Power grid material sampling inspection management method for realizing whole-course tracing based on intelligent sample sealing

Abstract

The invention discloses a power grid material sampling inspection management method for realizing whole-course tracing based on intelligent sample sealing, and relates to the technical field of electric power material management and intelligent perception, and the method comprises the following steps: S1, an initialization stage: executing a sample sealing coding segmentation pre-configuration process, distributing independent numbered segments to each sample sealing terminal, and the central control module synchronizes the use state of the terminal code to realize distributed isolation of code generation. According to the invention, coding uniqueness is realized through segmented pre-configuration and global duplicate checking, identity information security is guaranteed by adopting chip one-time write protection and tamper-proof technologies, and sample packet switching and data counterfeiting are effectively prevented by combining automatic acquisition and multi-round consistency comparison. Abnormal recognition and closed-loop processing ensure that abnormal events are frozen in time and responsibility is traced, and irregular samples are prevented from flowing into subsequent links. The whole-process auditing and dynamic optimization enable the management process to be self-perfected, and the intelligence, standardization and reliability of sampling inspection of the power grid materials are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power material management and intelligent sensing technology, specifically to a method for power grid material sampling management based on intelligent sealing to achieve full traceability. Background Technology

[0002] "Power grid material sampling management based on intelligent sealing for full traceability" refers to the use of intelligent sealing devices with anti-tampering, unique coding, and data sensing functions in the power grid operation and maintenance process to ensure the quality of materials and the authenticity and controllability of the sampling process. These devices instantly seal and identify the sampled items, and throughout subsequent stages such as circulation, testing, retention, and problem tracing, IoT technologies (such as RFID, NFC, or QR codes) are used to record and dynamically update the sample's identity, circulation nodes, testing status, and results data. This management method establishes a closed-loop process of "sampling upon inspection, coding upon sealing, and traceability upon coding," effectively preventing problems such as sample substitution and data tampering. It improves the transparency, controllability, and traceability of power grid material sampling, supporting the intelligent and standardized upgrading of power grid equipment quality supervision.

[0003] The existing technology has the following shortcomings:

[0004] In existing intelligent sealing-based material sampling management systems, sealing codes typically rely on embedded chips (such as RFID and NFC) to generate unique identifiers to bind sample identity information. However, in practical applications, the system's sealing program suffers from instability, concurrency processing defects, or a lack of anti-duplicate mechanisms, potentially leading to technical malfunctions such as duplicate allocation or abnormal writing of identification codes during the sealing process. Specifically, on the one hand, when sealing multiple samples simultaneously or performing high-frequency batch operations, the system may, due to logical conflicts in identifier generation or database synchronization delays, mistakenly assign the same unique identification code to multiple different material samples, creating data chain overlaps and severely interfering with subsequent circulation monitoring and traceability of test results. On the other hand, after some sealing chips have completed sample binding, insufficient write permission control or abnormal system rewrite operations may cause the original sealing information to be overwritten with another material identifier, resulting in a mismatch between the sampling results and the actual sample. The aforementioned coding conflicts or overlay issues not only disrupt the correspondence between material samples and testing data, but may also be exploited by illegal operations to carry out sample replacement, falsification of random inspections, and other behaviors, thereby weakening the credibility of sealed samples and affecting the technical integrity and traceability of responsibility of the power grid material quality supervision system.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a power grid material sampling inspection management method based on intelligent sealing for end-to-end traceability. It achieves unique coding through segmented pre-configuration and global deduplication, safeguards identity information security with one-time chip write protection and anti-tampering technology, and effectively prevents sample substitution and data falsification by combining automated data collection and multi-round consistency comparison. Anomaly identification and closed-loop processing ensure timely freezing and accountability for abnormal events, preventing non-compliant samples from flowing into subsequent stages. End-to-end auditing and dynamic optimization enable self-improvement of the management process, significantly enhancing the intelligence, standardization, and reliability of power grid material sampling inspection, thereby addressing the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for power grid material sampling management based on intelligent sealing for full traceability, comprising the following steps:

[0008] S1, Initialization Phase: Execute the sealing sample coding segmentation pre-configuration process, allocate independent numbering segments to each sealing sample terminal, and synchronize the terminal coding usage status through the central control module to achieve distributed isolation of coding generation;

[0009] S2. After the pre-configuration process of segmented sealing coding is completed, the uniqueness verification process of coding is executed. Before each sealing operation, the data indexing mechanism is used to perform a global deduplication of the coding to be written to ensure that the coding has not been used.

[0010] S3. After the uniqueness verification process is completed, the chip write protection process is executed. The uniqueness verification code is written to the embedded chip, and the one-time write protection function is immediately activated to prevent subsequent writing and achieve chip anti-tampering.

[0011] S4. After the chip write protection process is completed, the sample sealing information consistency verification process is executed. At the sample transfer, testing and archiving nodes, the embedded chip code, sealing time and terminal identifier are automatically extracted and compared with the original sealing record to verify the consistency of the information.

[0012] S5, after the sealing information consistency verification process is completed, the abnormal data closed-loop processing process is executed. The inconsistent information detected in the consistency verification is automatically identified, the data is frozen, the responsibility is traced, and the flow is stopped to achieve process data closed-loop.

[0013] S6. After the abnormal data closed-loop processing is completed, the full-process audit feedback process of sealing is executed, the operation logs and abnormal records are summarized, the weak links are analyzed, and the initial segmentation pre-configuration strategy is adjusted and optimized to achieve dynamic optimization closed loop.

[0014] Preferably, step S1 includes:

[0015] During the initialization phase, each sample terminal is registered for identity, and the coding segment is divided based on physical characteristics, geographical location and management unit information using a segmented numbering strategy.

[0016] Write the coded segment corresponding to each sample terminal into the local data storage area and transmit it to the central control terminal for filing in an encrypted manner.

[0017] After generating a unique code, the terminal records the segment usage status and periodically feeds it back to the central control terminal via encrypted communication.

[0018] The central control unit audits and compares the coding usage status, and freezes the terminal coding permissions and adjusts the segmentation strategy when anomalies are detected.

[0019] Preferably, step S2 includes:

[0020] Before each sealing operation, a multi-factor coupling strategy is used to generate the code to be written based on the assigned numbering segment;

[0021] The generated code is uploaded to the central data interaction node through an encrypted channel to initiate a global uniqueness verification request;

[0022] Use data structures based on hash tables or B+ trees to perform deduplication and determine whether the code has been used.

[0023] If the verification passes, continue the sealing operation and temporarily lock the code; if the verification fails, interrupt the process and provide an error message.

[0024] Preferably, step S3 includes:

[0025] After the uniqueness verification of the code is completed, the unique code is encrypted using a preset encryption algorithm to generate ciphertext and then written to the storage area of ​​the embedded chip.

