Cold-chain logistics remote monitoring platform and method based on Internet of Things

Abstract

The invention relates to the technical field of cold-chain logistics monitoring, in particular to a cold-chain logistics remote monitoring platform and method based on the Internet of Things. An environment monitoring unit collects position and temperature and humidity data through a sensor array; the goods decay map generation unit is combined with a genotype biological activity database and a cross-stage accumulative exposure analysis model, a real-time quality decay coefficient is output through a space-time stress coupling algorithm and biological verification, the block chain dynamic evidence storage unit generates a traceability block containing decay data and an environment hash value, and the traceability block is used for storing the quality decay coefficient. And a cross-stage association chain is constructed to form a life cycle quality map, the adaptive regulation and control unit dynamically generates a threshold value according to a decay coefficient and a goods value loss function, and triggers responsibility report pushing and emergency goods scheduling, and the scheme improves the accuracy of quality evaluation, makes responsibility division clear, and ensures efficient and stable operation of cold-chain logistics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cold chain logistics monitoring, in particular to a cold chain logistics remote monitoring platform and method based on Internet of Things. BACKGROUND

[0002] Cold chain logistics monitoring is an important technology. In cold chain logistics, the quality of goods is easily affected by environmental factors such as temperature and humidity, vibration, etc. Real-time and accurate monitoring is the core means to ensure the freshness of goods and reduce loss.

[0003] This technology integrates transportation chain data to provide digital support for goods quality traceability, risk warning and responsibility definition, which is of great significance to improve cold chain efficiency and reduce loss rate. Traditional methods relying on single environmental monitoring or manual recording have been difficult to meet the needs of fine management in complex transportation scenarios.

[0004] However, the traditional cold chain logistics monitoring technology has the core problems of one-sided quality degradation evaluation and fuzzy responsibility traceability. The existing scheme only monitors real-time environmental data without considering the differences in goods genotype and the cumulative effect of historical environmental exposure, resulting in large prediction deviation of quality degradation. Different genotypes of fresh food have different tolerance to temperature and humidity fluctuations, and a unified warning standard may cause misjudgment of goods loss. At the same time, there is a lack of tamper-proof quality data evidence when goods are transferred, and the responsibility of each link is unclear. When goods are damaged, it is difficult to trace the specific responsibility node, leading to disputes. These problems have caused high loss rate, which not only increases logistics cost but also damages the reputation of upstream and downstream enterprises, making it difficult to meet the needs of accurate quality control and clear responsibility in cold chain logistics. In order to solve this problem, we provide a cold chain logistics remote monitoring platform and method based on Internet of Things. SUMMARY

[0005] The present application aims to provide a cold chain logistics remote monitoring platform and method based on Internet of Things to solve the problems raised in the background.

[0006] 1. Since traditional monitoring does not consider the cumulative effect of goods genotype and historical environment, the prediction deviation of quality degradation is large, therefore, the present case generates a goods degradation map through a goods degradation map generation unit, combines a biological activity database and a cross-stage cumulative exposure analysis model, and outputs a real-time quality degradation coefficient, which can accurately evaluate the quality of goods and reduce misjudgment of goods loss.

[0007] 2. Since traditional monitoring lacks tamper-proof quality evidence, the responsibility is unclear, therefore, the present case generates a traceability block through a blockchain dynamic evidence unit to build a cross-stage degradation correlation chain, which can clearly define the responsibility of each link and reduce disputes.

[0008] To achieve the above purpose, a cold chain logistics remote monitoring platform based on Internet of Things is provided, which comprises: The environmental monitoring unit is configured with a sensor array at each node of the cold chain transportation, and real-time collection of position coordinates, temperature and humidity, light intensity, vibration spectrum and cabin door opening and closing frequency is performed; The cargo degradation map generation unit internally has a biological activity database classified according to cargo genotypes, and deploys a cross-stage cumulative exposure analysis model, inputs current environmental data and historical cumulative environmental exposure values of the transportation stage, and outputs real-time quality degradation coefficients; The blockchain dynamic storage unit generates non-divisible traceability blocks at the cargo transfer nodes, the traceability blocks contain real-time quality degradation coefficients, historical cumulative degradation values and environmental data hash values, each traceability block is connected through a cross-stage degradation correlation chain to form a complete transportation life cycle quality map, the cargo value loss function is extracted from the life cycle quality map, and the following is performed: The adaptive control unit generates node differentiation thresholds according to the real-time quality degradation coefficients and the cargo value loss function, when the node differentiation thresholds exceed the standard thresholds, a responsibility definition report is pushed to the upstream and downstream associated parties and emergency replacement cargos in the cold chain resource pool within a preset range are locked.

[0009] The second object of the present application is to provide a method for implementing the above-mentioned cold chain logistics remote monitoring platform based on the Internet of Things, comprising the following steps: S1, through the sensor array deployed at the cold chain transportation nodes, real-time collection of position, temperature and humidity, light, vibration and cabin door opening and closing data is performed, at the same time, the degradation response curve in the biological activity database of the cargo genotype is called, based on the cross-stage cumulative exposure analysis model, the current environmental data and the historical cumulative environmental exposure values of the transportation stage are coupled and calculated in space and time, the time decay weighting mechanism is introduced to correct the influence of the historical data, the real-time quality degradation coefficients with credibility rating are output, and the model dynamic calibration is realized through the biological verification of the miniature cell culture cabin; S2, at the cargo transfer nodes, the blockchain dynamic storage is triggered to generate non-divisible traceability blocks containing real-time quality degradation coefficients, historical cumulative degradation values and environmental data hash values, the hierarchical encryption structure is used to store the core data and embed the multi-party consensus key, the cross-stage degradation correlation chain is constructed through the directional attenuation pointer technology, the responsibility contribution topology map is automatically generated to mark the degradation responsibility proportion of each node, the visual quality heat map is generated by binding the smart contract, and the electronic bill of lading containing the three-dimensional degradation traceability path is pushed to the associated parties; S3. Analyze the decay curve of cargo economic value from the life cycle quality map, generate cargo value loss function by combining real-time market pricing and cargo owner loss matrix, calculate cargo value loss sensitivity of the current transportation node through threshold game optimization algorithm, and superimpose the carrier's historical performance rating dynamic floating threshold baseline. When the real-time decay coefficient triggers the threshold, simultaneously execute the responsibility definition report push, smart contract automatic claims and emergency cargo scheduling in the same batch, and prioritize matching replacement cargo registered on the blockchain in the cold chain resource pool.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The cargo decay map generation unit has a built-in bioactivity database classified by genotype. Combined with a cross-stage cumulative exposure analysis model, it integrates current and historical environmental data through a spatiotemporal stress coupling algorithm, introduces a time decay weighting mechanism to correct for historical effects, and also uses a micro cell culture chamber biovalidation dynamic calibration model to output a real-time quality decay coefficient with a credibility rating. This process fully considers the differences in environmental tolerance of different cargo genotypes, accurately reflects the cumulative exposure effect, solves the assessment bias caused by traditional unified standards, and provides an accurate basis for quality control.

