Aquatic product cold chain logistics information traceability method and system based on blockchain

Abstract

The application relates to a blockchain-based aquatic product cold-chain logistics information traceability method and system, which comprises the following steps: collecting parameter information of aquatic products at each node of cold-chain transportation, combining the parameter information to generate a data digest, and calculating a hash value of the data digest; based on historical data of the node corresponding to the hash value, extracting the uploading success rate, average delay time and abnormal rate of the node, and calculating the credibility of the node; calculating the cold-chain risk index of the node; constructing a risk chain state matrix based on the cold-chain risk index and the credibility of each node, and calculating the integrity score of the cold-chain path; constructing a block of the node, and a blockchain composed of blocks of all nodes; based on the risk chain state matrix and the integrity score, generating a visual traceability report; the visual traceability report comprises a risk trend chart and trust signal prompt information based on the integrity score. The application can improve the safety and reliability of aquatic product cold-chain logistics information traceability.

Description

Technical Field

[0001] This invention relates to the field of information traceability technology, and in particular to a blockchain-based method, system, electronic device, and non-transitory computer-readable storage medium for information traceability in cold chain logistics of aquatic products. Background Technology

[0002] Currently, to ensure the quality and safety of aquatic products, existing technologies generally employ cold chain logistics systems for end-to-end preservation management, supplemented by information traceability systems to record and track product distribution. Common practices include using RFID tags, QR codes, and sensor devices to collect data on aquatic products' storage temperature, transportation time, and geographical location, and then uploading this information to a centralized database platform for regulatory agencies, businesses, and consumers to view, achieving visualized management "from source to table."

[0003] However, existing information traceability systems generally rely on centralized data management architectures, which suffer from problems such as data tampering, broken traceability chains, and insufficient credibility, making them ineffective in dealing with complex cold chain transportation environments involving multiple stakeholders. Furthermore, inconsistent system standards across different transportation nodes lead to frequent delays or missing information uploads, resulting in incomplete traceability information that fails to accurately reflect the true status of aquatic products at each stage of the cold chain, thus impacting regulatory efficiency and consumer trust. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a blockchain-based method, system, electronic device, and non-transitory computer-readable storage medium for tracing information in the cold chain logistics of aquatic products.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] This invention provides a blockchain-based method for tracing information in the cold chain logistics of aquatic products, the method comprising:

[0007] Parameter information of aquatic products is collected at each node of the cold chain transportation, the parameter information is combined to generate a data digest, and the hash value of the data digest is calculated.

[0008] Based on the historical data of the node corresponding to the hash value, the upload success rate, average latency, and anomaly rate of the node are extracted, and the credibility of the node is calculated.

[0009] Calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data summary;

[0010] A risk chain status matrix is ​​constructed based on the cold chain risk index and credibility of each node, and the integrity score of the cold chain path is calculated.

[0011] Based on the hash value, credibility, and risk index of the node, construct the block of the node, and a blockchain composed of the blocks of all nodes;

[0012] Based on the risk chain status matrix and the integrity score, a visual traceability report is generated to demonstrate the quality of the cold chain; the visual traceability report includes a risk trend chart and trust signal prompts based on the integrity score.

[0013] Optionally, calculating the trustworthiness of the node includes:

[0014] Obtain the normalized upper limit of the confidence level;

[0015] Obtain the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient corresponding to the upload success rate, the average latency time, and the anomaly rate, respectively;

[0016] Based on the upload success rate, the average latency, the anomaly rate, and each adjustment coefficient, a corresponding exponential function is constructed.

[0017] The credibility of the node is determined based on the normalization upper limit and the exponential function.

[0018] Optionally, the credibility is expressed as:

[0019]

[0020] in, It represents the credibility of the i-th node. It represents the success rate of data upload to the i-th node. It is the average latency of data upload to the i-th node. It is the anomaly rate of the i-th node. It is the upper limit of normalization. These are the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient, respectively.

[0021] Optionally, calculating the cold chain risk index of the node based on the temperature, humidity, and credibility in the data digest includes:

[0022] Obtain the first difference between the stated temperature and the standard storage temperature;

[0023] Obtain the second difference between the humidity and the standard storage humidity;

[0024] The confidence level is adjusted based on a positive constant to obtain the adjusted confidence level;

[0025] The first difference, the second difference, and the adjusted credibility are weighted and summed to obtain the cold chain risk index of the node.

