Chip product whole life cycle management system fusing blockchain and smart contract

Abstract

This invention relates to the field of chip product lifecycle management technology, specifically a chip product lifecycle management system integrating blockchain and smart contracts. The system includes a design specification solidification module that stores design documents and bill of materials data on the blockchain; a production data acquisition module that compares process parameters to generate production process records; a cross-stage verification module that identifies environmental anomalies and assesses a quality deviation index; a quality anomaly monitoring module that performs cross-stage traceability against performance requirements; and a lifecycle management module that outputs lifecycle management results in conjunction with warehousing and logistics. This invention ensures authenticity by storing design data and hash values ​​on the blockchain, generates deviation features by collecting production line parameters in real time, verifies material consistency to construct a quality deviation index, uses smart contracts to determine exceedances and establishes batch associations by combining test and warehousing data, and writes all data to generate electronic tags by timestamp, achieving full lifecycle continuity and accurate anomaly traceability, improving traceability efficiency and reducing quality risks.

Description

Technical Field

[0001] This invention relates to the field of chip product lifecycle management technology, and in particular to a chip product lifecycle management system that integrates blockchain and smart contracts. Background Technology

[0002] The field of chip product lifecycle management technology encompasses the entire process management of chip products, including design and development, wafer manufacturing, packaging and testing, warehousing and distribution, end-user applications, and end-of-life recycling. The core of this technology lies in establishing a data recording and traceability mechanism around the unique identifier of each chip, spanning all stages. This is achieved through specific technical means such as product coding rule formulation, batch information registration, production process parameter collection, quality inspection result archiving, warehousing and logistics status updates, and after-sales maintenance record storage. This constructs a data collaboration system covering upstream and downstream entities in the supply chain and forms a systematic management framework based on serial number generation rule settings, barcode or RFID tag binding, production equipment data interface integration, quality inspection item data structure definition, and warehousing inbound and outbound process settings.

[0003] This technical field also involves the organization and integration of various data recording methods in the chip production and distribution chain, including the systematic setting of production process numbering, quality inspection item structure, logistics node registration method, distribution link responsibility definition method, and maintenance record archiving method. By defining and continuously recording the above information in a structured manner, a management chain is formed that runs through all stages of raw material input, production and processing, warehousing and circulation and end use, so that the chip product forms a sustainable and interconnected data system throughout its life cycle.

[0004] Among them, the chip product lifecycle management system integrating blockchain and smart contracts refers to a system solution that introduces the distributed ledger structure of blockchain and smart contract rule scripts into the chip product lifecycle management process. It records and constrains data at each stage of the chip in a chain. Addressing technical issues such as the susceptibility of chip production data to tampering, unclear division of responsibilities in the circulation process, and broken quality traceability chains, it generates a unique digital identity for each chip and binds it to a blockchain address. Data such as design version number, wafer batch number, packaging and testing number, quality inspection report number, logistics order number, and maintenance record number are written into the block structure in chronological order. The system uses preset smart contract scripts to stipulate the data submission format of production nodes, the signature confirmation process of circulation nodes, the conditions for reporting quality anomalies, and the rules for confirming the responsible party. Participants complete data submission and confirmation by signing with private keys, thus forming a chip product lifecycle management system based on blockchain ledger storage structure and smart contract execution rules.

[0005] Existing technologies rely on unique identifiers to build data recording mechanisms but lack the ability to dynamically verify real-time production parameters and design specifications. When production environment parameters such as temperature and pressure fluctuations exceed the tolerance range, early warnings cannot be triggered in a timely manner. The data submission process relies on manual registration, resulting in abnormal features not being graded and evaluated. The delayed identification of key deviations increases the risk of quality problems. Material consumption verification is based on static registration information and does not automatically compare with the design list, so potential inconsistencies are not captured. Quality test results are independent of production process data. Anomaly attribution lacks cross-stage correlation evidence. Warehouse environment monitoring is not bound to production batch mapping relationships, making it difficult to trace temperature and humidity anomalies back to their source. The decentralized data collaboration model increases operational complexity and weakens the efficiency of full-cycle management. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing a chip product lifecycle management system that integrates blockchain and smart contracts.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a chip product lifecycle management system integrating blockchain and smart contracts, the system comprising: The design specification solidification module collects chip design document content, circuit layout hash value, manufacturing process tolerance range and bill of materials information, converts the design document content into a byte sequence, forms the first set of associated data, writes it to the blockchain to trigger smart contract node signature verification, and generates a chip design specification blockchain record. The production data acquisition module collects the temperature, pressure, speed and production batch number of the production equipment, extracts the material number and usage, retrieves the process tolerance range and bill of materials in the chip design specification blockchain, generates production environment offset characteristics and material consistency verification results, and generates a blockchain record of the chip production process. The cross-stage verification module extracts the production environment offset features, material consistency verification results and production batch numbers from the blockchain record of the chip production process, identifies abnormal items and material mismatch items, rates them according to sensitivity and associates them to form a production quality offset index, determines the threshold of the smart contract and generates the threshold determination result, associates and stores it in the state space, and obtains the cross-stage verification conclusion of the chip smart contract. The quality anomaly monitoring module acquires the electrical performance, functional, and reliability test results of the chip, retrieves the design specification record requirements, compares them to form a set of performance compliance states, and if there are any non-compliance states, it calls the offset index of the cross-stage verification conclusion of the chip smart contract and traces the state space anomaly items to obtain the anomaly event attribution mapping results and integrates them to generate the chip smart contract quality monitoring record.

[0008] As a further aspect of the present invention, the system further includes: The lifecycle management module calls the performance compliance status set and abnormal event attribution mapping results of the chip smart contract quality monitoring records, extracts the production batch number, collects product identification code, inbound and outbound circulation records, temperature and humidity sequence, establishes batch product mapping and evaluates compliance, and generates full lifecycle management results of chip products by uploading them to the blockchain according to timestamps.