[0011] 2. The blockchain dynamic evidence storage unit uses a layered encryption structure to store core data, embeds multi-party consensus keys, and constructs a cross-stage decay association chain through directional decay pointer technology. It automatically generates a responsibility contribution topology map to mark the responsibility ratio of each node. When goods are handed over, an indivisible traceability block is generated and a complete life cycle quality map is formed, ensuring that the data is tamper-proof and making the responsibility of each link clear at a glance, reducing disputes caused by ambiguity of responsibility.

[0012] 3. The adaptive control unit dynamically adjusts the threshold based on the real-time quality decay coefficient and cargo value loss function through a threshold game optimization algorithm. When the threshold is triggered, a responsibility determination report is pushed out, and emergency replacement goods are locked and automatic claims are initiated. Priority is given to matching the same batch of goods in the cold chain resource pool, which improves the efficiency of risk response, reduces the economic losses caused by cargo damage, and ensures the stability and reliability of cold chain logistics. Attached Figure Description

[0013] Figure 1 This is an overall block diagram of the present invention; Figure 2 This is the overall flowchart of the present invention.

[0014] The meanings of the labels in the diagram are as follows: 1. Environmental monitoring unit; 2. Cargo decay map generation unit; 3. Blockchain dynamic evidence storage unit; 4. Adaptive control unit. Detailed Implementation

[0015] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0016] The present application provides a cold chain logistics remote monitoring platform based on Internet of Things, please refer to Figure 1 As shown in the figure, comprising: The environmental monitoring unit 1 is configured in the sensor array of each node of the cold chain transportation, and real-time collects the position coordinates, temperature and humidity, light intensity, vibration spectrum and cabin door opening and closing frequency; The goods decay map generation unit 2 internally has a biological activity database classified according to the genotype of goods, and deploys a cross-stage cumulative exposure analysis model, inputs the current environmental data and the cumulative value of environmental exposure in the historical transportation stage, and outputs the real-time quality decay coefficient; The biological activity database classified according to the genotype of goods in the goods decay map generation unit 2 is constructed through multi-dimensional biological stress characteristic mapping technology, realizing fine evaluation of the quality decay of goods, and the specific implementation manner is as follows: For typical goods in cold chain transportation (such as meat, fruits and vegetables, dairy products), collect their cell-level decay data under different environmental stresses according to genotype classification, simulate the stress conditions of temperature and humidity fluctuations, vibration intensity, and light duration in the transportation scenario, record the decay indicators of cell activity changes and enzyme activity fluctuations of different genotypes of goods through microscope observation and biosensor monitoring, track and record the decay data every hour for 72 hours, for example, collect the cell juice loss of A genotype strawberries under temperature fluctuations (0℃→3℃→0℃), record it every hour, get the complete decay response curve, establish the genotype-environment stress mapping table, organize the collected cell-level decay data according to the structure of "genotype-environment stress-decay indicator", form the mapping table, the horizontal dimension is different environmental stress combinations (such as "temperature 5℃+vibration 20 hertz" "humidity 80%+light 4 hours"), the vertical dimension is the genotype of goods, such as A, B, C genotypes of strawberries and X, Y genotypes of beef, the table content is the decay indicator value under the corresponding combination (such as the cell juice loss rate of A genotype strawberries under "temperature 5℃+vibration 20 hertz" is 15% after 6 hours), the role of the mapping table is to clarify the decay law of specific genotype goods under specific environment, for example, the decay speed of B genotype blueberries under humidity 70% is twice that under humidity 90%, introduce the cross-species decay acceleration factor library, which records the difference in decay sensitivity of different species to the same environmental stress, and classify goods into three basic lineages according to biological characteristics, high sensitivity lineage goods (such as strawberries, fresh-cut flowers) that react strongly to environmental stresses such as temperature and humidity fluctuations and vibration (decay acceleration factor ≥2.0, i.e. the decay rate significantly accelerates with slight environmental changes), medium sensitivity lineage goods (such as apples, pork) that react moderately to environmental stress (decay acceleration factor between 1.0 and 2.0), and low sensitivity lineage goods (such as potatoes, frozen meat) that are resistant to environmental stress (decay acceleration factor ≤1.0), by quantifying the environmental sensitivity of different goods through acceleration factor, goods of the same lineage can share similar decay evaluation models, reducing database redundancy.For example, strawberries and fresh-cut flowers both belong to the high-sensitivity spectrum and can share a set of basic decay parameters. Based on the above classification, a hierarchical bioactivity database is finally formed. The database is divided into levels according to "basic spectrum → genotype → specific goods" (such as "high-sensitivity spectrum → A genotype → strawberry"). Each type of goods is associated with a bioactivity decay curve in the hierarchical bioactivity database. The curve takes time as the horizontal axis, decay coefficient (0-1, 0 represents fresh, and 1 represents complete decay) as the vertical axis, and marks the decay trend under different environmental stresses (such as the decay curve of A genotype strawberry at 0°C is flat, and the curve is steep at 5°C). For example, under the "medium-sensitivity spectrum → X genotype → pork" entry in the database, the associated decay curve shows that when stored at 4°C, the decay coefficient is 0.2 at 24 hours and 0.5 at 48 hours; when stored at 8°C, the decay coefficient reaches 0.6 at 24 hours, directly reflecting the effect of temperature on its decay, providing accurate basic data for subsequent cross-stage cumulative exposure analysis models, and ensuring that the quality decay evaluation of different goods can be tailored to their biological characteristics.