[0026] Optionally, the cold chain risk index is expressed as:

[0027]

[0028] in, It is a cold chain risk index. It is the ambient temperature of the i-th node. It is the standard storage temperature. It is the ambient humidity of the i-th node. For standard storage humidity, This is the temperature fluctuation sensitivity coefficient. It is a positive constant. These are the weighting coefficients for temperature deviation, humidity deviation, and credibility influencing factors, respectively. It represents the credibility of the i-th node.

[0029] Optionally, the construction of the risk chain state matrix based on the cold chain risk index and credibility of each node includes:

[0030] The cold chain risk index and credibility of each node are sorted.

[0031] Based on the cold chain risk index and credibility of each node after sorting, determine the quality status mapping structure of each node;

[0032] The risk chain state matrix is ​​constructed based on the quality state mapping structure of each node.

[0033] Optionally, the calculation of the cold chain path integrity score includes:

[0034] The cold chain risk index of each node is adjusted to obtain the corresponding attenuation conversion value;

[0035] The attenuation reduction value and the confidence level of each node are weighted and summed to obtain the intermediate calculated value;

[0036] The integrity score is obtained by averaging the intermediate calculated values ​​with the total number of nodes.

[0037] Optionally, constructing the block of the node based on its hash value, trustworthiness, and risk index includes:

[0038] Obtain the hash header information of the previous node of the given node;

[0039] Obtain the private key signature information of the node;

[0040] The private key signature information, temperature, trustworthiness, and cold chain risk index of the node are concatenated with the hash header information of the previous node to obtain the block of the node.

[0041] Optionally, the step of generating a visual traceability report to demonstrate cold chain quality based on the risk chain status matrix and the integrity score includes:

[0042] The geographical location information and timestamps of each node in the risk chain state matrix are analyzed to construct a time-stamped transportation route map;

[0043] Extract the temperature and humidity data of each node in the risk chain state matrix, and plot a biaxial fluctuation curve of temperature and humidity over time.

[0044] Extract the cold chain risk index according to the node order and generate a cold chain risk index trend chart.

[0045] The route integrity score is compared with a preset threshold. If the integrity score is not less than the preset threshold, a green trust indicator is displayed in the visualization report. If the integrity score is less than the preset threshold, a red warning indicator is displayed and the location of the abnormal node is marked in the transportation route map.

[0046] The transportation route map, temperature and humidity fluctuation curve, cold chain risk trend chart, and trust indicator are integrated and output into a single interactive interface.

[0047] This invention also provides a blockchain-based information traceability system for cold chain logistics of aquatic products, the system comprising:

[0048] The first calculation module is used to collect parameter information of aquatic products at each node of cold chain transportation, combine the parameter information to generate a data digest, and calculate the hash value of the data digest.

[0049] The second calculation module is used to extract the upload success rate, average latency and anomaly rate of the node based on the historical data of the node corresponding to the hash value, and to calculate the credibility of the node.

[0050] The third calculation module is used to calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data digest;

[0051] The fourth calculation module is used to construct a risk chain status matrix based on the cold chain risk index and credibility of each node, and to calculate the integrity score of the cold chain path.

[0052] The blockchain module is used to construct the blocks of the nodes and the blockchain composed of the blocks of all nodes based on the hash value, credibility and risk index of the nodes.

[0053] The information traceability module is used to generate a visual traceability report to display the quality of the cold chain based on the risk chain status matrix and the integrity score; the visual traceability report includes a risk trend chart and trust signal prompts based on the integrity score.

[0054] In addition, to achieve the above objectives, the present invention also proposes 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 information traceability method for cold chain logistics of aquatic products as described above.

[0055] Furthermore, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the blockchain-based information traceability method for cold chain logistics of aquatic products as described above.

[0056] The beneficial effects of this invention are:

[0057] (1) This invention generates a data digest containing information such as temperature, humidity, time, location, and status at each transportation node, and encrypts it through a hash function before uploading it to the blockchain, ensuring that all node data is immutable and verifiable, thus significantly improving the authenticity and integrity of cold chain traceability data.

[0058] (2) Based on the node trust quantification formula of upload success rate, latency time and anomaly rate, this invention can dynamically evaluate the reliability of transportation nodes. This trust value is not only used as a risk control input, but is also introduced into the blockchain consensus process to support a trust-driven weighted consensus mechanism, effectively reducing the risk of low-quality nodes interfering with the system consensus.

