An internet-of-things management method and management system for cold-chain cargo full-life-cycle traceability

Abstract

The application discloses a cold-chain cargo full-life-cycle traceability Internet of Things management method and management system, relates to the Internet of Things management technical field of cold-chain logistics, and has the technical scheme that traceability information of a transportation unit is established and physical characteristic reference information is acquired when cold-chain cargo enters a circulation link; current physical characteristics are acquired when each node is handed over, consistency is judged with the reference, handover proof information is generated in combination with state information and node identification information; risk representation information and responsibility segment information are generated based on period state information held by the node; responsibility segments are spliced based on the handover proof information to form a full-life-cycle segment chain and consistency verification is performed; and responsibility contribution results and traceability evidence information are output when an exception or a traceability request occurs.The application has the beneficial effects that the identity verification capability of the transportation unit is improved, handover evidence non-repudiation is improved, node holding period risk quantification and responsibility segmentation management are realized, and traceability chain continuity, consistency and exception accountability efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of IoT management technology for cold chain logistics, and in particular to an IoT management method and system for tracing the entire lifecycle of cold chain goods. Background Technology

[0002] Cold chain goods have stringent requirements regarding temperature, humidity, door opening frequency, transport vibration, and the operational status of refrigeration equipment throughout the production, warehousing, transportation, transshipment, and distribution stages. Any abnormality in temperature control, improper loading and unloading, or unclear handover management at any stage can lead to a decline in product quality, increased compliance risks, and ultimately, liability disputes. Therefore, full lifecycle traceability management for cold chain goods has become a crucial technological requirement in the cold chain logistics field.

[0003] In existing technologies, cold chain traceability solutions typically monitor the transportation process using temperature and humidity sensors, positioning devices, and data acquisition terminals, and upload the collected data to a platform for storage and retrieval. While these solutions can track the status of the cold chain to a certain extent, they still have the following problems: First, most solutions focus on recording and displaying status data, lacking a reliable verification mechanism for the "identity" of transport units. This makes them prone to issues like label replacement, seal tampering, and historical data playback during handover, leading to insufficient credibility of subsequent traceability evidence. Second, existing handover records often rely on manual entry, QR code confirmation, or simple timestamps, making it difficult to form a verifiable and undeniable chain of handover evidence. In case of disputes, it is often difficult to accurately reconstruct the handover facts at each node. Third, existing technologies typically only provide anomaly alerts or data curve displays, lacking the ability to quantify the risk of holding periods between adjacent handovers, making it difficult to establish a structured correspondence between status anomalies and specific responsible parties. Fourth, in complex cold chain scenarios involving multiple nodes and multiple carriers, existing solutions generally lack a mechanism for splicing responsibility segments and verifying consistency based on handover boundaries, failing to provide continuous and verifiable chain management of responsibility throughout the entire lifecycle. Fifth, when quality anomalies, customer complaints, or claims disputes occur, existing technologies struggle to quickly output responsibility contribution results and corresponding evidence, resulting in low accountability efficiency and long dispute resolution cycles. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an IoT management method and system for the full lifecycle traceability of cold chain goods.

[0005] The technical solution includes the following steps: When cold chain goods enter the circulation chain, traceability information is established for the transportation unit of the goods, and physical characteristic reference information associated with the transportation unit is obtained; When a transport unit is handed over at any node, the current physical characteristic information is obtained and its consistency with the physical characteristic reference information is judged. The handover certificate information is generated by combining the status information at the handover time and the node identification information. Risk characterization information is generated based on the state information of the node holding period between two adjacent handovers, and corresponding responsibility segment information is generated. Based on the handover proof information, adjacent responsibility segment information is associated and spliced ​​to form a full life cycle segment chain, and the segment chain is verified for consistency. When an anomaly occurs or a tracing request is received, the target responsibility fragment set is determined based on the fragment chain, the responsibility contribution result is output according to preset rules, and the tracing evidence information associated with it is output.

[0006] Preferably, obtaining the physical feature reference information includes: When the transport unit enters the cold chain circulation, the physical carrier on the transport unit used for anti-tampering or identification is selected as the collection object. The physical carrier includes at least one of the seal, packaging surface, and label carrier. A preset acquisition area is determined on the acquisition object according to a preset acquisition rule, and acquisition is performed on the preset acquisition area. The acquisition includes at least one of image acquisition and signal acquisition. Feature extraction is performed on the collected results to generate a physical feature template as the physical feature benchmark information, and summary information is generated on the physical feature template for subsequent consistency judgment.

[0007] Preferably, the step of acquiring current physical characteristic information when the transport unit is handed over at any node includes a challenge-based acquisition step, which includes: The server generates and distributes collection challenge parameters for the transportation unit. The collection challenge parameters include at least the coordinates of the collection area, the collection angle, the lighting mode, and the collection time window. Within the collection time window, the node terminal collects data from the preset collection area according to the collection challenge parameters to obtain the current collection result; Feature extraction is performed on the current acquisition results to generate the current physical feature template, and a consistency score is obtained by matching and calculating the physical feature template corresponding to the physical feature benchmark information. When the consistency score is lower than the preset threshold, the collection challenge parameters are reissued and the collection is completed again. The consistency scores obtained from the two collections and the corresponding collection challenge parameters are recorded together in the handover proof information for anti-copying and anti-replay verification.