[0017] To accurately calculate the cumulative decay impact of goods throughout the entire transportation cycle using the cross-stage cumulative exposure analysis model, the training process uses a space-time stress coupling algorithm to integrate historical environmental influences, real-time stress superposition, and stage critical parameters to generate real-time quality decay coefficients with credibility. The specific implementation is as follows: The decay curve of the cargo genotype in the bioactive database, such as the temperature-decay curve of the highly sensitive spectrum strawberry, is used as the benchmark input for model training, ensuring that the model calculation fits the biological characteristics of the cargo. On this basis, the cumulative value of environmental exposure in the historical transportation stage (such as the cumulative impact of "temperature fluctuation 3℃ for 2 hours" and "vibration 10 Hz for 4 hours" in the previous transportation) is loaded, and a time decay weighting mechanism is introduced to allocate weights according to the distance from the current stage. The recent historical data (such as within 12 hours) has a high weight (such as 0.8), and the long-term historical data (such as 48 hours ago) has a low weight (such as 0.3), avoiding the interference of outdated data on the current evaluation. For example, in the previous transportation of a certain strawberry, there was a "temperature fluctuation of 5℃" 24 hours ago and a "vibration of 20 Hz" 12 hours ago. The former weight is 0.4 and the latter weight is 0.7. Multiply the corresponding exposure values and add them up to get the weighted historical cumulative impact. The environmental stress superposition verification channel converts temperature and humidity fluctuations (such as temperature fluctuation every 10 minutes between 0℃ and 4℃) and vibration frequency spectrum (such as 20 Hz low-frequency vibration and 50 Hz high-frequency vibration appearing alternately) into energy values. The greater the temperature fluctuation amplitude and the higher the vibration frequency, the greater the energy integral value (such as the energy value of 4℃ fluctuation is twice that of 2℃ fluctuation). The energy integral values of temperature and humidity and vibration are superimposed according to the influence weight (such as temperature weight 0.6 and vibration weight 0.4) to get the real-time environmental stress total value of the current stage.For example, the temperature energy value is 50, the vibration energy value is 30, and after superposition, it is 50*0.6+30*0.4=42. The core of this process is to convert multi-dimensional environmental parameters into a unified energy index, which is convenient for quantitative evaluation of the impact of comprehensive stress on goods, and outputs real-time cumulative exposure equivalent. Combined with the critical deterioration coefficient of the current transportation stage of the goods, the decay rate matching operation is performed in parallel in the cloud edge computing node, and the real-time cumulative exposure equivalent is calculated by combining the historical weighted cumulative value and the current stress superposition value. Real-time cumulative exposure equivalent=historical weighted cumulative value+current stress superposition value. At the same time, the critical deterioration coefficient of the current transportation stage of the goods (such as the critical deterioration coefficient of strawberries in the middle of transportation is 80, that is, when the cumulative exposure equivalent exceeds 80, the quality will decrease sharply) is introduced as a reference threshold for subsequent decay rate calculation. The real-time cumulative exposure equivalent and the critical deterioration coefficient are input into the cloud edge computing node, and the decay rate matching operation is performed in parallel. The decay curve of the corresponding genotype goods is retrieved from the biological activity database, and the decay rate corresponding to the real-time cumulative exposure equivalent is found (such as the decay rate of strawberries corresponding to the equivalent of 72 is 0.05 per hour). According to the completeness of the current environmental data (such as the sensor data missing rate <5% is "high reliability", and 5%-10% is "medium reliability") and the matching degree of historical data (such as the deviation of the current decay rate from the historical same type transportation <10%), the reliability rating is given (such as "high reliability" and "medium reliability"). Combined with the decay rate and the transportation time, the real-time quality decay coefficient is calculated (such as transporting for 10 hours, the decay rate is 0.05 per hour, and the coefficient is 0.5), and the reliability rating is attached (such as "0.5 (high reliability)"). For example, the real-time cumulative exposure equivalent of a certain X genotype beef is 60, the critical deterioration coefficient is 100, the matched decay rate is 0.03 per hour, and after 8 hours of transportation, the real-time quality decay coefficient is 0.24, and the rating is "high reliability". The real-time quality decay coefficient with reliability rating is generated to accurately reflect the cumulative decay state of the goods in the whole transportation period and provide reliable basis for subsequent blockchain notarization and adaptive regulation.

[0018] To ensure the accuracy of the real-time quality decay coefficient, its output process is dynamically calibrated through a double feedback verification mechanism, combined with biological simulation verification and model retraining, so that the coefficient always fits the actual decay state of the goods. The specific implementation is as follows: When the cross-stage cumulative exposure analysis model is based on current environmental data and historical cumulative values, the first output of the real-time quality decay coefficient is immediately started (such as the decay coefficient of a strawberry is 0.3, and the reliability rating is high), and a short-period biological simulation verification process is started. A small amount of actual goods is extracted from the transport carrier as a sample (such as 3 representative A genotype strawberries are selected from the entire batch of strawberries), and is placed in the micro cell culture cabin carried by the transport carrier. The culture cabin can simulate the temperature, humidity, vibration and other conditions of the current transport environment to ensure that the sample decay state is consistent with the entire batch of goods. Real-time actual decay data of the sample (such as the cell activity of the strawberry sample decreases by 5% per hour, and the actual decay degree after 2 hours corresponds to a coefficient of 0.1) is recorded by the biological sensor (such as a sensor that detects cell activity and a weight sensor that monitors juice loss) built in the culture cabin. The actual decay coefficient of the sample is compared with the predicted coefficient output by the model, and the deviation value (such as the model predicts a coefficient of 0.3, the actual coefficient is 0.1, and the deviation is 0.2) is calculated. That is, by comparing the actual decay deviation of the sample and the predicted value through the micro cell culture cabin carried by the transport carrier, the accuracy of the model prediction is verified by the decay state of the real sample, and the deviation caused by the fixed model parameters is avoided. The decay of biological samples is the "actual result", and the model prediction needs to be calibrated to it. A preset fault tolerance threshold (such as a deviation value ≤0.1 is acceptable) is set, and when the deviation exceeds the preset fault tolerance threshold, a dynamic model retraining loop is triggered: Collecting environmental data (such as real-time temperature and humidity curves, vibration spectrum records) and actual decay data (such as changes in cell activity and juice loss of strawberry samples within 2 hours) of the sample during the current transport stage forms an incremental training set. These data directly reflect the real decay law of goods under the current environment and are the key basis for model calibration. Under the supervision of the dynamic storage unit 3 of the blockchain (to ensure that the data cannot be tampered with), the incremental training set is input into the environmental stress index model (which is used to calculate the correlation between environmental stress and decay coefficient), and the model parameters are adjusted (such as correcting the influence weight of temperature fluctuations on decay speed from 0.6 to 0.7). The real-time quality decay coefficient is recalculated (such as correcting the original predicted coefficient 0.3 to 0.15) by the updated model, and a version traceability identifier (such as "V2-20240520-14:30", including version number and correction time) is added to the coefficient to facilitate traceability and record adjustment. For example, the actual decay coefficient of a beef sample deviates from the model prediction by 0.25 (out of limits), and the environmental data (such as "temperature 8°C for 1 hour + vibration 15 Hz") and the actual fat oxidation rate of the beef in the current environment are collected to retrain the model. The temperature influence weight is adjusted from 0.5 to 0.6, and the corrected decay coefficient is changed from 0.4 to 0.3, and the version identifier is marked to ensure the accuracy of the decay data and the evaluation of the value loss of the subsequent blockchain storage.

