Logistics operation performance data evidence storage and intelligent assessment method and system based on block chain

Abstract

The invention provides a logistics operation performance data evidence storage and intelligent assessment method and system based on a block chain, and the method comprises the steps: classifying performance data to obtain subclass data, and constructing a unique data identifier in combination with a timestamp, a data type code and a random encryption character of the subclass data; performing asymmetric encryption on the performance sub-category data to obtain first encrypted data, and performing secondary symmetric encryption to obtain second encrypted data; constructing a privacy protection smart contract according to a logistics business process and a data sharing demand, and determining a block chain node in combination with logistics parameters, reputation rating of each party and network stability; storing the second encrypted data, the unique data identifier and the smart contract into a node to form a target block chain; and finally, based on an intelligent assessment strategy pre-embedded in the chain, performing performance assessment on the decrypted original data and obtaining a result. According to the method, data privacy security is guaranteed, and the problem that the assessment result is lagged due to data processing delay is solved.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and in particular to a blockchain-based method and system for storing and intelligently evaluating logistics operation performance data. Background Technology

[0002] With the booming development of the logistics industry, the storage and intelligent assessment of logistics operation performance data plays a crucial role in enhancing the competitiveness of logistics companies and optimizing operational efficiency. Blockchain-based methods for storing and intelligently assessing logistics operation performance data have emerged, leveraging the decentralized and tamper-proof characteristics of blockchain to provide a new approach for the reliable recording and assessment of logistics data.

[0003] However, existing methods in logistics operations involve numerous stakeholders, including suppliers, transporters, warehousing companies, and customers. The data from these parties contains a large amount of sensitive information, such as customer personal details and trade secrets. While existing technologies utilize blockchain for data storage and performance evaluation processes, they employ only basic encryption methods for data privacy protection. This makes it difficult to effectively guarantee data privacy and security in the face of increasingly complex cyberattacks and the privacy requirements of data-sharing scenarios. Furthermore, traditional performance data evaluation methods suffer from serious deficiencies in accuracy and timeliness. The evaluation process relies heavily on manual operations for data collection, processing, and calculation, which not only consumes significant human and material resources but is also prone to data distortion due to human factors. Additionally, data processing delays cause evaluation results to be outdated, failing to reflect operational status in real time and hindering timely decision-making by enterprises. Summary of the Invention

[0004] This invention provides a blockchain-based method and system for storing and intelligently assessing logistics operation performance data, which aims to ensure data privacy and security and solve the problem of delayed assessment results caused by data processing delays.

[0005] In a first aspect, the present invention provides a blockchain-based method for storing and intelligently evaluating logistics operation performance data, including:

[0006] The comprehensive performance data obtained from logistics operations is classified to obtain multiple performance subcategories. A unique data identifier is constructed based on the timestamp, data type code, and random encrypted characters of each performance subcategory.

[0007] The performance sub-category data is initially asymmetric encrypted to obtain first encrypted data, and the first encrypted data is then subjected to secondary symmetric encryption to obtain second encrypted data.

[0008] Based on logistics business processes and data sharing needs, a privacy-protecting smart contract for the second encrypted data is constructed, and blockchain nodes are determined based on logistics parameters, the reputation ratings of each participant, and network stability.

[0009] The second encrypted data, the unique data identifier, and the privacy-protecting smart contract are stored on the blockchain node to obtain the target blockchain chain;

[0010] The performance evaluation is conducted on the original data after the second encrypted data is decrypted based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and the evaluation result is obtained.

[0011] Secondly, the present invention also provides a blockchain-based logistics operation performance data storage and intelligent assessment system, applied to the blockchain-based logistics operation performance data storage and intelligent assessment method described in the first aspect; the blockchain-based logistics operation performance data storage and intelligent assessment system includes:

[0012] The data classification and identification generation module is used to classify the comprehensive performance data obtained from logistics operations, obtain multiple performance sub-categories of data, and construct a unique data identifier based on the timestamp, data type code, and random encrypted characters of each performance sub-category of data.

[0013] A multi-encryption module is used to perform preliminary asymmetric encryption on the performance sub-category data to obtain first encrypted data, and to perform secondary symmetric encryption on the first encrypted data to obtain second encrypted data;

[0014] The smart contract construction and node determination module is used to construct a privacy-protected smart contract for the second encrypted data based on logistics business processes and data sharing needs, and to determine blockchain nodes based on logistics parameters, the reputation ratings of each participant, and network stability.

[0015] A blockchain data storage module is used to store the second encrypted data, the unique data identifier, and the privacy-protecting smart contract on the blockchain node to obtain the target blockchain chain;

[0016] The performance evaluation module is used to perform performance evaluation on the original data after decryption of the second encrypted data based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and obtain the evaluation result.

[0017] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the blockchain-based logistics operation performance data storage and intelligent assessment method as described above.

[0018] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the blockchain-based logistics operation performance data storage and intelligent assessment method as described above.

[0019] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the blockchain-based logistics operation performance data storage and intelligent assessment method as described above.

[0020] The blockchain-based logistics operation performance data storage and intelligent assessment method provided in this invention achieves dual encryption of data through preliminary asymmetric encryption and secondary symmetric encryption of the categorized performance sub-data. Compared with a single basic encryption method, this enhances data encryption strength, effectively resists complex network attacks, and ensures the security of sensitive data such as customer personal information and trade secrets during storage and sharing. Furthermore, it is coupled with privacy-protecting smart contracts built according to logistics business processes and data sharing needs, achieving data privacy and security while ensuring orderly data sharing among all participants. In addition, performance assessment is conducted on the decrypted raw data based on a pre-embedded intelligent assessment strategy on the target blockchain chain, replacing the traditional manual data collection, processing, and calculation methods. This significantly reduces manpower and material resources, avoids data distortion caused by human factors, and eliminates data processing delays, enabling assessment results to reflect logistics operation status in real time. This provides strong support for timely optimization decisions by enterprises and improves the accuracy and timeliness of performance assessment. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the blockchain-based logistics operation performance data storage and intelligent assessment method provided in this embodiment of the invention.

[0022] Figure 2 This is a schematic diagram of the structure of the blockchain-based logistics operation performance data storage and intelligent assessment system provided in an embodiment of the present invention;

[0023] Figure 3 An embodiment diagram of the electronic device provided in this invention;

[0024] Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0028] See Figure 1 , Figure 1 This is a flowchart illustrating the blockchain-based logistics operation performance data storage and intelligent assessment method provided by the present invention. In this embodiment, the executing entity of the blockchain-based logistics operation performance data storage and intelligent assessment method is the storage and intelligent assessment system. Therefore, the blockchain-based logistics operation performance data storage and intelligent assessment method includes:

[0029] Step 10: Classify the comprehensive performance data obtained in logistics operations to obtain multiple performance sub-categories of data, and construct a unique data identifier based on the timestamp, data type code, and random encrypted characters of each performance sub-category of data.

[0030] Optionally, the evidence storage and intelligent assessment system first comprehensively reviews the collected logistics operation performance data. This data covers multiple aspects such as transportation timeliness, cargo integrity rate, warehouse turnover rate, delivery accuracy, and customer satisfaction. Then, it categorizes the data into various performance subcategories according to preset classification standards. These classification standards can be determined based on logistics business processes (such as transportation, warehousing, and delivery) and data attributes (such as timeliness data, cost data, and quality data). Finally, for each performance subcategory, the system extracts its timestamp, generates a data type code according to internally preset encoding rules, and randomly generates an encrypted string. These three elements are combined to construct a unique data identifier, ensuring that each performance subcategory data can be uniquely identified.

[0031] In one embodiment, taking the comprehensive performance data obtained by the logistics company, which includes transportation time and cost in the transportation stage, and warehouse turnover rate and inventory accuracy in the warehousing stage, as an example, the evidence storage and intelligent assessment system classifies the comprehensive performance data into four sub-categories: transportation timeliness data, transportation cost data, warehouse turnover data, and inventory accuracy data. For transportation timeliness data, its timestamp is 2025-08-05, 08:00:00, the data type code is set to YSX001 (the first letter of transportation timeliness plus a number), and the random encryption character is a7b3c9d1. Then, the constructed unique data identifier is 20250805080000-YSX001-a7b3c9d1.

[0032] Step 20: Perform preliminary asymmetric encryption on the performance sub-category data to obtain the first encrypted data, and then perform secondary symmetric encryption on the first encrypted data to obtain the second encrypted data.

[0033] Optionally, the evidence storage and intelligent assessment system first uses an asymmetric encryption algorithm (such as RSA asymmetric encryption algorithm) to perform preliminary encryption processing on the performance sub-category data, generating the first encrypted data. The asymmetric encryption algorithm uses a public key and a private key, and the system uses the corresponding public key to encrypt the data. Then, the system uses a symmetric encryption algorithm to perform a second encryption on the first encrypted data, obtaining the second encrypted data, as described in steps 201-205, ultimately achieving dual encryption of the performance sub-category data.