[0009] As a further embodiment of the present invention, the design specification solidification module includes a data acquisition submodule, an association encapsulation submodule, and a contract verification submodule; The data acquisition submodule collects design document content, circuit layout file hash value, manufacturing process fault tolerance range, and bill of materials information during the chip design phase. It calls character encoding rules to convert the design document content into a byte sequence, performs field validation on the circuit layout file hash value and extracts a fixed-length summary fragment, sorts the upper and lower limits of the manufacturing process fault tolerance range in order, and performs corresponding arrangement of the material number and usage fields in the bill of materials information to generate a design load identifier. The associated encapsulation submodule, based on the design payload identifier, calls the byte sequence, circuit layout file hash digest fragment, production process fault tolerance range field and bill of materials arrangement result to perform sequential splicing, writes the identifier segment and length segment according to the preset data frame format, performs field consistency verification on the spliced ​​data frame and forms a verification fragment, appends the verification fragment to the end of the data frame to obtain the on-chain associated certificate; The contract verification submodule calls the on-chain associated credentials to write to the blockchain transaction network to generate transaction records to be verified, retrieves the public key of the smart contract node to perform signature matching verification on the transaction records to be verified, counts the number of signature matching nodes and performs consistency judgment with the preset signature number threshold, writes the judgment result into the contract state space and completes the transaction confirmation process, and generates a chip design specification blockchain record.

[0010] As a further embodiment of the present invention, the production data acquisition module includes a running acquisition submodule, an offset identification submodule, and an on-chain writing submodule; Run the data acquisition submodule to collect temperature data, pressure data, speed data and production batch number from the chip production line equipment operation records. Extract material consumption records, material number and real-time usage information. Perform sequence alignment processing on the temperature data, pressure data and speed data according to the timestamp order. Establish a corresponding mapping between the material number and the real-time usage information and bind it to the production batch number to generate production operation correlation. The offset identification submodule calls the production operation correlation degree, retrieves the chip design specification blockchain record of the production process fault tolerance range and bill of materials information, performs boundary comparison between temperature data, pressure data, speed data and production process fault tolerance range and records the number of times the range is exceeded, performs number consistency check with material number and counts the number of difference items in combination with material number and bill of materials information, and obtains the environmental material offset. The on-chain writing submodule extracts the production batch number based on the environmental material offset, constructs a transaction field sequence containing production environment offset features, material consistency verification results, and production batch number, writes it into the blockchain transaction network, calls the smart contract node signature verification rules to perform signature quantity consistency judgment, writes the judgment identifier into the contract state space, and generates a blockchain record of the chip production process.

[0011] As a further aspect of the present invention, the cross-stage verification module includes an anomaly identification submodule, a rating association submodule, and a contract determination submodule; The anomaly identification submodule extracts the production environment offset features, material consistency verification results, and production batch numbers from the blockchain records of the chip manufacturing process. It performs boundary comparison between the production environment offset features and the process tolerance boundary and filters records that exceed the tolerance. It extracts material mismatch items from the material consistency verification results and counts the number of items. It merges the number of abnormal environment records and the number of material mismatch items to generate an anomaly summary. The rating association submodule retrieves a preset sensitivity attribute table based on the anomaly summary degree, performs interval matching in the sensitivity attribute table for the number of abnormal environment records and marks the corresponding rating number, combines the rating number and the number of abnormal environment records and writes them into the production batch number identifier segment to form an association field sequence and obtain the quality offset index. The contract determination submodule calls the quality offset index, retrieves the fault tolerance threshold, performs a numerical comparison to determine whether the fault tolerance threshold is exceeded, records the comparison output identifier and binds it with the production batch number to the smart contract state space, confirms the state writing result on the chain, and generates the chip smart contract cross-stage verification conclusion.

[0012] As a further aspect of the present invention, the quality anomaly monitoring module includes a performance comparison submodule and a link tracing submodule; The performance comparison submodule obtains the chip's electrical performance, functional, and reliability test results, retrieves the performance requirement items recorded in the chip design specification blockchain, performs item-by-item numerical comparison between the electrical performance test results and the performance requirement items and records the deviation markers, performs status matching judgment between the functional test results and the performance requirement items and marks the difference items, performs interval verification between the reliability test results and the performance requirement items and counts the number of non-compliance items, combines the number of non-compliance items and generates the performance deviation degree. The link traceability submodule, based on the performance deviation degree, calls the chip smart contract cross-stage verification conclusion production quality offset index and smart contract state space abnormal environment feature items. It performs batch number matching and filters related records for the abnormal environment feature items and production quality offset index, binds the filtered records with the performance deviation degree execution items to form a traceability mapping sequence, writes it into the blockchain transaction network and completes the contract signature verification process, and generates chip smart contract quality monitoring records.

[0013] As a further aspect of the present invention, the lifecycle management module includes a batch mapping submodule, a warehouse evaluation submodule, and an on-chain archiving submodule; The batch mapping submodule calls the performance compliance status set and abnormal event attribution mapping results in the chip smart contract quality monitoring record, extracts the production batch number of the chip smart contract cross-stage verification conclusion, collects product identification codes, inbound and outbound circulation records, and environmental temperature and humidity sequences in the warehousing and logistics links, performs number matching and establishes a corresponding relationship between the production batch number and the product identification code, binds the corresponding relationship to the execution timestamp of the inbound and outbound circulation record, and generates the batch product mapping degree. The warehousing assessment submodule, based on the batch product mapping degree, calls the environmental temperature and humidity sequence and the compliant storage requirement range to perform interval comparison and record the number of times the limit is exceeded. The number of times the limit is exceeded is marked with the time period of the inbound and outbound flow record, and the number of times the limit is exceeded in each time period is merged to form a summary count, thus obtaining the logistics warehousing assessment degree. The on-chain archiving submodule, based on the logistics and warehousing assessment, retrieves the performance compliance status set, the abnormal event attribution mapping results, and the inbound and outbound flow records. It sorts the fields according to the timestamp sequence and constructs a transaction data segment. The transaction data segment is written into the blockchain transaction network and triggers smart contract signature verification. The signature count judgment identifier is recorded and written into the contract state space to generate the chip product's full lifecycle management results.

[0014] As a further aspect of the present invention, the process of performing number matching and establishing a corresponding relationship between the production batch number and the product identification code specifically includes: Perform coding rule consistency verification and field integrity verification on the production batch number and the product identification code respectively. Establish a corresponding relationship record between the production batch number and the product identification code that pass the verification, and bind the corresponding relationship record to the inbound and outbound timestamps in the inbound and outbound flow record to form a batch product mapping degree.

