Aquatic product cold-chain logistics information tracing method and system based on block chain

Abstract

The invention relates to an aquatic product cold-chain logistics information tracing method and system based on a block chain, and the method comprises the steps: collecting the parameter information of aquatic products at each node of cold-chain transportation, combining the parameter information to generate a data abstract, and calculating the hash value of the data abstract; based on historical data of the nodes corresponding to the hash values, the uploading success rate, the average delay time and the abnormal rate of the nodes are extracted, and the credibility of the nodes is calculated; calculating cold chain risk indexes of the nodes; constructing a risk chain state matrix based on the cold chain risk index and the credibility of each node, and calculating an integrity score of a cold chain path; constructing blocks of the nodes and a block chain composed of the blocks of all the nodes; generating a visual traceability report based on the risk chain state matrix and the integrity score; the visual traceability report comprises a risk trend chart and trust signal prompt information based on integrity score. According to the invention, the safety and reliability of aquatic product cold-chain logistics information tracing can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information traceability, and in particular to a water product cold chain logistics information traceability method and system based on a block chain, an electronic device and a non-transitory computer readable storage medium. BACKGROUND

[0002] At present, in order to guarantee the quality and safety of water products, the cold chain logistics system is generally used for whole-process preservation management in the prior art, and an information traceability system is used to record and track the product circulation link. Common methods include collecting data such as storage temperature, transportation time, and geographic location of water products by using RFID tags, two-dimensional codes, and sensor devices, and uploading relevant information to a centralized database platform for viewing by regulatory agencies, businesses, and consumers, to realize visual management from the source to the table.

[0003] However, the existing information traceability system generally relies on a centralized data management architecture, and has problems such as data tampering, broken traceability chain, and insufficient credibility, which makes it difficult to effectively deal with the complex multi-party involved cold chain transportation environment. In addition, the system standards are not unified among different transportation nodes, and information uploading delays or missing phenomena occur from time to time, resulting in incomplete traceability information and difficulty in accurately reflecting the real state of water products at each link of the cold chain, thereby affecting the supervision efficiency and consumer trust. SUMMARY

[0004] The present application provides a water product cold chain logistics information traceability method, system, electronic device and non-transitory computer readable storage medium based on a block chain, which can improve the security and reliability of water product cold chain logistics information traceability.

[0005] The technical solution of the present application to solve the above technical problems is as follows: The present application provides a water product cold chain logistics information traceability method based on a block chain, which comprises: Collecting parameter information of water products at each node of the cold chain transportation, combining the parameter information to generate a data digest, and calculating a hash value of the data digest; Based on the historical data of the node corresponding to the hash value, extracting the upload success rate, average delay time, and abnormal rate of the node, and calculating the credibility of the node; According to the temperature and humidity in the data digest and the credibility, calculating the cold chain risk index of the node; Based on the cold chain risk index and credibility of each node, constructing a risk chain state matrix, and calculating the integrity score of the cold chain path; According to the hash value, the credibility and the risk index of the node, a block of the node is constructed, and a block chain composed of blocks of all nodes is constructed; Based on the risk chain state matrix and the integrity score, a visual traceability report for displaying cold chain quality is generated; the visual traceability report includes a risk trend chart and a trust signal prompt information based on the integrity score.

[0006] Optionally, the credibility of the node is calculated, including: Obtaining a normalized upper limit of the credibility; Obtaining first, second and third adjustment coefficients corresponding to the upload success rate, the average delay time and the abnormal rate respectively; According to the upload success rate, the average delay time, the abnormal rate and the adjustment coefficients, an exponential function is constructed; According to the normalized upper limit and the exponential function, the credibility of the node is determined.

[0007] Optionally, the credibility is represented as: ; Wherein, is the credibility of the i th node, is the upload success rate of the data of the i th node, is the average delay time of the data upload of the i th node, is the abnormal rate of the i th node, is the normalized upper limit, are first, second and third adjustment coefficients respectively.

[0008] Optionally, the cold chain risk index of the node is calculated according to the temperature, humidity in the data digest and the credibility, including: Obtaining a first difference value between the temperature and the standard storage temperature; Obtaining a second difference value between the humidity and the standard storage humidity; Adjusting the credibility according to a positive constant to obtain an adjusted credibility; The first difference value, the second difference value and the adjusted credibility are weighted and summed to obtain the cold chain risk index of the node.