[0008] Preferably, the generation of handover proof information includes: After completing the consistency judgment, the minimum set of fields for the handover proof information is determined. The minimum set of fields includes at least the transport unit identifier, node identifier information, handover time information, the consistency judgment result, status information summary, and a summary pointer to the previous handover proof information. The minimum set of fields is normalized and encoded in a preset order to generate a handover proof digest value; The handover proof digest value is associated with the node-side identity credential to form handover proof information, and the handover proof information is written into the boundary record of the full lifecycle fragment chain for boundary matching and consistency verification of adjacent responsibility fragment information.

[0009] Preferably, the generation of the handover proof information further includes a non-repudiation solidification step, wherein the non-repudiation solidification step includes: Perform signature solidification on the handover proof digest value to generate a digital signature; Generate a timestamp and solidify the received handover proof digest value into a record; At least one of the digital signature and the timestamp-based solidified record is bound and stored with the corresponding handover proof information so that the handover proof information can be verified to be unaltered and the generating entity and generation time can be determined during subsequent verification.

[0010] Preferably, the status information includes at least one of temperature information, humidity information, location information, vibration information, door opening event information, and chiller operating condition information; Furthermore, obtaining the state information includes an adaptive sampling step, which includes: When any of the following triggering conditions are detected, the sampling frequency of the corresponding state information is increased and a state data summary at the corresponding sampling frequency is generated. The triggering conditions include door opening event, temperature change gradient exceeding a preset threshold, and vibration amplitude exceeding a preset threshold. When the triggering condition is not detected, the sampling frequency of the corresponding state information is reduced and a state data summary at the corresponding sampling frequency is generated. The state data summary at the corresponding sampling frequency is associated with the corresponding node holding period to generate the risk characterization information.

[0011] Preferably, the generated risk characterization information includes: Based on the temperature information during the node holding period, at least one of the following is determined as the basic risk quantity: over-temperature amplitude, over-temperature duration, and over-temperature area integral. The severity index of abnormal events is determined based on door opening event information, vibration information, and chiller operating condition information; The basic risk quantity and the severity index of the abnormal event are fused together according to a preset weight to obtain risk characterization information that represents the impact of the node holding period on cold chain goods.

[0012] Preferably, the step of associating and splicing adjacent responsibility segment information based on the handover proof information to form a full lifecycle segment chain includes: Using the handover proof information corresponding to two adjacent handovers as the starting and ending boundaries of the responsibility segment, the responsibility segment information generated by the corresponding node holding period is bound to the starting and ending boundaries; When splicing adjacent responsibility segment information, a boundary matching check is performed. The boundary matching check includes comparing the consistency of the summary pointer recorded in the previous responsibility segment information with the summary pointer recorded in the next responsibility segment information, wherein each summary pointer points to the association relationship of the corresponding previous handover proof information. And perform time sequence verification, which includes verifying the chronological relationship of the handover time information in the handover certificate information corresponding to two adjacent handovers; When any verification fails, the corresponding responsible segment information is marked as a suspicious segment and the reason for the chain break is recorded.

[0013] Preferably, the step of outputting the responsibility contribution result according to preset rules and outputting the associated source tracing evidence information includes: In the set of target responsibility segments, risk characterization information corresponding to each responsibility segment is obtained, the risk proportion of each responsibility segment is calculated, and the risk proportion is used as the responsibility contribution. When there are responsibility segment information marked as suspicious segments in the target responsibility segment set, increase the responsibility contribution of the suspicious segment and output a suspicious identifier; When outputting the responsibility contribution result, the source tracing evidence information is also output. The source tracing evidence information includes at least the handover proof information corresponding to the target responsibility fragment set, the risk characterization information summary, and the fragment chain consistency verification result.

[0014] An IoT management system for the full lifecycle traceability of cold chain goods includes: The document establishment and anchoring module is used to establish traceability information for the transportation unit of cold chain goods when they enter the circulation link and to obtain physical characteristic reference information associated with the transportation unit. The handover verification module is used to obtain the current physical feature information when the transport unit is handed over at any node and to make a consistency judgment with the physical feature reference information, and to generate handover verification information by combining the status information at the time of handover and the node identification information. The proof solidification module is used to generate a handover proof digest value for the handover proof information and perform a non-repudiation solidification operation, and bind and store the solidification result with the corresponding handover proof information; The status data management module is used to acquire status information and generate a status data summary during the node holding period between two adjacent handovers. The risk and responsibility segment module is used to generate risk characterization information and corresponding responsibility segment information based on the status information of the node during its holding period. The segment chain and allocation output module is used to associate and splice adjacent responsibility segment information based on the handover proof information to form a full life cycle segment chain and perform consistency verification. When an anomaly occurs or a tracing request is received, the target responsibility segment set is determined based on the segment chain, and the responsibility contribution result is output according to preset rules, along with the associated tracing evidence information.