[0026] During the writing process, CRC redundancy check and bidirectional data readback comparison are performed to ensure that the written data is accurate and consistent.

[0027] Once the write operation is complete, the one-time write protection function is activated immediately to lock the storage area into an unmodifiable state;

[0028] The write-protected status and write logs are uploaded to the center, and the operation data is archived as a basis for traceability.

[0029] Preferably, step S4 includes:

[0030] When a sample is transferred to a testing or archiving node, the embedded chip code, sealing time, and terminal identifier are automatically extracted to form a sample identification data record.

[0031] The records are uploaded to the central data management terminal for multiple verifications against the original sealed sample records.

[0032] Consistency is determined by a comprehensive approach that includes full-byte comparison, time interval judgment, terminal legality verification, and flow logic validation.

[0033] If the verification matches, the data can continue to be processed; if the verification does not match, the data will be automatically frozen and an anomaly report will be sent.

[0034] Preferably, step S5 includes:

[0035] When inconsistencies in key information are detected during the consistency verification of sealing information, the system automatically identifies the anomaly and freezes the relevant data.

[0036] After freezing the associated records, the responsibility tracing process is initiated to analyze the operation logs, time sequence, terminal address and personnel historical behavior, and generate a responsibility chain report;

[0037] Suspend all transfer, testing, and archiving operations related to abnormal samples to prevent them from entering subsequent stages;

[0038] The processing results are generated into an abnormal process report and archived in the audit database for traceability and process optimization analysis.

[0039] Preferably, step S6 includes:

[0040] After each sealing, transfer, testing, archiving, and anomaly handling operation, collect and archive the entire process operation log and behavior records;

[0041] Regularly perform big data analysis and anomaly pattern recognition algorithms on logs and anomaly records to identify weak links in the coding allocation and transfer process;

[0042] Based on the identification results, optimization suggestions are generated and an audit feedback report is output;

[0043] Based on the audit feedback, the initial coding segmentation pre-configuration strategy was adjusted and the execution process was updated to form a closed-loop optimization mechanism.

[0044] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0045] This invention ensures the system-wide uniqueness of each code through segmented pre-configuration and global deduplication, and guarantees the immutability of identity information through chip one-time write protection technology and hardware anti-tampering mechanisms. Simultaneously, automated information collection and multi-round consistency comparisons effectively prevent operational loopholes such as sample substitution and data falsification. Anomaly identification and closed-loop processing mechanisms enable real-time freezing and precise tracing of abnormal events, preventing non-compliant samples from entering subsequent stages and improving the security and accountability of the entire process. Finally, through full-process auditing and dynamic optimization feedback, the sample sealing management process can self-identify and continuously correct potential weaknesses, promoting the intelligent, standardized, and highly reliable implementation of power grid material sampling management, and providing a solid and reliable technical guarantee and management foundation for industry quality supervision. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0047] Figure 1 This is a flowchart of the method for power grid material sampling management based on intelligent sealing to achieve full traceability, as described in this invention. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0049] This invention provides, for example Figure 1 The power grid material sampling management method based on intelligent sealing for full traceability, as shown, includes the following steps:

[0050] S1, Initialization Phase: The sealing sample coding segmentation pre-configuration process is executed, and an independent numbering segment is assigned to each sealing sample terminal. The coding usage status of the terminal is periodically synchronized through the central control module to achieve distributed isolation of coding generation.

[0051] The segmented pre-configuration process for sealing and coding during the initialization phase can significantly improve the reliability of unique identifier generation, stability under concurrent scenarios, and tamper-proof capability of the subsequent traceability chain throughout the entire process of power grid material sampling and sealing. The specific implementation steps are as follows:

[0052] During the initialization phase, all sealing terminals to be put into use are registered. Based on the unique physical characteristics, geographical location, and management responsibility unit of each sealing terminal, a segmented numbering strategy is adopted to scientifically divide the coding segments. These coding segments are not simply sequential numbers, but rather distributed coding segments are generated using algorithms based on multiple factors such as the characteristics of power grid material sampling inspection, the physical location distribution of each terminal, the frequency of daily sampling inspections, and the distribution of historical data. This ensures that each terminal can only generate a unique code within its pre-allocated segment during the subsequent sealing process. This segmentation strategy not only fully considers the sealing pressure and data throughput of different regions and management units, but also dynamically adjusts the segment size to prevent a single terminal from running out of numbers during peak working hours, thus affecting the sampling process.

[0053] After segmentation, the independent coded segment corresponding to each sealing terminal is written to the local data storage area, and the allocation results are transmitted to the central control terminal for record-keeping via secure encryption. Simultaneously, the central control terminal establishes a global coded segment allocation table to archive and monitor all allocated segments in real time, preventing the risk of cross-allocation or segment overlap between different terminals. This step not only ensures the security and uniqueness of data transmission but also improves the consistency and fault tolerance of distributed data through local and central two-level storage. Whenever a terminal starts or initializes a sealing task, it must retrieve the currently valid coded segment from the local storage area and perform a numbering operation according to the allocation table, eliminating cross-terminal numbering conflicts at the source.

[0054] During the actual sealing process, each terminal generates a unique code according to a pre-assigned numbering segment. Once a terminal successfully generates a new unique code and completes a sealing operation, it automatically records the current segment's usage status, including the number of used numbers, the remaining available number range, and the most recently assigned number. This status information is periodically fed back to the central control terminal via encrypted communication at predetermined time intervals, achieving two-way real-time synchronization between the terminal and the center. Through this mechanism, the central control terminal can monitor the code segment consumption of each terminal at any time, provide early warnings of terminals whose segments are about to run out, and dynamically expand or reallocate segments, avoiding the risk of code allocation interruptions during peak sampling periods.

[0055] To ensure the continuity and security of the entire segmented coding management process, the central control unit periodically audits and compares the coding usage status reported by all terminals, checking for abnormal phenomena such as abnormal coding growth, segment boundary violations, and abnormal usage rates. For detected coding usage anomalies, the central control unit immediately freezes the coding allocation permissions of the relevant terminals and initiates manual review and automatic data correction operations. Simultaneously, the center dynamically optimizes subsequent segment allocation strategies based on historical data statistics and trend analysis, continuously adjusting and improving coding segmentation rules. This ensures that the entire sample coding generation process maintains high reliability and security under distributed, concurrent, and high-intensity application scenarios. This segmented pre-configuration process based on dynamic adjustment and real-time monitoring greatly enhances the anti-duplicate and anti-tampering capabilities of coding generation, effectively supporting the uniqueness and traceability of identification identifiers throughout the entire process of power grid material sampling inspection.

