A method for tracing a smartphone repair process based on big data management

Abstract

The application discloses a kind of based on big data management's intelligent mobile phone maintenance process's traceability method, it is related to maintenance traceability technical field, including, the equipment identification information of maintenance equipment is obtained, and maintenance behavior description information set is collected, equipment identification information is associated with maintenance behavior description information, generates maintenance event unique identification number;Maintenance event unique identification number and maintenance behavior description information set are verified with context consistency, and verification result data containing digital signature is generated according to preset semantic rule;Verification result data containing digital signature is written as maintenance event node into chain traceability account book, and maintenance event node is integrated according to maintenance account dimension, and generates account behavior sequence set.It realizes the global modeling and clustering identification of maintenance behavior evolution path under multiple account dimensions, can classify analysis and quantitatively express behavior abnormal mode.

Description

Technical Field

[0001] This invention relates to the field of repair traceability technology, and in particular to a traceability method for the repair process of smartphones based on big data management. Background Technology

[0002] With the continuous expansion of the smartphone industry chain, quality control and process traceability in smartphone repair have gradually become core issues in equipment management. Current technologies typically employ information management of the repair process based on repair logs and device serial numbers. Repair data is generally entered manually, recorded in process platform logs, or generated by repair work order systems, and can be linked to device-level information using unique device identifiers to establish a basic repair history. Some solutions have integrated database technology and front-end platforms to provide visualized repair process displays and query functions, supporting after-sales maintenance, quality assessment, and responsibility allocation. Furthermore, in remote services for smart terminals, some technologies use process rules to determine the rationality of repair actions, assisting in diagnosis and standardizing repair processes. However, these methods primarily focus on recording and tracking single events, with less emphasis on big data semantic-level behavioral aggregation modeling and link-level tracing.

[0003] However, existing methods have the following limitations in identifying evolutionary patterns, verifying semantic consistency of operations, and analyzing behavioral links among multiple maintenance accounts in large-scale maintenance activities: First, at the level of structured expression of maintenance behavior, conventional methods mostly manage fields such as operation type, part number, and maintenance instructions through static field alignment, lacking a verification mechanism for semantic context relationships, making it difficult to determine whether maintenance behavior conforms to the logical requirements of standard maintenance procedures; Second, in terms of risk identification and visualization, existing solutions are mostly based on single-node scoring or event-level anomaly identification, making it difficult to perform anomaly clustering and graph display from the overall account behavior chain, thus limiting the ability to build behavioral risk control and path transparency in complex maintenance scenarios. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a traceability method for the smartphone repair process based on big data management to solve the problems of insufficient semantic consistency verification of repair behavior and difficulty in identifying abnormal account behavior links.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for tracing the repair process of smartphones based on big data management, comprising,

[0008] Obtain the equipment identification information of the equipment to be repaired, and collect a set of repair behavior description information. Associate the equipment identification information with the repair behavior description information to generate a unique identification number for the repair event.

[0009] Perform contextual consistency verification on the unique identifier of the maintenance event and the set of maintenance behavior description information, and generate verification result data containing digital signatures according to preset semantic rules;

[0010] Verification result data containing digital signatures is written as maintenance event nodes into the on-chain traceability ledger, and maintenance event nodes are integrated according to maintenance account dimensions to generate a set of account behavior sequences.

[0011] A multi-account behavior graph is constructed based on a set of account behavior sequences. The graph structure features are encoded and clustered to obtain anomaly scores for behavior links.

[0012] The abnormal behavior chain score is merged with the maintenance event node data to form a traceability node record with risk control identification, and a traceability visualization display structure is built based on multiple traceability node records.

[0013] As a preferred embodiment of the traceability method for smartphone repair process based on big data management described in this invention, the steps of obtaining the device identification information of the repair equipment, collecting a set of repair behavior description information, associating the device identification information with the repair behavior description information, and generating a unique identifier number for the repair event are as follows.

[0014] Read the hardware information of the mobile device to be repaired, standardize the hardware information, and generate device identification information;

[0015] Initialize the structure that records maintenance operation behavior, and collect a set of maintenance behavior description information;

[0016] The equipment identification information is associated with the maintenance behavior description information set to construct the initial structure data of the maintenance event;

[0017] Based on the initial structure data of the maintenance event, a unique identifier number corresponding to the current maintenance event is generated.

[0018] As a preferred embodiment of the traceability method for smartphone repair processes based on big data management as described in this invention, the steps of performing contextual consistency verification on the unique identifier of the repair event and the repair behavior description information set, and generating verification result data containing digital signatures according to preset semantic rules, are as follows:

[0019] Based on the maintenance equipment information corresponding to the unique identifier of the maintenance event, the standard maintenance process structure is retrieved from the standard maintenance process database. Then, the standard maintenance process structure is parsed to construct a maintenance process state diagram. Maintenance semantic rules are extracted from the maintenance process state diagram to form a semantic rule set.

[0020] Extract semantic feature fields of maintenance behavior from the unique identifier number of maintenance event, and combine them with the field specifications in the semantic rule set to generate a standardized set of semantic features of maintenance behavior;

[0021] The standardized semantic feature set of maintenance behavior is substituted into the maintenance process state diagram to determine the semantic consistency verification state, and at the same time, the semantic matching score is calculated.

[0022] Based on semantic matching score, semantic consistency verification status, unique identifier of maintenance event, and standardized semantic feature set of maintenance behavior, verification result data is generated by integrating and digitally signing the verification result data to generate verification result data containing digital signature.

[0023] As a preferred embodiment of the traceability method for smartphone repair process based on big data management described in this invention, the standard repair process database contains standard repair process specification information that predefines repair steps, operation sequences, state transition conditions, and process specifications for different device models and repair types.