[0015] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: 1. In view of the problem that the existing technology only records status data and it is difficult to verify the identity of the transport unit, this solution improves the ability to prevent tampering and replacement and enhances the authenticity of traceability by establishing a physical feature benchmark bound to the transport unit and making a consistency judgment at the time of handover.

[0016] 2. To address the issue of handover records relying on manual confirmation and insufficient credibility of evidence, this solution generates handover proof containing node identifiers, status summaries, and summary pointers, and then performs signature or timestamp-based solidification to improve the verifiability and non-repudiation of handover evidence.

[0017] 3. To address the lack of risk quantification capabilities in existing solutions, this solution generates risk characterization information based on node holding cycle status information, transforming temperature anomalies, door opening, vibration, and chiller operating conditions into calculable indicators, thereby improving the accuracy and interpretability of anomaly analysis.

[0018] 4. To address the issue of unclear responsibility boundaries in multi-node scenarios, this solution generates responsibility segment information and uses handover proof as the segment boundary to splice together the segments, forming a full lifecycle segment chain and realizing the structured division of responsibility intervals.

[0019] 5. To address the issues of existing traceability chains being prone to breakage and difficult to verify, this solution identifies suspicious segments and records the reasons for chain breaks through boundary matching verification and time sequence verification, thereby improving the continuity and consistency of the traceability chain.

[0020] 6. To address the issues of low efficiency in accountability for anomalies and incomplete evidence output, this solution outputs the results of responsibility contribution and related traceability evidence information when an anomaly or source tracing request is made, which facilitates claims settlement, supervision, and dispute resolution. Attached Figure Description

[0021] Figure 1This is an overall flowchart of an IoT management method for the entire lifecycle of cold chain goods, provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0024] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0025] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] Example 1 See Figure 1 This invention provides an IoT management method and system for the full lifecycle traceability of cold chain goods, comprising the following steps: S1. When cold chain goods enter the circulation link, establish traceability information for the transportation unit of the goods and obtain the physical characteristic benchmark information associated with the transportation unit; When performing step S1, the transport unit is used as the smallest traceability object. The transport unit can be a single box, pallet, turnover box, or outer packaging carrier bound to the goods. After the cold chain goods enter the circulation link and are packed, sealed, or labeled, the node terminal establishes traceability information for the transport unit and generates a transport unit identifier to uniquely identify the transport unit.

[0027] The traceability information includes at least the cargo category, batch information, packing time, initial node identification information, and initial status information. The initial status information includes at least the temperature at the time of packing and may further include humidity and location information. To ensure that the identity of the transport unit can be verified during subsequent handover and to provide basic data for handover certification, physical characteristic benchmark information associated with the transport unit is acquired and written into the traceability information during the filing stage.

[0028] The acquisition of physical feature reference information takes the physical carriers used for tamper-proofing or identification on the transport unit as the collection object. The physical carriers include at least one of the seals, packaging surfaces, and label carriers. When executing step S1, the node terminal determines the preset collection area according to the preset collection rules. The preset collection rules are used to limit the fixed positional relationship and size range of the collection area relative to the seal or label, so that different nodes can repeatedly collect the same preset area during subsequent handover and evidence collection.

[0029] In one implementation, the preset acquisition area is located on the seal surface or in the packaging texture area adjacent to the seal; in another implementation, the preset acquisition area is located within the marking frame of the label carrier. The node terminal acquires data from the preset acquisition area, including at least one of image acquisition and signal acquisition. When image acquisition is used, the node terminal obtains a reference image of the preset acquisition area and records key parameters during acquisition; when signal acquisition is used, the node terminal or an external acquisition device obtains the response signal output from the preset acquisition area and records the sampling parameters.

[0030] To improve robustness, multiple acquisitions can be performed on the same preset acquisition area, and quality screening can be carried out to remove acquisition results that are blurry, have abnormal exposure, or are too noisy.

[0031] Feature extraction is performed on the valid acquisition results to generate a physical feature template as physical feature reference information. The physical feature template is used to characterize the stable features of the preset acquisition area. In the implementation using image acquisition, the physical feature template is a texture feature vector extracted from the reference image. In the implementation using signal acquisition, the physical feature template is a signal feature vector extracted from the reference signal.

[0032] In a further implementation, physical feature templates can be generated for multiple preset collection areas and combined in a preset order to form a combined template, thereby improving resistance to occlusion and wear. Subsequently, summary information is generated from the physical feature templates for subsequent consistency judgment. The generation of summary information can be calculated based on the transport unit identifier, physical feature template, and collection area identifier, so that the summary information is bound to the transport unit and the collection area.

[0033] Finally, the physical feature template and its summary information are written into the traceability information and stored in a fixed manner. At the same time, the template version number, collection time and collection parameters are recorded so that the physical feature benchmark information can be called for consistency judgment during subsequent node handover, thereby realizing continuous verification of the identity of the transportation unit and providing benchmark data for the construction of the full life cycle traceability chain.