[0056] The core function of this step is to achieve distributed isolation and global uniqueness of coding allocation during the sampling and sealing process of power grid materials from the technical source, greatly improving the security and stability of subsequent sealing management. Specifically, through the segmented pre-configuration process of sealing coding in the initialization phase, each sealing terminal can be assigned an independent and non-overlapping numbering segment. This ensures that the code generated by each terminal during the sealing operation is limited to its preset segment range, fundamentally eliminating numbering conflicts and duplicate allocations caused by concurrent operations, network latency, or database synchronization anomalies between different terminals. At the same time, through the periodic synchronization of the coding usage status of each terminal by the central control module, not only can the dynamic monitoring of the numbering allocation and usage of terminals across the entire network be achieved, but also timely warnings and adjustments can be made when a terminal's coding segment is about to be exhausted or abnormal consumption occurs, preventing the risk of sealing operation interruption and illegal tampering. The distributed isolation mechanism can also effectively suppress the loss of global numbering control caused by malicious attacks or internal operational errors. By combining pre-allocation with real-time monitoring, it achieves high availability, high security, and high controllability of coding generation in the entire power grid material sampling process, laying a solid foundation for subsequent sample identity binding, traceability verification, and data consistency. This measure not only significantly improves the anti-duplicate and anti-tampering capabilities of the sampling process, but also provides technical support and management basis for the entire power grid material quality traceability system.

[0057] S2. After the encoding segmentation pre-configuration process is completed, the encoding uniqueness verification process is executed. Before each sealing operation, the data indexing mechanism is used to perform a global deduplication of the encoding to be written to ensure that the encoding to be written has not been used in the entire system.

[0058] To further ensure the uniqueness of each code throughout the system during the sealing process and prevent duplicate allocation due to concurrent processing, network latency, or historical issues, a code uniqueness verification process must be performed before each sealing operation. The specific steps are as follows:

[0059] Before sealing a material sample at the terminal, a unique code to be written is generated based on the previously assigned independent numbering segment. This code generation employs a multi-factor coupling strategy, incorporating not only the segment number and serial number, but also terminal identity information, sealing timestamp, and regional location parameters, significantly increasing the code's complexity and unpredictability. This approach not only prevents regular vulnerabilities arising from simple incremental numbering but also effectively distinguishes code ownership in cross-segment interactions or special scenarios, enhancing overall security and anti-counterfeiting capabilities.

[0060] The "multi-factor coupling strategy" refers to generating unique codes for material sampling and sealing that do not rely solely on a single number (such as a simple serial number or segment number). Instead, it combines and embeds multiple key information from different dimensions to form a complex and irreversible coding structure. These factors include, but are not limited to, the segment number assigned to the sealing terminal, the serial number of each sealing operation, the specific terminal's identity information, the precise timestamp of the operation, and the physical or geographical location information. The main advantage of adopting the multi-factor coupling strategy is that it significantly improves the security, uniqueness, and anti-counterfeiting capabilities of the code: on the one hand, codes generated by a single factor are highly susceptible to duplication or malicious prediction and tampering, while multi-factor combinations greatly increase the coding space and permutation complexity, almost eliminating the possibility of code collisions and forgery; on the other hand, the coupled code naturally possesses traceability capabilities, and once an anomaly is detected, it can accurately pinpoint the specific terminal, time, and location. The strategy is implemented as follows: During each sample preparation stage, a basic number is first generated from the segment number and local serial number assigned to the terminal. Then, the current terminal's identification code, the sample sealing timestamp, and location information are automatically collected. Finally, all factors are combined in a preset order using a specific encryption algorithm or a custom hash function to form the final code, thereby achieving uniqueness, unpredictability, and high security protection capabilities. This method not only improves the reliability of sample identification during the power grid material sampling inspection process but also provides a technical foundation for subsequent data traceability and risk control.

[0061] Once generated, the code is immediately uploaded to the central data interaction node via an encrypted channel, initiating a global uniqueness verification request. Upon receiving the verification request, the data interaction node employs an efficient data indexing mechanism to quickly check for duplicates across the entire system. This deduplication process utilizes data structures based on hash tables or B+ trees, enabling millisecond-level retrieval responses in large-scale, high-concurrency environments. The verification mechanism not only targets the segment codes currently being processed in this batch but also performs a full comparison of historical archives, abnormally frozen codes, and pre-allocated but inactive codes, ensuring that the code to be written is never used throughout its entire lifecycle.

[0062] In the deduplication process for verifying the uniqueness of codes, hash tables or B+ trees are used primarily to achieve efficient and accurate searching of codes to be written in a large-scale, high-concurrency data environment. A hash table is a data structure based on direct addressing of a key (i.e., the code itself), which can achieve constant time complexity (O(1)) search operations under ideal conditions, making it very suitable for quickly verifying whether a code has been used across the entire system. A B+ tree is a highly balanced multi-way search tree, widely used in database index structures, with good ordering and range search capabilities. When faced with a large amount of dynamically changing encoded data, it can stably support insertion, deletion, and range queries with logarithmic time complexity (O(log n)), which helps to continuously maintain the uniqueness and consistency of codes. The specific implementation method is as follows: at the central end or data interaction node, all assigned codes are stored in a hash table or B+ tree. When the terminal initiates a deduplication request, the code is automatically used as the key to directly locate the target code in the hash table, or the target code is searched layer by layer through the B+ tree index to see if it already exists. If the uniqueness check is found, the code fails and needs to be regenerated; if it does not exist, the check passes and the new code is promptly inserted into the data structure to maintain real-time uniqueness. Both structures can achieve efficient uniqueness verification at this stage. Hash tables are more suitable for high-frequency single-point queries, while B+ trees take into account both queries and ordered range operations, greatly improving the response speed and scalability of the entire sampling code allocation and verification process.

[0063] Once the data interaction node completes the uniqueness verification, it will return a pass or fail instruction in real time based on the verification result. If the verification passes, the terminal is allowed to continue executing subsequent encoding writing and sealing operations, and the encoding is temporarily locked to prevent it from being repeatedly used by other terminals in the short term. If the verification fails, the sealing process is immediately interrupted, and an exception message is displayed, requiring the operator to regenerate the encoding or contact the administrator for handling. This mechanism not only avoids encoding conflicts at the source but also effectively isolates technical risks such as data races and cache latency that may occur in concurrent scenarios.

[0064] To further strengthen uniqueness assurance, each uniqueness verification operation automatically generates a verification log, which is synchronized to the central log storage area for archiving and auditing. This log records detailed key data such as verification initiation time, terminal identity, coded content, verification time, and response result, providing objective evidence for subsequent anomaly tracing, responsibility identification, and process optimization. In addition, the central log storage area regularly runs consistency verification tasks, cross-referencing historical verification records with the current coding distribution to promptly identify potential coding conflicts, enabling proactive early warning and continuous optimization. Through this multi-dimensional, multi-stage uniqueness verification mechanism, a secure closed-loop for coding allocation is effectively achieved throughout the entire process of power grid material sampling inspection, ensuring the non-replicability and full traceability of sealed sample identities, laying a solid foundation for the intelligent and highly reliable operation of the power grid material quality supervision system.