[0059] (3) This invention calculates the cold chain risk index by integrating temperature and humidity deviation and node reliability through a multi-parameter function, thereby enabling quantitative judgment and early warning of abnormal situations such as cold chain breakage and environmental fluctuations, and improving the system's sensitivity to potential quality risks.

[0060] In summary, this invention uses blockchain as a foundation of trust and integrates multi-dimensional perception, risk modeling, and visualization technologies to comprehensively improve the data credibility, risk identification capabilities, and public transparency of the cold chain traceability system. It is applicable to complex cold chain scenarios involving multi-entity collaboration and has broad application prospects and significant industrial value. Attached Figure Description

[0061] Figure 1A flowchart of the blockchain-based information traceability method for cold chain logistics of aquatic products provided by the present invention;

[0062] Figure 2 A schematic diagram of the structure of the blockchain-based information traceability system for cold chain logistics of aquatic products provided by the present invention;

[0063] Figure 3 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0064] Figure 4 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0065] 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.

[0066] 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, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0067] 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.

[0068] Please see Figure 1 The present invention provides a flowchart of a blockchain-based method for tracing information in the cold chain logistics of aquatic products, comprising the following steps:

[0069] Step 201: Collect parameter information of aquatic products at each node of cold chain transportation, combine the parameter information to generate a data digest, and calculate the hash value of the data digest.

[0070] In a preferred embodiment of the present invention, during the cold chain logistics of aquatic products, each transportation node is equipped with a data acquisition device to acquire multi-dimensional sensing information related to the current node in real time. Specifically, the acquired information includes at least: temperature information of the environment in which the node is located. Humidity information Geographical location information of nodes The timestamp of the current collection The unique number of the transportation node and status identifiers used to identify the current transport or storage status of a node. .

[0071] The above information is formatted in a standardized way and then concatenated in a preset order (e.g., using the symbol ""). The "" indicates a join operation, generating a data digest containing all parameters including environment, space, time, and node status. Subsequently, a cryptographic hash function (such as SHA-256) is used to process this data digest, generating a hash value. ,Right now:

[0072]

[0073] The hash value It can uniquely identify the full source data corresponding to the node at a specific time point, and possesses tamper-resistance and irreversibility. To ensure the immutability and traceability of the data, the hash value... The data will be written onto the blockchain system as an on-chain record of key node data throughout the cold chain transportation process, allowing subsequent nodes to perform correlation verification, path tracking, and status assessment, thereby ensuring data credibility.

[0074] Step 202: Based on the historical data of the node corresponding to the hash value, extract the upload success rate, average latency time and anomaly rate of the node, and calculate the credibility of the node.

[0075] In some embodiments, step 202 may include:

[0076] Obtain the normalized upper limit of the confidence level;

[0077] Obtain the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient corresponding to the upload success rate, the average latency time, and the anomaly rate, respectively;

[0078] Based on the upload success rate, the average latency, the anomaly rate, and each adjustment coefficient, a corresponding exponential function is constructed.

[0079] The credibility of the node is determined based on the normalization upper limit and the exponential function.

[0080] In some embodiments, credibility is expressed as:

[0081]

[0082] in, It represents the credibility of the i-th node. It represents the success rate of data upload to the i-th node. It is the average latency of data upload to the i-th node. It is the anomaly rate of the i-th node. It is the upper limit of normalization. These are the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient, respectively.

[0083] In one specific embodiment of the present invention, in order to improve the data verification efficiency and credibility differentiation processing capability of the blockchain system with the participation of multiple transportation nodes, a node credibility calculation mechanism is introduced to evaluate the reliability of the data submitted by each transportation node in the cold chain logistics process, and then allocate reasonable weights to the consensus process.

[0084] Specifically, let the credibility of each transportation node be denoted as . This value is dynamically calculated based on the statistical analysis results of multi-dimensional performance indicators of the node during its historical operation cycle. These performance indicators include at least: the node's data upload success rate. Average latency of data upload (Unit: seconds), and the anomaly rate in the node's historical data records. .