[0034] 2. When a transport unit is handed over at any node, the current physical characteristic information is obtained and its consistency with the physical characteristic reference information is judged. The handover certificate information is generated by combining the status information at the time of handover with the node identification information. In this embodiment, when a transport unit arrives at any transfer node and a handover operation occurs, the node terminal reads the physical characteristic reference information corresponding to the transport unit from the traceability information and initiates a handover evidence collection request to the server. The server dynamically generates collection challenge parameters for the transport unit and sends them to the node terminal. The collection challenge parameters are used to limit the specific conditions of this collection, thereby improving the unpredictability and verifiability of the evidence collection process.

[0035] In one example, the acquisition challenge parameters include at least the area coordinates of the preset acquisition area, the acquisition angle range, the lighting method, and the acquisition time window. The acquisition time window is used to constrain the node terminal to complete the acquisition within a specified time to reduce the possibility of subsequent forgery and playback. Within the acquisition time window, the node terminal acquires the preset acquisition area according to the acquisition challenge parameters, obtains the current acquisition result, and performs the same feature extraction process as the archiving stage to generate the current physical feature template.

[0036] Subsequently, the node terminal or server matches and calculates the current physical feature template with the physical feature template in the physical feature benchmark information to obtain a consistency score, and determines the consistency judgment result based on the comparison result of the consistency score and the preset threshold.

[0037] When the consistency score is lower than the preset threshold, a supplementary evidence collection mechanism is triggered. The server regenerates and issues new collection challenge parameters. The node terminal completes the collection again and obtains the consistency score obtained from the collection. The consistency scores obtained from multiple collections and the corresponding collection challenge parameters are written into the handover proof information as evidence, so as to verify that the collection process meets the challenge constraints and achieve anti-copying and anti-replay verification during subsequent review.

[0038] After the consistency check is completed, handover proof information is generated. The handover proof information is organized using a minimum set of fields, which includes at least the transport unit identifier, node identifier information, handover time information, consistency check result, a status information summary at the time of handover, and a summary pointer to the previous handover proof information.

[0039] The status information summary is generated from the status information collected at the handover time or within a preset time window before and after the handover. The status information includes at least temperature information and may further include at least one of humidity information, location information, vibration information, door opening event information, and chiller operating condition information. To reduce storage and transmission overhead, the status information summary may be a compressed or characteristic representation of the status information and is used together with the handover time information to describe the objective state at the time of the handover.

[0040] The digest pointer pointing to the previous handover proof information is used to establish a chain association between the current handover proof and the previous node's handover proof, thereby providing a basis for the boundary matching of subsequent responsibility segments and the consistency verification of the segment chain.

[0041] To ensure the consistency and verifiability of handover proof information, the minimum set of fields is standardized and encoded in a preset order. The encoding rules are used to limit the field arrangement, field length, or field separation method, so that the handover proof summary values ​​generated by different nodes are deterministic.

[0042] After completing the standardized encoding, a handover proof digest value is generated, and the handover proof digest value is associated with the node-side identity credential to form handover proof information. The node-side identity credential can be the device identity of the node terminal, the digital certificate of the node subject, or a pre-set key identifier, which is used to represent the subject that generates the handover proof information.

[0043] The handover proof information is written into the boundary record of the entire lifecycle segment chain, enabling adjacent responsible segments to establish a verifiable boundary connection relationship with the current handover proof summary value through the summary pointer of the previous handover proof information.

[0044] In a further embodiment, to achieve the non-repudiation of the handover proof information, a non-repudiation solidification operation is performed simultaneously with the generation of the handover proof information. The non-repudiation solidification operation includes performing signature solidification on the handover proof digest value to generate a digital signature, and generating a timestamp solidification record on the received handover proof digest value. At least one of the digital signature and the timestamp solidification record is bound and stored with the corresponding handover proof information, so that subsequent verification can verify that the handover proof information has not been tampered with and can determine the generating entity and the generation time, thereby providing verifiable handover evidence in case of disputes.

[0045] Through the above step S2, the physical identity evidence, handover time status information summary, chained summary pointer, and non-repudiation solidification results obtained by challenge collection are solidified into handover proof information, providing a credible boundary and evidentiary basis for subsequent responsibility fragment splicing and responsibility apportionment.

[0046] S3. Generate risk characterization information based on the status information of the node holding period between two adjacent handovers, and generate corresponding responsibility segment information; In this embodiment, the time interval between two adjacent handover proof information is used as the node holding period, and the status information collected within the node holding period is used as the input for risk calculation. The status information includes at least one of temperature information, humidity information, location information, vibration information, door opening event information, and chiller operating condition information. Among them, temperature information is used to reflect the core compliance status of the cold chain, door opening event information and vibration information are used to reflect the loss of cold energy and mechanical impact risk caused by loading, unloading, handling, door opening, etc., and refrigeration unit operating condition information is used to reflect the refrigeration capacity and operational stability.