[0024] As a preferred embodiment of the traceability method for smartphone repair processes based on big data management as described in this invention, the steps of writing verification result data containing digital signatures as repair event nodes into the on-chain traceability ledger and integrating the repair event nodes according to the repair account dimension to generate an account behavior sequence set are as follows:

[0025] Based on the verification result data containing digital signatures, an on-chain writable maintenance event structure is constructed, and the on-chain contract interface is called to write the maintenance event structure into the traceability ledger, and the storage address of the on-chain maintenance event node is obtained as the behavior node positioning index.

[0026] Based on the storage address of the on-chain maintenance event node and the corresponding maintenance account identifier, the set of behaviors of the target maintenance account is searched from the index structure of the stored account behavior data;

[0027] The storage addresses of on-chain maintenance event nodes are added to the end of the target maintenance account's behavior set in chronological order of event occurrence, generating an account behavior sequence set.

[0028] As a preferred embodiment of the traceability method for the smartphone repair process based on big data management described in this invention, the on-chain contract interface refers to a set of data submission functions predefined in a smart contract deployed in a blockchain network, used to enable interaction between external entities and on-chain data structures.

[0029] As a preferred embodiment of the traceability method for smartphone repair process based on big data management described in this invention, the steps of constructing a multi-account behavior graph based on a set of account behavior sequences, and performing graph structure feature encoding and cluster analysis on the multi-account behavior graph to obtain anomaly scores for behavior links are as follows.

[0030] Each account behavior sequence in the account behavior sequence set is arranged in chronological order, cleaned, and uniformly encoded to generate an account behavior sequence dataset with a uniform format.

[0031] The standardized account behavior sequence dataset is transformed into a behavior path structure, and maintenance events are used as behavior nodes to construct a multi-account behavior graph.

[0032] A holistic analysis of the multi-account behavior graph is performed to generate account behavior paths that represent the semantic features of nodes, and then these paths are integrated to generate a set of expressive features of the account behavior paths.

[0033] The expression feature set of account behavior paths is compared and grouped to identify abnormal account behavior paths with highly similar behavior and obvious deviations in behavior. Based on the degree of feature difference between abnormal account behavior paths and regular account behavior paths, an abnormal behavior link score is calculated.

[0034] As a preferred embodiment of the traceability method for smartphone repair processes based on big data management as described in this invention, the steps of merging behavioral link anomaly scores with repair event nodes to form traceability node records with risk control identifiers, and constructing a traceability visualization display structure based on multiple traceability node records, are as follows:

[0035] Using the unique identifier of the maintenance event as the key index, each maintenance event node is matched and integrated with the abnormal behavior link score corresponding to the maintenance event node at the field level to generate a set of traceability node paths with risk control identifiers;

[0036] The set of traceability node paths with risk control identifiers is categorized and sorted, and then sorted in ascending order according to the timestamp field of the traceability node to generate a set of equipment maintenance traceability path chains categorized by equipment.

[0037] The information fields of maintenance traceability event nodes in the equipment maintenance traceability path chain set after equipment classification are summarized, a node summary information structure is constructed, and then integrated in chronological order to generate a structured traceability chain summary dataset.

[0038] A directed graph structure for maintenance paths is constructed based on a structured traceability chain summary dataset. On-chain maintenance traceability event nodes are mapped to node units in the graph. Directed edges are established based on the time sequence and behavioral logic relationship of on-chain maintenance traceability event nodes. Combined with node behavior link anomaly scoring, a visual traceability graph structure supporting risk level identification is generated.

[0039] Call the preset cross-platform display interface to encode the visual traceability diagram structure that supports risk level identification into a standard interface format and obtain the traceability visualization display structure.

[0040] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the traceability method for the smartphone repair process based on big data management as described in the first aspect of the present invention.

[0041] Thirdly, the present invention provides 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 traceability method for the smartphone repair process based on big data management as described in the first aspect of the present invention.

[0042] The beneficial effects of this invention are as follows: by constructing a multi-account behavior graph based on a set of account behavior sequences, and by performing graph structure feature encoding and cluster analysis on the multi-account behavior graph, the abnormal scoring steps of the behavior link are obtained. This realizes the global modeling and cluster identification of the evolution path of maintenance behavior under multiple account dimensions, and enables the classification analysis and quantitative expression of abnormal behavior patterns, providing data support for identifying potential risk accounts and assessing the credibility of maintenance behavior. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a traceability method for the smartphone repair process based on big data management.

[0045] Figure 2 A diagram illustrating the generation of a unique identifier for a maintenance event.

[0046] Figure 3 A schematic diagram for context consistency verification and digital signature generation.

[0047] Figure 4 A schematic diagram for constructing and visualizing multi-account behavior graphs. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for tracing the source of smartphone repair processes based on big data management, including the following steps:

[0052] S1: Obtain the equipment identification information of the equipment under maintenance, collect the maintenance behavior description information set, associate the equipment identification information with the maintenance behavior description information, and generate a unique identification number for the maintenance event.

[0053] Specifically, the steps are as follows:

[0054] S1.1: Read the hardware information of the mobile device to be repaired, standardize the hardware information, and generate device identification information.

[0055] Specifically, a physical connection is made to the mobile device to be repaired. The hardware information field set inside the mobile device is read through a standardized device communication interface. Then, the hardware information field set is structured and encapsulated according to a preset field standard. The field names are uniformly named according to the standard. The numerical fields in the hardware information field set are converted to units and the format is validated to form a structured device identification field set that conforms to a unified format. Finally, the structured device identification field set is hashed according to the field hash weighted encoding rule to generate device identification information.

[0056] The hash calculation formula is as follows:

[0057] ;

[0058] in, This indicates the generated device representation information. This indicates that a hash operation will be performed on the entire concatenated result. This indicates that the concatenated string will be encoded in UTF-8 and then converted to hexadecimal. This represents the field number index in the structured device identifier field set. Indicates the first The weighting coefficients of each field, Indicates the first Field names, Indicates the first Standardized field values.