[0034] In one embodiment, taking transportation cost data as an example, the original data is 10,000 yuan. The system uses the RSA asymmetric encryption algorithm to encrypt the data with the public key, obtaining the first encrypted data.

[0035] Step 30: Based on the logistics business process and data sharing needs, construct a privacy-protecting smart contract for the second encrypted data, and determine the blockchain nodes based on logistics parameters, the reputation rating of each participant, and network stability.

[0036] Optionally, the evidence storage and intelligent assessment system can construct privacy-preserving smart contracts based on the specific processes of logistics operations, such as the flow of goods from suppliers to transporters, then to warehousing providers, and finally to customers, as well as the data sharing needs among different participants (such as cargo owners, logistics companies, and transport drivers). These privacy-preserving smart contracts clearly define data access permissions, sharing scope, and usage rules to protect data privacy. Simultaneously, the system will comprehensively analyze and evaluate logistics parameters (such as transport distance and cargo weight), the reputation ratings of each participant (assessed through historical cooperation records), and network stability (node ​​online time, data transmission speed, etc.), selecting qualified blockchain nodes from a large pool of nodes based on preset weights and evaluation criteria.

[0037] In one embodiment, the logistics process is illustrated by supplier A entrusting goods to transporter B, then to warehouse C for storage, and finally to delivery provider D for customer E. Based on the principle that all parties need to share the location information of the goods but cannot access each other's cost information (e.g., customer E needs real-time location and delivery time data, while transporter B only needs information about the goods they are responsible for), a privacy-preserving smart contract is constructed. This contract stipulates that each party can only access encrypted data related to the location of the goods and must not disclose it to entities outside the contract (e.g., customer E can access delivery time and location data, while transporter B can only access data related to the goods they transport). For determining the blockchain nodes, the following factors are considered: transportation distance (weight 30%), reputation rating (weight 50%), and network stability (weight 20%). Assuming there are nodes 1, 2, and 3, with transportation distance scores of 80, 90, and 70 respectively, reputation ratings of 90, 85, and 95 respectively, and network stability scores of 85, 90, and 80 respectively. After calculation, the scores for node 1 are: 80×30%+90×50%+85×20%=86; the scores for node 2 are: 90×30%+85×50%+90×20%=87.5; and the scores for node 3 are: 70×30%+95×50%+80×20%=85.5. Node 2 was ultimately selected as the blockchain node.

[0038] Step 40: Store the second encrypted data, the unique data identifier, and the privacy-protected smart contract on the blockchain node to obtain the target blockchain chain.

[0039] Optionally, the evidence storage and intelligent assessment system will store the second encrypted data (obtained through secondary encryption), the unique data identifier constructed in step 10, and the privacy-protected smart contract constructed in step 30, according to the data storage format and rules of the blockchain, on a predetermined blockchain node, as described in steps 401-405. This will enable the continuous and immutable target blockchain chain to form as data is continuously stored and accumulated.

[0040] Step 50: Based on the intelligent assessment strategy pre-embedded in the target blockchain chain, the original data after decryption of the second encrypted data is assessed to obtain the assessment results.

[0041] Optionally, the evidence storage and intelligent assessment system utilizes the intelligent assessment strategy pre-embedded in the target blockchain chain to first decrypt the second encrypted data to obtain the original performance sub-category data, and then conduct performance assessment on the original data according to the intelligent assessment strategy to finally obtain the assessment result, as described in steps 501-505.

[0042] This invention achieves dual encryption of data by performing preliminary asymmetric encryption and secondary symmetric encryption on the categorized performance sub-data. Compared to a single basic encryption method, this enhances data encryption strength, effectively resists complex network attacks, and ensures the security of sensitive data such as customer personal information and trade secrets during storage and sharing. Furthermore, it utilizes privacy-protecting smart contracts built according to logistics business processes and data sharing needs to ensure data privacy while maintaining orderly data sharing among all participants. In addition, performance evaluation is conducted on the decrypted raw data based on a pre-embedded intelligent assessment strategy on the target blockchain, replacing traditional manual data collection, processing, and calculation methods. This significantly reduces manpower and material resources, avoids data distortion caused by human factors, and eliminates data processing delays, enabling assessment results to reflect logistics operations in real time. This provides strong support for timely optimization decisions by enterprises and improves the accuracy and timeliness of performance evaluation.

[0043] In one embodiment, steps 201-205 are described as follows:

[0044] Step 201: Based on the length of the first encrypted data and the feature value in the unique data identifier, construct a target symmetric key, and divide the first encrypted data into multiple sub-data blocks based on the binary conversion value of the first preset bit of the target symmetric key.

[0045] Optionally, the evidence storage and intelligent assessment system first obtains the length of the first encrypted data and extracts a feature value from the unique data identifier. The feature value can be a character at a specific position in the unique data identifier or a calculated value (such as a timestamp, data type encoding, and random encrypted characters). Then, based on the length of the first encrypted data and the feature value, a target symmetric key is constructed using a preset key generation algorithm. Next, the first preset bits (e.g., the first 8 bits) of the target symmetric key are truncated, converted into a binary value, and then a corresponding segmentation rule is determined based on this binary value (e.g., the segmentation size is determined based on the number of consecutive 1s in the binary value). The first encrypted data is then segmented to obtain multiple sub-data blocks.

[0046] In one embodiment, assuming the length of the first encrypted data obtained in step 20 is 128 bytes, and the unique data identifier is 20250805080000-YSX001-a7b3c9d1, the feature value "a7b3" (taking the first 4 bits of the random encryption character) is extracted from it. The system uses an algorithm to convert the length of the first encrypted data 128 bytes and the feature value "a7b3" to construct the target symmetric key "m8n2p5q7r1s3t6u9v4". The preset bits are the first 8 bits, i.e. "m8n2p5q7", which are converted into binary values ​​(assuming the converted value is 100101100110100100111010010111000011010101011100100100110100101111). The size of the segment is determined by the number of consecutive 1s in the binary value. If the number of consecutive 1s is 3, then the 128 bytes of the first encrypted data are divided into 4 sub-data blocks, each consisting of 32 bytes: sub-data block 1 (bytes 1-32), sub-data block 2 (bytes 33-64), sub-data block 3 (bytes 65-96), and sub-data block 4 (bytes 97-128).

[0047] Step 202: Based on the byte distribution characteristics within the sub-data block, determine the feature value of each sub-data block, and integrate it with the target symmetric key to obtain the encryption offset of each sub-data block.

[0048] Optionally, the evidence storage and intelligent assessment system analyzes each sub-data block, such as statistically analyzing the frequency of different bytes and the relationships between bytes, to determine the feature value of each sub-data block. This feature value can be a numerical value that comprehensively reflects the byte distribution. Then, the system integrates the feature value of each sub-data block with the target symmetric key using a preset encryption function to obtain the encryption offset corresponding to each sub-data block. This encryption offset is used to adjust the encryption position or strength of the data during subsequent encryption processes.

[0049] Furthermore, the encryption function is as follows:

[0050]

[0051] Where EO is the encryption offset; SK i Let be the ASCII value of the i-th bit in the ASCII sequence of the target symmetric key; L is the length of the ASCII sequence of the target symmetric key. For XOR operation; FV is the feature value of each sub-data block.

[0052] In one embodiment, taking sub-data block 1 obtained in step 201 as an example, its byte distribution characteristics are analyzed, and the feature value is determined to be 58 (calculated by the frequency of byte occurrence, etc.). The target symmetric key is "m8n2p5q7r1s3t6u9v4", which is converted into the numerical sequence corresponding to ASCII code (assuming m corresponds to 109, 8 corresponds to 56, n corresponds to 110, 2 corresponds to 50, p corresponds to 112, 5 corresponds to 53, q corresponds to 113, 7 corresponds to 55, r corresponds to 114, 1 corresponds to 49, s corresponds to 115, 3 corresponds to 51, t corresponds to 116, 6 corresponds to 54, u corresponds to 117, 9 corresponds to 57, v corresponds to 118, and 4 corresponds to 52). The encryption offset of sub-data block 1 is calculated to be 127 using the encryption function. Similarly, the feature value of sub-data block 2 is calculated to be 63, and the encryption offset is 98; the feature value of sub-data block 3 is 49, and the encryption offset is 112; the feature value of sub-data block 4 is 55, and the encryption offset is 86.

[0053] Step 203: Encrypt each sub-data block with its corresponding encryption offset to obtain an intermediate encryption block. Then, for each sub-data block's intermediate encryption block, perform association encryption based on the intermediate encryption blocks of adjacent sub-data blocks to obtain an associated encryption block.