[0047] To reduce storage and communication overhead while ensuring sufficient evidence, an adaptive sampling mechanism is adopted when acquiring status information. In this embodiment, the node terminal continuously monitors trigger conditions. When any of the following trigger conditions are detected: a door opening event, a temperature change gradient exceeding a preset threshold, or a vibration amplitude exceeding a preset threshold, the sampling frequency of the corresponding status information is increased, and a status data summary is generated at this high sampling frequency. When no trigger condition is detected, the sampling frequency of the corresponding status information is decreased, and a status data summary is generated at this low sampling frequency.

[0048] The state data summary is used to compress and express state information and is stored in association with the corresponding node holding period, so that risk calculation and evidence verification can be completed without retrieving the full amount of original data. In one implementation, the state data summary contains at least the statistical feature value and time boundary information of the sampling period. The statistical feature value includes at least one of the features of mean, extreme value, variance and rate of change. The time boundary information is used to characterize the start and end time covered by the summary.

[0049] When generating risk characterization information based on temperature information during the node holding period, the basic risk quantity is first determined. The basic risk quantity is determined by at least one of the following: over-temperature amplitude, over-temperature duration, and over-temperature area integral. The over-temperature amplitude is used to characterize the severity of the deviation from the target temperature zone, the over-temperature duration is used to characterize the duration of the deviation, and the over-temperature area integral is used to comprehensively reflect the cumulative impact of the deviation amplitude and duration. In one example, the over-temperature area integral is obtained by accumulating the deviation of the temperature curve from the threshold temperature over time, and is thus used to characterize the total over-temperature load during the holding period.

[0050] Subsequently, based on information about door opening events, vibration data, and refrigeration unit operating conditions, an abnormal event severity index is determined. This index characterizes the risk amplification effect introduced by non-temperature factors; for example, the longer the duration and the more frequent the door opening events, the higher the severity; the larger the peak value of the vibration impact, the higher the severity; and the higher the severity is indicated by insufficient cooling, frequent start-ups and shutdowns, or abnormal shutdowns in the refrigeration unit. Finally, the basic risk quantity and the abnormal event severity index are integrated and calculated according to preset weights to obtain risk characterization information that reflects the impact of the node holding period on cold chain goods. This ensures that the risk characterization information can simultaneously reflect the combined impact of temperature control deviations and abnormal events on quality or compliance.

[0051] After obtaining the risk characterization information, the corresponding responsibility segment information is further generated in step S3. The responsibility segment information is used to form a structured record that can be spliced ​​and can be divided for each node holding period. The responsibility segment information is associated with at least the start and end boundaries of the node holding period. The start and end boundaries are determined by the handover proof information of two adjacent times, thereby ensuring that the responsibility segment and the handover boundary can correspond.

[0052] The information on the responsibility segment includes at least node identification information, the time range of the node holding period, the handover proof summary pointer corresponding to the boundary, risk characterization information, and status data summary index information; among which, the status data summary index information is used to indicate the set of status data summaries associated with the responsibility segment, so as to facilitate the retrieval of the summary or original data of the corresponding time period as needed in subsequent review or dispute resolution.

[0053] To improve verifiability, a fragment digest can be generated for the responsibility fragment information and bound to the node-side identity credentials for storage. This allows the responsibility fragment information to achieve consistency verification through boundary pointers and digest checks when it is subsequently spliced ​​into a full lifecycle fragment chain.

[0054] Through the above step S3, the multi-source state information within the node holding period is organized into a state data summary corresponding to the sampling frequency under adaptive sampling reminders, and further converted into risk characterization information that can be used for responsibility division. At the same time, responsibility fragment information strongly correlated with the handover boundary is formed, thus providing a unified data structure and quantitative basis for the subsequent fragment chain splicing and consistency verification in step S4 and the responsibility contribution calculation in step S5.

[0055] S4. Based on the handover proof information, associate and splice adjacent responsibility segment information to form a full life cycle segment chain, and perform consistency verification on the segment chain; The handover certificate information generated in step S2 is used as the boundary anchor point of the responsibility segment, and the responsibility segment information of each node generated in step S3 is chained together according to the cargo flow sequence to form a segment chain covering the entire life cycle of cold chain goods.

[0056] Specifically, the handover proof information corresponding to two adjacent handovers serves as the starting and ending boundaries of the responsibility segment of the same node's holding period, respectively. The responsibility segment information is bound to the starting and ending boundaries when it is generated. Therefore, when executing step S4, the starting and ending range of each responsibility segment can be directly located according to the boundary information and arranged in sequence to form a segment chain structure, so that the segment chain can reflect the continuous flow process of "handover-holding-handover".

[0057] When splicing adjacent responsibility segment information, a boundary matching check is performed to verify whether the connection relationship between responsibility segments is genuine and continuous. The boundary matching check is accomplished by comparing the summary pointer recorded in the previous responsibility segment information with the summary pointer recorded in the next responsibility segment information. Each summary pointer is used to point to the corresponding previous handover proof information and characterize the association relationship between adjacent handover proofs. In one implementation, the summary pointer of the previous responsibility segment information should be able to correspond to the start boundary handover proof information bound to the next responsibility segment information, thereby proving that the next responsibility segment is indeed inherited after the previous handover proof boundary.