[0059] The encoding operation involves concatenating the field name and field value into a string, converting it into a byte sequence according to the UTF-8 encoding standard, and then encoding each byte in the byte sequence according to the hexadecimal format corresponding to the byte value, ultimately generating an encoded string containing only hexadecimal characters.

[0060] It should be explained that the hardware information field set includes, but is not limited to, the device motherboard serial number, CPU model, memory capacity, storage device identification code, wireless communication module identifier, camera module number, and battery component number.

[0061] It should be explained that the preset field standards refer to a set of field naming conventions, field type definitions, unit standards, and value range rules for the set of hardware information fields of the mobile devices to be repaired. The preset field standards include a unified name, data type requirements, and valid value range for each hardware information field. Furthermore, the preset field standards are predefined based on mainstream smartphone hardware structure specifications, device manufacturer interface protocol descriptions, and traceability data consistency requirements. They serve as a general constraint template in the repair data management process to ensure that hardware information read from different devices can be parsed and structurally encapsulated in a standardized manner.

[0062] S1.2: Initialize the structure that records maintenance operation behavior and collect the set of maintenance behavior description information.

[0063] Specifically, the structure for recording maintenance operation behavior is initialized. According to the preset equipment identification field specifications, a structure framework is established that includes multiple descriptive equipment identification fields such as operation timestamp, operation type, part number, operator identification, maintenance instructions, and diagnostic conclusion. Then, the maintenance behavior information collection process is started. The descriptive content corresponding to the equipment identification fields is extracted item by item from multiple data sources such as maintenance execution records, manual input data, and process log records. The extracted results are then filled into the corresponding equipment identification fields of the structure, completing the collection process of the maintenance behavior description information set. Finally, a maintenance operation behavior structure containing structured equipment identification field content is generated.

[0064] For example, during the collection of maintenance behavior description information, the obtained operation type field is "replace motherboard", the component number field is "MB-AX9234", the operator identifier field is "Tech_021", the maintenance description field is "power-on abnormality, power chip overheating, motherboard replacement recommended", the diagnosis conclusion field is "motherboard short circuit", and the operation timestamp field is "2020-07-10 09:45:00". The structured device identifier field content is filled into the maintenance operation behavior structure in sequence to form a structured maintenance behavior description information set with complete semantic description.

[0065] It should be explained that the structure for recording maintenance operation behavior refers to a standardized data structure established to carry various semantic fields of maintenance behavior based on the structured management requirements of the maintenance behavior description information set. The structure of maintenance operation behavior includes multiple field items, mainly including operation timestamp field, operation type field, part number field, operator identification field, maintenance description field, diagnostic conclusion field, operation result field, and abnormal situation marker field, etc.

[0066] It should be explained that the preset equipment identification field specification refers to a set of standardized data field rules that are predetermined to achieve structured traceability of the maintenance process. The preset equipment identification field specification includes standardized field definitions, logical constraints on fields, and data source mapping rules.

[0067] S1.3: Associate the equipment identification information with the maintenance behavior description information set to construct the initial structure data of the maintenance event.

[0068] Specifically, the equipment identification information and the maintenance behavior description information set are bound in the order of fields. The equipment identification information is used as the equipment identification field in the initial structure data of the maintenance event, and the maintenance behavior description information set is used as the behavior description field in the initial structure data of the maintenance event. They are filled into a unified structure in sequence according to the field naming convention, so as to complete the combination of equipment static attributes and dynamic behaviors in the same data structure and construct the initial structure data of the maintenance event.

[0069] It should be explained that static attributes refer to the fields in the equipment identification information that are related to the inherent identity of the equipment and do not usually change during the maintenance cycle; dynamic behavior refers to the fields in the maintenance behavior description information that are related to the maintenance operation and change with each maintenance event.

[0070] S1.4: Based on the initial structure data of the maintenance event, generate a unique identifier number corresponding to the current maintenance event.

[0071] Specifically, based on the initial structured data of the maintenance event, the structured field content is extracted sequentially according to the preset field order (i.e., the ordered arrangement of each structured field of the maintenance event in the static attribute field and the dynamic behavior field). Then, the field content of the structured field of the maintenance event is processed by concatenating the structured field content in a unified format to form a structured field concatenation string. A hash operation is performed on the structured field concatenation string to generate a hash value of fixed length as a unique identifier number of the maintenance event.

[0072] It should be explained that the structured fields of maintenance events refer to the initial structured data for constructing maintenance events. They consist of static attribute fields extracted from equipment identification information and dynamic behavior fields extracted from the maintenance behavior description information set. They are used to characterize the static identity attributes of the equipment and the process semantic features of the current maintenance operation.

[0073] S2: Perform contextual consistency verification on the unique identifier of the maintenance event and the set of maintenance behavior description information, and generate verification result data containing digital signatures according to preset semantic rules.

[0074] Specifically, the steps are as follows:

[0075] S2.1: Based on the maintenance equipment information corresponding to the unique identifier of the maintenance event, retrieve the standard maintenance process structure from the standard maintenance process database, then parse the standard maintenance process structure, construct the maintenance process state diagram, and extract maintenance semantic rules from the maintenance process state diagram to form a semantic rule set.

[0076] Specifically, based on the maintenance equipment information corresponding to the unique identifier of the maintenance event, the system retrieves matching standard maintenance process structures from the standard maintenance process database according to the equipment model field and maintenance type field in the maintenance equipment information. Then, the retrieved standard maintenance process structures are parsed, and each process step is identified according to the node definition specifications in the process structure. The node number field, node type field, and node description field of each process step are extracted to construct a set of maintenance operation nodes. Based on the node connection relationship field in the process structure, the conditional expressions and jump logic describing the state transitions between different process nodes are extracted to construct a set of state transition relationships. Finally, a maintenance process state diagram is constructed based on the set of maintenance operation nodes and the set of state transition relationships. Semantic constraint rules are identified based on the maintenance process state diagram and finally integrated to form a complete set of semantic rules.