[0019] The blockchain dynamic evidence storage unit 3 generates an indivisible traceability block at the cargo handover node, the traceability block containing a real-time quality decay coefficient, a historical cumulative decay value and an environmental data hash value, each traceability block being connected through a cross-stage decay correlation chain to form a complete transport life cycle quality map, and a cargo value loss function of the cargo being extracted from the life cycle quality map.

[0020] To ensure the security, integrity and responsibility traceability of the cargo quality decay data in cold chain transportation, the traceability block of the blockchain dynamic evidence storage unit 3 adopts a hierarchical encryption structure, and through the cooperative operation of the core layer, the verification layer and the access layer, the data is managed in stages and the responsibility is clear, and the specific implementation is as follows: The block core layer stores the original data of the real-time quality decay coefficient and the historical cumulative decay value. The core layer is the basis of the traceability block and is used to store the unencrypted original data as the benchmark for the entire blockchain storage. The storage content includes real-time quality decay coefficient (such as the current decay coefficient of a strawberry 0.3), historical cumulative decay value (such as the total decay coefficient from shipment to the current 1.2), and corresponding timestamp (accurate to seconds, such as "2024-05-20 08:30:00"). The data is stored in a structured table form, and each record is associated with a unique identification of the goods (such as batch number "20240520-ST-001"). This ensures that the full-cycle decay data of a certain batch of goods can be quickly located through the identification. The design logic of the core layer is that the original data is the basis for subsequent verification and responsibility division, and it needs to be kept intact and cannot be tampered with. The chain storage structure of the blockchain naturally guarantees this. Any modification of the core layer data will cause the subsequent block verification to fail. The verification layer embeds a multi-party consensus key generated based on the environmental data hash value. The verification layer is built on top of the core layer. A multi-party consensus key is generated based on the environmental data hash value to ensure the authenticity of the data and the common recognition of the participants. The environmental data of the current transportation stage (temperature and humidity curve, vibration spectrum, cabin door opening and closing record, etc.) is subjected to hash operation (an algorithm that converts arbitrary data into a fixed-length string), and a unique hash value (such as "a3b7c9…") is obtained. This value can be used to verify whether the environmental data has been tampered with (data changes will cause the hash value to change). Based on the above hash value, a complete consensus key is generated, which is then split into three independent key fragments, held by the carrier, the consignee, and the regulatory agency respectively. Any party cannot obtain the complete key alone and needs to combine the key fragments of the three parties to unlock the verification layer. When verifying the block data, the three parties submit the key fragments, which are combined by the system and matched with the hash value. If the match is successful, it proves that the core layer data and the environmental data are consistent (have not been tampered with). For example, the environmental data hash value of a certain batch of blueberries is "x5y8z2…", and the generated consensus key is split into "x5…" held by the carrier, "y8…" held by the consignee, and "z2…" held by the regulatory agency. The three-party key combination is matched with the hash value to verify the authenticity of the data. The access layer adds a decay correlation identifier. The responsibility contribution topology graph is automatically generated by analyzing the cross-stage decay correlation chain. The access layer, as the outer layer of the block, adds a decay correlation identifier (marks the decay influence correlation degree of the current block and the previous and subsequent blocks, such as "previous influence degree 30%" indicating that 30% of the current decay is caused by the previous transportation stage). The responsibility contribution topology graph is generated by analyzing the cross-stage decay correlation chain. The system reads the decay correlation identifier of each block, traces the decay data of the current goods at each transportation node (such as warehouse, road transportation, and railway transportation), divides the decay coefficient of each node by the total decay coefficient (such as warehouse 0.3 ÷ total 1.2=25%, highway 0.5÷1.2≈42%), to obtain the contribution percentage of each node to the total decay, to take the node as the vertex and the contribution percentage as the weight, to draw a responsibility contribution topology graph (such as the storage node is marked 25% and the highway node is marked 42%), and to intuitively display the responsibility proportion of each link, for example, the total decay coefficient of a batch of beef is 1.0, among which the processing node contributes 0.2 (20%), the transportation node contributes 0.5 (50%), and the storage node contributes 0.3 (30%), each node is marked in proportion in the topology graph, and the responsibility of the transportation node is the largest, when the quality problem of the goods occurs, the responsibility proportion of each node can be directly obtained from the topology graph, which provides a basis for dispute handling.