[0058] If the summary pointers do not match, the system determines that there is a risk of breakage in the boundary connection. This may be caused by missing handover documents, replacement of handover documents, alteration of the responsible segment, or abnormal replacement of the transport unit. The system will mark the connection as boundary inconsistency and record the reason for the breakage.

[0059] In addition to boundary matching verification, time sequence verification is also performed to verify the temporal rationality of the fragment chain. The time sequence verification is based on the handover time information in the handover proof information corresponding to two adjacent handovers. It requires that the handover time of the previous handover proof is earlier than the handover time of the next handover proof, and that the interval between adjacent handover times meets the preset reasonable range constraint. In one example, when adjacent handover times are reversed, the time interval is zero, or is significantly less than the minimum loading / unloading time threshold, it is determined that there is a risk of time alteration, playback, or recording anomalies. If the time sequence verification fails, the corresponding responsible segment information is also marked as a suspicious segment and the reason for the chain break is recorded.

[0060] In a further implementation, the system generates a chain-level digest after forming the fragment chain to represent the overall state of the current fragment chain, and updates the chain-level digest each time a new responsible fragment or a new handover certificate is added, thereby facilitating rapid verification of whether the fragment chain has been tampered with.

[0061] When any verification fails, the system writes the suspicious fragment and the reason for the break in the fragment chain into the abnormal record area and can trigger a supplementary evidence collection mechanism, such as prompting to increase the intensity of challenge collection or increase the frequency of state sampling when the next node is handed over, so as to enhance the evidence density of subsequent links.

[0062] Through the above step S4, the handover proof information and responsibility fragment information are formed into a verifiable full life cycle fragment chain under the constraints of boundary pointers and time verification, which provides consistency guarantee for the subsequent step S5 to calculate responsibility contribution and output evidence based on the fragment chain.

[0063] S5. When an anomaly occurs or a tracing request is received, determine the target responsibility fragment set based on the fragment chain, output the responsibility contribution result according to the preset rules, and output the tracing evidence information associated with it.

[0064] In this embodiment, when an abnormal event is detected or a tracing request is received, the set of target responsibility segments related to the target event is first located based on the full lifecycle segment chain formed in step S4. The abnormal event may include at least one of the following: over-temperature alarm, refrigeration unit abnormal alarm, door opening event abnormality, transportation route abnormality, arrival quality abnormality, and query events triggered by supervision or claims; the tracing request may come from the cargo owner, carrier, warehousing party, supervisor, or claims party.

[0065] To ensure the interpretability of the location, the location process is based on the event time window and segment boundaries. The time of the anomaly and the preset time windows before and after it are mapped onto the time axis of the segment chain, thereby determining one or more responsible segments covering the time window as the target responsible segment set. In one implementation, when the abnormal event continuously crosses multiple segment boundaries, all responsible segments within the crossed range are incorporated into the target responsible segment set.

[0066] After determining the target responsibility segment set, the responsibility contribution results are output according to preset rules. The preset rules are based on risk characterization information. The risk characterization information of each responsibility segment in the target responsibility segment set is read separately, and the risk proportion of each responsibility segment in the target set is calculated. The risk proportion can be obtained by dividing the risk characterization value of a single responsibility segment by the sum of the risk characterization values ​​in the target responsibility segment set, and this risk proportion is used as the responsibility contribution, thereby realizing the quantitative allocation of responsibility in multi-node, multi-carrier scenarios.

[0067] To improve the robustness of the allocation results, in one example, when there are segments in the target responsibility segment set with a risk characterization value of zero or abnormally missing, a default risk lower limit can be set for that segment or it can be compensated by the statistical characteristics of adjacent segments, so that the responsibility contribution calculation can still be completed and the missing data explanation can be marked in the results.

[0068] When the target responsibility segment set contains responsibility segment information that was marked as a suspicious segment in step S4, the responsibility contribution of the suspicious segment is increased, and a suspicious flag is output to indicate the risk of insufficient evidence due to broken links, inconsistent boundaries, or temporal anomalies. In one implementation, the increase can be achieved by multiplying the responsibility contribution of the suspicious segment by a preset penalty coefficient and renormalizing it, so that the sum of the responsibility contributions is ultimately kept to 1. The output also provides a summary of the reasons for the broken links corresponding to the suspicious identifiers, such as boundary matching failure, time sequence verification failure, or missing handover proof, so that third parties can quickly locate the focus of the dispute.