[0085] In this invention, the following formula is used to quantitatively model the node credibility:

[0086]

[0087] in, This is the normalized upper limit for credibility (usually set to 1). These are the system's preset first, second, and third adjustment coefficients, used to control the impact of each indicator on the final reliability value, adapting to the node stability characteristics under different cold chain scenarios. Among them, the higher... This helps to improve credibility, and a larger [percentage] and This will lead to a decrease in credibility. It is an exponential function constructed based on a data summary.

[0088] This credibility value This not only serves as a comprehensive score of a node's historical behavior but also as a dynamic weighting factor in the subsequent blockchain consensus process. It is used to determine the priority or allocate voting weight for the data submitted by the node during the on-chain confirmation process, thereby achieving a more granular and trust-driven multi-node collaboration mechanism. Furthermore, the trust parameter will also be incorporated into the calculation of the cold chain risk index, working together with environmental perception parameters to construct a high-dimensional anomaly assessment model, further improving the system's accuracy in identifying quality risks in complex transportation scenarios.

[0089] Step 203: Calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data summary.

[0090] In some embodiments, step 203 may include:

[0091] Obtain the first difference between the stated temperature and the standard storage temperature;

[0092] Obtain the second difference between the humidity and the standard storage humidity;

[0093] The confidence level is adjusted based on a positive constant to obtain the adjusted confidence level;

[0094] The first difference, the second difference, and the adjusted credibility are weighted and summed to obtain the cold chain risk index of the node.

[0095] In some embodiments, the cold chain risk index is expressed as:

[0096]

[0097] in, It is a cold chain risk index. It is the ambient temperature of the i-th node. It is the standard storage temperature. It is the ambient humidity of the i-th node. For standard storage humidity, This is the temperature fluctuation sensitivity coefficient. It is a positive constant. These are the weighting coefficients for temperature deviation, humidity deviation, and credibility influencing factors, respectively. It represents the credibility of the i-th node.

[0098] In a preferred embodiment of the present invention, in order to achieve a quantitative assessment of the quality risk of aquatic products during cold chain transportation, the system constructs a cold chain abnormal risk index calculation model based on the environmental parameters and node credibility values ​​collected at each transportation node, in order to determine whether there is an abnormal risk at a specific transportation node that may lead to a decline in the quality of aquatic products.

[0099] Specifically, for any transportation node, let the ambient temperature collected be... Ambient humidity is The system presets the standard storage temperature and humidity according to the product category. and Combined with the node's credibility value The cold chain anomaly risk index for this node is calculated using the following formula. :

[0100]

[0101] in, These are the weighting coefficients for temperature deviation, humidity deviation, and credibility influencing factors, used to adjust the degree of influence of different factors on the risk index; This is the temperature fluctuation sensitivity coefficient, with a value greater than 1, designed to strengthen the punitive judgment of abnormal temperature changes; It is a positive constant used to prevent issues with node credibility. When the value is too low, an abnormal value will appear when divided by zero.

[0102] The above risk index This study comprehensively characterizes the potential for cold chain failures or product quality variations at transportation nodes by considering both the degree of deviation from environmental factors and the level of trust in the nodes. The risk assessment occurs when the calculated risk index exceeds the system's pre-set risk threshold. At that time, that is:

[0103]

[0104] The transportation node will be automatically marked as a "high-risk node" and will trigger preset response processes such as on-chain anomaly recording, alarm push or manual review to help regulators identify cold chain breaks or potential risk sources in a timely manner.

[0105] Through the above mechanism, this invention realizes risk perception modeling of aquatic products during transportation, providing a key data foundation for subsequent chain integrity assessment and consumer trust information display, and significantly improving the comprehensive identification capability of cold chain traceability system for temperature and humidity anomalies and data anomalies.

[0106] Step 204: Construct a risk chain status matrix based on the cold chain risk index and credibility of each node, and calculate the integrity score of the cold chain path.

[0107] In some embodiments, step 204 may include:

[0108] The cold chain risk index and credibility of each node are sorted.

[0109] Based on the cold chain risk index and credibility of each node after sorting, determine the quality status mapping structure of each node;

[0110] The risk chain state matrix is ​​constructed based on the quality state mapping structure of each node.

[0111] In a preferred embodiment of the present invention, in order to comprehensively evaluate the transportation quality and data reliability of aquatic products throughout the entire cold chain logistics process, the system constructs a risk chain state matrix based on risk parameters and reliability parameters of multiple transportation nodes, and calculates a comprehensive quality score at the link level accordingly to determine the reliability and integrity of the entire cold chain path.