[0054] Optionally, the evidence storage and intelligent assessment system uses the encryption offset corresponding to each sub-data block and employs a preset encryption algorithm (such as shift encryption combined with XOR encryption) to encrypt the corresponding sub-data block, obtaining an intermediate encryption block. Then, for each sub-data block's intermediate encryption block, referencing the intermediate encryption blocks of its adjacent sub-data blocks (the previous one; if the current sub-data block is the first sub-data block, a random value is taken as the default encryption block), it is processed using an association encryption algorithm (such as mixing and encrypting a portion of the bytes from the adjacent intermediate encryption block with the current intermediate encryption block) to obtain an associated encryption block. This establishes a connection between the encryption blocks, enhancing the encryption complexity.

[0055] Continuing with the above embodiment, sub-data block 1 is encrypted using an encryption offset of 127. Each byte is then added to 127 and modulo 256 to obtain intermediate encryption block 1. Sub-data block 2 is encrypted using an encryption offset of 98 to obtain intermediate encryption block 2, sub-data block 3 is encrypted using 112 to obtain intermediate encryption block 3, and sub-data block 4 is encrypted using 86 to obtain intermediate encryption block 4. For associated encryption, taking intermediate encryption block 2 as an example, its first 16 bytes are XORed with the last 16 bytes of intermediate encryption block 1, and then combined with the last 16 bytes of intermediate encryption block 2 to obtain associated encryption block 2. Similarly, associated encryption block 1 is processed using a standard encryption block, associated encryption block 3 is associated with associated encryption block 2, and associated encryption block 4 is associated with a portion of the data in associated encryption block 3, ultimately yielding associated encryption blocks 1, 2, 3, and 4.

[0056] Step 204: Integrate the associated encrypted blocks based on the original order of the sub-data blocks to obtain a continuous encrypted data stream, and determine the verification value based on the encrypted data stream.

[0057] Optionally, the evidence storage and intelligent assessment system concatenates the corresponding associated encrypted blocks sequentially according to the original order of the sub-data blocks (i.e., sub-data block 1, sub-data block 2, sub-data block 3, and sub-data block 4) to form a continuous encrypted data stream. Then, a specific verification algorithm (such as a partial verification mechanism of CRC32 or MD5) is used to calculate the verification value of the encrypted data stream. This verification value is used to verify whether the data has been damaged or tampered with during transmission or storage.

[0058] Continuing with the above embodiment, associated encryption block 1, associated encryption block 2, associated encryption block 3, and associated encryption block 4 are concatenated sequentially to form a continuous encrypted data stream. The CRC32 algorithm is used to calculate the checksum of this encrypted data stream, yielding a value of 0x8F3A2B1C.

[0059] Step 205: Combine the encrypted data stream with the hash value of the target symmetric key and the feature value in the unique data identifier to obtain a verification segment, and then concatenate the verification segment to the encrypted data stream to obtain the second encrypted data.

[0060] Optionally, the evidence storage and intelligent assessment system first calculates the hash value of the target symmetric key (using the SHA-256 algorithm), and then extracts a feature value from the unique data identifier (consistent with the feature value extracted in step 201). Next, the encrypted data stream (containing the checksum), the hash value of the target symmetric key, and the feature value of the unique data identifier are combined according to a preset format to form a checksum segment. Finally, the checksum segment is appended to the end of the encrypted data stream to obtain the second encrypted data. This checksum segment is used to verify the integrity and correctness of the data during decryption.

[0061] In one embodiment, the target symmetric key “m8n2p5q7r1s3t6u9v4” is hashed using the SHA-256 algorithm to a value of “3a7f9d2b4e6c8g1h5j0k3l7m9n2p4q6r8s0t5u1v3w5x7y9z2”. The unique data identifier has a feature value of “a7b3”. The encrypted data stream, the hash value “3a7f9d2b4e6c8g1h5j0k3l7m9n2p4q6r8s0t5u1v3w5x7y9z2”, and the feature value “a7b3” are combined to form a check segment (format: hash value + feature value + encrypted data stream identifier). This check segment is then appended to the encrypted data stream to obtain the second encrypted data.

[0062] This invention, through the construction of a target symmetric key related to data characteristics, segments the first encrypted data and determines the encryption offset by combining feature values ​​and the key. Then, it performs associated encryption, integration, and verification segment processing to form the second encrypted data. This enhances the complexity and security of data encryption, making the encrypted data difficult to crack. Simultaneously, the verification segment ensures the integrity and verifiability of the data during transmission and storage, further protecting the privacy and security of logistics operation performance data and providing a reliable encrypted data foundation for subsequent data notarization and intelligent assessment.

[0063] In one embodiment, steps 401-405 are described as follows:

[0064] Step 401: Associate and map the second encrypted data and the unique data identifier to generate an associated data packet containing the correspondence between the two.

[0065] Optionally, the evidence storage and intelligent assessment system associates and maps the second encrypted data with the unique data identifier. A one-to-one correspondence is established between the two through preset mapping rules, generating an associated data packet containing this correspondence. This associated data packet clearly shows the unique data identifier corresponding to each piece of second encrypted data, ensuring that the corresponding second encrypted data can be accurately located through the unique data identifier.

[0066] In one embodiment, taking transportation timeliness data as an example, the second encrypted data is data that has undergone secondary encryption processing, with a unique data identifier of 20250805080000-YSX001-a7b3c9d1. The system associates these two data through mapping rules, and the generated associated data packet contains the following correspondence information: "Second encrypted data: [specific encrypted content], corresponding unique data identifier: 20250805080000-YSX001-a7b3c9d1".

[0067] Step 402: Based on the associated data packet, the parameters of the privacy-preserving smart contract are adapted to obtain the adapted target smart contract, and the blockchain nodes are divided into regions based on the target smart contract to obtain the target storage sub-region.

[0068] Optionally, the evidence storage and intelligent assessment system extracts key information (such as data type and identification features) from the associated data packet generated in step 401, adjusts and adapts the parameters of the privacy-protected smart contract, so that the smart contract can match the associated data packet, resulting in an adapted target smart contract. Then, based on the requirements of the target smart contract and factors such as the distribution of storage resources and data processing capabilities of the blockchain nodes, the system divides the blockchain nodes into regions, creating a target storage sub-region specifically for storing the associated data packet and the target smart contract.

[0069] In one embodiment, information such as the type characteristics of transportation timeliness data in the associated data packets is extracted. Based on this information, the system adapts and adjusts the access permission parameters and data processing rule parameters of the privacy-preserving smart contract to obtain the target smart contract. For the determined node 2, the system divides part of the storage area of ​​node 2 into a target storage sub-region A, which is specifically used to store the associated data packets related to transportation timeliness data and the target smart contract, according to the storage requirements of the target smart contract and the storage partitioning of node 2.

[0070] Step 403: Deploy the target smart contract to the target storage sub-area of ​​the blockchain node, and write the associated data packet after verification into the target storage sub-area of ​​the deployed target smart contract to obtain the sub-area deployment result.

[0071] Optionally, the evidence storage and intelligent assessment system deploys the target smart contract obtained in step 402 to the target storage sub-area of ​​the blockchain node. After deployment, the associated data packet is verified, including data integrity and the accuracy of its correspondence with the unique data identifier. After verification and confirmation, the associated data packet is written into the target storage sub-area where the target smart contract has been deployed, ultimately obtaining the sub-area deployment result. This result reflects the deployment and storage status of the target smart contract and associated data packet in the target storage sub-area.

[0072] Continuing with the above embodiment, the evidence storage and intelligent assessment system deploys the adapted target smart contract to the target storage sub-region A of node 2. Then, it verifies the associated data packet of the transportation timeliness data, confirming that the second encrypted data is complete and correctly corresponds to the unique data identifier. After successful verification, the associated data packet is written to the target storage sub-region A, and the sub-region deployment result shows that the target smart contract and associated data packet have been successfully stored in the target storage sub-region A.

[0073] Step 404: Establish a chain link between the associated data packets and the target smart contract stored in the sub-region deployment results to form a preliminary blockchain fragment.

[0074] Optionally, the evidence storage and intelligent assessment system, based on the sub-region deployment results obtained in step 403, utilizes the chain structure characteristics of blockchain to establish a chain association between the associated data packets and the target smart contract stored in the target storage sub-region. That is, through specific hash pointers or other methods, the associated data packets and the target smart contract are connected in a certain order to form preliminary blockchain fragments, making them part of the blockchain structure and possessing the continuity and correlation characteristics of blockchain data.