[0077] It should be explained that the standard maintenance process database is a dedicated data collection that stores the standardized maintenance process structure corresponding to various maintenance equipment. It includes standard maintenance process specification information such as predefined maintenance steps, operation sequence, state transition conditions, and related process specifications for different equipment models and maintenance types. The standard maintenance process database organizes maintenance process content in a structured data form, which facilitates accurate retrieval and retrieval based on equipment information of maintenance events. It serves as the process basis for constructing maintenance process state diagrams and extracting maintenance semantic rules.

[0078] It should be explained that semantic constraint rules refer to the set of rules extracted from the maintenance process state diagram, which are used to limit the logical relationships and execution conditions between each step in the maintenance operation process. These rules include: operation sequence constraints, precondition constraints, event restriction constraints, resource dependency constraints, and exception handling constraints.

[0079] S2.2: Extract the semantic feature field of maintenance behavior from the unique identifier number of maintenance event, and standardize the format by combining it with the field specifications in the semantic rule set to generate a standardized semantic feature set of maintenance behavior.

[0080] Specifically, the semantic feature fields of maintenance behavior are parsed and extracted from the unique identifier number of the maintenance event. Then, in combination with the standard requirements of the semantic rule set for field name, field format, value range and data type, the extracted semantic feature fields of maintenance behavior are standardized. Finally, a standardized semantic feature set of maintenance behavior that conforms to the standard requirements of the semantic rule set is generated.

[0081] It should be explained that the semantic feature field of maintenance behavior refers to a set of structured fields used to characterize the semantic content of operational behavior in maintenance events. It reflects key semantic elements such as the occurrence time of maintenance behavior, operation content, executing subject, target object and diagnosis result. Each semantic feature field of maintenance behavior corresponds to the time sequence information, action category, target component, operator identity, operation content description, technical judgment conclusion, result status and abnormality identification of maintenance operation.

[0082] It should be explained that format standardization refers to the process of performing unified format conversion and compliance verification on each field in the semantic feature fields of maintenance behavior, according to the predefined field format specifications, field value ranges, and data type requirements in the semantic rule set. For example, the operation type field is converted into a standardized Chinese operation category name according to the operation type coding specification in the semantic rule set; the part number field is uniformly format verified and cleaned according to the standard part naming rules to ensure that the order of letters and numbers and the number of digits conform to the coding specification.

[0083] S2.3: Substitute the standardized semantic feature set of maintenance behavior into the maintenance process state diagram, and determine whether it meets the semantic constraints of the current process stage based on the context logic in the semantic rule set. Finally, determine the semantic consistency verification state and calculate the semantic matching score.

[0084] Specifically, each semantic field in the standardized maintenance behavior semantic feature set is matched to a process node in the maintenance process state diagram. The semantic field matching ratio is calculated by comparing the semantic constraints associated with the current stage of the process node. Then, based on the predefined operation sequence restrictions, field value logical ranges, and dependency judgment conditions in the process node, the percentage of satisfied semantic constraints is statistically analyzed to obtain the degree of satisfaction of the semantic constraints of the process node. Combined with the contextual logical relationships defined in the semantic rule set, the logical consistency verification rules are used to judge the consistency of semantic dependencies, field value linkage relationships, and time sequence rationality between the preceding and following nodes to obtain the contextual logical consistency verification result. Finally, the semantic field matching ratio, the degree of satisfaction of the semantic constraints of the process node, and the contextual logical consistency verification result are combined to calculate the semantic matching score.

[0085] S2.4: Based on semantic matching score, semantic consistency verification status, maintenance event unique identifier number and standardized maintenance behavior semantic feature set, the verification result data is generated by integrating and digitally signing the verification result data to generate verification result data containing digital signature.

[0086] Specifically, based on semantic matching scores, semantic consistency verification status, unique identifiers of maintenance events, and standardized semantic feature sets of maintenance behaviors, various semantic verification-related fields are structurally integrated according to verification field arrangement rules to construct verification result data containing scoring indicators, verification conclusions, event identification information, and semantic behavior features. Subsequently, the verification result data is encoded according to a unified structural format, and a digital signature algorithm is used to perform a signature operation on the encoded verification result data to generate verification result data containing digital signatures.

[0087] It should be explained that the validation field arrangement rules refer to the rule operations used to structure, set the field order, and align the format of fields such as semantic matching score value, semantic consistency verification status, maintenance event unique identifier number, and standardized maintenance behavior semantic feature set when generating validation result data.

[0088] It needs to be explained that a digital signature algorithm refers to a cryptographic signature operation performed on encoded verification result data. The algorithm extracts a digest from the encoded verification result data, encrypts the digest using the signer's private key, and generates a unique digital signature. This digital signature is then appended to the original verification result data, forming verification result data containing the digital signature. During verification, the signer's public key is used to decrypt the appended digital signature, and the decryption is compared with the recalculated digest from the received verification result data to determine whether the verification result data has been tampered with or forged during transmission or storage.

[0089] Cryptographic methods refer to a complete processing approach based on modern cryptographic theory, using techniques such as hash functions, asymmetric encryption algorithms, and key management mechanisms to perform operations such as digest calculation, digital signature, and authentication on the encoded verification result data. Modern cryptographic theory includes a security theory system based on the assumption of computational complexity, such as the large integer factorization problem, the elliptic curve discrete logarithm problem, and the theory of one-way function construction, providing mathematical security support for encryption and signature algorithms.

[0090] S3: Write the verification result data containing digital signatures as maintenance event nodes into the on-chain traceability ledger, and integrate the maintenance event nodes according to the maintenance account dimension to generate a set of account behavior sequences.

[0091] Specifically, the steps are as follows:

[0092] S3.1: Based on the verification result data containing digital signatures, construct an on-chain writable maintenance event structure, call the on-chain contract interface to write the maintenance event structure into the traceability ledger, obtain the storage address of the on-chain maintenance event node, and use it as the location index for the behavior node.