[0065] The core function of this step is to ensure that the unique code generated for each sample during the power grid material sampling inspection process is truly non-repeatable and globally unique across the entire system, thereby eliminating risks such as data mismatch, identity confusion, and broken traceability chains that may be caused by coding conflicts. In practical applications, although the pre-configuration of coding segments has assigned independent numbering segments to each terminal, there is still a technical risk that the same code may be accidentally generated or reused in multiple terminals and multiple stages under high-concurrency operations, network fluctuations, human error, or extreme scenarios. Therefore, before each sealing operation, a system-wide deduplication check is initiated on the code to be written through a data indexing mechanism, which can effectively strengthen the uniqueness guarantee of segment isolation. This deduplication check process is supported by a high-performance data structure (such as a hash table or B+ tree), which can accurately locate whether the target code has been used from a large historical coding dataset in a very short time. Once a duplicate code is found, the sealing process can be stopped immediately, and a new code must be generated to eliminate the risk of duplicate allocation. This step not only fundamentally prevents problems such as sample identity mismatch, data tampering, and subsequent traceability obstacles caused by coding conflicts, but also provides a solid data foundation for accountability tracking and full-process auditing of power grid material sampling inspection. In summary, the coding uniqueness verification process is a key technical measure to improve the data security, sample sealing credibility, and regulatory compliance of the entire process, playing an irreplaceable and important role in ensuring the authority and scientific nature of the sampling inspection process.

[0066] S3. After the uniqueness verification process is completed, the chip write protection process is executed. The uniqueness verification code is written into the embedded chip, and the one-time write protection function is immediately activated to prevent all subsequent write operations, thereby achieving chip-level anti-tampering.

[0067] To ensure that the uniqueness verification code achieves the technical goals of being immutable, unrewritable, and unforgeable at the physical media level, the chip write protection process is strictly executed after the code uniqueness verification process is completed. The specific steps of this process are as follows:

[0068] After verifying the uniqueness of the code, the sampling terminal automatically loads the verified unique code into the write buffer and performs an initial handshake with the embedded chip to verify the chip's physical state, free area, and write permissions. At this point, the chip returns its unique physical identification information and current storage area status. The sampling terminal compares this information with the central record to confirm that the chip has not been written to and is in its initial sealing state. This step, through a two-way authentication mechanism, eliminates the risk of reusing old, recycled, or already written chips, ensuring data security at the physical interface level.

[0069] After confirming the chip's status is correct, the inspection terminal initiates a secure write process. The unique code in the buffer is converted into ciphertext using a secure encryption algorithm and then written to the chip's dedicated storage area. This storage area supports One-Time Programmable (OTP) write functionality. During the write process, the written data undergoes integrity verification, including CRC redundancy checks and bidirectional data readback comparison, ensuring that the actual written data is completely consistent with the expected code, with no errors and no information loss. Once the data writing is complete, the chip automatically generates a write log and provides real-time feedback on the write status and verification results, further guaranteeing the non-repudiation of the operation and the authenticity of the data.

[0070] Before writing the unique code in the buffer to the chip's dedicated storage area, various secure encryption algorithms are typically used, such as symmetric encryption algorithms (e.g., AES, SM4), asymmetric encryption algorithms (e.g., RSA, ECC), or block hash encryption (e.g., SHA-256 with salt), to convert the plaintext code into ciphertext. The core purpose of encrypting the unique code is to prevent attacks on the chip's physical read / write operations, malicious theft, reverse engineering, or tampering of information during transmission or storage, thereby enhancing the security and anti-counterfeiting capabilities of the sample data. Even if unauthorized personnel read the encrypted code, they cannot directly recover the true number and associated information, effectively preventing data leakage and identity theft, and ensuring the security and traceability of the power grid material sampling inspection process. The specific operation steps are as follows: First, the sampling terminal retrieves the code that has passed the uniqueness verification from the buffer. Then, based on the preset key or public key, a specified encryption algorithm is selected to encrypt the code, converting the plaintext code into a ciphertext string. Next, the ciphertext is securely written to the chip's dedicated storage area. Finally, an operation log for this encryption and writing is generated and saved, including the algorithm type used, encryption time, and encryption success flag, ensuring subsequent traceability and verification. Through this mechanism, the security of the code is protected, and the anti-attack capability of the sample chip in complex application scenarios is enhanced, providing a crucial technical guarantee for the unforgeable and tamper-proof identification of materials.

[0071] CRC redundancy check is a commonly used cyclic redundancy check technique. It calculates and appends a checksum to the data to be written to the chip (such as a unique code and its encrypted content), and then calculates and compares the checksum again after writing to detect whether errors or damage occurred during transmission and writing. Its function is to ensure the integrity and correctness of the data content, avoiding single-bit or multi-bit errors caused by electromagnetic interference, hardware failure, etc. Bidirectional data readback comparison, on the other hand, involves the terminal reading the written data from the chip's dedicated storage area after the write operation is completed, comparing it byte-by-byte with the original data to be written to confirm that the actual content stored in the chip is consistent with the content before writing, without any deviation or omission. This mechanism can promptly detect abnormalities such as physical damage to the chip, storage failure, or illegal tampering. When used together, the former ensures the overall correctness of transmission and writing through algorithms, while the latter verifies the authenticity and validity of the writing results through actual read and write operations, greatly improving the security and reliability of coded data storage and providing technical assurance for the uniqueness and immutability of the identity information of power grid material samples.

[0072] After data writing and verification are completed, the sampling terminal immediately initiates a one-time write protection command to the chip, activating the write protection mechanism. This mechanism physically locks the written storage area, preventing the chip from modifying or overwriting the contents of that area in any subsequent operation. Neither normal commands nor unauthorized intrusions can breach this hardware defense. This write protection can be implemented through the chip's internal fuse circuit or combined with external physical encryption measures, such as pin fuses or data locking, to permanently put the written content into a read-only state. The write protection status and the chip's unique identification information are uploaded to the center, forming a link-level data mapping, providing an immutable physical basis for subsequent sample traceability and information verification.

[0073] "One-Time Programmable (OTP) write protection command" refers to the control command issued by the sampling inspection terminal to the embedded chip immediately after completing the encrypted writing and integrity verification of the encoded data. This command triggers the chip's internal one-time write protection mechanism. "One-time write protection" means that once data is written to a specific area of ​​the chip, that area will be permanently unreadable and unalterable, accessible only through hardware fuses, electrical locking, or physical fuses. This mechanism aims to completely prevent the sample data from being tampered with, overwritten, or counterfeited at the physical level, ensuring the uniqueness, irreversibility, and high security of each sample's identity.