[0075] Continuing with the above embodiment, in the target storage sub-region A of node 2, the system establishes a chain association between the stored transportation timeliness data associated data packet and the target smart contract. Using hash pointers, the hash value of the target smart contract is used as the forward pointer of the associated data packet, and the hash value of the associated data packet is used as the backward pointer of the target smart contract, thereby forming a preliminary blockchain fragment containing both.

[0076] Step 405: Integrate the preliminary blockchain fragment with the historical blockchain data of the blockchain node to obtain the target blockchain chain.

[0077] Optionally, the evidence storage and intelligent assessment system integrates the preliminary blockchain fragment formed in step 404 with the existing historical blockchain data in the blockchain nodes. During the integration process, by verifying the consistency and correlation between the preliminary blockchain fragment and the historical blockchain data, the preliminary blockchain fragment is embedded into the historical blockchain data in chronological or logical order, ultimately forming a complete and continuous target blockchain chain. This chain contains both newly added data and historical data to maintain the integrity and immutability of the blockchain.

[0078] Continuing with the above embodiment, node 2 already contains historical blockchain data regarding previous transportation tasks. The system integrates a preliminary blockchain fragment containing transportation timeliness data with this historical blockchain data. After verifying the legality and relevance of the preliminary blockchain fragment, it adds it to the end of the historical blockchain data in chronological order, forming the target blockchain chain containing the transportation timeliness data.

[0079] This invention, through associating and mapping the second encrypted data and the unique data identifier, adapting smart contracts, dividing storage areas, deploying storage, and establishing a chain-like association, ultimately integrates with historical data to form a target blockchain chain. This ensures that new data can be securely and accurately integrated into the blockchain system, guaranteeing the relevance, integrity, and immutability of data during storage. Simultaneously, through smart contract adaptation and storage area division, the standardization and access efficiency of data management are improved, making the storage of logistics operation performance data more reliable. This provides a solid data foundation for subsequent intelligent assessments, further enhancing the scientific and efficient nature of logistics operation management.

[0080] In one embodiment, steps 501-505 are described as follows:

[0081] Step 501: Based on the access permission rules of the privacy protection smart contract in the target blockchain chain, call the preset decryption trigger function on the chain, extract the second encrypted data and the corresponding unique data identifier that match the assessment period in the blockchain node, and obtain the target encrypted data.

[0082] Optionally, the evidence storage and intelligent assessment system first reads the access permission rules in the privacy-protected smart contract of the target blockchain to determine whether it has permission to access the relevant data. After confirming permission, the system calls the preset decryption trigger function on the chain. This function will filter out the second encrypted data that matches the set assessment period (e.g., monthly, quarterly) from the blockchain nodes, and extract the unique data identifiers corresponding to these data. The two are then integrated to obtain the target encrypted data, preparing for subsequent decryption and assessment.

[0083] In one embodiment, with the assessment period set for August 2025, the privacy-protected smart contract in the target blockchain stipulates that the notarization and smart assessment system has the authority to access transportation timeliness-related data. The system triggers a decryption function to filter out the second encrypted data of transportation timeliness for August from node 2, along with its corresponding unique data identifier 20250805080000-YSX001-a7b3c9d1, etc., and integrates them to form the target encrypted data.

[0084] Step 502: Decrypt the target encrypted data based on the symmetric encryption key and the asymmetric encryption key to obtain the original performance data, and verify the original performance data based on the hash value of the target blockchain chain to obtain the verification result.

[0085] Optionally, the evidence storage and intelligent assessment system uses the symmetric encryption key generated in step 20 (such as the AES key "k29f4j7d1") to perform a first-level decryption on the second encrypted data in the target encrypted data, obtaining the first encrypted data. Then, it uses the asymmetric encryption private key to perform a second-level decryption on the first encrypted data, obtaining the original performance data. Afterwards, it calculates the hash value of the target blockchain chain and compares it with the hash value stored corresponding to the original performance data. If they match, the verification result is that the data has not been tampered with; otherwise, it has been tampered with, thus obtaining the verification result.

[0086] Continuing with the above embodiment, the system uses the AES key "k29f4j7d1" to decrypt the second encrypted data in the target encrypted data to obtain the first encrypted data, and then uses the RSA private key to decrypt it to obtain the original transportation timeliness data (e.g., the actual transportation time for a batch of goods is 2.5 hours). By calculating the hash value of the target blockchain chain as "h3k5m7p9", and comparing it with the hash value corresponding to the original performance data storage, the verification result is that the data has not been tampered with.

[0087] Step 503: If the verification result shows that the data has not been tampered with, the original performance data is mapped to the corresponding assessment dimensions according to the performance subcategories based on the logistics business process nodes to obtain the dimension data mapping table.

[0088] Optionally, when the evidence storage and intelligent assessment system receives the verification result from step 502 indicating that the data has not been tampered with, it maps the original performance data to the corresponding assessment dimensions (such as transportation timeliness data, transportation cost data, etc.) according to the previously divided performance subcategories (such as transportation timeliness data, transportation cost data, etc.) based on the logistics business process nodes (such as transportation, warehousing, distribution, etc.), and organizes them into a dimension data mapping table to clearly present the correspondence between each data and the assessment dimensions.

[0089] Continuing with the above embodiment, the original performance data is transportation timeliness data (actual transportation time 2.5 hours, prescribed transportation time 2.3 hours, etc.). The system maps these data to transportation timeliness assessment dimensions according to the transportation nodes of the logistics business process, forming a dimension data mapping table. The table clearly states "Transportation timeliness dimension: actual transportation time 2.5 hours, prescribed transportation time 2.3 hours, etc."

[0090] Step 504: Extract and analyze the time series and spatial features of each assessment dimension data in the dimension data mapping table to obtain abnormal data points.

[0091] Optionally, the evidence storage and intelligent assessment system analyzes the data of each assessment dimension in the dimensional data mapping table. It extracts time series features (such as the trend of data change over time, periodicity, etc.) and spatial features (such as data differences in different transportation routes and storage locations, etc.) to identify abnormal data points, as described in steps 5041-5044.

[0092] Step 505: Based on the abnormal data points, dimensional data mapping table, and intelligent assessment strategy, a comprehensive analysis is conducted to determine the assessment results.

[0093] Optionally, the evidence storage and intelligent assessment system combines the abnormal data points obtained in step 504, the dimensional data mapping table in step 503, and the intelligent assessment strategy pre-embedded in the target blockchain chain for comprehensive analysis to determine the assessment results, as described in steps 5051-5054.

[0094] This invention ensures the legality of performance evaluation, the authenticity and accuracy of data by compliantly retrieving data, securely decrypting, rigorously verifying, accurately mapping dimensions, identifying anomalies, and comprehensively assessing the data. It fully utilizes the immutability of blockchain and the rule constraints of smart contracts, reduces human intervention, and makes the evaluation results more objective and fair, truly reflecting the performance of logistics operations.

[0095] In one embodiment, steps 5041-5044 are described as follows:

[0096] Step 5041: Based on the original performance data of each assessment dimension in the dimensional data mapping table, extract time series features and spatial features respectively, and perform spatiotemporal collaborative analysis on the feature extraction results to obtain collaborative feature values.

[0097] Optionally, the evidence storage and intelligent assessment system extracts time-series and spatial features from the raw performance data of each assessment dimension in the dimensional data mapping table. Time-series features include the rate of change of data over time and the magnitude of periodic fluctuations; spatial features cover the differences in data distribution and the degree of dispersion at different spatial nodes (such as transportation routes and storage locations). Subsequently, a spatiotemporal collaborative analysis is performed on the time-series and spatial features of the same dimension. The mutual influence relationship between the two is quantified through a preset feature correlation function to obtain a collaborative feature value, which comprehensively reflects the overall characteristics of the data in the spatiotemporal dimensions.

[0098] Furthermore, the feature association function is as follows:

[0099]

[0100] Where: CE is the collaborative eigenvalue (values ​​0-1); TS i The standardized value of the i-th time series feature (such as the daily rate of change in transportation duration, periodic fluctuation amplitude, etc., which have been normalized to 0-1); SS j The j-th spatial feature is a standardized value (e.g., route variation, coefficient of variation, etc., normalized to 0-1); θ i,j Let be the angle between the i-th temporal feature and the j-th spatial feature (calculated using vector similarity to reflect feature correlation); α is the spatiotemporal feature distance decay coefficient (α>0, reflecting the impact of feature index differences on correlation); m is the number of time series features; n is the number of spatial features; β is the spatiotemporal balance coefficient (β=0.5, balancing the weights of time and spatial features). This formula introduces cosine similarity to measure the intrinsic correlation between temporal and spatial features; through e... -α×|i-j| The impact of differences in simulated feature indices is simulated. This allows for the simultaneous inclusion of multiple temporal and spatial features, avoiding the limitations of a single feature, comprehensively reflecting spatiotemporal relationships, and better aligning with actual business scenarios.