[0009] Optionally, the cold chain risk index is represented as: ; Wherein, is the cold chain risk index, is the environmental temperature of the i th node, is a standard storage temperature, is an ambient humidity of the ith node, is a standard storage humidity, is a temperature fluctuation sensitivity coefficient, is a positive constant, are weight coefficients of temperature deviation, humidity deviation and credibility influence factor respectively, is a credibility of the ith node.

[0010] Optionally, the risk chain state matrix is constructed based on the cold chain risk index and the credibility of each node, and the method comprises: sorting the cold chain risk index and the credibility of each node; determining a quality state mapping structure of each node according to the sorted cold chain risk index and the credibility of each node; constructing the risk chain state matrix according to the quality state mapping structure of each node.

[0011] Optionally, the integrity score of the cold chain path is calculated, and the method comprises: adjusting the cold chain risk index of each node to obtain a corresponding decay conversion value; weighting and summing the decay conversion value and the credibility of each node to obtain an intermediate calculation value; averaging the intermediate calculation value in combination with the total number of nodes to obtain the integrity score.

[0012] Optionally, the block of the node is constructed according to the hash value, the credibility and the risk index of the node, and the method comprises: obtaining hash header information of a previous node of the node; obtaining private key signature information of the node; connecting the private key signature information, the temperature, the credibility, the cold chain index and the private key signature information of the node, and the hash header information of the previous node to obtain the block of the node.

[0013] Optionally, the visualized traceability report for displaying the cold chain quality is generated based on the risk chain state matrix and the integrity score, and the method comprises: analyzing geographical location information and time stamps of each node in the risk chain state matrix to construct a time-marked transportation path map; extracting temperature data and humidity data of each node in the risk chain state matrix to draw a double-axis fluctuation curve graph of temperature and humidity changing over time; extracting the cold chain risk index according to the node order to generate a cold chain risk index trend graph; The path integrity score is compared with a preset threshold, if the integrity score is not less than the preset threshold, a green trust identification is displayed in a visual report; if the integrity score is less than the preset threshold, a red early warning identification is displayed and the position corresponding to the abnormal node is marked in the transportation path map; The transportation path map, the temperature and humidity fluctuation graph, the cold chain risk trend graph and the trust identification are integrated and output in a single interactive interface.

[0014] The application also provides a cold chain logistics information traceability system for aquatic products based on a blockchain, and the system comprises: A first computing module is configured 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 a hash value of the data digest; A second computing module is configured to extract an upload success rate, an average delay time and an abnormal rate of the node based on historical data of the node corresponding to the hash value, and calculate a trust degree of the node; A third computing module is configured to calculate a cold chain risk index of the node according to temperature and humidity in the data digest and the trust degree; A fourth computing module is configured to construct a risk chain state matrix based on the cold chain risk index and the trust degree of each node, and calculate an integrity score of a cold chain path; A blockchain module is configured to construct a block of the node according to the hash value, the trust degree and the risk index of the node, and construct a blockchain composed of blocks of all nodes; An information traceability module is configured to generate a visual traceability report for displaying cold chain quality based on the risk chain state matrix and the integrity score; the visual traceability report comprises a risk trend graph and trust signal prompt information based on the integrity score.

[0015] In addition, to achieve the above-mentioned purpose, the application further provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing the above-mentioned cold chain logistics information traceability method for aquatic products based on a blockchain.

[0016] In addition, to achieve the above-mentioned purpose, the application further provides a non-transitory computer readable storage medium, wherein the storage medium stores a computer software program, and the computer software program is executed by a processor to realize the above-mentioned cold chain logistics information traceability method for aquatic products based on a blockchain.

[0017] The application has the following beneficial effects: (1) The present application generates a data digest containing temperature and humidity, time, location, state and other information at each transport node, and encrypts it through a hash function and then chains it, ensuring that all node data is tamper-proof and verifiable, significantly improving the authenticity and integrity of cold chain traceability data.

[0018] (2) The present application can dynamically evaluate the reliability of the transport node based on the node credibility quantification formula of upload success rate, delay time and abnormal rate. The credibility value is not only used as a risk control input, but also introduced into the blockchain consensus process to support the trust-driven weighted consensus mechanism, effectively reducing the interference risk of low-quality nodes on system consensus.

[0019] (3) The present application calculates the cold chain risk index by synthesizing the multi-parameter function of temperature and humidity deviation and node credibility, realizes the quantitative judgment and early warning of cold chain breakage, environmental fluctuation and other abnormal situations, and improves the perception sensitivity of the system to potential quality risks.