[0074] The specific implementation steps are as follows: First, the sampling terminal encrypts and writes the uniquely coded data, and performs CRC redundancy check and bidirectional readback comparison to confirm the integrity and consistency of the written data. Second, the terminal calls the corresponding one-time write protection control command according to the chip model and instruction set structure, and sends it to the chip's write protection control pin or logic control channel. Third, after receiving the write protection command, the chip starts the OTP locking logic, permanently locking the corresponding memory unit to a read-only state through electrical fuse, charge injection, or logic latching. Finally, the chip transmits the write protection status and execution result back to the terminal through a feedback signal. The terminal then uploads the write protection status and the chip's unique identification code to the data center for archiving and evidence preservation, forming a hardware-level termination mark for the sealing process. Through this mechanism, even if the chip subsequently encounters illegal read / write, physical stripping, or reverse operation, the original coding information cannot be changed or overwritten, thereby effectively preventing the sample identity from being forged, replaced, or tampered with, providing an irrefutable physical-level security guarantee for the power grid material sampling process.

[0075] After a one-time write protection operation is completed, the sampling terminal generates a complete operation log for the entire writing process, including the encoded content, chip identification code, write timestamp, write success flag, write protection status, and other information, which is then archived in the central data storage area. Simultaneously, the terminal automatically triggers the collection of on-site photographs, environmental parameters, and other supporting evidence data, forming an inseparable, holographic record of the sealed sample together with the chip writing operation. All this data is transmitted remotely in encrypted form to ensure transmission security and is backed up at the central facility at multiple levels.

[0076] The core function of this step is to achieve permanent protection and tamper-proofing of the identification codes of power grid material sampling samples from both physical media and information security perspectives, thereby maximizing the authenticity of the sealing process and the traceability of the entire subsequent process. After the code uniqueness verification process is completed, the verified unique code is written to the dedicated storage area of ​​the embedded chip using encryption or other secure methods. This operation ensures the precise binding between the sample and the identification code. More importantly, a one-time write protection function is immediately activated after writing, completely locking the storage area so that the chip cannot be rewritten or modified under any subsequent operation. Neither normal commands nor malicious attacks can breach this hardware defense. In this way, even if the sampling terminal, operating network, or backend database is attacked, the coded information recorded in the chip cannot be tampered with or forged, greatly improving the anti-counterfeiting capabilities and technical credibility of the sealing process. In addition, the one-time write protection mechanism can also prevent the chip from being reused or the information from being maliciously overwritten, eliminating security risks such as code replacement and sample swapping, and effectively ensuring the unique correspondence between the sample and the test data. This step not only achieves physical-level tamper-proof protection of identity information but also provides irrefutable objective evidence for subsequent processes such as transfer, testing, archiving, and traceability. This ensures that the regulatory compliance, data reliability, and traceability of the entire power grid material sampling inspection process reach industry-leading levels. This method, combining chip write protection with a one-time write lock mechanism, is a key technological foundation for achieving highly secure and robust power grid material management.

[0077] S4. After the chip write protection process is completed, the sealing information consistency verification process is executed. At each key node such as sample transfer, testing and archiving, the embedded chip code, sealing time and terminal identifier are automatically extracted and compared with the original sealing record item by item to verify the consistency of information.

[0078] To ensure that the identity information of each material sample remains consistent and traceable throughout the entire process, a sample sealing information consistency verification process must be performed after the chip write protection process is completed. This process uses the unique code in the embedded chip as the main thread, combined with time information and terminal identification, to construct a dynamic verification chain spanning multiple key stages such as sample transfer, testing, and archiving. The specific steps are as follows:

[0079] As the sample moves from the sealing stage to subsequent stages, the receiving terminal automatically extracts the encoded information from the embedded chip using a dedicated reading device, and simultaneously collects the current operation time and terminal identification information. All data collection is conducted under strict automated control, avoiding manual intervention or information omissions. At this point, the receiving terminal packages the chip code, collection time, and terminal identification together to form a complete sample identity data record. The innovation of this step lies in the fact that each stage uses hardware-level automatic data collection and real-time binding to ensure seamless and unforgeable data flow throughout the entire process, laying a solid foundation for information consistency.

[0080] The collected sample identification information is uploaded to the central data management terminal and automatically compared with the original sealing process records. The comparison includes not only the unique code itself, but also the sealing time, the sealing operation terminal identifier, and the item-by-item correspondence with the information collected at the current node. The central terminal employs a multi-verification algorithm, performing precise comparison of the code itself and fault-tolerant matching of the timestamp and terminal identifier. It can intelligently identify minor delays in normal processes and time differences caused by network fluctuations, and immediately trigger alarms for unexpected modifications or abnormal changes. This step, through full cross-verification of information, effectively prevents data mismatches and identity interference caused by code copying, manual input, equipment malfunctions, etc.

[0081] The "multi-verification algorithm" refers to a process in which the consistency of sample identity information is verified not only by a single comparison method, but also by comprehensively utilizing multiple information items and multi-dimensional rules to jointly verify key data in a layered and itemized manner, thereby significantly improving the rigor and fault tolerance of data verification. Specifically, the role of the multi-verification algorithm in the above steps is twofold: firstly, it ensures the absolute uniqueness and correctness of the chip code, that is, confirming through full-byte comparison that the read code is completely consistent with the original sealed sample record, preventing forgery or tampering; secondly, it employs fault tolerance and interval matching mechanisms for timestamps and terminal identifiers. By setting reasonable time tolerances and terminal identity whitelists, it intelligently filters out minor information deviations caused by non-malicious factors such as network fluctuations and delays, avoiding misjudging compliant flows as abnormal. Furthermore, multi-verification also performs logical chain analysis by comparing operation logs, flow order, and sample status changes at each stage, triggering alarms and data freezes immediately in cases of unexpected modifications, data jumps, or multi-node anomalies. The implementation method is as follows: whenever a key node reads the chip code and related information, it automatically calls a multi-verification algorithm to sequentially perform code equality comparison, time interval judgment, terminal legality verification, and flow logic verification. All verification results are comprehensively judged, and only if all meet the preset standards is the sample allowed to continue its subsequent flow. This ensures high security and accuracy in the verification process while accommodating reasonable operational deviations in the actual process, providing intelligent and refined technical support for the integrity, compliance, and traceability of the power grid material sampling inspection data chain.