[0101] In one embodiment, taking transportation timeliness as an example, the original performance data includes the transportation duration of different routes each day in August. The system extracts time series features, such as the average daily transportation duration variation rate of 2% (TS1 = 0.2) and the periodic fluctuation amplitude of 0.3 hours (TS2 = 0.3); spatial features, such as the difference in transportation duration between route A and route B of 15% (SS1 = 0.15), the coefficient of variation of each route's data of 0.12 (SS2 = 0.15), α = 0.2, and β = 0.5. The calculated cosθ 1,1≈0.866, cosθ 1,2 =0.5, cosθ 2,1 ≈0.707, cosθ 2,2 ≈0.866, therefore, through spatiotemporal synergistic analysis, the synergistic feature value of this dimension is calculated to be approximately 0.85 (the value ranges from 0 to 1, and the higher the value, the stronger the spatiotemporal feature correlation).

[0102] Step 5042: Determine the feature trend coupling degree between adjacent time windows and adjacent spatial nodes based on collaborative feature values, and perform preliminary screening of outliers based on feature trend coupling degree to obtain candidate outliers; feature trend coupling degree is used to quantify the correlation of feature changes.

[0103] Optionally, the evidence storage and intelligent assessment system calculates the feature trend coupling degree between adjacent time windows (e.g., two consecutive days, two consecutive weeks) and adjacent spatial nodes (e.g., adjacent transportation routes, storage points in the same area) based on the collaborative feature values ​​obtained in step 5041. The feature trend coupling degree quantifies the correlation by analyzing the consistency of the direction and magnitude of spatiotemporal feature changes, with a value ranging from 0 to 1; the closer the value is to 1, the stronger the correlation. The system sets a coupling degree threshold (e.g., 0.6), and considers datasets corresponding to features below the threshold as candidate anomalies, because the spatiotemporal trends of these data have weak correlation with surrounding data and may contain anomalies.

[0104] Furthermore, the formula for calculating the characteristic trend coupling degree is as follows:

[0105]

[0106] Where CTC is the characteristic trend coupling degree; ΔT t , These represent the changes in the t-th time feature within adjacent time windows; ΔS s , These represent the changes in the s-th spatial feature of adjacent spatial nodes; W t,s The weights of the spatiotemporal features are dynamically adjusted based on feature importance; T is the number of temporal features; and S is the number of spatial features.

[0107] Continuing with the above embodiment, for the transportation timeliness dimension, the characteristic trend coupling degree of adjacent time windows (August 5th and August 6th) and adjacent spatial nodes (Route A and Route C) is calculated. Since the coupling degree of normal data is mostly above 0.7, while the coupling degree of transportation time data for Route A on August 5th with data on August 6th is 0.4, and with data for Route C is 0.3, both below the threshold of 0.6, this data point is listed as a candidate outlier.

[0108] Step 5043: Analyze and evaluate the diffusion index of candidate anomalies in time series and spatial distribution to obtain the anomaly diffusion index.

[0109] Optionally, the evidence storage and intelligent assessment system analyzes the diffusion of candidate anomalies obtained in step 5042 in terms of time series (e.g., whether similar anomalies continue to appear in subsequent time windows) and spatial distribution (e.g., whether they spread to other spatial nodes), and calculates an anomaly diffusion index. This index is obtained by weighting the diffusion range, diffusion speed, and influence intensity, and takes a value of 0-1. The higher the value, the stronger the anomaly diffusion, and the more likely it is a systematic anomaly rather than an isolated error.

[0110] Furthermore, the formula for calculating the abnormal diffusion index is as follows:

[0111]

[0112] Where: ADI is the anomalous diffusion index (value 0-1); N t Let N be the number of outliers within the t-th time window. t-1 M represents the number of outliers in the (t-1)th time window (N0 = 1 at t = 1); s M represents the number of anomalous nodes at the s-th spatial diffusion level. s-1 λ represents the number of anomalous nodes at the (s-1)th level (M0 = 1 when s = 1); λ and μ are the temporal and spatial attenuation coefficients, respectively (λ, μ > 0, reflecting the attenuation effect as the diffusion distance increases); D is the deviation between anomalous data and normal data (e.g., the deviation between a transportation time of 4 hours and the average of 2.2 hours is (4-2.2) / 2.2≈0.818); W T W S W I The weights for temporal diffusion, spatial diffusion, and influence intensity are dynamically adjusted, with a total sum of 1; γ is the deviation amplification coefficient (γ>0, enhancing the impact of deviation on the index); T is the total number of windows for temporal diffusion; and S is the total number of levels for spatial diffusion. By incorporating temporal diffusion speed, spatial spread range, and data deviation intensity, the one-sidedness of single-dimensional judgment is avoided, and temporal and spatial attenuation coefficients are introduced to simulate the attenuation characteristics of abnormal diffusion in reality, ensuring that the final abnormal diffusion index aligns with business understanding.

[0113] Continuing with the above example, the candidate anomaly is the 4-hour transport time of Route A on August 5th. System analysis revealed that no similar anomaly occurred on Route A in the following 3 days, and it did not spread to other routes. The spread was limited to one time window and one spatial node, with a spread rate of 0 and a low impact intensity, thus it was identified as W. T =0.3,W S =0.3,W I=0.4; the calculated abnormal diffusion index is 0.15.

[0114] Step 5044: Based on the abnormal diffusion index and the generation time and node information of the original performance data stored in the target blockchain chain, trace the root cause of the abnormality to obtain abnormal data points.

[0115] Optionally, the evidence storage and intelligent assessment system combines the anomaly diffusion index from step 5043 with the generation time and node information (such as transport vehicle ID, warehousing operation records, etc.) of the original performance data stored in the target blockchain chain to trace the root cause of anomalies. If the anomaly diffusion index is low (e.g., <0.3), and the blockchain record shows that a special event occurred when the data was generated (e.g., weather warning, equipment failure record), it is determined to be a genuine anomaly data point; if the diffusion index is high and there is no reasonable record, it may be a data entry error and needs to be excluded. The finally determined genuine anomaly data point is the result required in step 504.

[0116] Continuing with the above embodiment, for the candidate anomaly point (Route A, transportation time of 4 hours on August 5th), the blockchain record shows that there was a rainstorm weather warning for the route on that day, and the anomaly diffusion index of 0.15 < 0.3, which is determined to be a real anomaly. Therefore, this data point is identified as an anomaly data point.

[0117] This invention constructs a multi-dimensional anomaly identification mechanism through spatiotemporal collaborative analysis, feature coupling degree screening, diffusion index evaluation, and root cause tracing. This mechanism can accurately distinguish between real abnormal data and data errors. Furthermore, by utilizing the spatiotemporal correlation of data and the traceability capabilities of blockchain, the false judgment rate is reduced, making the identification of abnormal data points more objective and reliable.

[0118] In one embodiment, steps 5051-5054 are described as follows:

[0119] Step 5051: Based on the business operation logs and node interaction records stored in the target blockchain chain, perform source tracing analysis on abnormal data points and generate an anomaly attribution report.

[0120] Optionally, the evidence storage and intelligent assessment system retrieves business operation logs (such as transportation vehicle dispatch records, cargo loading and unloading time records, etc.) and node interaction records (such as data transmission records and operation instruction records between participants) stored in the target blockchain chain. By sorting and analyzing these records, the system tracks the time of occurrence of abnormal data points, related business operations, and involved nodes, determines the cause of abnormal data points (such as sudden weather, equipment failure, human error, etc.), and generates an anomaly attribution report containing the cause of the anomaly, the scope of impact, and related operations.

[0121] In one embodiment, for an anomaly data point within the 4-hour timeliness dimension, the system retrieves business operation logs stored in the blockchain. It finds that the corresponding transportation task was executed on August 5, 2025, a day with a rainstorm warning, and the vehicle's GPS track shows a prolonged stop along that route. Node interaction records indicate that the transporter had previously sent feedback to the system regarding abnormal road conditions. Based on this information, the system generates an anomaly attribution report, clarifying that the anomaly data point was caused by a sudden rainstorm delay.

[0122] Step 5052: For each assessment dimension, based on the benchmark value and dynamic adjustment coefficient of the intelligent assessment strategy, analyze and sort the matching degree between the data of each assessment dimension and the benchmark value in the dimension data mapping table to obtain the performance achievement degree of each assessment dimension.

[0123] Optionally, the evidence storage and intelligent assessment system extracts the baseline value (e.g., the baseline value for transportation timeliness is the specified transportation time) and a dynamic adjustment coefficient (this coefficient changes dynamically based on factors such as season, weather, and traffic conditions; for example, the dynamic adjustment coefficient for transportation timeliness during heavy rain is 1.2) from the intelligent assessment strategy for each assessment dimension. Then, the actual data for each assessment dimension in the dimension data mapping table is compared with the baseline value, and the degree of matching between the two is calculated in conjunction with the dynamic adjustment coefficient to obtain the performance compliance rate for each assessment dimension. The higher the performance compliance rate, the more the actual performance of that dimension meets expectations.