[0112] Specifically, the cold chain transportation route includes... For each of the transportation nodes, the system calculates its cold chain risk index separately. and node credibility And organize the two in sequence to form a risk chain state matrix. Its structure is as follows:

[0113]

[0114] The above matrix Essentially, it is a quality status mapping structure for the entire cold chain process with multiple nodes, which can comprehensively reflect the status distribution of each node in terms of both risk level and trust level.

[0115] In some embodiments, step 204 may further include:

[0116] The cold chain risk index of each node is adjusted to obtain the corresponding attenuation conversion value;

[0117] The attenuation reduction value and the confidence level of each node are weighted and summed to obtain the intermediate calculated value;

[0118] The integrity score is obtained by averaging the intermediate calculated values ​​with the total number of nodes.

[0119] To further quantify the overall quality level of the entire link, the system is based on a matrix. Calculate the integrity score of link quality. The calculation formula is as follows:

[0120]

[0121] in, To score for completeness, and The first and second weights set by the system respectively satisfy the following conditions: This is used to adjust the relative impact of risk index and credibility in the scoring; This is the decayed value of the cold chain risk index for the i-th node, used to apply exponential penalties to high-risk nodes in the scoring. It directly reflects the trust value of a node in the consensus system; the higher the value, the more positive the contribution to the score.

[0122] The above scoring results This system achieves a normalized evaluation of the overall status of multiple nodes throughout the cold chain path; a higher score indicates a more reliable overall link and lower risk. When the calculated link quality score falls below the system's preset link integrity threshold... At that time, that is:

[0123]

[0124] The system will determine that the cold chain path is an "untrusted link" and will trigger intervention mechanisms, including anomaly alarms, regulatory warnings, data audits, or resampling, to ensure that product quality risks are identified and addressed before consumption.

[0125] By constructing the aforementioned risk chain state matrix and quality scoring mechanism, this invention achieves reliable modeling and anomaly screening at the cold chain path level, providing a computational foundation for reliable integration of multi-node and multi-source data, and improving the overall reliability and responsiveness of cold chain information traceability.

[0126] Step 205: Based on the hash value, credibility, and risk index of the node, construct the block of the node, and the blockchain composed of the blocks of all nodes.

[0127] In some embodiments, step 205 may further include:

[0128] Obtain the hash header information of the previous node of the given node;

[0129] Obtain the private key signature information of the node;

[0130] The private key signature information, temperature, trustworthiness, and cold chain risk index of the node are concatenated with the hash header information of the previous node to obtain the block of the node.

[0131] In one embodiment of the present invention, to achieve immutable storage and multi-party sharing of data at each transportation node throughout the entire cold chain logistics process of aquatic products, the system constructs a node-level data on-chain mechanism based on blockchain technology. Specifically, after each transportation node completes operations such as data collection, summary generation, and credibility and risk index calculation, and after the data validity is confirmed through a consensus mechanism, a corresponding local on-chain data structure, namely a node block, is generated. .

[0132] The node block Includes the following field content:

[0133] Hash header information of the previous node It is used to form a chain connection with the previous block;

[0134] Current node's data hash value Due to temperature ,humidity Location information timestamp Node number and status indicators The concatenation is then used to generate a hash function (such as SHA-256);

[0135] The confidence value calculated by the current node This reflects the reliability of the node's historical data behavior;

[0136] Cold chain risk index at the current node The likelihood of cold chain anomalies is determined by combining temperature and humidity deviations with the level of trust between nodes.

[0137] Node's private key signature information This is used to verify that the data was actually generated by the corresponding node and has not been tampered with.

[0138] The above fields constitute the complete block structure of this node, as follows:

[0139]

[0140] Blocks generated by each node The data will be submitted to the consortium blockchain network and undergo multi-node consistency verification through a pre-defined consensus mechanism (such as PBFT, or Practical Byzantine Fault Tolerance) to ensure synchronized confirmation and consistent writing of the data. After on-chain confirmation, the block is not only stored in the consortium blockchain belonging to the transportation company, but also linked to the regulatory blockchain and consumer query blockchain through a hash anchoring mechanism to achieve cross-chain information sharing.