[0069] When outputting the responsibility contribution results, the associated traceability evidence information is output simultaneously for verification and evidence presentation. The traceability evidence information includes at least the handover proof information corresponding to the target responsibility segment set, the risk characterization information summary, and the segment chain consistency verification result. The handover proof information is used to prove the handover facts at the boundary of the responsibility segment and the identity of the transportation unit. The risk characterization information summary is used to explain the basis for risk quantification of each segment. The segment chain consistency verification result is used to explain whether the connection relationship of the target responsibility segment set in the segment chain throughout the entire life cycle is continuous and whether there are any breaks or suspicious markers.

[0070] In one example, the source tracing evidence information may also include a state data digest index associated with the target responsibility segment set, to support further verification by retrieving the state data digest or original state information for the corresponding time period when necessary. To facilitate cross-entity sharing and third-party verification, the output source tracing evidence information can be further used to generate an evidence package digest and bind it to the corresponding request identifier for storage, enabling the recipient to verify that the evidence package has not been tampered with and to confirm its source consistency without obtaining the full amount of original data.

[0071] Through the above step S5, the responsible fragments on the fragment chain are mapped into quantifiable responsibility contribution results, supplemented by verifiable source tracing evidence information, to achieve interpretable output of anomaly accountability and source tracing query.

[0072] Example 2, an embodiment of the present invention, proposes an IoT management system for the full lifecycle traceability of cold chain goods, comprising: The document establishment and anchoring module is used to establish traceability information for the transportation units of cold chain goods and obtain physical characteristic benchmark information associated with the transportation units when the goods enter the circulation link. The handover verification module is used to obtain the current physical feature information and make a consistency judgment with the physical feature benchmark information when the transport unit is handed over at any node, and generate handover verification information by combining the status information at the time of handover and the node identification information. The proof solidification module is used to generate a handover proof summary value from the handover proof information and perform a non-repudiation solidification operation, binding and storing the solidification result with the corresponding handover proof information; The status data management module is used to acquire status information and generate a status data summary during the node holding period between two adjacent handovers. The Risk and Responsibility Segment Module is used to generate risk characterization information and corresponding responsibility segment information based on the status information of the node during its holding period. The fragment chain and allocation output module is used to associate and splice adjacent responsibility fragment information based on handover proof information to form a full life cycle fragment chain and perform consistency verification. When an anomaly occurs or a tracing request is received, the target responsibility fragment set is determined based on the fragment chain, and the responsibility contribution result is output according to preset rules, along with the associated tracing evidence information.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An IoT management method for full lifecycle traceability of cold chain goods, characterized in that, Includes the following steps: When cold chain goods enter the circulation chain, traceability information is established for the transportation unit of the goods, and physical characteristic reference information associated with the transportation unit is obtained; When a transport unit is handed over at any node, the current physical characteristic information is obtained and its consistency with the physical characteristic reference information is judged. The handover certificate information is generated by combining the status information at the handover time and the node identification information. Risk characterization information is generated based on the state information of the node holding period between two adjacent handovers, and corresponding responsibility segment information is generated. Based on the handover proof information, adjacent responsibility segment information is associated and spliced ​​to form a full life cycle segment chain, and the segment chain is verified for consistency. When an anomaly occurs or a tracing request is received, the target responsibility fragment set is determined based on the fragment chain, the responsibility contribution result is output according to preset rules, and the tracing evidence information associated with it is output.

2. The IoT management method for full lifecycle traceability of cold chain goods according to claim 1, characterized in that, Obtaining the physical feature reference information includes: When the transport unit enters the cold chain circulation, the physical carrier on the transport unit used for anti-tampering or identification is selected as the collection object. The physical carrier includes at least one of the seal, packaging surface, and label carrier. A preset acquisition area is determined on the acquisition object according to a preset acquisition rule, and acquisition is performed on the preset acquisition area. The acquisition includes at least one of image acquisition and signal acquisition. Feature extraction is performed on the collected results to generate a physical feature template as the physical feature benchmark information, and summary information is generated on the physical feature template for subsequent consistency judgment.

3. The IoT management method for full lifecycle traceability of cold chain goods according to claim 2, characterized in that, The step of acquiring current physical characteristic information when a transport unit is handed over at any node includes a challenge-based acquisition step, which includes: The server generates and distributes collection challenge parameters for the transportation unit. The collection challenge parameters include at least the coordinates of the collection area, the collection angle, the lighting mode, and the collection time window. Within the collection time window, the node terminal collects data from the preset collection area according to the collection challenge parameters to obtain the current collection result; Feature extraction is performed on the current acquisition results to generate the current physical feature template, and a consistency score is obtained by matching and calculating the physical feature template corresponding to the physical feature benchmark information. When the consistency score is lower than the preset threshold, the collection challenge parameters are reissued and the collection is completed again. The consistency scores obtained from the two collections and the corresponding collection challenge parameters are recorded together in the handover proof information for anti-copying and anti-replay verification.

4. The IoT management method for full lifecycle traceability of cold chain goods according to claim 3, characterized in that, The generated handover proof information includes: After completing the consistency judgment, the minimum set of fields for the handover proof information is determined. The minimum set of fields includes at least the transport unit identifier, node identifier information, handover time information, the consistency judgment result, status information summary, and a summary pointer to the previous handover proof information. The minimum set of fields is normalized and encoded in a preset order to generate a handover proof digest value; The handover proof digest value is associated with the node-side identity credential to form handover proof information, and the handover proof information is written into the boundary record of the full lifecycle fragment chain for boundary matching and consistency verification of adjacent responsibility fragment information.

