation list entries, generates reconciliation and recalculation tasks, and calls to re-acquire the corresponding query time. Configuration view and query point-in-time confidence The recalculation task retains the original conclusion as a historical record and writes it to a new reference anchor.

[0139] First, the root of the evidence set Bind the validation results and generate a set of differential operations. Then by event identifier The idempotent procedure is written into the immutable event library and triggers the re-arbitration and partial replay of steps two and three. Then, the reference anchors are reconciled and recalculated according to the impact propagation list.

[0140] When in use, the verification results are appended to the same event chain to avoid changing differential records in an overwrite manner, which would compromise audit traceability; the version chain repair and downstream conclusion updates share the trigger chain of the impact propagation list, thereby reducing the coexistence of configuration repair and conclusion reference inconsistencies.

[0141] Those skilled in the art will recognize that the units 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 design 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.

[0142] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] 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.

Claims

1. A product lifecycle traceability and after-sales service system based on multi-source heterogeneous data, characterized in that: include, By integrating manufacturing history, after-sales records, and equipment logs, a primary key mapping is established between product instances, components, and software. Records are unified into configuration change events and written to an immutable event library. Events include effective time, entry time, differential operation, evidence package, and idempotent event identifier. For product instances, configuration change events are aggregated according to configuration goals and time windows to construct conflict sets. Uniqueness, causality, and compatibility hard constraints are performed on candidate events. Based on evidence packages, weighted arbitration is used to determine the effective event, and arbitration records and confidence levels are output. Based on the effective event, the effective interval is divided into a configuration version chain according to the effective time, the difference is stored and a snapshot is generated according to the predetermined conditions; When a late or error correction event occurs, the affected version segment is located, incremental replay is performed, and an impact propagation list of the dependent configuration version chain is generated; when responding to a query at any point in time, the nearest snapshot is loaded and replayed differentially to the query point in time to obtain the configuration set, and the evidence chain and confidence level are output; when the confidence level is lower than the threshold or the conflict is not resolved, active verification is triggered and an error correction event backflow is formed. The primary key mapping establishes a unified primary key for product instances, components and software respectively, and the records from each source are unified into configuration change events according to the configuration target and written into the immutable event library. The evidence package contains evidence item identifiers, collection time and content summary, and generates an evidence package integrity summary. Hard constraint verification includes: verifying that a component is not occupied by different product instances simultaneously within the same validity period based on the component occupancy index; verifying that differential operations satisfy the causal order of installation, disassembly, and replacement; and verifying that the component and software match the configuration target based on the compatibility matrix. Those that fail are written into the arbitration record. The weighted arbitration of the evidence package extracts the consistency of work orders and inbound / outbound documents, on-site barcode scanning, upgrade signature, and equipment log fingerprint as evidence features for each candidate event. The arbitration score is calculated according to the predetermined weights, and the event with the highest score is selected as the effective event. At the same time, the confidence level and the reason for rejection are written into the arbitration record. When a late or error correction event occurs, the insertion point is located based on the effective time. Incremental playback is performed on the version segment between the insertion point and the recalculation watermark, and the snapshot and differential storage are updated. It also generates an impact propagation list, which includes the affected warranty determination identifier, recall scope identifier, spare parts recommendation conclusion identifier, and corresponding time interval; The three types of features are uniformly restricted to... to Within the range, and by feature weights Configure the arbitration score; use logarithmic summation and exponential aggregation, and subtract the penalty function derived from the soft constraint flag. : ； Where: Arbitration score Scoring function; range is real numbers; candidate ranking; candidate event identifier. : Unique key, ranging from 128 to 512 bits, serving as index evidence and marker; Number of features : Number of features, in positive integers, representing the limited number of aggregation terms; Feature index : Indexed variable; range is Up to the number of features The integers are the enumerated feature terms; feature weights : Weight parameter; ranges from non-negative real numbers, used to configure the importance of evidence; Evidence feature function Feature mapping; ranging from 0 to 1, for quantization closure, closed-loop, and time consistency; associated closed features. If the set of event association numbers is in the product primary key The above satisfies If true, set the value to 1; otherwise, set the value to 0. Physical closed-loop characteristics If the evidence package contains both an outbound order index and a return order index, and both point to the same configured target identifier. If the primary key is a before / after component, then the value is 1; otherwise, it is 0. Among them, time consistency feature :make ,Pick: ； time constant : Positive number; its function is to control the rate at which the delay affects the feature reduction; warehousing delay Non-negative time difference; its function is to reflect the degree of supplementary recording; penalty coefficient. : Scaling parameter; range is to To adjust the intensity of punishment; penalty function : Penalty mapping; the range is non-negative real numbers, used to convert compatibility risks, difference non-closure, and uniqueness doubts into deductions; the penalty function is defined as a weighted sum of constraint doubt indicators: ； in: : Uniqueness indicator function; returns 1 if there is a violation or conflict, otherwise returns 0. : Indicator function for non-closed difference; : Compatibility risk indicator function; Evidence gap indicator function Weight For non-negative configuration parameters, the version number is written into the arbitration record for recalculation.

2. The product lifecycle traceability and after-sales service system according to claim 1, characterized in that: The idempotent event identifier is generated by encoding the unified primary key, configuration target, effective time, entry time, differential operation, and evidence package integrity summary of the product instance in a predetermined order. The immutable event library performs deduplication based on the idempotent event identifier and retains the source record identifier.

3. The product lifecycle traceability and after-sales service system according to claim 2, characterized in that: The conflict set is constructed by aggregating configuration change events using product instance, configuration target, and time window obtained by discretizing the effective time as keys; when the same configuration change event corresponds to multiple configuration targets, it is first split into single-target events according to the configuration target before entering the conflict set.

4. The product lifecycle traceability and after-sales service system according to claim 3, characterized in that: Configure the version chain to divide the effective interval by the effective time, write the differential operation of the effective event to the differential storage, and generate a snapshot when the cumulative number of changes reaches the threshold and when the effective interval span reaches the threshold; the snapshot records the location information of the starting point of the corresponding version segment and the differential storage.

5. The product lifecycle traceability and after-sales service system according to claim 4, characterized in that: When querying at any point in time, the base state is loaded from the nearest snapshot and the differential operation is replayed to the query point in time to obtain the configuration set. The evidence chain is output, and the evidence chain is associated with the effective event, the evidence package integrity summary and the arbitration record. The confidence level is obtained by combining the confidence level recorded in the arbitration record with the evidence integrity summary represented by the evidence package integrity, and is output along with the query results.

6. The product lifecycle traceability and after-sales service system according to claim 5, characterized in that: When the confidence level is below the threshold, an active verification task is generated. When the conflict set is not resolved, an active verification task is generated. When there is a component conflict, the active verification task specifies the configuration target to perform on-site scanning and return the serial number. When there is a software inconsistency, the device is triggered to return the firmware signature digest. When there is a document gap, the warehouse is triggered to review the document index. The returned results are encapsulated as error correction events and fed back.

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