[0093] Specifically, based on the verification result data containing digital signatures, a maintenance event structure is constructed according to the on-chain storage field specifications. The semantic consistency verification status, semantic matching score, unique identifier of the maintenance event, and standardized semantic feature set of maintenance behavior from the verification result data are written into the fields of the maintenance event structure according to the field mapping rules. Then, the maintenance event structure is submitted to the traceability ledger through the on-chain contract interface call. After the traceability ledger completes the data writing operation, it returns the storage address of the on-chain maintenance event node and uses the storage address as the unique positioning index of the maintenance behavior node.

[0094] It should be explained that the on-chain storage field specification refers to the set of rules used to store the maintenance event structure in the traceability ledger, including field names, field types, field lengths, encoding formats, and field order, to ensure that the maintenance event structure can be correctly identified, parsed, and stored by the on-chain contract.

[0095] It's important to explain that an on-chain contract interface refers to a predefined set of data submission functions within a smart contract deployed on a blockchain network. These functions enable interaction between external entities and on-chain data structures, typically including data writing, state updates, or verification triggering. During smart contract execution, the on-chain contract interface ensures that input data meets format specifications and logical verification requirements, and writes legitimate data into the blockchain ledger, thereby achieving trusted data registration and tamper-proof evidence preservation.

[0096] S3.2: Based on the storage address of the on-chain maintenance event node and the corresponding maintenance account identifier, search for the set of behaviors of the target maintenance account from the index structure of the stored account behavior data.

[0097] Specifically, based on the storage address of the on-chain maintenance event node and the corresponding maintenance account identifier, the index structure of the stored account behavior data is accessed. The maintenance account identifier field is used as the index basis to match and locate the account behavior record entry corresponding to the target maintenance account. Then, based on the set of maintenance event node addresses maintained in the account behavior record entry, the behavior node that matches the current on-chain maintenance event node record address is extracted, and the behavior set of the target maintenance account is restored from it.

[0098] S3.3: Add the storage addresses of the on-chain maintenance event nodes to the end of the target maintenance account's behavior set in chronological order of event occurrence, generating an account behavior sequence set.

[0099] Specifically, the storage address of the on-chain maintenance event node is paired with the event occurrence time field corresponding to the current maintenance event. Using the event occurrence time field as the sorting basis, the storage address of the current on-chain maintenance event node is inserted according to the time order of the event occurrence time field in the account behavior set. If the event occurrence time field of the current on-chain maintenance event node is later than the time field values ​​of all existing nodes in the account behavior set, the storage address of the current on-chain maintenance event node is appended to the end of the account behavior set, ultimately generating an account behavior sequence set that meets the time sequence order requirements.

[0100] It should be explained that the time of occurrence field refers to the timestamp field used in the maintenance behavior description information set to record the actual time of each maintenance operation. It is usually represented in a uniform format, such as "year-month-day, hour:minute:second".

[0101] S4: Construct a multi-account behavior graph based on the set of account behavior sequences, and perform graph structure feature encoding and cluster analysis on the multi-account behavior graph to obtain anomaly scores for behavior links.

[0102] Specifically, the steps are as follows:

[0103] S4.1: Arrange each account behavior sequence in the account behavior sequence set in chronological order, and clean and uniformly encode the maintenance event nodes in the account behavior sequence to generate an account behavior sequence dataset with a unified format.

[0104] Specifically, each account behavior sequence in the account behavior sequence set is sorted in ascending order according to the event occurrence time field corresponding to each on-chain maintenance event node in the account behavior sequence. Then, data cleaning operations are performed on each on-chain maintenance event node in the ascending sorted account behavior sequence to remove on-chain maintenance event nodes that do not conform to the field format specifications or have missing information. The retained on-chain maintenance event nodes are uniformly encoded according to the preset on-chain storage field specifications. Finally, all the cleaned and encoded on-chain maintenance event nodes are organized in chronological order into an account behavior sequence dataset with a unified format standard.

[0105] It should be explained that data cleaning refers to the process of performing field validity checks, missing field removal, invalid value filtering, and redundant field removal on each on-chain maintenance event node in the account behavior sequence, in accordance with preset data integrity requirements and field format specifications, to ensure that each on-chain maintenance event node contains only data content with complete field specifications, unified format structure, and accurate semantic information.

[0106] Among them, the preset data integrity requirements refer to the set of field-level data quality standards that are pre-defined in order to ensure the availability and consistency of on-chain maintenance event node data during the construction of a standardized account behavior sequence dataset. These standards mainly include field existence requirements, field value validity standards, logical consistency conditions between fields, and rules for the rationality of event time sequence.

[0107] S4.2: Transform the standardized account behavior sequence dataset into a behavior path structure, use maintenance events in the standardized account behavior sequence dataset as behavior nodes, and establish connections between nodes based on the temporal relationship or operational correlation between maintenance events to construct a multi-account behavior graph.

[0108] Specifically, in the process of transforming the standardized account behavior sequence dataset into a behavior path structure, each maintenance event in the standardized account behavior sequence dataset is identified as an independent behavior node, and a unique identifier value is assigned to each behavior node. Then, based on the event occurrence time field of adjacent maintenance events in the account behavior sequence, the time series connection relationship between behavior nodes is constructed. At the same time, combined with the operation type field and part number field in the maintenance event, the possible logical causal relationship before and after the operation is determined, and the operation correlation connection between behavior nodes is further supplemented. Finally, based on all behavior nodes and their connection relationships, the behavior paths of each account are aggregated to construct a multi-account behavior graph representing the evolution process of maintenance behavior.