[0082] If any inconsistency is found in any key information during the verification process, such as a discrepancy between the chip code and the original sealing record, an abnormally early or late sealing time, or a mismatch between the terminal identifier and the expected responsibility node, the sample information will be automatically risk-classified and subsequent operations will be frozen. At this point, all related flow and testing stages will be temporarily suspended, and relevant data will be marked with a risk flag to prevent abnormal samples from entering the testing and archiving stages. An anomaly report will also be automatically generated and pushed to the responsible management personnel, requiring manual review and traceability investigation of the entire process data for that sample. This automated freezing and risk reporting mechanism not only significantly reduces the scope for human manipulation such as product substitution and identity forgery, but also provides detailed data support for subsequent responsibility identification and error correction.

[0083] For samples that are verified and comply with the procedures, the aforementioned automatic extraction, uploading, comparison, and feedback loop will be continuously executed at key stages such as sample transfer to the testing and archiving stages, forming a multi-node, multi-round information consistency verification chain that spans the entire lifecycle. All verification processes and results are archived in the form of immutable logs, ensuring that any subsequent spot checks, dispute resolution, and accountability can be traced back to every operational detail. This dynamic, multi-dimensional, and multi-node automatic verification method greatly improves the uniqueness of sample identity, the integrity of the data chain, and the scientific nature of supervision in the power grid material sampling process.

[0084] The core function of the sample sealing information consistency verification process is to provide a robust means of identity verification, data chain solidification, and risk control for the entire process of power grid material sampling inspection. This step automatically reads the unique code within the embedded chip at each key node of sample transfer, testing, and archiving, and simultaneously collects key data such as sealing time and terminal identification, comparing it item by item with the original record from the initial sealing. The primary purpose of this is to ensure that the identity information of each material sample remains unchanged, unaltered, or misused after multiple stages, effectively preventing risks such as human substitution and data tampering. Especially in the complex real-world environment of power grid material management involving multiple departments, multiple locations, and long-term testing, manual recording or traditional labels are prone to errors. However, relying on automatic chip extraction and data comparison, the authenticity and uniqueness of each sample at each stage can be identified in real time and accurately. Furthermore, the application of item-by-item comparison and multiple verification algorithms allows it to tolerate reasonable process delays or minor operational differences, while also sensitively identifying abnormal modifications, unauthorized operations, and identity changes. When problematic samples are detected, the process is automatically halted, relevant data is frozen, and risk warnings are issued. Ultimately, this process not only ensures the integrity of the data chain and the closed-loop traceability, but also provides irrefutable evidence for subsequent quality responsibility determination, dispute resolution, and regulatory inspection. It greatly enhances the credibility and management technology content of the entire power grid material sampling inspection and is a fundamental link in achieving highly credible and transparent sample sealing management.

[0085] S5, after the sealing information consistency verification process is completed, the abnormal data closed-loop processing process is executed. The inconsistent information detected in the consistency verification is automatically identified, the relevant data is frozen, the source of responsibility is traced, and subsequent transfer operations are stopped, so as to realize the closed-loop management of process data.

[0086] After the sealing information consistency verification process is completed, an abnormal data closed-loop processing process must be executed to ensure that any detected inconsistencies in identity information, coding anomalies, or mismatches in data transfer can be automatically identified, processed in real time, and completely isolated, thereby guaranteeing the integrity and reliability of the process data. The specific steps are as follows:

[0087] During the consistency verification of sealing information, if the automatic comparison detects any discrepancies, omissions, abnormal changes, or unauthorized modifications between any key piece of information (such as chip code, sealing time, terminal identifier, etc.) and the original sealing record, an anomaly detection mechanism will be immediately triggered. This mechanism is not merely a simple comparison of a single field, but rather a comprehensive comparison of multiple factors, including operation logs, data flow sequence, timestamp chains, and matching of responsible nodes, to achieve highly sensitive detection of complex anomaly scenarios. For example, if the chip code collected at a certain node is completely consistent with the original allocation number, but the terminal identifier does not match the expected responsible unit, it can be judged as a suspected case of unauthorized sample transfer or abnormal handover of responsibility. This comprehensive intelligent identification greatly reduces false alarms and false negatives, improving the level of intelligence in anomaly detection.

[0088] Once an anomaly is identified, all related records involving the anomalous data will be automatically frozen. Specifically, all data entries related to the anomalous sample, including operation logs, workflow nodes, test results, and archived information, will be immediately locked in read-only mode, prohibiting any user from modifying, deleting, or continuing workflow operations. The freeze operation covers not only the sample's own sealing data but also critical information related to upstream and downstream samples, batch transfer slips, and quality inspection reports, ensuring that the anomalous sample cannot enter subsequent stages or be obscured in the normal data flow. Simultaneously, a unique anomaly event number will be generated, forming an independent data tracking chain, providing comprehensive evidence for subsequent problem tracing and review.

[0089] Upon data freezing, the accountability process is automatically initiated. By analyzing multi-dimensional information such as operation logs, time sequence, terminal physical address, and personnel operation history, the system can intelligently infer the key nodes leading to the anomaly and the relevant responsible persons. For example, by comparing recent operator, operating equipment, location changes, and historical compliance behavior, the source of suspected operational errors, unauthorized access, or malicious tampering can be efficiently identified. All accountability results generate a detailed accountability chain report, which is automatically pushed to the regulatory authorities, data administrators, and on-site supervisors, requiring them to intervene in the investigation and handling immediately. This accountability process not only improves problem-solving efficiency but also provides authoritative data support for subsequent accountability determination and rectification.

[0090] After data freezing and accountability tracing are completed, all subsequent operations related to the abnormal sample, including processing, testing, and archiving, are automatically suspended to prevent the abnormal sample from continuing to participate in the process or affecting normal data flow. An abnormal process handling report is automatically generated, detailing all operational details such as anomaly identification, data freezing, accountability tracing, and suspension of processing, and archived in a dedicated audit database as a long-term basis for quality traceability and compliance monitoring. Simultaneously, the causes of the abnormal event, process loopholes, and responsible nodes are statistically analyzed, and an abnormal data closed-loop processing analysis report is regularly pushed to management, providing decision support for subsequent optimization of management processes, improvement of prevention and control measures, and enhancement of robustness.

[0091] The core function of the closed-loop abnormal data processing procedure is to establish an automated and intelligent risk control barrier for the entire process of power grid material sampling inspection. This ensures that when issues such as data anomalies, identity discrepancies, or mismatches in the consistency verification of sealing information are discovered, they can be automatically identified and addressed immediately, thus completely preventing non-compliant samples and abnormal data from entering subsequent processes. This step uses automation technology to achieve real-time monitoring and comprehensive analysis of the verification results of all key nodes. Once inconsistencies are detected between key information such as chip encoding, sealing time, and terminal identification and the original sealing record, all relevant data, operation logs, and transfer links are immediately frozen. Any modification, overwriting, or transfer of abnormal data by any personnel or equipment is strictly prohibited, ensuring that potential risks are contained at the source. Simultaneously, the frozen data is automatically traced back to its source in multiple dimensions. By analyzing operation history, node locations, and personnel records, the specific link and responsible person causing the anomaly are intelligently located, prompting on-site management personnel or relevant departments to conduct precise review and accountability. This process not only eliminates the risk of abnormal samples entering the testing, archiving, or even actual operation stages due to operational oversights, unauthorized access, or internal fraud, but also provides tamper-proof audit evidence for subsequent quality dispute resolution, compliance supervision, and accident investigation. Ultimately, all abnormal events and processing logs are archived as closed-loop analysis reports, forming a complete basis for data traceability and rectification. This process significantly enhances the risk prevention capabilities, data credibility, and accountability of power grid material sampling management, driving the entire quality supervision system towards higher standards, greater safety, and greater transparency.