[0020] In summary, the present application takes blockchain as the trust basis, integrates multi-dimensional perception, risk modeling and visualization technology, and comprehensively improves the data credibility, risk identification ability and public transparency of the cold chain traceability system. It is suitable for complex cold chain scenarios under multi-agent collaboration, has wide application prospects and significant industrial value. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The flowchart of the cold chain logistics information traceability method for aquatic products based on the blockchain provided by the present application is provided. Figure 2 The structure schematic diagram of the cold chain logistics information traceability system for aquatic products based on the blockchain provided by the present application is provided. Figure 3 The hardware structure schematic diagram of a possible electronic device provided by the present application is provided. Figure 4 The hardware structure schematic diagram of a possible computer readable storage medium provided by the present application is provided. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] In the description of the present application, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can be explicitly or implicitly included one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly specified.

[0024] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can recognize that the present application can be implemented without using these specific details. In other examples, well-known structures and processes will not be described in detail to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope consistent with the principles and characteristics disclosed.

[0025] Please refer to Figure 1 , the flowchart of the blockchain-based aquatic product cold chain logistics information traceability method of the present application is provided, including the following steps: Step 201, 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.

[0026] In a preferred embodiment of the present application, during the aquatic product cold chain logistics process, each transportation node is configured with a data acquisition device for real-time acquisition of multi-dimensional perception information related to the current node. Specifically, the collected information at least includes: temperature information , humidity information of the environment where the node is located, geographic location information of the node, current acquisition time stamp , unique number of the transportation node , and state identification for identifying the current transportation or storage state of the node .

[0027] After the above types of information are uniformly formatted and spliced in a predetermined order (for example, the symbol " " represents the connection operation), a data digest containing all parameters such as environment, space, time and node state is formed. Then, the data digest is processed by using an encryption hash function (such as SHA-256) to generate a hash value , that is: ; The hash value can uniquely identify the full traceability data of the node corresponding to the time point, and has tamper resistance and irreversibility. In order to guarantee the data tamper resistance and traceability, the hash value will be written into the blockchain system as the on-chain record of the key node data in the whole cold chain transportation process, for subsequent node correlation verification, path tracking and state evaluation, so as to realize data credibility guarantee.

[0028] Step 202, based on the historical data of the node corresponding to the hash value, extracting the upload success rate, average delay time and abnormal rate of the node, calculating the credibility of the node.

[0029] In some embodiments, step 202 can include: obtaining a normalized upper limit of the credibility; obtaining first, second and third adjustment coefficients corresponding to the upload success rate, the average delay time and the abnormal rate respectively; According to the upload success rate, the average delay time, the abnormal rate, and each adjustment coefficient, a corresponding exponential function is constructed; According to the normalized upper limit and the exponential function, the credibility of the node is determined.

[0030] In some embodiments, the credibility is represented as: ; Wherein, is the credibility of the i-th node, is the upload success rate of the data of the i-th node, is the average delay time of the data upload of the i-th node, is the abnormal rate of the i-th node, is the normalized upper limit, are first, second and third adjustment coefficients respectively.

[0031] In a specific embodiment of the present application, in order to improve the data verification efficiency of the blockchain system under the participation of multiple transportation nodes and the differentiation processing ability of credibility, 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 reasonably weight the consensus process.

[0032] Specifically, let the credibility corresponding to each transportation node be , the value is dynamically calculated based on the statistical analysis results of the multi-dimensional performance indicators of the node in the historical running period. The performance indicators at least include: the upload success rate of node data , the average delay time of data upload (unit: seconds), and the abnormal rate in the node historical data record .

[0033] In the present application, the node credibility is quantitatively modeled by the following formula: ; wherein, is the normalized upper limit of the credibility (usually set to 1), are the first adjustment coefficient, the second adjustment coefficient and the third adjustment coefficient preset by the system, respectively, for controlling the influence degree of each indicator on the final credibility value to adapt to the node stability characteristics in different cold chain scenarios. Among them, a higher helps to improve the credibility, a larger and will lead to a decrease in credibility. is an exponential function constructed according to the data summary.