[0109] For example, if a standardized account behavior sequence dataset contains three maintenance event nodes, recording "battery replacement on July 3, 2020", "motherboard driver update on July 4, 2020", and "motherboard function test on July 5, 2020", then these three maintenance event nodes are first added to the behavior path structure as three independent behavior nodes. Then, based on the event occurrence time field "July 3 to July 4 to July 5", a chronological connection is established. Next, combining the component number field and the operation type field, an operational correlation is identified between "motherboard driver update" and "motherboard function test", and additional connections are constructed, ultimately forming a multi-account behavior graph structure containing both chronological paths and operational logic paths.

[0110] It should be explained that the behavior path structure refers to an ordered graph structure formed by establishing connections between maintenance event nodes based on their temporal order and operational relevance. In the behavior path structure, each maintenance event is represented as an independent behavior node, and the behavior nodes are connected by directed edges. The direction of the directed edges indicates the order in which the events occur or the causal dependency of the operations.

[0111] S4.3: Perform an overall analysis of all maintenance event nodes and their connections in the multi-account behavior graph to generate account behavior paths that represent the semantic features of the nodes, and then integrate them to generate a set of expressive features of the account behavior paths.

[0112] Specifically, in the process of conducting a comprehensive analysis of all maintenance event nodes and their connections in the multi-account behavior graph, based on each complete behavior path in the multi-account behavior graph, starting from the first maintenance event node, the temporal sequence connections and operational relevance connections of the first maintenance event node are traversed sequentially to identify the account behavior paths that form complete links. Subsequently, semantic features are extracted from the maintenance event nodes in each account behavior path. Based on the serialization information of the maintenance event nodes, the connection patterns between maintenance event nodes, and the overall path structure, the account behavior paths are expressed in a unified format, and finally, a set of expression features of the account behavior paths is generated.

[0113] It needs to be explained that temporal sequence connection refers to the directed connection relationship established between two adjacent maintenance event nodes in the multi-account behavior graph based on the event occurrence time field of the maintenance event node in the account behavior sequence, according to the chronological order. Temporal sequence connection reflects the time flow of maintenance operations and is used to describe the time sequence evolution of maintenance events in the account operation trajectory. Operation-related connection refers to the directed connection relationship established in the multi-account behavior graph after identifying the logical dependency relationship between two maintenance event nodes on the maintenance operation content in the maintenance behavior description information set, based on the operation type field and the part number field in the maintenance event node. By analyzing the semantic relationship of the operation type field and the logic of the operation sequence, the functional dependency and process continuity between maintenance operation contents can be determined.

[0114] The maintenance operations include: fault detection, component disassembly, component replacement, firmware flashing, function verification, software initialization, and maintenance record registration.

[0115] S4.4: Compare and group the expression feature set of account behavior paths, identify abnormal account behavior paths with highly similar behavior and obvious deviations in behavior, and calculate the abnormal behavior link score based on the degree of feature difference between abnormal account behavior paths and regular account behavior paths.

[0116] Specifically, based on the expression feature set of account behavior paths, the feature dimensions of the expression feature sets of all account behavior paths are aligned, and a distance metric is used to compare the similarity of the expression feature sets between different account behavior paths. The obtained distance value is used as input, and the account behavior paths are aggregated and grouped by a similarity clustering algorithm. During the clustering process, the corresponding cluster center feature is calculated based on the average vector or weighted center of all expression feature sets in each group of account behavior paths. Based on the cluster center feature, account behavior paths that deviate from the cluster center feature are identified as abnormal account behavior paths. Subsequently, the difference in expression feature sets between abnormal account behavior paths and normal account behavior paths is measured, and the abnormal score of the behavior link is calculated by combining the feature deviation value, the cluster boundary distance, and the distribution density of the feature space.

[0117] The formula for calculating the behavioral link anomaly score is as follows:

[0118] ;

[0119] Specifically, This is represented as a behavioral link anomaly score. This is expressed as a measure of the difference between the behavioral path feature set and the standard feature set. It is represented as a distance index between the behavioral path and the boundary in the feature clustering space. It is represented as a density index in the feature space where the behavior path is located. This represents the weighted coefficient of the behavior link anomaly score, which is an indicator of the difference between the corresponding behavior path feature set and the standard feature set. This represents the weighted coefficient of the behavior link anomaly score, which is the distance index of the corresponding behavior path relative to the boundary in the feature clustering space. This represents the weighting coefficient for behavior link anomaly scoring, expressed as the density index of the feature space where the corresponding behavior path resides, and must satisfy... + + =1.

[0120] It should be explained that feature dimension alignment refers to unifying the order and type of each expressive feature in the dimensional space of all account behavior paths before comparing and analyzing the set of expressive features of account behavior paths, so as to ensure that each account behavior path has consistent feature meaning and quantity in the same position.

[0121] Among them, the various expression features include time series features, operation type distribution features, behavior path length features, process deviation features, node connection features, node semantic features, and context continuity features.

[0122] It should be explained that the distance measurement method refers to the process of comparing the similarity of the set of expressive features of account behavior paths, and measuring the degree of similarity or deviation between behavior paths by quantifying the numerical differences between the corresponding dimensions in the feature set.

[0123] S5: Merge the abnormal behavior chain score with the maintenance event node data to form a traceability node record with risk control identification, and build a traceability visualization display structure based on multiple traceability node records.

[0124] Specifically, the steps are as follows:

[0125] S5.1: Use the unique identifier of the maintenance event as the key index, and perform field-level matching and integration of each maintenance event node with the abnormal score of the corresponding behavior link to generate a set of traceability node paths with risk control identifiers.

[0126] Specifically, the unique identifier of the maintenance event is used as the key index. Each maintenance event node in the multi-account behavior graph is matched and integrated with the corresponding behavior link anomaly score at the field level. Based on the field correspondence, the unique identifier field of the maintenance event and the behavior link anomaly score field are aligned and bound in the structured field set. Then, based on the numerical performance of the behavior link anomaly score field, a risk control identifier field representing the risk control status is generated. Finally, a set of traceability node paths with risk control identifiers is constructed, including the unique identifier field of the maintenance event, the behavior link anomaly score field, and the risk control identifier field of the risk control status.