[0141] Through the above design, the system ensures the immutability of node-level data on the blockchain while achieving permission isolation and reliable data sharing among different entities. This ensures that regulatory agencies can obtain information on high-risk nodes in real time, and consumers can easily query traceability data confirmed by consensus, thereby significantly improving the transparency and traceability of cold chain logistics information.

[0142] Step 206: Based on the risk chain status matrix and the integrity score, generate a visual traceability report to demonstrate the quality of the cold chain.

[0143] The visual traceability report includes a risk trend chart and trust signal prompts based on the integrity score.

[0144] In some embodiments, step 206 may further include:

[0145] The geographical location information and timestamps of each node in the risk chain state matrix are analyzed to construct a time-stamped transportation route map;

[0146] Extract the temperature and humidity data of each node in the risk chain state matrix, and plot a biaxial fluctuation curve of temperature and humidity over time.

[0147] Extract the cold chain risk index according to the node order and generate a cold chain risk index trend chart.

[0148] The route integrity score is compared with a preset threshold. If the integrity score is not less than the preset threshold, a green trust indicator is displayed in the visualization report. If the integrity score is less than the preset threshold, a red warning indicator is displayed and the location of the abnormal node is marked in the transportation route map.

[0149] The transportation route map, temperature and humidity fluctuation curve, cold chain risk trend chart, and trust indicator are integrated and output into a single interactive interface.

[0150] In one embodiment of the present invention, in order to improve the readability and public transparency of cold chain logistics traceability information, the system generates a set of visualized cold chain quality traceability reports for end consumers based on the confirmed link status data in the blockchain, so as to achieve transparent presentation and risk warning prompts for the entire product transportation process.

[0151] The process of generating the visualization report is based on the following two core data structures:

[0152] Link State Matrix Cold chain risk index of each transportation node With node credibility It consists of records of the quality risks and data trust status of the product at each node in the transportation route;

[0153] Link quality integrity score The path-level quality evaluation value, calculated based on the state matrix, comprehensively reflects the risk level and reliability of the entire cold chain process.

[0154] Based on this, the system can automatically generate the following visual displays and embed them into the source tracing report interface:

[0155] The route map display module shows the geographical location information of each transportation node. and corresponding timestamp The timeline is mapped onto the map to form a complete transportation trajectory map, which is used to show the spatial and temporal flow of products from the source of production to the final sales point, and supports clicking on nodes to view detailed environmental and quality information.

[0156] The node temperature and humidity fluctuation curve module is based on the temperature collected from each node. With humidity This information is used to create a line trend chart that reflects the temperature and humidity changes during transportation. This chart helps consumers identify whether cold chain conditions are consistently stable and allows setting standard temperature and humidity ranges to highlight abnormal periods.

[0157] The whole-chain risk index trend graph module extracts the risk index of each node in the state matrix. This generates a risk change curve at the chain level. This graph is used to visually display the risk distribution at each node in cold chain transportation. If a significant peak appears, it can be marked as a key monitoring point, and it supports a risk threshold line prompt function.

[0158] The trust scoring and traffic light indicator module prominently displays the link quality integrity score at the core of the report. And set up a traffic light level indication mechanism based on the scoring results, for example:

[0159] when When the (high confidence threshold) is reached, a green indicator (confidential) will be displayed.

[0160] when When the value is in the middle confidence interval, a yellow warning will be displayed (attention is required);

[0161] when When the confidence threshold is low, a red warning (untrusted) will be displayed.

[0162] The above traffic light mechanism is used in conjunction with the scoring results. Visual charts and graphs effectively enhance consumers' ability to intuitively judge the quality level of the entire cold chain process and encourage all parties involved to improve data compliance and transportation standards.

[0163] In summary, this invention establishes a transparent, reliable, and highly interactive cold chain quality display mechanism by automatically generating a multi-dimensional visual traceability report based on blockchain on-chain state data, significantly enhancing consumers' perception and trust in the quality and safety of aquatic products.

[0164] Please see Figure 2 , Figure 2 A schematic diagram of the structure of the blockchain-based aquatic product cold chain logistics information traceability system provided by the present invention.

[0165] like Figure 2 As shown in the embodiment of the present invention, the blockchain-based information traceability system for cold chain logistics of aquatic products includes:

[0166] The first calculation module 301 is used to collect parameter information of aquatic products at each node of cold chain transportation, combine the parameter information to generate a data digest, and calculate the hash value of the data digest.