[0021] In order to realize the coherent tracing and responsibility closed loop of the decay data of the goods in the whole transportation cycle, the cross-stage decay correlation chain is constructed by the directional attenuation pointer technology, so that the decay data of each transportation node can be correlated and checked, and the specific implementation manner is as follows: When each traceability block is generated, a decay accumulation pointer to the previous block and a decay prediction pointer to the subsequent block are automatically created. The decay accumulation pointer points to the traceability block of the previous transport stage, and the pointer encapsulates the environmental stress conduction coefficient (representing the degree of influence of the previous environment on the current goods decay, such as the conduction coefficient of the temperature fluctuation of the previous storage stage on the current road transport stage decay being 0.3, i.e. 10% of the previous decay will cause 3% of additional decay). For example, when the block of the road transport stage is generated, its accumulation pointer points to the block of the storage stage, and the encapsulated conduction coefficient is 0.2, indicating that the decay of the storage stage has a 20% continuation effect on the current stage, and the decay prediction pointer points to the block of the subsequent possible transport stage (such as predicting railway transport from road transport), and the pointer also encapsulates the conduction coefficient (such as the conduction coefficient of road transport to railway transport 0.4, which is used to predict the subsequent decay trend).For example, the prediction pointer of the highway transportation block points to the railway transportation block, with a conduction coefficient of 0.3, indicating that the decay state of the current highway transportation may cause a 30% decay increment of the railway transportation stage. The design logic of the pointer is that the cargo decay has continuity, and the influence of the previous environment will be "conducted" to the subsequent stage. The pointer association can realize the connection of the whole chain decay data, avoid the isolation of each stage data, and encapsulate the environmental stress conduction coefficient in the pointer. When a new block is added, the system automatically backtracks and recalculates the historical cumulative decay value along the pointer chain, forming a closed loop verification channel. The new block finds the previous highway transportation block through the decay cumulative pointer, and then traces back to the more previous warehouse block through the cumulative pointer of the highway transportation block, forming a complete pointer chain: warehouse→highway→railway. Based on the conduction coefficient encapsulated in each pointer, the contribution value of each stage to the total decay is recalculated. For example, the original decay coefficient of the warehouse stage is 0.2, and the contribution to the total decay is 0.04 after being calculated by the highway transportation conduction coefficient of 0.2. The highway transportation itself has a decay coefficient of 0.3, and the total contribution to the decay is 0.3. The railway transportation decay coefficient is 0.4, and the total contribution after superimposing the previous conduction influence is 0.4+0.3x0.3(highway conduction to railway)=0.49. The total cumulative decay value is 0.04+0.3+0.49=0.83. Compare the recalculated cumulative value with the cumulative value recorded in each stage. If the deviation is within the allowed range (e.g. ≤0.05), the verification is passed. If the deviation exceeds the limit, a data anomaly alarm is triggered, prompting to check the sensor data or pointer conduction coefficient of the corresponding stage. By backtracking and recalculating, the relevance and accuracy of the decay data of each stage are ensured, and the deviation of the whole chain evaluation caused by a single stage data error is avoided. The addition of each new block is equivalent to a "review" of the historical data. When the transportation task is completed, all pointers are solidified on the block chain to generate an indestructible decay trajectory chain. The trajectory chain node contains the handover timestamp and the responsibility person's biometric signature. Each node (i.e. each stage block) supplements the record of the time stamp (accurate to seconds, e.g. "2024-06-1014:30:00") and the responsibility person's biometric signature (e.g. the fingerprint of the delivery driver or the face recognition record of the receiving personnel) of the cargo handover, clearly defining the responsibility person of each node. The encryption feature of the block chain ensures that the solidified trajectory chain cannot be modified or deleted. Any attempt to modify the data of a node will be rejected by all network nodes, ensuring the authenticity of the trajectory chain. For example, the trajectory chain of a batch of cold-chain fruits contains three nodes: warehouse (timestamp 2024-06-0808:00, responsibility person A signature), highway transportation (timestamp 2024-06-0910:00, responsibility person B signature), and railway transportation (timestamp 2024-06-10, 14:30, responsibility person C signature). Each node is associated through the pointer, and the whole cycle decay data and responsibility attribution from warehouse to delivery are recorded, providing a coherent and reliable basis for the quality evaluation and responsibility division of the whole life cycle of the goods.

[0022] In order to make the life cycle quality map accurately reflect the state of goods at the time of transfer of ownership and trigger associated operations, the generation process implants a transfer of ownership trigger mechanism, which automatically performs three core operations at the moment of each handover of goods through the blockchain dynamic evidence unit 3. The specific implementation is as follows: At the moment of handover of goods (such as from the warehouse link to the transportation link, and from the transportation link to the consignee), the blockchain dynamic evidence unit 3 automatically retrieves the current decay trajectory chain (i.e. the full-stage decay data chain from delivery to the current node), and generates a visual quality heat map: a) Based on the current decay trajectory chain, a visual quality heat map is generated, with a spectral color scale indicating the decay risk level of each stage. The transportation time is taken as the horizontal axis (such as from 8:00 on June 1st to 10:00 on June 3rd), and the transportation nodes are taken as the vertical axis (such as warehouse, highway transportation, and railway transportation), forming a two-dimensional grid. Each grid corresponds to a specific node within a certain time period, and the decay risk level of each stage is represented by a spectral color scale. Green represents low risk (decay coefficient ≤ 0.3), yellow represents medium risk (0.3 < decay coefficient ≤ 0.6), and red represents high risk (decay coefficient > 0.6). For example, the grid for the warehouse stage from 8:00 to 12:00 on June 1st is green (decay coefficient 0.2), and the grid for highway transportation from 14:00 to 18:00 on June 2nd is red (decay coefficient 0.7). The generation logic of the heat map is to visually present the decay risk of each stage through color, allowing both parties to quickly judge the quality change of the goods in different links and solve the problem of traditional text reports that are difficult to quickly identify risks.

[0023] b) Bind the key nodes of the visual quality heat map to the smart contract terms, and automatically freeze a preset proportion of the goods when the cumulative decay value exceeds the limit. The key nodes in the visual quality heat map (such as the start and end points of each transportation stage and the time points of sudden increase in decay coefficient) are bound to the pre-set smart contract terms, enabling automatic risk control. Key nodes in the heat map are marked, such as "the time point of sudden increase in decay coefficient from 0.4 to 0.6 in the highway transportation stage (6 / 2 16:00)" and "the cumulative decay value at the end of railway transportation (0.8)". The smart contract has pre-set terms such as "when the cumulative decay value exceeds 0.7, automatically freeze 30% of the goods" and "when a red high-risk grid appears in a certain stage, freeze 10% of the goods". The decay data of the key nodes of the map automatically match the terms. If the cumulative decay value or the stage risk level reaches the contract threshold at the time of handover, the system immediately executes the goods freezing, such as freezing 30% of the goods when the cumulative decay value of a batch of goods is 0.8 (exceeding 0.7), and then unfreezing after both parties confirm the responsibility. For example, the heat map shows that the railway transportation stage of strawberries has a 3-hour red grid, with a cumulative decay value of 0.75, and the smart contract automatically freezes 30% of the goods, avoiding the risk of the consignee facing quality problems while having to pay the full amount.

[0024] c)push the electronic bill of lading containing the three-dimensional decay trace to the associated party, the consignee scans the see-through arbitrary section environmental data, takes time, spatial position (latitude and longitude), and decay coefficient as three-dimensional coordinate axes to construct the decay trajectory of the goods from shipment to the current node, such as decay coefficient 0.1 at A warehouse (north latitude 30°, east longitude 120°) on June 1, 8:00, decay coefficient 0.4 at B road section (north latitude 31°, east longitude 121°) on June 2, 10:00, forming a continuous three-dimensional curve, the consignee scans the two-dimensional code on the electronic bill of lading, and can click on the section on the three-dimensional path to view the environmental data of the corresponding period (such as clicking on the node of June 2, 10:00, the temperature is 8℃, the humidity is 70%, and the vibration frequency is 15 Hz), the electronic bill of lading is sent to the carrier (for proving the compliance of the transportation process) and the regulatory agency (for tracing the supervision) at the same time, ensuring that the information obtained by each party is consistent, for example, after the consignee scans the electronic bill of lading, clicks on the red risk section of the road transportation stage, and can view the detailed environmental record of the period due to the rapid rise of temperature leading to accelerated decay, providing basis for responsibility definition, the heat map intuitively displays the risk, the smart contract automatically safeguards the safety of funds, the electronic bill of lading realizes data transparent traceability, and the three work together to ensure information symmetry and clear responsibility in the transfer of ownership, reducing handover disputes.