[0127] It should be explained that field-level matching and integration refers to the process of constructing traceability data by matching the structured fields between maintenance event nodes and behavior link anomaly scores one-to-one based on the consistency of field names, the compatibility of field data types, and the correspondence of field semantic meanings, according to the consistency of field names such as the maintenance event unique identifier field and the behavior link anomaly score field. The matched field content is then written into a unified data record unit, thereby generating a structured data set containing the maintenance event unique identifier field and the behavior link anomaly score field.

[0128] It needs to be explained that field mapping refers to mapping fields with the same semantic meaning or business function in different data sets based on the field's name definition, data type, business meaning, and role in the data entity. This establishes a pairing relationship between data fields that reflect the same entity characteristics or are associated with the same event.

[0129] S5.2: Classify and sort the set of traceability node paths with risk control identifiers, arrange them in ascending order according to the timestamp field of the traceability nodes, and generate a set of equipment maintenance traceability path chains classified by equipment.

[0130] Specifically, based on the equipment identifier field corresponding to the unique maintenance event identifier field of each traceability node in the traceability node path set with risk control identification, all traceability node path data are grouped and categorized according to the equipment identifier field. Then, based on the timestamp field in the traceability node path data as the sorting criterion, all traceability node path data belonging to the same equipment identifier field are sorted in ascending order according to the chronological order of the event occurrence time, and finally, a set of equipment maintenance traceability path chains organized by time sequence under each equipment dimension is generated.

[0131] S5.3: Extract summaries from the information fields of maintenance traceability event nodes in each chain of the equipment maintenance traceability path chain set after equipment classification, construct node summary information structures, and then integrate the node summary information structures in chronological order to generate a structured traceability chain summary dataset.

[0132] Specifically, based on representative key information fields such as the unique identifier field, timestamp field, operation type field, component number field, and behavior link anomaly score field contained in each maintenance traceability event node, field extraction and compression operations are performed to construct a node summary information structure to represent the core behavior information of the node. Subsequently, in each equipment maintenance traceability path chain set, according to the timestamp field of the maintenance traceability event node, the summary information structures of each node are integrated and spliced ​​in chronological order to form a structured traceability chain summary dataset with complete traceability trajectory and risk control feature description capabilities.

[0133] S5.4: Construct a directed graph structure for maintenance paths based on the structured traceability chain summary dataset, map on-chain maintenance traceability event nodes to node units in the graph, generate directed edges according to the time sequence and behavioral logic relationship of on-chain maintenance traceability event nodes, configure status identifier attributes according to the node behavior link anomaly score, and finally generate a visual traceability graph structure that supports risk level identification.

[0134] Specifically, each maintenance traceability event node summary in the structured traceability chain summary dataset is used as an independent node unit in the graph structure, and an identifier value corresponding to the unique identifier number field of the maintenance event is assigned to each independent node unit. Then, based on the timestamp field in the maintenance traceability event node summary, chronological directed edges are established between adjacent node units in chronological order. At the same time, combined with the operation type field and the component number field, behavioral logic connections are established for node units with causal relationships between operations, supplementing to form a directed edge structure with logical dependencies. Finally, based on the behavioral link anomaly score calculated from the behavioral link anomaly score field in each node summary, a status identifier attribute is configured for the node unit, and a visual traceability graph structure supporting risk level identification is constructed.

[0135] S5.5: Call the preset cross-platform display interface to encode the visual traceability diagram structure that supports risk level identification into a standard interface format and obtain the traceability visualization display structure.

[0136] Specifically, based on the visual traceability graph structure that supports risk level identification, the preset cross-platform display interface is called. The graph node units, directed edge connections, and node status identification attributes in the visual traceability graph structure that supports risk level identification are formatted according to the field mapping specifications and data structure format requirements of the cross-platform display interface. The node encoding, edge relationship encoding, and global attribute encoding of the graph structure are completed in sequence, and then uniformly organized into a data carrier that conforms to the standard interface format, finally generating the traceability visual display structure.

[0137] It should be explained that the preset cross-platform display interface refers to an interface specification defined in advance based on the cross-platform data visualization application requirements and the general graph structure display standard. When designing the cross-platform display interface, requirements such as multi-terminal display compatibility, graph structure expression universality, and data exchange format consistency are taken into account. The interface clearly specifies the organization form, field name, and data type of graph node fields, edge relationship fields, and graph attribute fields in the interface data structure, so as to ensure that the visualized graph structure can be accurately parsed and consistently presented in different platforms or front-end display environments.

[0138] This embodiment also provides a computer device applicable to the traceability method of smartphone repair process based on big data management, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the traceability method of smartphone repair process based on big data management as proposed in the above embodiment.

[0139] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0140] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the traceability method for smartphone repair processes based on big data management as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0141] In summary, this invention achieves global modeling and clustering identification of maintenance behavior evolution paths under multiple account dimensions by constructing a multi-account behavior graph based on a set of account behavior sequences, encoding graph structure features and performing cluster analysis on the multi-account behavior graph, obtaining abnormal behavior scoring steps, and classifying and quantifying abnormal behavior patterns. This provides data support for identifying potential risky accounts and assessing the credibility of maintenance behaviors.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for tracing the smartphone repair process based on big data management, characterized in that: The method comprises the following steps: obtaining equipment identification information of the maintenance equipment, collecting a set of maintenance behavior description information, associating the equipment identification information with the maintenance behavior description information, and generating a unique identification number of a maintenance event; context consistency verification is performed on the unique identification number of the maintenance event and the set of maintenance behavior description information, and verification result data containing a digital signature is generated according to a preset semantic rule; the verification result data containing the digital signature is written into a chain-based traceability ledger as a maintenance event node, and the maintenance event nodes are integrated according to a maintenance account dimension to generate a set of account behavior sequences; a multi-account behavior graph is constructed based on the set of account behavior sequences, and graph structure feature coding and clustering analysis are performed on the multi-account behavior graph to obtain an abnormal score of a behavior link; the abnormal score of the behavior link is combined with the maintenance event node to form a traceability node record with a risk control identifier, and a traceability visualization display structure is constructed based on a plurality of traceability node records.