5. The IoT management method for full lifecycle traceability of cold chain goods according to claim 4, characterized in that, The generation of the handover proof information also includes a non-repudiation solidification step, wherein the non-repudiation solidification step is: Perform signature solidification on the handover proof digest value to generate a digital signature; Generate a timestamp and solidify the received handover proof digest value into a record; At least one of the digital signature and the timestamp-based solidified record is bound and stored with the corresponding handover proof information so that the handover proof information can be verified to be unaltered and the generating entity and generation time can be determined during subsequent verification.

6. The IoT management method for full lifecycle traceability of cold chain goods according to claim 5, characterized in that, The status information includes at least one of the following: temperature information, humidity information, location information, vibration information, door opening event information, and chiller operating condition information; Furthermore, obtaining the state information includes an adaptive sampling step, which includes: When any of the following triggering conditions are detected, the sampling frequency of the corresponding state information is increased and a state data summary at the corresponding sampling frequency is generated. The triggering conditions include door opening event, temperature change gradient exceeding a preset threshold, and vibration amplitude exceeding a preset threshold. When the triggering condition is not detected, the sampling frequency of the corresponding state information is reduced and a state data summary at the corresponding sampling frequency is generated. The state data summary at the corresponding sampling frequency is associated with the corresponding node holding period to generate the risk characterization information.

7. The IoT management method for full lifecycle traceability of cold chain goods according to claim 6, characterized in that, The generated risk characterization information includes: Based on the temperature information during the node holding period, at least one of the following is determined as the basic risk quantity: over-temperature amplitude, over-temperature duration, and over-temperature area integral. The severity index of abnormal events is determined based on door opening event information, vibration information, and chiller operating condition information; The basic risk quantity and the severity index of the abnormal event are fused together according to a preset weight to obtain risk characterization information that represents the impact of the node holding period on cold chain goods.

8. The IoT management method for full lifecycle traceability of cold chain goods according to claim 7, characterized in that, The step of associating and splicing adjacent responsibility segment information based on the handover proof information to form a full lifecycle segment chain includes: Using the handover proof information corresponding to two adjacent handovers as the starting and ending boundaries of the responsibility segment, the responsibility segment information generated by the corresponding node holding period is bound to the starting and ending boundaries; When splicing adjacent responsibility segment information, a boundary matching check is performed. The boundary matching check includes comparing the consistency of the summary pointer recorded in the previous responsibility segment information with the summary pointer recorded in the next responsibility segment information, wherein each summary pointer points to the association relationship of the corresponding previous handover proof information. And perform time sequence verification, which includes verifying the chronological relationship of the handover time information in the handover certificate information corresponding to two adjacent handovers; When any verification fails, the corresponding responsible segment information is marked as a suspicious segment and the reason for the chain break is recorded.

9. The IoT management method for full lifecycle traceability of cold chain goods according to claim 8, characterized in that, The step of outputting the responsibility contribution result according to preset rules and outputting the associated source tracing evidence information includes: In the set of target responsibility segments, risk characterization information corresponding to each responsibility segment is obtained, the risk proportion of each responsibility segment is calculated, and the risk proportion is used as the responsibility contribution. When there are responsibility segment information marked as suspicious segments in the target responsibility segment set, increase the responsibility contribution of the suspicious segment and output a suspicious identifier; When outputting the responsibility contribution result, the source tracing evidence information is also output. The source tracing evidence information includes at least the handover proof information corresponding to the target responsibility fragment set, the risk characterization information summary, and the fragment chain consistency verification result.

10. An IoT management system for full lifecycle traceability of cold chain goods using any one of the methods described in claims 1-9, characterized in that, include: The document establishment and anchoring module is used to establish traceability information for the transportation unit of cold chain goods and obtain physical characteristic reference information associated with the transportation unit when the cold chain goods enter the circulation link; The handover verification module is used to obtain the current physical feature information when the transport unit is handed over at any node and to make a consistency judgment with the physical feature reference information, and to generate handover verification information by combining the status information at the time of handover and the node identification information. The proof solidification module is used to generate a handover proof digest value for the handover proof information and perform a non-repudiation solidification operation, and bind and store the solidification result with the corresponding handover proof information; The status data management module is used to acquire status information and generate a status data summary during the node holding period between two adjacent handovers. The risk and responsibility segment module is used to generate risk characterization information and corresponding responsibility segment information based on the status information of the node during its holding period. The segment chain and allocation output module is used to associate and splice adjacent responsibility segment information based on the handover proof information to form a full life cycle segment chain and perform consistency verification. When an anomaly occurs or a tracing request is received, the target responsibility segment set is determined based on the segment chain, and the responsibility contribution result is output according to preset rules, along with the associated tracing evidence information.

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