[0092] S6. After the abnormal data closed-loop processing process is completed, the full-process audit feedback process of the sealing process is executed. The full-process operation log and abnormal behavior records are summarized, and the weak links in the coding allocation link and sealing process are analyzed and identified. Based on the analysis results, the initial segmentation pre-configuration strategy is adjusted and optimized to form a dynamic optimization closed loop.

[0093] After the abnormal data closed-loop processing is completed, in order to further improve the data security, execution efficiency, and continuous optimization capabilities of the entire sealing process, a full-process audit feedback process for sealing needs to be implemented. This process, centered on full lifecycle data traceability, automatic analysis, and strategy adjustment, constructs a "self-learning, adaptive" management closed loop through four stages. The specific steps are as follows:

[0094] After each sealing, transfer, testing, archiving, and anomaly handling operation, all operation logs and behavior records are automatically collected and summarized. These logs include not only detailed operational information for core steps such as sealing code generation, uniqueness verification, chip write protection, information consistency verification, and anomaly detection and handling, but also multi-dimensional data such as the operator's identity information, operation time, geographical location, environmental parameters, and operator's digital signature. All collected raw logs are archived in chronological order, linked to the sample's unique code, forming a complete and tamper-proof data chain for the entire sealing process. This ensures accurate reconstruction of the operation site and transfer path during any regulatory audit or accountability investigation.

[0095] Regularly conduct automated statistical and in-depth analysis of the archived full-process logs and abnormal behavior records. Specifically, using big data analytics and anomaly pattern recognition algorithms, focus on the coding allocation stage and the sealing sample transfer chain to identify high-incidence points, fault concentration areas, and operation nodes with excessive processing time in the logs. For example, it will statistically analyze indicators such as the number of conflicts during coding allocation, the proportion of uniqueness verification failures, the frequency of chip write anomalies, the number of samples that failed consistency verification during the transfer process, and the average response time for freezing and tracing abnormal data. Through comprehensive comparison and trend analysis of this data, it is possible to intelligently identify process shortcomings, potential risks, and optimization opportunities in the sealing sample process.

[0096] Anomaly pattern recognition algorithms utilize machine learning, data mining, and other methods to deeply analyze large amounts of historical operation logs and process data, automatically identifying abnormal phenomena, patterns, or potential problems that deviate significantly from normal behavior. In the power grid material sampling management process, this algorithm helps to accurately locate weak points, high-risk fault nodes, and potential process bottlenecks in the coding allocation and sample sealing processes. The implementation method is as follows:

[0097] Continuously collect detailed operation logs and timestamps from all terminals at each stage, including sealing, encoding allocation, uniqueness verification, chip writing, circulation, and archiving, and standardize the log processing.

[0098] Based on statistical features (such as mean, standard deviation, distribution pattern) and supervised / unsupervised learning algorithms (such as clustering, isolated forest, LOF, neural network, etc.), it automatically discovers anomalies in log data that differ significantly from normal patterns, such as frequent coding conflicts, uniqueness verification failures, abnormal flow time, duplicate node operations, and excessive data rollback times.

[0099] Visual analysis of discovered anomaly patterns is performed, and high-frequency anomalies are associated with corresponding times, responsible terminals, and operation categories to generate anomaly distribution heatmaps, process bottleneck lists, and response time rankings.

[0100] Based on these analysis results, optimization suggestions are pushed to managers, such as adjusting the operating permissions of high-risk nodes, optimizing allocation strategies, and adding automatic alarms for anomalies, thereby achieving data-driven process self-optimization. Through the above process, the anomaly pattern recognition algorithm can make the power grid material sampling management more intelligent and proactive, promptly identifying and resolving key issues that may threaten process safety and efficiency.

[0101] After identifying problematic stages and weak points, the system automatically generates targeted optimization suggestions and adjustment strategies. Combining historical logs, anomaly cases, and workflow bottlenecks, specific measures are proposed, such as adjusting terminal number segment allocation, optimizing uniqueness verification logic, strengthening chip write protection processes, refining anomaly identification standards, and increasing data synchronization frequency. For example, for terminals that frequently experience code exhaustion or conflicts in a certain segment, it may be recommended to expand the segment in advance or adjust the allocation weight; for stages that frequently experience consistency verification failures at specific nodes, it may be recommended to add a manual review process or optimize the automatic comparison algorithm. All optimization suggestions are archived as audit feedback reports and reviewed and adopted by management.

[0102] Based on the audit feedback and recommendations mentioned above, management personnel regularly make dynamic adjustments and process optimizations to the initial segmented pre-configuration strategy. This optimization includes, but is not limited to, segment re-division, terminal priority adjustment, increased verification frequency, and refined anomaly handling procedures. The adjusted strategy will take effect in the next round of sealing operations and will continuously link with subsequent full-process audit data, forming a closed-loop mechanism of "operation—audit—analysis—optimization—reoperation." Through this dynamic optimization process throughout the entire process, the power grid material sampling management method can not only identify and correct potential problems and defects in the sealing process in real time, but also promote the self-evolution of the management system, achieving efficient, safe, and transparent full lifecycle management of sealing.

[0103] The core function of the end-to-end sealing audit feedback process is to systematically analyze operational data, behavioral records, and anomaly logs generated throughout the sealing lifecycle after completing the closed-loop processing of abnormal data. This identifies weaknesses, structural defects, and high-risk nodes in the sealing management process and drives continuous optimization of the entire coding allocation mechanism and sealing process through data-driven approaches. Specifically, this step comprehensively reconstructs the complete chain of operations such as code generation, uniqueness verification, chip writing, sample transfer, data verification, and anomaly handling, uncovering high-frequency anomalies, recurring failure patterns, or bottlenecks in process execution. By combining big data statistics and anomaly pattern recognition algorithms, it can accurately locate problems such as frequent coding conflicts on specific terminals, abnormally high verification failure rates, write delays, or transfer interruptions. It can also establish multi-dimensional risk profiles from time, space, and operational behavior dimensions. More importantly, the analysis results and optimization suggestions summarized in this step will feed back into the initial segmented pre-configuration strategy, enabling dynamic adjustments to coding allocation logic, optimization of verification strategies, or strengthening of write protection mechanisms in subsequent sealing tasks, thereby achieving a dual improvement in coding allocation efficiency and data security. Ultimately, this step establishes an intelligent optimization closed loop in the sampling management process, characterized by "full-process visibility, full-link controllability, and full-chain traceability." This not only significantly enhances the risk resistance and response speed of power grid material sealing management but also provides solid technical support and data foundation for the long-term formation of a standardized, refined, and highly reliable quality supervision system.