[0167] The second calculation module 302 is used to extract the upload success rate, average latency and anomaly rate of the node based on the historical data of the node corresponding to the hash value, and to calculate the credibility of the node.

[0168] The third calculation module 303 is used to calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data digest;

[0169] The fourth calculation module 304 is used to construct a risk chain status matrix based on the cold chain risk index and credibility of each node, and to calculate the integrity score of the cold chain path.

[0170] Blockchain module 305 is used to construct the blocks of the nodes and the blockchain composed of the blocks of all nodes based on the hash value, credibility and risk index of the nodes.

[0171] The information traceability module 306 is used to generate a visual traceability report to display the quality of the cold chain based on the risk chain status matrix and the integrity score; the visual traceability report includes a risk trend graph and trust signal prompts based on the integrity score.

[0172] It should be noted that the specific implementation methods and beneficial effects of the above modules 301 to 306 can be found in the detailed description of steps 201 to 206 above, and will not be repeated here.

[0173] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps:

[0174] Parameter information of aquatic products is collected at each node of the cold chain transportation, the parameter information is combined to generate a data digest, and the hash value of the data digest is calculated.

[0175] Based on the historical data of the node corresponding to the hash value, the upload success rate, average latency, and anomaly rate of the node are extracted, and the credibility of the node is calculated.

[0176] Calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data summary;

[0177] A risk chain status matrix is ​​constructed based on the cold chain risk index and credibility of each node, and the integrity score of the cold chain path is calculated.

[0178] Based on the hash value, credibility, and risk index of the node, construct the block of the node, and a blockchain composed of the blocks of all nodes;

[0179] Based on the risk chain status matrix and the integrity score, a visual traceability report is generated to demonstrate the quality of the cold chain; the visual traceability report includes a risk trend chart and trust signal prompts based on the integrity score.

[0180] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 4 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, it performs the following steps:

[0181] Parameter information of aquatic products is collected at each node of the cold chain transportation, the parameter information is combined to generate a data digest, and the hash value of the data digest is calculated.

[0182] Based on the historical data of the node corresponding to the hash value, the upload success rate, average latency, and anomaly rate of the node are extracted, and the credibility of the node is calculated.

[0183] Calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data summary;

[0184] A risk chain status matrix is ​​constructed based on the cold chain risk index and credibility of each node, and the integrity score of the cold chain path is calculated.

[0185] Based on the hash value, credibility, and risk index of the node, construct the block of the node, and a blockchain composed of the blocks of all nodes;

[0186] Based on the risk chain status matrix and the integrity score, a visual traceability report is generated to demonstrate the quality of the cold chain; the visual traceability report includes a risk trend chart and trust signal prompts based on the integrity score.

[0187] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0188] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0189] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0190] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0191] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0192] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0193] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A blockchain-based method for tracing information in the cold chain logistics of aquatic products, characterized in that: The method includes: Parameter information of aquatic products is collected at each node of the cold chain transportation, the parameter information is combined to generate a data digest, and the hash value of the data digest is calculated. Based on the historical data of the node corresponding to the hash value, the upload success rate, average latency, and anomaly rate of the node are extracted, and the credibility of the node is calculated. Calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data summary; A risk chain status matrix is ​​constructed based on the cold chain risk index and credibility of each node, and the integrity score of the cold chain path is calculated. Based on the hash value, credibility, and risk index of the node, construct the block of the node, and a blockchain composed of the blocks of all nodes; Based on the risk chain status matrix and the integrity score, a visual traceability report is generated to demonstrate the quality of the cold chain; the visual traceability report includes a risk trend chart and trust signal prompts based on the integrity score; The calculation of the node's credibility includes: Obtain the normalized upper limit of the confidence level; Obtain the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient corresponding to the upload success rate, the average latency time, and the anomaly rate, respectively; Based on the upload success rate, the average latency, the anomaly rate, and each adjustment coefficient, a corresponding exponential function is constructed. The credibility of the node is determined based on the normalization upper limit and the exponential function; The credibility is expressed as follows: in, It represents the credibility of the i-th node. It represents the success rate of data upload to the i-th node. It is the average latency of data upload to the i-th node. It is the anomaly rate of the i-th node. It is the upper limit of normalization. These are the first adjustment coefficient, the second adjustment coefficient, and the third adjustment coefficient, respectively.