[0124] In one embodiment, the baseline value for the transportation timeliness dimension is a specified transportation time of 2.3 hours. Due to heavy rain on August 5th, the dynamic adjustment coefficient is 1.2. The actual transportation time for that day in the dimension data mapping table is 4 hours. Based on the performance compliance rate = min(baseline value × dynamic adjustment coefficient / actual value × 100%, 100%), with values ​​ranging from 0-100%, a higher value indicates better compliance. Values ​​exceeding 100% are counted as 100% (to avoid excessive compliance leading to unreasonable scores), the calculation shows (2.3 × 1.2) / 4 × 100% ≈ 69%. Therefore, the performance compliance rate for that day is 69% (not meeting the standard). Combining the performance compliance rates for other days in the month, the overall monthly performance compliance rate for the transportation timeliness dimension is 88%.

[0125] Step 5053: Based on the mutual influence relationship between different assessment dimensions, perform collaborative analysis on the data mapped to the corresponding assessment dimensions in the dimension data mapping table to obtain the synergistic effect value of multi-dimensional data.

[0126] Optionally, the evidence storage and intelligent assessment system analyzes the interrelationships between different assessment dimensions (e.g., there is a correlation between transportation timeliness and transportation cost; improved transportation timeliness may lead to increased transportation costs, and vice versa). Based on these relationships, the data of each assessment dimension in the dimensional data mapping table are analyzed collaboratively. By calculating the correlation and interaction degree of changes in data of different dimensions, the synergistic effect value of multi-dimensional data is obtained. This value reflects the impact of the combined effect of the data of each assessment dimension on the overall operational performance.

[0127] Furthermore, the formula for calculating the synergistic effect value of multi-dimensional data is as follows:

[0128]

[0129] Wherein, CEV is the synergistic effect value of multi-dimensional data (ranging from 0 to 1, with higher values ​​indicating better synergistic effects); P i P j These represent the performance achievement rates (standardized to 0-1) for the i-th and j-th assessment dimensions, respectively; U i,j ε is the correlation coefficient between the i-th assessment dimension and the j-th assessment dimension (take the absolute value to eliminate the interference of negative correlation on the synergistic effect); ε is the dimension distance decay coefficient (ε>0, reflecting the impact of dimension number difference on the synergistic effect); R is the total number of assessment dimensions; ∈ is the synergistic effect correction coefficient (dynamically adjusted based on historical synergistic data, with a value of 0.5 to 1.5).

[0130] In one embodiment, the transportation timeliness and transportation cost dimensions are mutually influential. System analysis revealed that the overall data for transportation timeliness in that month showed a slight downward trend (compliance rate 88%, i.e., P1 = 0.88), and the data for transportation cost also showed a downward trend (compliance rate 92%, i.e., P2 = 0.92). Analysis showed a negative correlation between transportation timeliness and cost; improved timeliness may increase costs. Based on historical data, U was determined... 1,2 = -0.6 (negative values ​​indicate reverse correlation, and an absolute value of 0.6 indicates moderate strength), and transportation timeliness (dimension 1) and transportation cost (dimension 2) are adjacent correlation dimensions. The dimension distance decay coefficient is taken as ε = 0.3. If only two dimensions are involved, then R = 2. The synergy effect correction coefficient is taken as ∈ = 1.1 (determined based on historical synergy data for this quarter, with the overall synergy effect slightly better than the average level). Through synergy analysis, the synergy effect value of the two is calculated to be approximately 0.62 (range 0-1, with higher values ​​indicating better synergy effect), indicating that transportation timeliness and transportation cost show a good synergy optimization effect in this month.

[0131] Step 5054: Integrate the anomaly attribution report, performance achievement rate, and synergy effect value to obtain the assessment results.

[0132] Optionally, the evidence storage and intelligent assessment system integrates the anomaly attribution report generated in step 5051, the performance compliance of each assessment dimension obtained in step 5052, and the multi-dimensional data synergy value obtained in step 5053 to finally determine the assessment result, as described in steps 50541-50545.

[0133] This invention, through source tracing analysis of abnormal data points, calculation of performance compliance for each assessment dimension, collaborative analysis of multi-dimensional data, and integrated information evaluation, not only considers the individual performance of each assessment dimension but also focuses on the synergistic effects between dimensions and the impact of anomalies, making the assessment results more scientific and reasonable. Simultaneously, relying on real data stored on the blockchain and the rule constraints of smart contracts reduces interference from human factors, ensuring the fairness and reliability of the assessment results.

[0134] In one embodiment, steps 50541-50545 are described as follows:

[0135] Step 50541: Based on the correlation pattern between performance achievement and synergy value, and the impact path of the abnormal attribution report on both, generate a correlation matrix; the rows of the correlation matrix represent the performance achievement of each assessment dimension, and the columns represent the cross-dimensional synergy combination and abnormal attribution type.

[0136] Optionally, the evidence storage and intelligent assessment system first analyzes the correlation pattern between performance achievement and synergy value (such as the positive correlation between transportation timeliness achievement and cost-timeliness synergy value), and then combines the impact path of abnormal causes on both in the anomaly attribution report (such as heavy rain causing a decrease in transportation timeliness achievement, indirectly reducing cost-timeliness synergy value) to construct a correlation matrix. The rows of the matrix correspond to the performance achievement of each assessment dimension (such as transportation timeliness, transportation cost, etc.), and the columns include cross-dimensional synergy combination (such as transportation timeliness-cost, transportation timeliness-warehouse turnover, etc.) and anomaly attribution type (such as abnormal weather, equipment failure, etc.). The matrix elements are correlation strength values ​​(ranging from -1 to 1, with larger absolute values ​​indicating stronger correlations).

[0137] In one embodiment, two dimensions are involved: transportation timeliness (row 1) and transportation cost (row 2). The synergistic effect combination is "timeliness-cost" (column 1), and the anomaly attribution type is "heavy rain weather" (column 2). Analysis shows that the correlation strength between transportation timeliness achievement rate (88%) and the "timeliness-cost" synergistic effect value (0.85) is 0.7, while the correlation strength with "heavy rain weather" is -0.6. Similarly, the correlation strength between transportation cost achievement rate (92%) and the "timeliness-cost" synergistic effect value is 0.8, while the correlation strength with "heavy rain weather" is -0.3. The resulting correlation matrix is ​​as follows:

[0138] Performance targets met Timeliness-cost coordination Heavy rain Delivery time (88%) 0.7 -0.6 Transportation costs (92%) 0.8 -0.3 .

[0139] Step 50542: Based on the correlation matrix, dynamically calibrate the basic scores corresponding to the performance attainment of each assessment dimension to obtain the calibrated quantitative scores.

[0140] Optionally, the evidence storage and intelligent assessment system dynamically calibrates the base score (e.g., base score = achievement level × 100) corresponding to the performance achievement level of each assessment dimension based on the correlation matrix in step 50541. The calibration rule is as follows: if the performance achievement level is positively correlated with the synergy effect value (strength > 0), the base score is increased; if it is negatively correlated with the attribution of anomalies (strength < 0), the score is adjusted according to the degree of impact of the anomaly (e.g., appropriately reducing the score for force majeure anomalies), and finally, the calibrated quantitative score is obtained.

[0141] Continuing with the above example, the basic score for transportation timeliness is 88 points, which is positively correlated with the "timeliness-cost" synergy (0.7), so 3 points are added; it is negatively correlated with "heavy rain" (-0.6), but since heavy rain is an uncontrollable factor, only 2 points are deducted. The calibrated quantitative score is 88 + 3 - 2 = 89 points. The basic score for transportation cost is 92 points, which is positively correlated with the synergy (0.8), so 4 points are added; it is correlated with heavy rain (-0.3), so 1 point is deducted. The calibrated score is 92 + 4 - 1 = 95 points.

[0142] Step 50543: Based on the quantitative score and synergy effect value, analyze and rank the scope of impact and urgency of the improvement items extracted from the anomaly attribution report to obtain ranked improvement suggestions.

[0143] Optionally, the evidence storage and intelligent assessment system extracts improvement items (such as "optimizing route planning in rainstorm weather") from the anomaly attribution report, combines quantitative scores (reflecting the importance of dimensions) and synergy values ​​(reflecting the impact of improvements on cross dimensions), analyzes the scope of impact of improvement items (such as a large scope if it affects the two dimensions of transportation timeliness and cost) and urgency (such as a high urgency if there are frequent rainstorms recently), and ranks the improvement suggestions through multi-factor evaluation.