[0104] The aforementioned method for power grid material sampling management based on intelligent sealing and full traceability systematically eliminates key risks in existing technologies, such as duplicate sealing codes, mismatched identity information, and broken data chains caused by chaotic code allocation, concurrency processing defects, and weak access control. This method ensures the system-wide uniqueness of each code through segmented pre-configuration and global deduplication, and guarantees the immutability of identity information through chip one-time write protection technology and hardware anti-tampering mechanisms. Simultaneously, automated information collection and multi-round consistency comparison effectively prevent operational loopholes such as sample substitution and data falsification. Anomaly identification and closed-loop processing mechanisms enable real-time freezing and precise traceability of abnormal events, preventing non-compliant samples from flowing into subsequent stages and improving the security and accountability of the entire process. Finally, through full-process auditing and dynamic optimization feedback, the sealing management process can self-discover and continuously correct potential weaknesses, promoting the intelligent, standardized, and highly reliable implementation of power grid material sampling management, and providing a solid and reliable technical guarantee and management foundation for industry quality supervision.

[0105] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0106] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0107] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0112] 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.

[0113] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for power grid material sampling inspection management based on intelligent sealing to achieve full traceability, characterized in that, Includes the following steps: S1, Initialization Phase: Execute the sealing sample coding segmentation pre-configuration process, allocate independent numbering segments to each sealing sample terminal, and synchronize the terminal coding usage status through the central control module to achieve distributed isolation of coding generation; S2. After the pre-configuration process of segmented sealing coding is completed, the uniqueness verification process of coding is executed. Before each sealing operation, the data indexing mechanism is used to perform a global deduplication of the coding to be written to ensure that the coding has not been used. S3. After the uniqueness verification process is completed, the chip write protection process is executed. The uniqueness verification code is written to the embedded chip, and the one-time write protection function is immediately activated to prevent subsequent writing and achieve chip anti-tampering. S4. After the chip write protection process is completed, the sealing information consistency verification process is executed. At the sample transfer, testing and archiving nodes, the embedded chip code, sealing time and terminal identifier are automatically extracted and compared with the original sealing record to verify the consistency of the information. S5, after the sealing information consistency verification process is completed, the abnormal data closed-loop processing process is executed. The inconsistent information detected in the consistency verification is automatically identified, the data is frozen, the responsibility is traced, and the flow is stopped to achieve process data closed-loop. S6. After the abnormal data closed-loop processing is completed, the full-process audit feedback process of sealing is executed, the operation logs and abnormal records are summarized, the weak links are analyzed, and the initial segmentation pre-configuration strategy is adjusted and optimized to achieve dynamic optimization closed loop.

2. The method for power grid material sampling management based on intelligent sealing for full traceability as described in claim 1, characterized in that, Step S1 includes: During the initialization phase, each sample terminal is registered for identity, and the coding segment is divided based on physical characteristics, geographical location and management unit information using a segmented numbering strategy. Write the coded segment corresponding to each sample terminal into the local data storage area and transmit it to the central control terminal for filing in an encrypted manner. After generating a unique code, the terminal records the segment usage status and periodically feeds it back to the central control terminal via encrypted communication. The central control unit audits and compares the coding usage status, and freezes the terminal coding permissions and adjusts the segmentation strategy when anomalies are detected.

3. The method for power grid material sampling management based on intelligent sealing for full traceability as described in claim 1, characterized in that, Step S2 includes: Before each sealing operation, a multi-factor coupling strategy is used to generate the code to be written based on the assigned numbering segment; The generated code is uploaded to the central data interaction node through an encrypted channel to initiate a global uniqueness verification request; Use data structures based on hash tables or B+ trees to perform deduplication and determine whether the code has been used. If the verification passes, continue the sealing operation and temporarily lock the code; if the verification fails, interrupt the process and provide an error message.

4. The method for power grid material sampling management based on intelligent sealing for full traceability as described in claim 1, characterized in that, Step S3 includes: After the uniqueness verification of the code is completed, the unique code is encrypted using a preset encryption algorithm to generate ciphertext and then written to the storage area of ​​the embedded chip. During the writing process, CRC redundancy check and bidirectional data readback comparison are performed to ensure that the written data is accurate and consistent. Once the write operation is complete, the one-time write protection function is activated immediately to lock the storage area into an unmodifiable state; The write-protected status and write logs are uploaded to the center, and the operation data is archived as a basis for traceability.

5. The method for power grid material sampling management based on intelligent sealing for full traceability as described in claim 1, characterized in that, Step S4 includes: When a sample is transferred to a testing or archiving node, the embedded chip code, sealing time, and terminal identifier are automatically extracted to form a sample identification data record. The records are uploaded to the central data management terminal for multiple verifications against the original sealed sample records. Consistency is determined by a comprehensive approach that includes full-byte comparison, time interval judgment, terminal legality verification, and flow logic validation. If the verification matches, the data can continue to be processed; if the verification does not match, the data will be automatically frozen and an anomaly report will be sent.

6. The method for power grid material sampling management based on intelligent sealing for full traceability as described in claim 1, characterized in that, Step S5 includes: When inconsistencies in key information are detected during the consistency verification of sealing information, the system automatically identifies the anomaly and freezes the relevant data. After freezing the associated records, the responsibility tracing process is initiated to analyze the operation logs, time sequence, terminal address and personnel historical behavior, and generate a responsibility chain report; Suspend all transfer, testing, and archiving operations related to abnormal samples to prevent them from entering subsequent stages; The processing results are generated into an abnormal process report and archived in the audit database for traceability and process optimization analysis.

7. The method for power grid material sampling management based on intelligent sealing for full traceability as described in claim 1, characterized in that, Step S6 includes: After each sealing, transfer, testing, archiving, and anomaly handling operation, collect and archive the entire process operation log and behavior records; Regularly perform big data analysis and anomaly pattern recognition algorithms on logs and anomaly records to identify weak links in the coding allocation and transfer process; Based on the identification results, optimization suggestions are generated and an audit feedback report is output; Based on the audit feedback, the initial coding segmentation pre-configuration strategy was adjusted and the execution process was updated to form a closed-loop optimization mechanism.

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