2. The blockchain-based method for tracing the cold chain logistics information of aquatic products according to claim 1, characterized in that, The step of calculating the cold chain risk index of the node based on the temperature, humidity, and credibility in the data digest includes: Obtain the first difference between the stated temperature and the standard storage temperature; Obtain the second difference between the humidity and the standard storage humidity; The confidence level is adjusted based on a positive constant to obtain the adjusted confidence level; The first difference, the second difference, and the adjusted credibility are weighted and summed to obtain the cold chain risk index of the node.

3. The blockchain-based information traceability method for aquatic product cold chain logistics according to claim 2, characterized in that, The cold chain risk index is expressed as follows: in, It is a cold chain risk index. It is the ambient temperature of the i-th node. It is the standard storage temperature. It is the ambient humidity of the i-th node. For standard storage humidity, This is the temperature fluctuation sensitivity coefficient. It is a positive constant. These are the weighting coefficients for temperature deviation, humidity deviation, and credibility influencing factors, respectively. It represents the credibility of the i-th node.

4. The blockchain-based information traceability method for aquatic product cold chain logistics according to claim 3, characterized in that, The risk chain state matrix, constructed based on the cold chain risk index and credibility of each node, includes: The cold chain risk index and credibility of each node are sorted. Based on the cold chain risk index and credibility of each node after sorting, determine the quality status mapping structure of each node; The risk chain state matrix is ​​constructed based on the quality state mapping structure of each node.

5. The blockchain-based method for tracing the cold chain logistics information of aquatic products according to claim 4, characterized in that, The calculation of the cold chain path integrity score includes: The cold chain risk index of each node is adjusted to obtain the corresponding attenuation conversion value; The attenuation reduction value and the confidence level of each node are weighted and summed to obtain the intermediate calculated value; The integrity score is obtained by averaging the intermediate calculated values ​​with the total number of nodes.

6. The blockchain-based information traceability method for aquatic product cold chain logistics according to claim 5, characterized in that, The step of constructing the block of the node based on the node's hash value, credibility, and risk index includes: Obtain the hash header information of the previous node of the given node; Obtain the private key signature information of the node; The private key signature information, temperature, trustworthiness, and cold chain risk index of the node are concatenated with the hash header information of the previous node to obtain the block of the node.

7. The blockchain-based method for tracing information in the cold chain logistics of aquatic products according to claim 6, characterized in that, The process of generating a visual traceability report showcasing cold chain quality based on the risk chain status matrix and the integrity score includes: The geographical location information and timestamps of each node in the risk chain state matrix are analyzed to construct a time-stamped transportation route map; Extract the temperature and humidity data of each node in the risk chain state matrix, and plot a biaxial fluctuation curve of temperature and humidity over time. Extract the cold chain risk index according to the node order and generate a cold chain risk index trend chart. The route integrity score is compared with a preset threshold. If the integrity score is not less than the preset threshold, a green trust indicator is displayed in the visualization report. If the integrity score is less than the preset threshold, a red warning indicator is displayed and the location of the abnormal node is marked in the transportation route map. The transportation route map, temperature and humidity fluctuation curve, cold chain risk trend chart, and trust indicator are integrated and output into a single interactive interface.

8. A blockchain-based aquatic product cold chain logistics information traceability system, applied to the blockchain-based aquatic product cold chain logistics information traceability system as described in any one of claims 1-7, characterized in that, The system includes: The first calculation module is used to collect parameter information of aquatic products at each node of cold chain transportation, combine the parameter information to generate a data digest, and calculate the hash value of the data digest. The second calculation module is used to extract the upload success rate, average latency and anomaly rate of the node based on the historical data of the node corresponding to the hash value, and to calculate the credibility of the node. The third calculation module is used to calculate the cold chain risk index of the node based on the temperature, humidity and credibility in the data digest; The fourth calculation module is used to construct a risk chain status matrix based on the cold chain risk index and credibility of each node, and to calculate the integrity score of the cold chain path. The blockchain module is used to construct the blocks of the nodes and the blockchain composed of the blocks of all nodes based on the hash value, credibility and risk index of the nodes. The information traceability module is used to generate a visual traceability report to display the quality of the cold chain based on the risk chain status matrix and the integrity score; the visual traceability report includes a risk trend chart and trust signal prompts based on the integrity score.

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