[0025] And perform: The adaptive regulation unit 4 generates a node differentiation threshold value according to the real-time quality decay coefficient and the cargo value loss function, and when the node differentiation threshold value breaks through the standard threshold value, triggers the push of a responsibility definition report to the upstream and downstream associated parties and locks the emergency replacement goods in the cold chain resource pool within the preset range.

[0026] To quantify the economic loss caused by the quality decay of goods, the extraction of the cargo value loss function starts from the life cycle quality map, combines market pricing and consignor loss rules, and establishes a nonlinear relationship between quality and value. The specific implementation is as follows: The economic value decay curve is parsed from the life cycle quality map, which records the full-cycle quality decay data of the goods from shipment to the current node (such as real-time quality decay coefficient and cumulative decay value in each stage), and the steps for parsing the economic value decay curve are as follows: From the atlas, the nodes where the quality of the goods changes significantly (such as the time point where the decay coefficient jumps from 0.2 to 0.5, and the cumulative decay value at the transportation stage transition) are screened out, which correspond to the key turning points of value. For example, in the atlas of a batch of imported beef, the decay coefficient increases from 0.3 to 0.6 at the 12th hour of transportation, which is marked as a key node of value decay. The key decay nodes are arranged in chronological order, and the actual value of the goods at the corresponding time points is associated (such as the value of 100,000 yuan at the time of delivery, 60,000 yuan at the 12th hour, and 30,000 yuan at the 24th hour), forming a three-dimensional data set of "time-decay coefficient-goods value". The economic value decay curve is analyzed. According to the market real-time pricing database and the consignor's preset loss matrix, based on the analyzed initial value decay curve, the market real-time pricing and the consignor's rules are introduced for calibration. The database includes the market transaction prices of similar goods in different quality states (such as fresh strawberries (decay coefficient 0.1) 20 yuan per kilogram, mild decay (0.3) 15 yuan per kilogram, and severe decay (0.6) 5 yuan per kilogram). By comparing the genotype and quality state of the current goods, the market-recognized value reference is obtained. For example, a batch of B genotype strawberries with a current decay coefficient of 0.4 matches the market average price of similar strawberries at this coefficient from the database, which is 10 yuan per kilogram. The loss matrix is the quality-value loss rule set by the consignor according to the characteristics of the goods (such as "decay coefficient increases by 0.1, value loss 20%" and "cumulative decay value exceeds 0.8, value zero"). It is used to correct the individualized demand not considered in market pricing (such as higher quality requirements for high-end food materials).For example, the consignor sets the loss matrix for a batch of organic vegetables as follows: decay coefficient within 0.2, loss 10%; 0.2-0.5, loss 50%; 0.5 or more, loss 80%. Through the combination of market pricing and consignor loss matrix, the value assessment is ensured to comply with market rules, the individualized quality requirements of the consignor are met, the value misjudgment caused by a single standard is avoided, the nonlinear mapping relationship between the quality decay coefficient and the monetary value is established, the cargo value loss function is obtained, the "quality decay coefficient-monetary value" data after calibration is fitted, the nonlinear mapping relationship is established, and finally the cargo value loss function is formed. Since the influence of quality decay on value is not linear (e.g., the value decreases slowly when the decay is slight, and decreases sharply when the decay exceeds the critical value), the nonlinear characteristics are captured through curve fitting, such as when the decay coefficient is 0.1-0.3, the value decreases from 100% to 70%; when the decay coefficient is 0.3-0.5, the value decreases from 70% to 30%; and when the decay coefficient is 0.5 or more, the value decreases from 30% to 0. The cargo value loss function takes the "current quality decay coefficient" as input and outputs the corresponding cargo value loss ratio (e.g., input 0.4, output 50%, indicating a loss of half the value). At the same time, the function applicable to the cargo genotype and the transportation stage is marked (to ensure relevance), for example, the cargo value loss function of a batch of dairy products shows that a decay coefficient of 0.2 corresponds to a loss of 15%, a decay coefficient of 0.4 corresponds to a loss of 40%, and a decay coefficient of 0.6 corresponds to a loss of 80%. The nonlinear correlation between quality and value is accurately reflected. Through this process, the cargo value loss function converts the abstract quality decay coefficient into specific economic loss quantitative indicators, providing decision-making basis for the economic value level of the subsequent adaptive control unit 4 threshold setting and emergency dispatch. When the loss reaches the preset proportion, the system can trigger the early warning or replacement mechanism in time to reduce economic loss.