[0034] The credibility value not only serves as a comprehensive score of the historical behavior of the node, but also as a dynamic weighting factor in the subsequent blockchain consensus process, for priority judgment or voting weight allocation of the data submitted by the node in the chain confirmation process, so as to realize a more fine-grained and credibility-driven multi-node collaboration mechanism. In addition, the credibility parameter will also import the calculation process of the cold chain risk index, and the environmental perception parameter to construct a high-dimensional abnormality evaluation model, further improving the identification accuracy of the system for quality risks in complex transportation scenarios.

[0035] Step 203, calculating the cold chain risk index of the node according to the temperature, humidity in the data summary and the credibility.

[0036] In some embodiments, step 203 can include: obtaining a first difference value between the temperature and the standard storage temperature; obtaining a second difference value between the humidity and the standard storage humidity; adjusting the credibility according to a positive constant to obtain an adjusted credibility; performing weighted sum processing on the first difference value, the second difference value and the adjusted credibility to obtain the cold chain risk index of the node.

[0037] In some embodiments, the cold chain risk index is represented as: ; wherein, is a cold chain risk index, is the ambient temperature of the ith node, is the standard storage temperature, is the ambient humidity of the ith node, is the standard storage humidity, is the temperature fluctuation sensitivity coefficient, is a positive constant, are the weight coefficients of the temperature deviation, humidity deviation and credibility impact factor, respectively, is the credibility of the ith node.

[0038] In a preferred embodiment of the present application, to realize the quantitative evaluation of the quality risk of aquatic products in the cold chain transportation process, the system constructs a set of cold chain abnormal risk index calculation model based on the environmental parameters collected at each transportation node and the node credibility value, to determine whether there is an abnormal risk that may cause the quality of aquatic products to decline at a specific transportation node.

[0039] Specifically, for any transportation node, let the ambient temperature collected by the node be , the ambient humidity be , and the system preset the standard storage temperature and humidity of the product according to the product category to be and respectively. Combined with the credibility value of the node, the cold chain abnormal risk index of the node is calculated by the following formula : ; wherein, are the weight coefficients of the temperature deviation, humidity deviation and credibility impact factor, respectively, used to adjust the influence degree of different factors on the risk index; is the temperature fluctuation sensitivity coefficient, which is greater than 1, aiming to strengthen the punitive judgment of abnormal temperature change; is a positive constant, used to prevent the numerical anomaly of division by zero when the node credibility is too low.

[0040] The above risk index starts from two dimensions of the deviation degree of environmental factors and the trust degree of the node, and comprehensively depicts whether there is a possibility of potential cold chain failure or product quality variation at the transportation node. When the calculated risk index exceeds the risk judgment threshold set by the system, ; The transport node is automatically marked as a "high-risk node", and will trigger preset response processes such as abnormal record on the chain, alarm push or manual review, to assist supervisors in identifying cold chain breakpoints or potential risk sources in a timely manner.

[0041] Through the above mechanism, the present application realizes the risk perception modeling of aquatic products in the transportation process, provides key data basis for subsequent link integrity evaluation and consumer trusted information display, and significantly improves the comprehensive identification ability of the cold chain traceability system for temperature and humidity abnormalities and data abnormalities.

[0042] Step 204, 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.

[0043] In some embodiments, step 204 can include: sorting the cold chain risk index and the credibility of each node; determining the quality state mapping structure of each node according to the sorted cold chain risk index and credibility of each node; constructing the risk chain state matrix according to the quality state mapping structure of each node.

[0044] In a preferred embodiment of the present application, in order to comprehensively evaluate the transportation quality and data credibility of aquatic products in the whole process of cold chain logistics, the system constructs a risk chain state matrix based on the risk parameters and credibility parameters of multiple transport nodes, and calculates the comprehensive quality score of the link level according to the matrix, which is used to judge the credibility and integrity of the whole cold chain path.

[0045] Specifically, assuming that the cold chain transportation path contains transport nodes, the system calculates the cold chain risk index and the node credibility of each node respectively, and organizes them in order to form a risk chain state matrix , the structure of which is as follows: ; The above matrix is essentially a quality state mapping structure of multiple nodes in the whole process of cold chain, which can comprehensively reflect the state distribution of each node in the two dimensions of risk level and trust level.

[0046] In some embodiments, step 204 can further include: adjusting the cold chain risk index of each node to obtain the corresponding decay conversion value; weighting and summing the decay conversion value and the credibility of each node to obtain an intermediate calculation value; The integrity score is obtained by averaging the intermediate calculated values ​​with the total number of nodes.

[0047] 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: ; 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.