[0144] Continuing with the above examples, the anomaly attribution report extracts the following improvement items: ① Optimize route planning during heavy rain; ② Strengthen vehicle maintenance (no related anomalies). Quantitative scores show that transportation timeliness (89) and cost (95) are both key dimensions, and the synergy value of 0.85 indicates a strong linkage between the two dimensions. ① The impact covers both dimensions, and recent heavy rains are frequent, indicating a high degree of urgency; ② It only affects a single dimension, indicating a low degree of urgency. Ranking results: ① takes precedence over ②.

[0145] Step 50544: Integrate the quantitative scores, ranked improvement suggestions, and target association information in the association matrix according to the preset report structure framework to obtain structured report content; target association information refers to information in the association matrix whose absolute value of association strength is greater than a preset threshold.

[0146] Optionally, the evidence storage and intelligent assessment system can filter target association information in the association matrix whose absolute value of association strength is greater than a preset threshold (e.g., 0.5) (e.g., 0.7 for transportation timeliness and "timeliness-cost" synergy, and -0.6 for rainstorm). The system can then integrate the quantitative scores, ranked improvement suggestions, and target association information according to a preset report structure framework (e.g., "dimensional score-association analysis-improvement suggestions") to form a structured report, ensuring that the information is clear and the key points are highlighted.

[0147] Continuing with the above embodiment, the preset threshold is 0.5, and the target correlation information is transportation timeliness and synergy effect (0.7), transportation timeliness and rainstorm (-0.6), and transportation cost and synergy effect (0.8). The structured content includes: quantitative scores (transportation timeliness 89, cost 95); target correlation analysis (strong synergy between timeliness and cost, and rainstorm has a significant impact on timeliness); improvement suggestions (① optimize rainstorm routes, ② strengthen vehicle maintenance).

[0148] Step 50545: Based on the report content, call the report generation template pre-deployed on the intelligent assessment strategy, automatically fill in the data to generate a comprehensive assessment report, and determine the comprehensive assessment report as the assessment result.

[0149] Optionally, the evidence storage and intelligent assessment system, based on the structured report content from step 50544, calls the pre-deployed report generation template in the intelligent assessment strategy (containing fixed modules: assessment overview, dimension scores, correlation analysis, improvement suggestions, etc.), automatically fills data into the corresponding modules, and generates a comprehensive assessment report. This report covers key performance information, correlations, and improvement directions, and is ultimately determined as the assessment result, providing a direct basis for decision-making.

[0150] Continuing with the above implementation, after calling the template, the system automatically populates the data to generate a comprehensive assessment report: Assessment Overview (Overall rating: Good); Dimension Scores (Transportation Timeliness 89, Cost 95); Correlation Analysis (Significant synergistic effect between timeliness and cost, with heavy rain causing a decrease in timeliness); Improvement Suggestions (Prioritize optimizing routes during heavy rain). This report represents the final assessment result.

[0151] This invention, through constructing an association matrix, calibrating scores, providing ranking suggestions, integrating content, and generating reports, achieves dynamic calibration of performance scores based on the quantification of association relationships, ensuring that assessment results are more aligned with actual operations. Through structured integration and template-based generation, the assessment report is both professional and readable, presenting the performance of each assessment dimension while revealing internal connections and directions for improvement. Furthermore, relying on the reliability of blockchain data and the automation of intelligent strategies, the objectivity of the assessment results and its decision support capabilities are significantly enhanced.

[0152] Furthermore, the blockchain-based logistics operation performance data storage and intelligent assessment system provided by the present invention will be described below. The blockchain-based logistics operation performance data storage and intelligent assessment system described below can be referred to in correspondence with the blockchain-based logistics operation performance data storage and intelligent assessment method described above.

[0153] Optional, refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of the blockchain-based logistics operation performance data storage and intelligent assessment system provided by the present invention. The blockchain-based logistics operation performance data storage and intelligent assessment system includes:

[0154] The data classification and identification generation module 210 is used to classify the comprehensive performance data obtained in logistics operations, obtain multiple performance sub-categories of data, and construct a unique data identifier based on the timestamp, data type code, and random encrypted characters of each performance sub-category of data.

[0155] The multi-encryption module 220 is used to perform preliminary asymmetric encryption on the performance sub-category data to obtain the first encrypted data, and to perform secondary symmetric encryption on the first encrypted data to obtain the second encrypted data.

[0156] The smart contract construction and node determination module 230 is used to construct privacy-protected smart contracts for the second encrypted data based on logistics business processes and data sharing needs, and to determine blockchain nodes based on logistics parameters, the reputation ratings of each participant, and network stability.

[0157] The blockchain data storage module 240 is used to store the second encrypted data, the unique data identifier and the privacy-protected smart contract on the blockchain node to obtain the target blockchain chain.

[0158] The performance evaluation module 250 is used to evaluate the original data after decryption of the second encrypted data based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and obtain the evaluation results.

[0159] This invention achieves dual encryption of data by performing preliminary asymmetric encryption and secondary symmetric encryption on the categorized performance sub-data. Compared to a single basic encryption method, this enhances data encryption strength, effectively resists complex network attacks, and ensures the security of sensitive data such as customer personal information and trade secrets during storage and sharing. Furthermore, it utilizes privacy-protecting smart contracts built according to logistics business processes and data sharing needs to ensure data privacy while maintaining orderly data sharing among all participants. In addition, performance evaluation is conducted on the decrypted raw data based on a pre-embedded intelligent assessment strategy on the target blockchain, replacing traditional manual data collection, processing, and calculation methods. This significantly reduces manpower and material resources, avoids data distortion caused by human factors, and eliminates data processing delays, enabling assessment results to reflect logistics operations in real time. This provides strong support for timely optimization decisions by enterprises and improves the accuracy and timeliness of performance evaluation.

[0160] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, this embodiment of the invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps:

[0161] The comprehensive performance data obtained from logistics operations is classified to obtain multiple performance subcategories. A unique data identifier is constructed based on the timestamp, data type code, and random encrypted characters of each performance subcategory.

[0162] The performance subcategory data is initially encrypted using asymmetric encryption to obtain the first encrypted data, and then the first encrypted data is further encrypted using symmetric encryption to obtain the second encrypted data.

[0163] Based on the logistics business process and data sharing needs, a privacy-protecting smart contract for the second encrypted data is constructed, and the blockchain nodes are determined based on logistics parameters, the reputation ratings of each participant, and network stability.

[0164] The second encrypted data, the unique data identifier, and the privacy-protected smart contract are stored on the blockchain node to obtain the target blockchain chain;

[0165] The performance evaluation is conducted on the original data after the second encrypted data is decrypted based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and the evaluation results are obtained.

[0166] Please see Figure 4 , Figure 4An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it performs the following steps:

[0167] The comprehensive performance data obtained from logistics operations is classified to obtain multiple performance subcategories. A unique data identifier is constructed based on the timestamp, data type code, and random encrypted characters of each performance subcategory.

[0168] The performance subcategory data is initially encrypted using asymmetric encryption to obtain the first encrypted data, and then the first encrypted data is further encrypted using symmetric encryption to obtain the second encrypted data.

[0169] Based on the logistics business process and data sharing needs, a privacy-protecting smart contract for the second encrypted data is constructed, and the blockchain nodes are determined based on logistics parameters, the reputation ratings of each participant, and network stability.

[0170] The second encrypted data, the unique data identifier, and the privacy-protected smart contract are stored on the blockchain node to obtain the target blockchain chain;

[0171] The performance evaluation is conducted on the original data after the second encrypted data is decrypted based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and the evaluation results are obtained.

[0172] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the blockchain-based logistics operation performance data storage and intelligent assessment method provided by the above methods. This method includes:

[0173] The comprehensive performance data obtained from logistics operations is classified to obtain multiple performance subcategories. A unique data identifier is constructed based on the timestamp, data type code, and random encrypted characters of each performance subcategory.

[0174] The performance subcategory data is initially encrypted using asymmetric encryption to obtain the first encrypted data, and then the first encrypted data is further encrypted using symmetric encryption to obtain the second encrypted data.

[0175] Based on the logistics business process and data sharing needs, a privacy-protecting smart contract for the second encrypted data is constructed, and the blockchain nodes are determined based on logistics parameters, the reputation ratings of each participant, and network stability.

[0176] The second encrypted data, the unique data identifier, and the privacy-protected smart contract are stored on the blockchain node to obtain the target blockchain chain;

[0177] The performance evaluation is conducted on the original data after the second encrypted data is decrypted based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and the evaluation results are obtained.