[0015] As a further aspect of the present invention, the process of calling the environmental temperature and humidity sequence and performing an interval comparison with the compliant storage requirement interval and recording the number of times the limit is exceeded specifically includes: The environmental temperature and humidity sequence is divided into time periods based on the timestamps of the inbound and outbound flow records. For the temperature and humidity values ​​in each time period, interval inclusion judgment is performed and an out-of-limit marker is generated. The out-of-limit marker is associated with the inbound and outbound flow records of the corresponding time period and written into the summary record to obtain the logistics warehousing evaluation degree.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, design documents are converted into byte sequences and merged with layout hash values ​​before being written into the blockchain. This triggers node signature verification to ensure the authenticity of the design data. Real-time collection of production line temperature, pressure, speed, and batch numbers is performed. Process tolerance intervals are dynamically called for comparison to generate environmental offset features. Material consumption and bill of quantities consistency are verified simultaneously to form results. Exceeding-limit anomalies are automatically identified and associated with sensitivity ratings to construct a quality offset index. Smart contracts determine threshold exceeding limits and store conclusions. In the testing phase, performance requirement items are extracted and compared to form a compliance set. For non-compliant items, traceability is performed based on the offset index and anomaly features to generate attribution mapping. Warehouse temperature and humidity sequences are integrated to assess compliance and establish batch product associations. Full data is written according to timestamps to generate electronic tags, achieving dynamic parameter monitoring, real-time deviation warnings, accurate anomaly tracing, and full-cycle data connectivity, thereby improving traceability efficiency and reducing quality risks. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 A flowchart for obtaining the design specification solidification module of this invention; Figure 3 This is a flowchart illustrating the data acquisition process of the production data acquisition module of the present invention. Figure 4 This is a flowchart illustrating the acquisition process of the cross-stage verification module of the present invention. Figure 5 This is a flowchart illustrating the acquisition process of the quality anomaly monitoring module of the present invention. Figure 6 This is a flowchart illustrating the acquisition process of the lifecycle management module of this invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] Please see Figure 1 This invention provides a technical solution: a chip product lifecycle management system integrating blockchain and smart contracts, the system comprising: The design specification solidification module collects design document content, circuit layout file hash value, manufacturing process fault tolerance range, and bill of materials information during the chip design stage. It converts the design document content into a byte sequence, merges the byte sequence, circuit layout file hash value, manufacturing process fault tolerance range, and bill of materials information to form the first set of associated data, writes the first set of associated data into the blockchain transaction network, triggers the smart contract node signature verification mechanism, and generates a chip design specification blockchain record after the node signature verification is passed. The production data acquisition module collects temperature, pressure, speed, and production batch numbers from the chip production line equipment operation records. It extracts material numbers and real-time usage information from the material consumption records, calls the production process fault tolerance range and bill of materials information from the chip design specification blockchain record, compares the temperature, pressure, and speed data with the production process fault tolerance range, generates production environment offset features, verifies the consistency of material numbers, real-time usage information, and bill of materials information, forms a material consistency verification result, and writes the production environment offset features, material consistency verification result, and production batch number to the blockchain transaction network to generate a blockchain record of the chip production process. The cross-stage verification module extracts production environment offset features, material consistency verification results, and production batch numbers from the blockchain records of the chip manufacturing process. It identifies abnormal environment feature items that exceed the process tolerance boundary in the production environment offset features, as well as material mismatch items in the material consistency verification results. Based on preset sensitivity attributes, it divides the sensitivity rating labels of abnormal environment feature items into sensitivity rating labels, associates the sensitivity rating labels with abnormal environment feature items, and forms a production quality offset index. Through smart contracts, it determines whether the production quality offset index exceeds the set fault tolerance threshold, generates a threshold determination result, and associates and stores the threshold determination result, the production quality offset index, and the production batch number in the smart contract state space to obtain the cross-stage verification conclusion of the chip smart contract. The quality anomaly monitoring module acquires the electrical performance, functional, and reliability test results of the chip, retrieves the performance requirement items from the chip design specification blockchain record, compares the electrical performance, functional, and reliability test results with the performance requirement items, and forms a performance compliance status set. When there are non-compliant items in the performance compliance status set, it calls the production quality deviation index in the cross-stage verification conclusion of the chip smart contract and the abnormal environment feature items stored in the smart contract state space to perform a cross-stage link tracing operation, obtains the abnormal event attribution mapping result, and merges it with the performance compliance status set to generate a chip smart contract quality monitoring record. The lifecycle management module calls upon the performance compliance status set and abnormal event attribution mapping results from the chip smart contract quality monitoring records, extracts the production batch number from the cross-stage verification conclusion of the chip smart contract, collects product identification codes, inbound and outbound flow records, and environmental temperature and humidity sequences from the warehousing and logistics links, associates the production batch number with the product identification code, establishes a batch-product mapping relationship, evaluates the compliance of the environmental temperature and humidity sequence with compliant storage requirements, outputs the logistics and warehousing status assessment conclusion, and writes the performance compliance status set, abnormal event attribution mapping results, logistics and warehousing status assessment conclusion, and inbound and outbound flow records to the blockchain according to the timestamp sequence, generating the chip product's full lifecycle management results.