2. The big data management based traceability method of smartphone repair process as claimed in claim 1, wherein: The step of obtaining equipment identification information of the maintenance equipment, collecting a set of maintenance behavior description information, associating the equipment identification information with the maintenance behavior description information, and generating a unique identification number of a maintenance event comprises the following steps: reading hardware information of a mobile phone device to be maintained, and performing standardized processing on the hardware information to generate equipment identification information; initializing a structure body for recording maintenance operation behaviors, and collecting a set of maintenance behavior description information; associating the equipment identification information with the set of maintenance behavior description information to construct initial structure data of a maintenance event; generating a unique identification number corresponding to the current maintenance event based on the initial structure data of the maintenance event.

3. The method for tracing of smartphone repair process based on big data management as claimed in claim 1, wherein: The step of performing context consistency verification on the unique identification number of the maintenance event and the set of maintenance behavior description information, and generating verification result data containing a digital signature according to a preset semantic rule comprises the following steps: based on the maintenance equipment information corresponding to the unique identification number of the maintenance event, retrieving a standard maintenance process structure from a standard maintenance process database, then analyzing the standard maintenance process structure to construct a maintenance process state diagram, and extracting maintenance semantic rules from the maintenance process state diagram to form a set of semantic rules; extracting maintenance behavior semantic feature fields from the unique identification number of the maintenance event, and combining the fields in the set of semantic rules to generate a set of standardized maintenance behavior semantic features; substituting the set of standardized maintenance behavior semantic features into the maintenance process state diagram to determine a semantic consistency verification state, and simultaneously calculating a semantic matching score value; based on the semantic matching score value, the semantic consistency verification state, the unique identification number of the maintenance event, and the set of standardized maintenance behavior semantic features, generating verification result data by integration, digitally signing the verification result data, and generating verification result data containing a digital signature.

4. The method for tracing of smartphone repair process based on big data management as claimed in claim 3, wherein: The standard maintenance process database contains standard maintenance process specification information of maintenance steps, operation sequences, state transition conditions, and process specifications for different equipment models and maintenance types.

5. The method for tracing of smartphone repair process based on big data management as claimed in claim 1, wherein: The step of writing the verification result data containing the digital signature into a chain-based traceability ledger as a maintenance event node, and integrating the maintenance event nodes according to a maintenance account dimension to generate a set of account behavior sequences comprises the following steps: Based on the verification result data containing the digital signature, a repair event structure writable on the chain is constructed, and a chain contract interface is called to write the repair event structure into the traceability ledger, and the storage address of the chain repair event node is obtained as the behavior node positioning index; Based on the storage address of the chain repair event node and the corresponding repair account identifier, the behavior set of the target repair account is searched from the index structure of the storage account behavior data; The storage address of the chain repair event node is added to the end of the behavior set of the target repair account in chronological order of the event occurrence, generating an account behavior sequence set.

6. The method for tracing of smartphone repair process based on big data management as claimed in claim 5, wherein: The chain contract interface refers to a set of data submission functions predefined in the smart contract deployed in the blockchain network, which is used to realize the interaction between external entities and chain data structures.

7. The big data management based traceability method of smartphone repair process as claimed in claim 1 wherein: Based on the account behavior sequence set, a multi-account behavior graph is constructed, and graph structure feature coding and clustering analysis are performed on the multi-account behavior graph to obtain a behavior link abnormal score, which is as follows, The account behavior sequence set is arranged in chronological order, and the account behavior sequence data set with uniform format is generated by cleaning and uniform coding; The account behavior sequence data set with uniform format is converted into a behavior path structure, and the repair event in it is taken as a behavior node to construct a multi-account behavior graph; The multi-account behavior graph is analyzed as a whole to generate an account behavior path representing the semantic features of the node, and then the expression feature set of the account behavior path is generated by integration; The expression feature set of the account behavior path is compared and grouped to identify abnormal account behavior paths with high similarity in behavior performance and obvious deviation in behavior performance, and the behavior link abnormal score is calculated according to the feature difference between the abnormal account behavior path and the regular account behavior path.

8. The big data management based traceability method of smartphone repair process as claimed in claim 1 wherein: The behavior link abnormal score is merged with the repair event node to form a traceability node record with risk control identification, and a traceability visualization display structure is constructed based on multiple traceability node records, which is as follows, The unique identification number of the repair event is taken as the key index, and each repair event node and the corresponding behavior link abnormal score of the repair event node are matched and integrated at the field level to generate a traceability node path set with risk control identification; The traceability node path set with risk control identification is sorted and arranged in ascending order according to the timestamp field of the traceability node to generate a device repair traceability path chain set classified by devices; The information field of the chain repair traceability event node in the device repair traceability path chain set is extracted to construct a node abstract information structure, and then integrated in chronological order to generate a structured traceability chain abstract data set; Based on the structured traceability chain abstract data set, a repair path directed graph structure is constructed, the chain repair traceability event node is mapped to a node unit in the graph, the directed edge is established according to the time sequence and behavior logic relationship of the chain repair traceability event node, and the visualization traceability graph structure supporting risk level identification is generated by combining the node behavior link abnormal score; Call the preset cross-platform display interface, encode the visual traceability diagram structure supporting risk level identification into a standard interface format, and obtain a traceability visual display structure. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the traceability method of the smartphone repair process based on big data management according to any one of claims 1-8.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the traceability method of the smartphone repair process based on big data management according to any one of claims 1-8.

Images