[0178] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0179] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain-based method for storing and intelligently evaluating logistics operation performance data, characterized in that: include: The comprehensive performance data obtained from logistics operations is classified to obtain multiple performance subcategories. A unique data identifier is constructed based on the timestamp, data type code, and random encrypted characters of each performance subcategory. The performance sub-category data is initially asymmetric encrypted to obtain first encrypted data, and the first encrypted data is then subjected to secondary symmetric encryption to obtain second encrypted data. Based on logistics business processes and data sharing needs, a privacy-protecting smart contract for the second encrypted data is constructed, and blockchain nodes are determined based on logistics parameters, the reputation ratings of each participant, and network stability. The second encrypted data, the unique data identifier, and the privacy-protecting smart contract are stored on the blockchain node to obtain the target blockchain chain; The performance evaluation is conducted on the original data after the second encrypted data is decrypted based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and the evaluation result is obtained.

2. The blockchain-based logistics operation performance data storage and intelligent assessment method according to claim 1, characterized in that, The performance evaluation of the original data after decryption of the second encrypted data is performed based on the intelligent evaluation strategy pre-embedded in the target blockchain chain to obtain the evaluation results, including: Based on the access permission rules of the privacy-protecting smart contract in the target blockchain chain, the preset decryption trigger function on the chain is invoked to extract the second encrypted data and the corresponding unique data identifier that match the assessment period in the blockchain node, thereby obtaining the target encrypted data; The target encrypted data is decrypted using symmetric and asymmetric encryption keys to obtain the original performance data. The original performance data is then verified based on the hash value of the target blockchain chain to obtain the verification result. If the verification result shows that the data has not been tampered with, the original performance data is mapped to the corresponding assessment dimensions according to the performance subcategories based on the logistics business process nodes to obtain the dimension data mapping table. The time series and spatial features of the assessment dimension data in the dimensional data mapping table are extracted and analyzed to obtain abnormal data points; The assessment result is determined by comprehensively analyzing the abnormal data points, the dimensional data mapping table, and the intelligent assessment strategy.

3. The blockchain-based logistics operation performance data storage and intelligent assessment method according to claim 2, wherein the step of determining the assessment result based on the abnormal data points, the dimensional data mapping table, and the intelligent assessment strategy includes: Based on the business operation logs and node interaction records stored in the target blockchain chain, the abnormal data points are traced and analyzed to generate an anomaly attribution report. For each assessment dimension, the matching degree between the data of each assessment dimension and the benchmark value in the dimension data mapping table is analyzed and sorted based on the benchmark value and dynamic adjustment coefficient of the intelligent assessment strategy to obtain the performance achievement degree of each assessment dimension. Based on the mutual influence relationship between different assessment dimensions, the data mapped to each assessment dimension in the dimension data mapping table are analyzed collaboratively to obtain the synergistic effect value of multi-dimensional data. The assessment results are obtained by integrating the anomaly attribution report, the performance achievement rate, and the synergy effect value.

4. The blockchain-based logistics operation performance data storage and intelligent assessment method according to claim 2, wherein the step of extracting and analyzing the time series characteristics and spatial characteristics of each assessment dimension data in the dimensional data mapping table to obtain abnormal data points includes: Based on the original performance data of each assessment dimension in the dimensional data mapping table, time series features and spatial features are extracted respectively, and spatiotemporal collaborative analysis is performed on the feature extraction results to obtain collaborative feature values; Based on the collaborative feature values, the feature trend coupling degree between adjacent time windows and adjacent spatial nodes is determined, and based on the feature trend coupling degree, preliminary screening of outliers is performed to obtain candidate outliers. The feature trend coupling degree is used to quantify the correlation of feature changes; An anomaly diffusion index is obtained by analyzing and evaluating the diffusion index of the candidate anomalies in the time series and spatial distribution. Based on the anomaly diffusion index and the generation time and node information of the original performance data stored in the target blockchain chain, the root cause of the anomaly is traced to obtain the anomaly data point.

5. The blockchain-based logistics operation performance data storage and intelligent assessment method according to claim 3, wherein the assessment result is obtained by integrating the anomaly attribution report, the performance achievement rate, and the synergy effect value, includes: Based on the correlation pattern between the performance achievement and the synergy effect value, and the impact path of the anomaly attribution report on both, a correlation matrix is ​​generated. The rows of the correlation matrix represent the performance achievement of each assessment dimension, and the columns represent the combination of cross-dimensional synergistic effects and the types of abnormal attribution. Based on the aforementioned correlation matrix, the basic scores corresponding to the performance attainment of each assessment dimension are dynamically calibrated to obtain the calibrated quantitative scores. Based on the quantitative scores and the synergistic effect values, the impact scope and urgency of the improvement items extracted from the anomaly attribution report are analyzed and ranked to obtain ranked improvement suggestions; The quantitative scores, the ranked improvement suggestions, and the target association information in the association matrix are integrated according to a preset report structure framework to obtain structured report content; The target association information refers to information in the association matrix whose absolute value of association strength is greater than a preset threshold. Based on the report content, a report generation template pre-deployed on the intelligent assessment strategy is invoked, data is automatically filled in, a comprehensive assessment report is generated, and the comprehensive assessment report is determined as the assessment result.

6. The blockchain-based logistics operation performance data storage and intelligent assessment method according to claim 1, characterized in that, The step of storing the second encrypted data, the unique data identifier, and the privacy-preserving smart contract on the blockchain node to obtain the target blockchain chain includes: The second encrypted data and the unique data identifier are associated and mapped to generate an associated data packet containing the correspondence between the two; Based on the associated data packet, the parameters of the privacy-protecting smart contract are adapted to obtain the adapted target smart contract, and the blockchain node is divided into regions based on the target smart contract to obtain the target storage sub-region. The target smart contract is deployed to the target storage sub-area of ​​the blockchain node, and the associated data packet after verification is written into the target storage sub-area of ​​the deployed target smart contract to obtain the sub-area deployment result; A chain link is established between the associated data packets and the target smart contract stored in the deployment results of the sub-region, forming a preliminary blockchain fragment; The initial blockchain fragment is integrated with the historical blockchain data of the blockchain node to obtain the target blockchain chain.

7. The blockchain-based logistics operation performance data storage and intelligent assessment method according to claim 1, wherein performing secondary symmetric encryption on the first encrypted data to obtain the second encrypted data includes: Based on the length of the first encrypted data and the feature value in the unique data identifier, a target symmetric key is constructed, and the first encrypted data is divided into multiple sub-data blocks based on the binary conversion value of the first preset bit of the target symmetric key. Based on the byte distribution characteristics within the sub-data blocks, the feature value of each sub-data block is determined, and the feature value and the target symmetric key are integrated to obtain the encryption offset of each sub-data block; Each sub-data block is encrypted with its corresponding encryption offset to obtain an intermediate encryption block. For each sub-data block's intermediate encryption block, the intermediate encryption blocks of adjacent sub-data blocks are associated with each other to obtain an associated encryption block. The associated encrypted blocks are integrated based on the original order of the sub-data blocks to obtain a continuous encrypted data stream, and a check value is determined based on the encrypted data stream. The encrypted data stream is combined with the hash value of the target symmetric key and the feature value in the unique data identifier to obtain a verification segment, and the verification segment is concatenated to the encrypted data stream to obtain the second encrypted data.

8. A blockchain-based logistics operation performance data storage and intelligent assessment system, characterized in that: The method for storing and intelligently evaluating logistics operation performance data based on blockchain, as described in any one of claims 1 to 7, is applied; the blockchain-based logistics operation performance data storage and intelligent evaluation system comprises: The data classification and identification generation module is used to classify the comprehensive performance data obtained from logistics operations, obtain multiple performance sub-categories of data, and construct a unique data identifier based on the timestamp, data type code, and random encrypted characters of each performance sub-category of data. A multi-encryption module is used to perform preliminary asymmetric encryption on the performance sub-category data to obtain first encrypted data, and to perform secondary symmetric encryption on the first encrypted data to obtain second encrypted data; The smart contract construction and node determination module is used to construct a privacy-protected smart contract for the second encrypted data based on logistics business processes and data sharing needs, and to determine blockchain nodes based on logistics parameters, the reputation ratings of each participant, and network stability. A blockchain data storage module is used to store the second encrypted data, the unique data identifier, and the privacy-protecting smart contract on the blockchain node to obtain the target blockchain chain; The performance evaluation module is used to perform performance evaluation on the original data after decryption of the second encrypted data based on the intelligent evaluation strategy pre-embedded in the target blockchain chain, and obtain the evaluation result.

9. An electronic device, characterized in that, include: Memory, used to store computer software programs; A processor is configured to read and execute the computer software program, wherein when the processor executes the computer software program, it implements the blockchain-based logistics operation performance data storage and intelligent assessment method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the blockchain-based logistics operation performance data storage and intelligent assessment method as described in any one of claims 1 to 7.

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