[0024] Please see Figure 2 The design specifications define the modules as including a data acquisition submodule, an association encapsulation submodule, and a contract verification submodule. The data acquisition submodule collects design document content, circuit layout file hash value, manufacturing process fault tolerance range, and bill of materials information during the chip design phase. It calls character encoding rules to convert the design document content into a byte sequence, performs field validation on the circuit layout file hash value and extracts a fixed-length summary fragment, sorts the upper and lower limits of the manufacturing process fault tolerance range in order, and performs corresponding arrangement of the material number and usage fields in the bill of materials information to generate a design load identifier. The process involves collecting design documents, circuit layout file hash values, manufacturing process tolerance ranges, and bill of materials information during the chip design phase. Using UTF-8 encoding rules, the design document content, including textual descriptions of the design architecture, pin definitions, timing constraints, etc., is mapped to decimal encoding based on character occurrence positions and then converted to a binary 8-bit fixed-length byte sequence. The circuit layout is acquired using a high-precision scanner, and the file hash value is obtained using the SHA-256 algorithm. Its hexadecimal bit width is checked to ensure it meets the 256-bit checksum requirement. The first 64 bits of the hash value are extracted as a fixed-length digest. Semiconductor process simulation software is then used to obtain the upper temperature limit of 125°C within the manufacturing process tolerance range. The upper and lower limits are set at -40 degrees Celsius and 1.2 standard atmospheres, respectively, and the lower limit is set at 0.8 standard atmospheres. These upper and lower limits are then sorted in ascending order of temperature lower limit, temperature upper limit, pressure lower limit, and pressure upper limit. The silicon substrate numbered MAT-001 and its corresponding real-time usage of 500 units, and the metal interconnect lead numbered MAT-002 and its corresponding real-time usage of 200 units are extracted from the bill of materials information. The material number string and the usage value are concatenated to form a key-value pair sequence. The byte sequence, hash digest fragment, fault tolerance interval arrangement sequence, and material key-value pair sequence are then concatenated to obtain the design load identifier.

[0025] The associated encapsulation submodule, based on the design load identifier, calls the byte sequence, circuit layout file hash digest fragment, production process fault tolerance range field and bill of materials arrangement result to perform sequential splicing, writes the identifier segment and length segment according to the preset data frame format, performs field consistency verification on the spliced ​​data frame and forms a verification fragment, appends the verification fragment to the end of the data frame to obtain the on-chain associated certificate; Based on the design load identifier, the 32768-bit byte sequence converted from the design document, the 64-bit layout hash digest fragment, the 32-bit fault tolerance interval field containing four process extreme values, and the bill of materials arrangement result consisting of 10 sets of material key-value pairs are sequentially concatenated and spliced. A 16-bit fixed value 0xAAAA is inserted at the beginning of the spliced ​​binary sequence as an identifier segment, and a 32-bit binary value 0x00008060 describing the total length of subsequent valid data is inserted after the identifier segment as a length segment. Cyclic redundancy check is performed on the complete data frame containing the identifier segment, the length segment, and the spliced ​​data. Calling the generator polynomial Perform modulo-2 division on the data frame, calculate the 16-bit remainder and define it as a verification segment. Append the 16-bit verification segment to the end of the data frame to obtain the on-chain associated credential.

[0026] The contract verification submodule calls the on-chain associated credentials to write to the blockchain transaction network to generate transaction records to be verified, retrieves the public key of the smart contract node to perform signature matching verification on the transaction records to be verified, counts the number of signature matching nodes and performs consistency judgment with the preset signature number threshold, writes the judgment result into the contract state space and completes the transaction confirmation process, and generates a blockchain record of chip design specifications. The on-chain associated certificate is invoked, encapsulated into a transaction message body that conforms to the underlying blockchain protocol, and broadcast to the blockchain network consisting of 12 nodes. A transaction record to be verified with a unique transaction hash is generated. The public keys of the 12 nodes pre-stored in the smart contract script are retrieved, and the elliptic curve signature algorithm is used to verify the digital signature of the node private key carried in the transaction record. Configure the verification logic as follows ,in Indicates the node's public key. This represents a transaction message summary. The output value is calculated to determine if it is true. Each time a node successfully verifies the value, the counter parameter is updated. Perform an increment operation, with a preset signature count threshold. Set as the total number of nodes Adding 1 results in 9 nodes, representing the number of nodes matching the signature after statistical analysis. Perform a greater than or equal to condition operation with the threshold 9. If the condition is satisfied... The logical boolean value 1 is then stored in the smart contract storage slot State-0x01. After the nodes confirm that the consensus has been reached, the sorting node will package the transaction containing the design specifications into a block and update the global ledger to obtain the chip design specifications blockchain record.

[0027] Please see Figure 3 The production data acquisition module includes a running acquisition submodule, an offset identification submodule, and an on-chain writing submodule; Run the data acquisition submodule to collect temperature data, pressure data, speed data and production batch number from the chip production line equipment operation records. Extract material consumption records, material number and real-time usage information. Perform sequence alignment processing on the temperature data, pressure data and speed data according to the timestamp order. Establish a corresponding mapping between the material number and the real-time usage information and bind it to the production batch number to generate production operation correlation. The system collects temperature, pressure, and rotational speed data, as well as production batch numbers, from the chip production line equipment. Using a PLC controller in conjunction with infrared thermal imaging sensors, MEMS pressure sensors, and high-precision photoelectric encoders, it retrieves real-time sensor readings from equipment such as lithography machines and ion implanters. This yields the current time series temperature observation of 122 degrees Celsius, pressure observation of 1.1 standard atmospheres, and rotational speed observation of 3000 revolutions per minute. The system then aligns the temperature, pressure, and rotational speed data collected at the same time point using millisecond-level timestamps, forming a four-dimensional time vector. For silicon substrate with material number MAT-001 in the bill of materials, the real-time consumption of 498 units was collected using an industrial balance, and for metal interconnect lead with material number MAT-002, the real-time consumption of 205 units was collected. The material numbers were used as keys in a hash table, and the corresponding real-time consumption values ​​were used as values ​​in the hash table for one-to-one association and storage. The batch string BATCH-2026-A01 corresponding to the current production process was called, and this batch string was used as the main index identifier. Nested cascading operations were performed with the aligned sensor vector and the material hash table to establish the correlation between environmental parameters and material input in a specific production cycle.