[0027] To achieve precise adaptation of different transportation node early warning thresholds, the generation of node differentiated thresholds is completed by the adaptive control unit 4 through a threshold game optimization algorithm, combined with cargo value sensitivity and carrier performance dynamic adjustment, and triggering of linkage response. The specific implementation is as follows: The adaptive control unit 4 deploys a threshold game optimization algorithm: Calculate the cargo value loss sensitivity of the current transportation node, retrieve the cargo type of the current node (such as high-value seafood, ordinary fruits and vegetables), real-time quality decay coefficient (such as 0.4), remaining transportation time (such as 6 hours), and market price fluctuation data (such as the recent price fluctuation amplitude of similar goods is 5%). The cargo value loss sensitivity reflects the sensitivity of the value of the goods to the change in the decay coefficient. High-value goods (such as imported beefsteak) have high sensitivity (the value loss may reach 20% for every 0.1 increase in the decay coefficient), and ordinary goods (such as potatoes) have low sensitivity (the value loss is about 5% for every 0.1 increase in the decay coefficient). For example, the imported salmon being transported has a value loss of 15% when the decay coefficient increases from 0.4 to 0.5 in the remaining 6 hours, and its sensitivity is marked as "high". The logic of this step is: Different goods exhibit significantly varying tolerances to decay. Sensitivity calculations provide a value-level basis for threshold adjustments, preventing a uniform threshold from causing delayed warnings for high-value goods or excessive warnings for low-value goods. A dynamically floating threshold baseline is established, based on the carrier's historical performance rating. The threshold baseline is adjusted using the sensitivity to cargo value loss as a foundation, overlaid with the carrier's historical performance rating (e.g., AAA, A, B). For carriers with high historical performance ratings (e.g., AAA, with 90% of past shipments without damage), the threshold baseline is appropriately relaxed (e.g., the threshold for highly sensitive goods is adjusted from a decay coefficient of 0.5 to 0.6), giving them more flexibility. For carriers with lower ratings... For carriers (e.g., Class B, with 30% of past shipments experiencing cargo damage), the threshold baseline is tightened (e.g., the threshold for highly sensitive goods is adjusted from 0.5 to 0.4). For example, an AAA-rated carrier transporting highly sensitive salmon initially has a threshold baseline of 0.5, which is relaxed to 0.6 based on the rating. If the same shipment is transported by a Class B carrier, the threshold is tightened to 0.4. The core logic of this adjustment is that the carrier's performance capability directly affects the level of cargo quality assurance. By linking the rating to the threshold, a match between "risk and control intensity" is achieved, urging lower-rated carriers to improve transportation quality and strengthening the strictness of early warnings. When the real-time decay coefficient triggers the threshold, the following actions are simultaneously taken: The system invokes the responsibility contribution topology diagram generated by the blockchain dynamic evidence storage unit 3 (marking the percentage contribution of each transportation node to the total decay, such as the current node contributing 40% and the preceding warehousing node contributing 20%), allocates compensation weights according to the contribution ratio (e.g., the current carrier bears 40% of the compensation, and the warehousing party bears 20%), writes the compensation weight allocation scheme into the smart contract, and automatically deducts the compensation amount from the corresponding responsible party's deposit account (e.g., deducting 40% of the cargo value loss from the carrier's deposit account), and simultaneously pushes the compensation details to all parties. Based on the remaining value of the goods (e.g., the current salmon's remaining value is 60% of its original value), it selects blockchain-registered goods of the same batch from the cold chain resource pool (ensuring quality traceability), and initiates a competitive bidding procurement agreement. Qualified suppliers submit quotations, and the system prioritizes matching based on the principle of "lowest price + closest distance" to ensure the fastest replacement of damaged goods. For example, if a batch of beef reaches a threshold due to its real-time decay coefficient, the responsibility topology map shows that the transportation link contributes 50% of the responsibility. The smart contract automatically deducts 50% of the value of the goods as compensation from the carrier, and at the same time matches the same batch of beef from the cold chain resource pool within 300 kilometers, completing the procurement and replacement at the best price. Through this process, the generation of node differentiation thresholds takes into account both the sensitivity of the goods' value and the carrier's historical performance, making the early warning more accurate. The triggered linkage operation realizes a closed loop of responsibility definition, compensation, and emergency replacement, minimizing the impact of cargo damage and ensuring the economy and continuity of cold chain logistics.

[0028] The environmental monitoring unit 1 in the application collects position, temperature and humidity data through a sensor array, the goods decay pattern generation unit 2 combines a genotype biological activity database with a cross-stage cumulative exposure analysis model, and outputs a real-time quality decay coefficient through a space-time stress coupling algorithm and biological verification, the blockchain dynamic storage unit 3 generates a traceability block containing decay data and environmental hash values, constructs a cross-stage correlation chain to form a life cycle quality map, and the adaptive control unit 4 dynamically generates a threshold value according to the decay coefficient and the goods value loss function, triggers the responsibility report push and emergency goods scheduling, which improves the accuracy of quality evaluation, clarifies the responsibility division, and ensures the efficient and stable operation of the cold chain logistics.

[0029] The second object of the application is to provide a method for implementing the above-mentioned any one kind of cold chain logistics remote monitoring platform based on Internet of Things, comprising the following steps: S1, through the sensor array deployed in the cold chain transportation node, real-time collection of position, temperature and humidity, illumination, vibration and cabin door opening and closing data, at the same time, calling the decay response curve in the goods genotype biological activity database, based on the cross-stage cumulative exposure analysis model, the current environmental data and the historical transportation stage environmental exposure cumulative value are coupled and calculated, the time decay weighting mechanism is introduced to correct the influence of historical data, the real-time quality decay coefficient with credibility rating is output, and the model dynamic calibration is realized through the miniature cell culture cabin biological verification; S2, triggering the blockchain dynamic storage at the goods handover node, generating an indivisible traceability block containing real-time quality decay coefficient, historical cumulative decay value and environmental data hash value, using hierarchical encryption structure to store core data and embedding multi-party consensus key, constructing cross-stage decay correlation chain through directional attenuation pointer technology, automatically generating responsibility contribution topology to mark the decay responsibility proportion of each node, binding smart contract to generate visual quality heat map, and pushing the electronic bill of lading containing three-dimensional decay traceability path to the related parties; S3, analyzing the goods economic value decay curve from the life cycle quality map, combining the market real-time pricing with the consignor loss matrix to generate the goods value loss function, calculating the goods value loss sensitivity of the current transportation node through the threshold game optimization algorithm, superimposing the historical performance rating dynamic floating threshold baseline of the carrier, when the real-time decay coefficient triggers the threshold value, synchronously executing the responsibility definition report push, the intelligent contract automatic compensation and the emergency goods scheduling of the same batch, and preferentially matching the replacement goods registered in the blockchain of the cold chain resource pool.

[0030] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A remote monitoring platform for cold chain logistics based on the Internet of Things, characterized in that, include: The environmental monitoring unit (1) is configured in the sensor array at each node of the cold chain transportation to collect location coordinates, temperature and humidity, light intensity, vibration spectrum and door opening and closing frequency in real time. The cargo decay map generation unit (2) has a built-in bioactivity database classified by cargo genotype and deploys a cross-stage cumulative exposure analysis model. It inputs current environmental data and historical cumulative environmental exposure values ​​during transportation stages and outputs real-time quality decay coefficients. The blockchain dynamic evidence storage unit (3) generates an indivisible traceability block at the cargo handover node. The traceability block contains the real-time quality decay coefficient, historical cumulative decay value, and environmental data hash value. Each traceability block is connected through a cross-stage decay association chain to form a complete transportation lifecycle quality map. The cargo value loss function is extracted from the lifecycle quality map and executed: The adaptive control unit (4) generates a node differentiation threshold based on the real-time quality decay coefficient and the value loss function. When the node differentiation threshold exceeds the standard threshold, it triggers the push of a responsibility definition report to upstream and downstream related parties and locks the emergency replacement goods in the cold chain resource pool within the preset range.

2. The IoT-based remote monitoring platform for cold chain logistics according to claim 1, characterized in that, The bioactivity database classified by cargo genotype in the cargo decay map generation unit (2) is constructed using multi-dimensional biological stress feature mapping technology: Cellular-level decay response data of different cargo genotypes were collected, a genotype-environmental stress mapping table was established, and a cross-species decay acceleration factor library was combined to classify the cargoes into three basic lineages according to their biological characteristics, ultimately forming a hierarchical bioactivity database. Each type of cargo was associated with the bioactivity decay curve in the hierarchical bioactivity database.