[0048] 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: ; 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.

[0049] 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.

[0050] 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.

[0051] In some embodiments, step 205 may further include: Obtain the hash header information of the previous node of the given node; Obtain the private key signature information of the node; The node's private key signature information, temperature, trustworthiness, cold chain wind index, and private key signature information, along with the hash header information of the previous node, are concatenated to obtain the node's block.

[0052] In an embodiment of the present application, in order to realize the tamper-proof storage and multi-party sharing of data of each transportation node in the whole process of aquatic product cold chain logistics, the system constructs a node-level data on-chain mechanism based on blockchain technology. Specifically, after each transportation node completes data collection, digest generation, credibility and risk index calculation and other operations, and the data validity is confirmed through the consensus mechanism, the corresponding local on-chain data structure, i.e., the node block, is generated. .

[0053] The node block contains the following field contents: Hash header information of the previous node , used to form a chain connection with the previous block; Data hash value of the current node , generated by concatenating temperature , humidity , location information , timestamp , node number and state identifier and then generating by a hash function (such as SHA-256); Credibility value calculated by the current node , reflecting the reliability of the historical data behavior of the node; Cold chain risk index of the current node , judging the possibility of cold chain abnormality by comprehensively considering the temperature and humidity deviation and the trust degree of the node; Private key signature information of the node , used to verify that the data is truly generated by the corresponding node and has not been tampered with.

[0054] The above fields constitute the complete block structure of the node, which is specifically as follows: ; The block generated by each node will be submitted to the consortium chain network, and the consistency of multiple nodes will be verified through a preset consensus mechanism (such as PBFT, i.e., practical Byzantine fault tolerance algorithm) to ensure the synchronous confirmation and consistent writing of data. After the on-chain confirmation is completed, the block is not only stored in the consortium chain of the transportation enterprise, but also associated between chains through the hash anchoring mechanism and the supervision chain and the consumer query chain to realize cross-chain sharing of information.

[0055] Through the above design, the system not only ensures the tamper-proof nature of node-level data on-chain, but also realizes the permission isolation and data trusted sharing between different subjects, ensuring that the supervisory authority can obtain real-time high-risk node information, and consumers can also conveniently query the consensus-confirmed traceability data, thereby significantly improving the transparency and traceability of cold chain logistics information.

[0056] Step 206, based on the risk chain state matrix and the integrity score, generating a visual traceability report for displaying cold chain quality.

[0057] Wherein, the visual traceability report includes a risk trend chart and a trust signal prompt information based on the integrity score.

[0058] In some embodiments, step 206 can also include: Parsing the geographical location information and time stamp of each node in the risk chain state matrix to construct a time-stamped transportation path map; Extracting temperature data and humidity data of each node in the risk chain state matrix to draw a two-axis fluctuation curve graph of temperature and humidity over time; According to the node order, extracting the cold chain risk index to generate a cold chain risk index trend chart; Comparing the path integrity score with a preset threshold, if the integrity score is not less than the preset threshold, displaying a green trust identification in the visual report; if the integrity score is less than the preset threshold, displaying a red warning identification and marking the location of the abnormal node in the transportation path map; Integrating the transportation path map, temperature and humidity fluctuation curve graph, cold chain risk trend chart and trust identification into a single interactive interface.

[0059] In an embodiment of the present application, in order to improve the readability and public transparency of cold chain logistics traceability information, the system generates a set of visual cold chain quality traceability report for terminal consumers based on the confirmed link state data in the blockchain, so as to realize the transparent presentation and risk warning prompt of the whole product transportation process.

[0060] The generation process of the visual report is based on the following two core data structures: Link state matrix : composed of cold chain risk index of each transportation node And node credibility , recording the quality risk and data trust state of the product at each node in the transportation path; Integrity score of link quality : path-level quality evaluation value calculated based on the state matrix, comprehensively reflecting the risk level and reliability of the whole cold chain process.

[0061] On this basis, the system can automatically generate the following visual display contents and embed them into the traceability report interface: Path map display module, displaying the geographical location information And corresponding time stamp 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.

[0062] 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. The chart helps consumers identify whether cold chain conditions are consistently stable and allows setting standard temperature and humidity ranges to highlight abnormal periods.

[0063] 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.