[0028] The offset identification submodule calls the production operation correlation, retrieves the chip design specification blockchain record of the production process fault tolerance range and bill of materials information, performs boundary comparison between temperature data, pressure data, speed data and production process fault tolerance range and records the number of times the range is exceeded, performs number consistency check by combining material number and bill of materials information and counts the number of difference items to obtain the environmental material offset. The system invokes the production operation correlation function, retrieves the chip design specification blockchain record stored in the blockchain genesis state, and extracts the production process fault tolerance range: upper temperature limit of 125 degrees Celsius and lower limit of -40 degrees Celsius, and upper pressure limit of 1.2 standard atmospheres and lower limit of 0.8 standard atmospheres. The system performs a greater-than-normal operation on the actual collected temperature data of 122 degrees Celsius and a less-than-normal operation on the upper temperature limit. If the logical return value is false, it is considered to be within the safe range. If the real-time pressure data is 1.3 standard atmospheres, a greater-than-normal operation is performed on the upper limit of 1.2. If the logical return value is true, the environmental anomaly counter is activated. Increment by 1, retrieve the material number MAT-001 and expected usage of 500 units from the bill of materials arrangement results, and subtract it from the real-time usage of 498 units in the production operation correlation to obtain an absolute difference of 2 units. Compare the absolute difference of 2 units with the preset material loss coefficient of 1%. If the difference is within the range of 500 × 1% = 5 units, it is considered consistent. If the real-time usage is 510 units, it exceeds the preset error range, and the difference item counter is incremented. Record 1, combine the abnormal frequency of each sensor and the details of material differences to obtain the environmental material offset. The on-chain writing submodule extracts the production batch number based on the environmental material offset, constructs a transaction field sequence containing production environment offset characteristics, material consistency verification results, and production batch number, writes it into the blockchain transaction network, calls the smart contract node signature verification rules to perform signature quantity consistency judgment, writes the judgment identifier into the contract state space, and generates a blockchain record of the chip production process. Based on the environmental material offset, retrieve the production batch number BATCH-2026-A01 stored in the correlation structure, and set the environmental anomaly counter accordingly. The corresponding sequences of abnormal temperature, pressure, and rotational speed counts are mapped to hexadecimal feature fields, and the difference entry counters are used. The corresponding material comparison Boolean result is mapped to a consistency check field. This field, along with the batch number field, is encapsulated according to the blockchain standard transaction protocol format, generating a 256-byte transaction payload sequence. This sequence is sent to the blockchain consensus network node, triggering the signature verification logic within the smart contract. The node's public key set is retrieved, and a dot product operation and coordinate verification are performed on the ECDSA digital signature in the transaction. The total number of nodes with true verification results (10) is obtained. The pre-set weight judgment parameters of the smart contract are read to determine whether 10 falls within the range. If the judgment result is true, the transaction legality identifier 0x01 is written into the contract state variable. The load containing the production offset and material status is persistently stored through the block consensus engine, generating a blockchain record of the chip production process.

[0029] Please see Figure 4 The cross-stage verification module includes an anomaly identification submodule, a rating association submodule, and a contract determination submodule; The anomaly identification submodule extracts the production environment offset features, material consistency verification results, and production batch numbers from the blockchain records of the chip manufacturing process. It performs boundary comparisons between the production environment offset features and the process tolerance boundaries and filters out records that exceed the tolerance. It extracts material mismatch items from the material consistency verification results and counts the number of items. It merges the number of abnormal environment records and the number of material mismatch items to generate an anomaly summary. Extract the production environment offset characteristics, material consistency verification results, and production batch number BATCH-2026-A01 from the blockchain record of the chip manufacturing process. Retrieve the process tolerance boundary parameters stored at the design specification level, setting the temperature boundary to 125 degrees Celsius and the pressure boundary to 1.2 standard atmospheres. Extract the highest value of 128 degrees Celsius and the lowest value of 121 degrees Celsius from the real-time temperature observation sequence during the production process. Perform a greater-than-128 / 125 check operation. If the logical return value is true, the record is added to the tolerance set and an environmental warning interruption signal is sent to the control terminal by the smart contract. Then, execute the check operation on 121 / 125. The operation of determining if 25 is greater than or equal to 121 and -40 is less than or equal to 121 and -40. If the logical return value is false, no record is made. The maximum value of 1.3 standard atmospheres in the pressure sequence is compared with the boundary value of 1.2. If the result is true, an excess tolerance record is added. The number of abnormal environment records is counted as 2. The material consistency verification result field is checked for the existence of the Boolean value False. The material mismatch entry MAT-002 is extracted and counted. The number of material mismatch entries is found to be 1. The number of abnormal environment records 2 and the number of material mismatches 1 are added together to obtain an abnormality summary degree of 3.

[0030] The rating association submodule retrieves a preset sensitivity attribute table based on the anomaly aggregation degree. It performs interval matching in the sensitivity attribute table for the number of abnormal environment records and marks the corresponding rating number. The rating number and the number of abnormal environment records are combined and written into the production batch number identifier segment to form an association field sequence and obtain the quality offset index. Based on the anomaly summary value of 3, the smart contract's internal preset sensitivity attribute mapping table is retrieved. The anomaly frequency is divided into three levels based on process importance: the interval [0,1] corresponds to rating number 0x01 (normal fluctuation), the interval [2,5] corresponds to rating number 0x02 (significant deviation), and the interval [6,100] corresponds to rating number 0x03 (severe loss of control). An inclusion operation is performed between the current anomaly summary value 3 and the endpoints of each interval. If the judgment 3 falls within the closed interval [2,5], the corresponding rating number is marked as 0x02. The string identifier segment of the production batch number BATCH-2026-A01 is retrieved. The rating number 0x02, the number of anomaly environment records 2, and the number of material mismatches 1 are concatenated in hexadecimal complement format. The calculation formula is defined as follows: ,in The rating number is 2. The number of environmental anomalies is 2. The material variance number is 1; Substitute the values ​​and perform multiplication and addition operations to obtain The result 221 is defined as the quality offset index.

[0031] The contract determination submodule calls the quality offset index, retrieves the fault tolerance threshold, performs a numerical comparison to determine whether the fault tolerance threshold is exceeded, records the comparison output identifier and binds it with the production batch number to write it into the smart contract state space, confirms the state writing result on the chain, and generates the chip smart contract cross-stage verification conclusion. The quality offset index 221 is invoked to retrieve the fault tolerance threshold parameter from the smart contract configuration section. This threshold is set with reference to the historical distribution of high-quality products. The baseline value is 250. A numerical comparison is performed between the current quality offset index of 221 and the tolerance threshold of 250 to determine the appropriate action. The logical operation returns a Boolean value of True, and the comparison output identifier is recorded as 0x00 to indicate that it is qualified. If the offset index calculation result is 300, it is compared with 250 to perform a greater than judgment, and the result returns a Boolean value of False. The comparison output identifier is recorded as 0x01, indicating that there is a major quality risk in this batch of chips and triggering the contract to automatically execute the asset locking process. The identifier value 0x00 is hashed and associated with the production batch number BATCH-2026-A01 and written to the designated storage slot Slot-A2 in the smart contract state space. This triggers the on-chain node to perform PBFT consensus confirmation on this state change. After the confirmation block height increases, the data persistence is completed, and the chip smart contract cross-stage verification conclusion is generated.