3. The IoT-based remote monitoring platform for cold chain logistics according to claim 2, characterized in that, The training process of the cross-stage cumulative exposure analysis model adopts a spatiotemporal stress coupling algorithm: Using the bioactivity database as input, a time decay weighting mechanism is introduced when loading the cumulative environmental exposure value during the historical transportation stage. The frequency domain energy integral conversion of temperature and humidity fluctuations and vibration spectrum is performed through the environmental stress superposition verification channel to output the real-time cumulative exposure equivalent. Combined with the critical deterioration coefficient of the current transportation stage of the goods, the decay rate matching operation is performed in parallel on the cloud edge computing node to generate a real-time quality decay coefficient with a credibility rating.

4. The IoT-based remote monitoring platform for cold chain logistics according to claim 3, characterized in that, The output process of the real-time quality degradation coefficient includes a dual feedback verification mechanism: After the model outputs the decay coefficient for the first time, a short-cycle biological simulation validation is initiated. This involves using a miniature cell culture chamber carried by the transport vehicle to compare the decay deviation between the actual cargo sample and the predicted value in real time. When the deviation exceeds a preset fault tolerance threshold, a dynamic model retraining loop is triggered. The current environmental data and sample decay data are collected as incremental training sets. Under the supervision of the blockchain storage unit (3), the environmental stress index model parameters are updated to generate the corrected decay coefficient with version traceability.

5. A remote monitoring platform for cold chain logistics based on the Internet of Things according to claim 1, characterized in that, The traceability block of the blockchain dynamic evidence storage unit (3) adopts a layered encryption structure: The core layer of the block stores the raw data of real-time quality decay coefficient and historical cumulative decay value. The verification layer embeds a multi-party consensus key generated based on the hash value of environmental data, in which the carrier, the consignee and the regulatory agency each hold independent key fragments. The access layer adds decay correlation identifiers and automatically generates a responsibility contribution topology graph by parsing the cross-stage decay correlation chain, marking the percentage contribution of each transportation node to the total decay.

6. The IoT-based remote monitoring platform for cold chain logistics according to claim 5, characterized in that, The construction of the cross-stage decay association chain utilizes the directional decay pointer technique: When each traceability block is generated, a decay accumulation pointer pointing to the previous block and a decay prediction pointer pointing to the subsequent block are automatically created. The pointers encapsulate the environmental stress transmission coefficient. When a new block is added, the system automatically traces back along the pointer chain to recalculate the historical accumulated decay value, forming a closed-loop verification channel. When the transportation task ends, all pointers are solidified on the blockchain to generate an unremovable decay trajectory chain. The trajectory chain nodes contain a handover timestamp and the biometric signature of the responsible person.

7. A remote monitoring platform for cold chain logistics based on the Internet of Things according to claim 6, characterized in that, The generation process of the lifecycle quality map incorporates a ownership transfer trigger mechanism: At each moment of goods handover, the blockchain dynamic evidence storage unit (3) automatically executes: a) Generate a visual quality heat map based on the current decay trajectory chain, and use spectral color levels to indicate the decay risk level at each stage; b) Bind key nodes of the visualized quality heat map to smart contract terms, and automatically freeze a preset proportion of the payment when the accumulated decay value exceeds the limit; c) Push electronic bills of lading containing three-dimensional decay tracing paths to related parties, and the consignee can scan them to see the environmental data of any section.

8. A remote monitoring platform for cold chain logistics based on the Internet of Things according to claim 1, characterized in that, The extraction of the value loss function is as follows: The economic value decay curve of goods is analyzed from the life cycle quality map. The analysis of the economic value decay curve of goods is based on the real-time market pricing database and the loss matrix preset by the cargo owner. A non-linear mapping relationship between the quality decay coefficient and the monetary value is established to obtain the value loss function of goods.

9. A remote monitoring platform for cold chain logistics based on the Internet of Things according to claim 1, characterized in that, The generation of the node differentiation threshold specifically includes: Deploy a threshold game optimization algorithm in the adaptive control unit (4): Calculate the cargo value loss sensitivity at the current transportation node, and combine it with the carrier's historical performance rating dynamic floating threshold baseline. When the real-time decay coefficient triggers the threshold, execute the following synchronously: The system calls the responsibility contribution topology to generate a compensation weight allocation scheme, activates the smart contract to initiate automatic claims settlement, and initiates an emergency replacement goods bidding procurement agreement based on the remaining value of the goods, prioritizing matching with blockchain-registered goods of the same batch in the cold chain resource pool.

10. A method for implementing an Internet of Things-based remote monitoring platform for cold chain logistics as described in any one of claims 1-9, characterized in that: Includes the following steps: S1. Real-time data collection of location, temperature, humidity, light, vibration, and door opening and closing is achieved by sensor arrays deployed at cold chain transportation nodes. At the same time, decay response curves from the cargo genotype bioactivity database are called up. Based on the cross-stage cumulative exposure analysis model, the current environmental data and the cumulative environmental exposure values ​​of the historical transportation stages are coupled in a spatiotemporal stress calculation. A time decay weighting mechanism is introduced to correct the influence of historical data. The real-time quality decay coefficient with a credibility rating is output. The model is dynamically calibrated through biovalidation in a micro cell culture chamber. S2. At the cargo handover node, trigger the blockchain dynamic storage to generate an indivisible traceability block containing real-time quality decay coefficient, historical cumulative decay value and environmental data hash value. Use a layered encryption structure to store core data and embed multi-party consensus keys. Build a cross-stage decay association chain through directional decay pointer technology. Automatically generate a responsibility contribution topology map to mark the decay responsibility ratio of each node. At the same time, bind smart contracts to generate a visual quality heat map and push electronic bills of lading containing three-dimensional decay traceability paths to related parties. S3. Analyze the decay curve of cargo economic value from the life cycle quality map, generate cargo value loss function by combining real-time market pricing and cargo owner loss matrix, calculate cargo value loss sensitivity of the current transportation node through threshold game optimization algorithm, and superimpose the carrier's historical performance rating dynamic floating threshold baseline. When the real-time decay coefficient triggers the threshold, simultaneously execute the responsibility definition report push, smart contract automatic claims and emergency cargo scheduling in the same batch, and prioritize matching replacement cargo registered on the blockchain in the cold chain resource pool.

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