[0064] 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: when When the (high confidence threshold) is reached, a green indicator (confidential) will be displayed. when When the value is in the middle confidence interval, a yellow warning will be displayed (attention is required); when When the confidence threshold is low, a red warning (untrusted) will be displayed.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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: The first calculation module 301 is configured 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 a hash value of the data digest. The second calculation module 302 is configured to extract an upload success rate, an average delay time and an abnormality rate of the node based on historical data of the node corresponding to the hash value, and calculate a credibility of the node. The third calculation module 303 is configured to calculate a cold chain risk index of the node according to temperature and humidity in the data digest and the credibility. The fourth calculation module 304 is configured to construct a risk chain state matrix based on the cold chain risk index and the credibility of each node, and calculate an integrity score of a cold chain path. The blockchain module 305 is configured to construct a block of the node according to the hash value, the credibility and the risk index of the node, and construct a blockchain composed of blocks of all nodes. The information traceability module 306 is configured to generate a visual traceability report for displaying cold chain quality based on the risk chain state matrix and the integrity score, wherein the visual traceability report includes a risk trend chart and trust signal prompt information based on the integrity score.

[0069] It should be noted that the specific implementation and beneficial effects of the modules 301 to 306 can be referred to the specific description of the steps 201 to 206, which will not be repeated here.

[0070] Please refer to Figure 3 , Figure 3 An embodiment of an electronic device provided by the embodiment of the present application is shown in the figure. Figure 3 As shown in the figure, the embodiment of the present application provides an electronic device 400, which includes a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420, and the processor 420 implements the following steps when executing the computer program 411: The first calculation module 301 is configured 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 a hash value of the data digest. The second calculation module 302 is configured to extract an upload success rate, an average delay time and an abnormality rate of the node based on historical data of the node corresponding to the hash value, and calculate a credibility of the node. The third calculation module 303 is configured to calculate a cold chain risk index of the node according to temperature and humidity in the data digest and the credibility. The fourth calculation module 304 is configured to construct a risk chain state matrix based on the cold chain risk index and the credibility of each node, and calculate an integrity score of a cold chain path. constructing a block of the node according to the hash value, the credibility and the risk index of the node, and constructing a block chain composed of the blocks of all nodes; generating a visual traceability report for displaying cold chain quality based on the risk chain state matrix and the integrity score; the visual traceability report comprises a risk trend chart and trust signal prompt information based on the integrity score.

[0071] Please refer to Figure 4 , Figure 4 An embodiment of a computer readable storage medium provided by the embodiment of the present application is shown in the figure. As Figure 4 shown, the embodiment provides a computer readable storage medium 500, which stores a computer program 411, and the computer program 411 is executed by a processor to implement 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; extracting upload success rate, average delay time and abnormal rate of the node based on historical data of the node corresponding to the hash value, and calculating credibility of the node; calculating a cold chain risk index of the node according to temperature, humidity in the data digest and the credibility; constructing a risk chain state matrix based on the cold chain risk index and the credibility of each node, and calculating an integrity score of a cold chain path; constructing a block of the node according to the hash value, the credibility and the risk index of the node, and constructing a block chain composed of the blocks of all nodes; generating a visual traceability report for displaying cold chain quality based on the risk chain state matrix and the integrity score; the visual traceability report comprises a risk trend chart and trust signal prompt information based on the integrity score.

[0072] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0073] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented 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.

[0074] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0075] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0076] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0077] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims are intended to cover all such variations and modifications as falling within the scope of the application.

[0078] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

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.

2. The blockchain-based method for tracing the cold chain logistics information of aquatic products according to claim 1, characterized in that, 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.

3. The blockchain-based information traceability method for aquatic product cold chain logistics according to claim 2, characterized in that, 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.

4. The blockchain-based information traceability method for aquatic product cold chain logistics according to claim 3, 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.

5. The blockchain-based method for tracing the cold chain logistics information of aquatic products according to claim 4, 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.

6. The blockchain-based information traceability method for aquatic product cold chain logistics according to claim 5, 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.

7. The blockchain-based method for tracing information in the cold chain logistics of aquatic products according to claim 6, 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.

8. The blockchain-based method for tracing the cold chain logistics information of aquatic products according to claim 7, 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 node's private key signature information, temperature, trustworthiness, cold chain wind index, and private key signature information, along with the hash header information of the previous node, are concatenated to obtain the node's block.

9. The blockchain-based method for tracing information in the cold chain logistics of aquatic products according to claim 8, 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.

10. A blockchain-based information traceability system for cold chain logistics of aquatic products, 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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