[0032] Please see Figure 5 The quality anomaly monitoring module includes a performance comparison submodule and a link tracing submodule; The performance comparison submodule obtains the chip's electrical performance, functional, and reliability test results, retrieves the performance requirement items recorded in the chip design specification blockchain, performs item-by-item numerical comparison between the electrical performance test results and the performance requirement items and records the deviation markers, performs status matching judgment between the functional test results and the performance requirement items and marks the difference items, performs interval verification between the reliability test results and the performance requirement items and counts the number of non-compliance items, combines the number of non-compliance items and generates the performance deviation degree. The chip's electrical performance, functionality, and reliability test results were collected using automated test equipment (ATE). A 6.5-digit digital multimeter (DMM) was used to measure the static operating current. Performance requirements recorded in the blockchain ledger, such as a static operating current of 50 microamps, a logic flip-level threshold of 0.8 volts, and a high-temperature storage life of 1000 hours, were retrieved. The actual static current of 65 microamps, fed back from the test station, was extracted. A greater-than-expected operation was performed between 65 microamps and 50 microamps. When the actual value exceeded the design value, a deviation flag of 0x01 was recorded. The logic function self-test vector output sequence obtained from the logic analyzer was read. A bitwise XOR operation was performed between the actual output binary logic stream and the expected standard logic stream. The number of non-consistent bits with a value of 1 in the XOR result was counted as 12 bits. This 12-bit difference was marked as a functional item mismatch. The high-temperature accelerated aging test time, fed back from the reliability laboratory, was obtained. A less-than-expected operation was performed between the actual aging test pass time of 850 hours and the required value of 1000 hours. Records meeting the less-than condition were defined as failure items and counted as 1 item. The performance evaluation formula was retrieved. ,in The normalization bias is labeled 1. The number of functional difference items is 12. The reliability failure rate is 1. Substituting the value into the equation and performing an addition operation yields a performance deviation of 14.

[0033] The link traceability submodule, based on the performance deviation degree, calls the chip smart contract cross-stage verification conclusion production quality offset index and smart contract state space abnormal environment feature items. It performs batch number matching and filters related records for the abnormal environment feature items and production quality offset index, binds the filtered records with the performance deviation degree execution items to form a traceability mapping sequence, writes it into the blockchain transaction network and completes the contract signature verification process, and generates chip smart contract quality monitoring records. Based on the performance deviation value of 14, the production quality offset index 221 and its associated production batch number BATCH-2026-A01 stored in the blockchain smart contract state space are retrieved. Abnormal environmental characteristics, namely temperature offset of 128 degrees Celsius and pressure offset of 1.3 standard atmospheres, are searched in the state variables. A string consistency comparison is performed between the batch index corresponding to the performance deviation and the batch index corresponding to the offset index. If the two batch numbers are completely identical, a related record filtering action is performed. The static current exceeding the standard by 65 microamps in the performance non-compliance item is logically associated with the temperature offset of 128 degrees Celsius in the environmental characteristic item. The rating number 0x02 recorded in the offset index is extracted. The offset index 221, two environmental anomaly records, and performance deviation 14 are linearly concatenated according to the timestamp weight coefficient. The timestamp weight coefficient is set. The parameter is set to 1.0 and used as a connection factor. The above parameters are converted into a fixed-length byte array in hexadecimal two's complement format. The byte array is encapsulated into the blockchain transaction data field and sent to the consensus node. This triggers the smart contract to perform RSA-3072 public key algorithm verification on the transaction digest. The weight ratio of nodes with true verification results reaches 100% and is written into the block header, generating a chip smart contract quality monitoring record.

[0034] Please see Figure 6 The lifecycle management module includes a batch mapping submodule, a warehouse evaluation submodule, and an on-chain archiving submodule; The batch mapping submodule calls the performance compliance status set and abnormal event attribution mapping results in the chip smart contract quality monitoring record, extracts the production batch number of the chip smart contract cross-stage verification conclusion, collects product identification code, inbound and outbound circulation records, and environmental temperature and humidity sequence in the warehousing and logistics links, performs number matching and establishes a corresponding relationship between the production batch number and the product identification code, binds the corresponding relationship with the execution timestamp of the inbound and outbound circulation record, and generates batch product mapping degree. The process of matching production batch numbers with product identification codes and establishing a corresponding relationship is as follows: Perform coding rule consistency verification and field integrity verification on the production batch number and product identification code respectively. Establish a corresponding relationship record between the production batch number and product identification code that pass the verification, and bind the corresponding relationship record with the inbound and outbound timestamps in the inbound and outbound flow record to form a batch product mapping degree. The system retrieves the performance compliance status set and abnormal event attribution mapping results from the chip smart contract quality monitoring records. It extracts the production batch number BATCH-2026-A01 and the associated product identification code SN-2026-001. The identification code is collected using an RFID reader or barcode scanner. The system reads the 15-character prefix BATCH and length field of the production batch number, and the 12-character prefix SN and length field of the product identification code. Regular expression matching is performed on these codes to determine if the character set belongs to hexadecimal or a specified character range. If the verification result is true (Boolean value), field integrity extraction is performed. Write the verified BATCH-2026-A01 and SN-2026-001 into the key-value pair mapping table Map-ID. Retrieve the inbound operation sequence from the warehouse management database (WMS) inbound and outbound flow records, extract the Unix timestamp 1740902400 corresponding to the inbound operation, and extract the corresponding outbound operation timestamp 1741075200. Perform a tail append concatenation between the corresponding record in the Map-ID table and the inbound timestamp 1740902400, and perform a second append concatenation with the outbound timestamp 1741075200. Perform linear recombination of the concatenated binary sequence according to batch priority to generate the batch product mapping degree.

[0035] The warehouse assessment submodule, based on the batch product mapping degree, calls the environmental temperature and humidity sequence and the compliant storage requirement range to perform range comparison and record the number of times the limit is exceeded. The number of times the limit is exceeded is marked with the time period of the inbound and outbound flow record, and the number of times the limit is exceeded in each time period is merged to form a summary count, thus obtaining the logistics warehouse assessment degree. The process of calling the environmental temperature and humidity sequence and performing an interval comparison with the compliant storage requirement range, and recording the number of times the limit is exceeded, is as follows: The environmental temperature and humidity sequence is divided into time periods based on the timestamps of the inbound and outbound flow records. For the temperature and humidity values ​​in each time period, the interval inclusion judgment is performed and an out-of-limit mark is generated. The out-of-limit mark is associated with the inbound and outbound flow records of the corresponding time period and written into the summary record to obtain the logistics warehousing evaluation degree. Based on the batch product mapping degree, the LoRa / Zigbee IoT temperature and humidity transmitter is used to retrieve the environmental temperature and humidity sequence recorded by sensors in the logistics process. This includes temperature and humidity data collected every hour. The lower limit of temperature (5 degrees Celsius) and the upper limit of humidity (25 degrees Celsius) and the lower limit of humidity (30%) and the upper limit of humidity (60%) are extracted from the warehousing compliance storage requirement range. The corresponding time window is divided according to the timestamp span of the inbound and outbound flow records, i.e., from 1740902400 to 1741075200. The temperature sample value of 28 degrees Celsius and the upper limit of 25 degrees Celsius are greater than the upper limit, and the humidity sample value of 65% and the upper limit of 60% are also greater than the upper limit. When either judgment result is true, the sampling point is marked as 0x01, i.e., the over-limit mark, and the over-limit sampling point is added to the risk counter. The total number of over-limit marks in the entire time window is counted as 5. The value 5 is concatenated with the corresponding time period identifier. Calculate the warehouse risk coefficient %,in The number of times exceeded the limit was 5. The total number of samples during this period was 48. Substituting the values, the coefficient was calculated to be 10.42%. This coefficient was then associated with the entry location and logistics vehicle number in the summary record to obtain the logistics warehousing evaluation score.

[0036] The on-chain archiving submodule, based on the logistics and warehousing assessment, retrieves the performance compliance status set, the abnormal event attribution mapping results, and the inbound and outbound flow records. It sorts the fields according to the timestamp sequence and constructs the transaction data segment. The transaction data segment is written into the blockchain transaction network and triggers the smart contract signature verification. The signature count judgment identifier is recorded and written into the contract state space to generate the chip product full life cycle management results. Based on the logistics and warehousing assessment, the system retrieves the electrical performance deviation identifier 0x01 from the performance compliance status set, the temperature offset record of 128 degrees Celsius from the abnormal event attribution mapping results, and the inbound / outbound time 1740902400 from the inbound / outbound flow records. All these fields are then sorted in ascending order using a bubble sort based on their Unix timestamp values. A transaction data segment, Data-Segment, is constructed with a timeline as its axis. This Data-Segment is then injected into the consensus-pending transaction queue via the blockchain client API interface, triggering the smart contract to execute Keccak-... The 256-digest operation calls the node's private key to perform an ECDSA signature operation on the digest. The consensus plugin counts 11 signed response packets. The preset majority verification parameter 9 is retrieved, and a numerical comparison judgment of 11 being greater than or equal to 9 is performed. The judgment result logical value 1 is mapped to a hexadecimal identifier and written to the lifecycle management partition Slot-L1 of the contract state space. If the judgment result is inconsistent, the contract automatically rejects the archiving and triggers the traceability process, completing the final notarization and archiving of all data of the chip from design, production, verification to warehousing, and generating the chip product full lifecycle management result.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A chip product lifecycle management system integrating blockchain and smart contracts, characterized in that, The system includes: The design specification solidification module collects chip design document content, circuit layout hash value, manufacturing process tolerance range and bill of materials information, converts the design document content into a byte sequence, forms the first set of associated data, writes it to the blockchain to trigger smart contract node signature verification, and generates a chip design specification blockchain record. The production data acquisition module collects the temperature, pressure, speed and production batch number of the production equipment, extracts the material number and usage, retrieves the process tolerance range and bill of materials in the chip design specification blockchain, generates production environment offset characteristics and material consistency verification results, and generates a blockchain record of the chip production process. The cross-stage verification module extracts the production environment offset features, material consistency verification results and production batch numbers from the blockchain record of the chip production process, identifies abnormal items and material mismatch items, rates them according to sensitivity and associates them to form a production quality offset index, determines the threshold of the smart contract and generates the threshold determination result, associates and stores it in the state space, and obtains the cross-stage verification conclusion of the chip smart contract. The quality anomaly monitoring module acquires the electrical performance, functional, and reliability test results of the chip, retrieves the design specification record requirements, compares them to form a set of performance compliance states, and if there are any non-compliance states, it calls the offset index of the cross-stage verification conclusion of the chip smart contract and traces the state space anomaly items to obtain the anomaly event attribution mapping results and integrates them to generate the chip smart contract quality monitoring record.

2. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 1, characterized in that: The system also includes: The lifecycle management module calls the performance compliance status set and abnormal event attribution mapping results of the chip smart contract quality monitoring records, extracts the production batch number, collects product identification code, inbound and outbound circulation records, temperature and humidity sequence, establishes batch product mapping and evaluates compliance, and generates full lifecycle management results of chip products by uploading them to the blockchain according to timestamps.

3. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 1, characterized in that: The design specification solidification module includes a data acquisition submodule, an association encapsulation submodule, and a contract verification submodule; The data acquisition submodule collects design document content, circuit layout file hash value, manufacturing process fault tolerance range, and bill of materials information during the chip design phase. It calls character encoding rules to convert the design document content into a byte sequence, performs field validation on the circuit layout file hash value and extracts a fixed-length summary fragment, sorts the upper and lower limits of the manufacturing process fault tolerance range in order, and performs corresponding arrangement of the material number and usage fields in the bill of materials information to generate a design load identifier. The associated encapsulation submodule, based on the design payload identifier, calls the byte sequence, circuit layout file hash digest fragment, production process fault tolerance range field and bill of materials arrangement result to perform sequential splicing, writes the identifier segment and length segment according to the preset data frame format, performs field consistency verification on the spliced ​​data frame and forms a verification fragment, appends the verification fragment to the end of the data frame to obtain the on-chain associated certificate; The contract verification submodule calls the on-chain associated credentials to write to the blockchain transaction network to generate transaction records to be verified, retrieves the public key of the smart contract node to perform signature matching verification on the transaction records to be verified, counts the number of signature matching nodes and performs consistency judgment with the preset signature number threshold, writes the judgment result into the contract state space and completes the transaction confirmation process, and generates a chip design specification blockchain record.

4. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 1, characterized in that: The production data acquisition module includes a running acquisition submodule, an offset identification submodule, and an on-chain writing submodule; Run the data acquisition submodule to collect temperature data, pressure data, speed data and production batch number from the chip production line equipment operation records. Extract material consumption records, material number and real-time usage information. Perform sequence alignment processing on the temperature data, pressure data and speed data according to the timestamp order. Establish a corresponding mapping between the material number and the real-time usage information and bind it to the production batch number to generate production operation correlation. The offset identification submodule calls the production operation correlation degree, retrieves the chip design specification blockchain record of the production process fault tolerance range and bill of materials information, performs boundary comparison between temperature data, pressure data, speed data and production process fault tolerance range and records the number of times the range is exceeded, performs number consistency check with material number and counts the number of difference items in combination with material number and bill of materials information, and obtains the environmental material offset. The on-chain writing submodule extracts the production batch number based on the environmental material offset, constructs a transaction field sequence containing production environment offset features, material consistency verification results, and production batch number, writes it into the blockchain transaction network, calls the smart contract node signature verification rules to perform signature quantity consistency judgment, writes the judgment identifier into the contract state space, and generates a blockchain record of the chip production process.

5. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 1, characterized in that: The cross-stage verification module includes an anomaly identification submodule, a rating association submodule, and a contract determination submodule; The anomaly identification submodule extracts the production environment offset features, material consistency verification results, and production batch numbers from the blockchain records of the chip manufacturing process. It performs boundary comparison between the production environment offset features and the process tolerance boundary and filters records that exceed the tolerance. It extracts material mismatch items from the material consistency verification results and counts the number of items. It merges the number of abnormal environment records and the number of material mismatch items to generate an anomaly summary. The rating association submodule retrieves a preset sensitivity attribute table based on the anomaly summary degree, performs interval matching in the sensitivity attribute table for the number of abnormal environment records and marks the corresponding rating number, combines the rating number and the number of abnormal environment records and writes them into the production batch number identifier segment to form an association field sequence and obtain the quality offset index. The contract determination submodule calls the quality offset index, retrieves the fault tolerance threshold, performs a numerical comparison to determine whether the fault tolerance threshold is exceeded, records the comparison output identifier and binds it with the production batch number to the smart contract state space, confirms the state writing result on the chain, and generates the chip smart contract cross-stage verification conclusion.

6. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 1, characterized in that: The quality anomaly monitoring module includes a performance comparison submodule and a link tracing submodule; The performance comparison submodule obtains the chip's electrical performance, functional, and reliability test results, retrieves the performance requirement items recorded in the chip design specification blockchain, performs item-by-item numerical comparison between the electrical performance test results and the performance requirement items and records the deviation markers, performs status matching judgment between the functional test results and the performance requirement items and marks the difference items, performs interval verification between the reliability test results and the performance requirement items and counts the number of non-compliance items, combines the number of non-compliance items and generates the performance deviation degree. The link traceability submodule, based on the performance deviation degree, calls the chip smart contract cross-stage verification conclusion production quality offset index and smart contract state space abnormal environment feature items. It performs batch number matching and filters related records for the abnormal environment feature items and production quality offset index, binds the filtered records with the performance deviation degree execution items to form a traceability mapping sequence, writes it into the blockchain transaction network and completes the contract signature verification process, and generates chip smart contract quality monitoring records.

7. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 2, characterized in that: The lifecycle management module includes a batch mapping submodule, a warehouse evaluation submodule, and an on-chain archiving submodule; The batch mapping submodule calls the performance compliance status set and abnormal event attribution mapping results in the chip smart contract quality monitoring record, extracts the production batch number of the chip smart contract cross-stage verification conclusion, collects product identification codes, inbound and outbound circulation records, and environmental temperature and humidity sequences in the warehousing and logistics links, performs number matching and establishes a corresponding relationship between the production batch number and the product identification code, binds the corresponding relationship to the execution timestamp of the inbound and outbound circulation record, and generates the batch product mapping degree. The warehousing assessment submodule, based on the batch product mapping degree, calls the environmental temperature and humidity sequence and the compliant storage requirement range to perform interval comparison and record the number of times the limit is exceeded. The number of times the limit is exceeded is marked with the time period of the inbound and outbound flow record, and the number of times the limit is exceeded in each time period is merged to form a summary count, thus obtaining the logistics warehousing assessment degree. The on-chain archiving submodule, based on the logistics and warehousing assessment, retrieves the performance compliance status set, the abnormal event attribution mapping results, and the inbound and outbound flow records. It sorts the fields according to the timestamp sequence and constructs a transaction data segment. The transaction data segment is written into the blockchain transaction network and triggers smart contract signature verification. The signature count judgment identifier is recorded and written into the contract state space to generate the chip product's full lifecycle management results.

8. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 7, characterized in that: The process of matching production batch numbers with product identification codes and establishing a corresponding relationship is as follows: Perform coding rule consistency verification and field integrity verification on the production batch number and the product identification code respectively. Establish a corresponding relationship record between the production batch number and the product identification code that pass the verification, and bind the corresponding relationship record to the inbound and outbound timestamps in the inbound and outbound flow record to form a batch product mapping degree.

9. The chip product lifecycle management system integrating blockchain and smart contracts as described in claim 7, characterized in that: The process of calling the environmental temperature and humidity sequence and performing an interval comparison with the compliant storage requirement interval, and recording the number of times the limit is exceeded, is as follows: The environmental temperature and humidity sequence is divided into time periods based on the timestamps of the inbound and outbound flow records. For the temperature and humidity values ​​in each time period, interval inclusion judgment is performed and an out-of-limit marker is generated. The out-of-limit marker is associated with the inbound and outbound flow records of the corresponding time period and written into the summary record to obtain the logistics warehousing evaluation degree.

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