1059 results about "Cold chain" patented technology

The invention relates to a cold chain transportation intelligent early warning and abnormity processing method based on temperature sensor data, and the method comprises the following steps: carrying out the temperature data collection of different regions in a cold chain transportation carriage, and obtaining the temperature collection data; performing temperature fluctuation analysis based on the temperature acquisition data to obtain a temperature fluctuation trend parameter; based on the temperature fluctuation trend parameters, temperature threshold value judgment is conducted on the cold chain transportation compartment, and a temperature judgment result is obtained; and when the temperature judgment result is that the temperature of the core cargo area in the cold-chain transportation carriage exceeds the preset threshold range, temperature early warning grade information is determined according to the temperature judgment result, and an abnormity processing instruction is generated based on the temperature early warning grade information, so that the problem that potential temperature risks cannot be predicted in advance is solved, and the safety of the cold-chain transportation carriage is improved. And the exception processing is often lagged.

The invention relates to the technical field of cold chain warehouse logistics, and discloses a cold chain warehouse logistics intelligent distribution management system based on the Internet of Things technology. The system comprises a multi-source data processing module which collects storage environment and equipment operation data through a heterogeneous sensor, and preprocesses the data to generate a normalized matrix containing parameters such as temperature and humidity; the dynamic topology updating module is used for constructing an equipment association topology library, updating a relationship according to real-time data and generating a dynamic topological graph; the goods fresh-keeping reference configuration module inputs goods characteristics and equipment operation duration based on a topological graph mapping neural network model, and outputs a goods deterioration risk coefficient; an abnormal early warning decision module generates an early warning rule according to the data, and triggers delay verification in combination with real-time data; the scheduling decision optimization module generates an optimal scheduling scheme through a decision tree in combination with early warning and a resource state; the three-dimensional reservoir area mapping module constructs a three-dimensional grid model and maps environmental parameters; and the path optimization module optimizes the optimal path of the logistics equipment by using a genetic algorithm.

The invention relates to the field of agricultural product cold-chain storage, and discloses an intelligent management monitoring system for agricultural product cold-chain storage and transportation, which comprises the following steps: synchronously sensing operation states and operation behaviors of agricultural product storage units and cold source equipment to form a data input sequence with operation association semantics; introducing a dynamic sampling mechanism based on operation event triggering into the data input sequence, and constructing a continuous evolution feature set reflecting local cold chain stability; performing time sequence analysis on a temperature and humidity parameter recovery track and a cooling capacity response rhythm presented in the continuous evolution characteristic set; delimiting a storage area corresponding to the risk evolution fragment as a key monitoring unit, and constructing an operation influence association model for describing a local cold chain constraint relationship; and performing linkage adjustment on the sensing node acquisition strategy, the monitoring update frequency and the cold source scheduling parameters under the constraint relation limited by the operation influence correlation model. The method has the advantage that the controllability of the cold chain operation process is improved.

The invention relates to the field of cold-chain transportation, and discloses an aquatic product cold-chain quality monitoring and tracing method, which comprises the following steps: acquiring multivariable environmental data in cold-chain transportation; based on the multivariable environment data, constructing a dynamic coupling model of a cold chain environment for representing a nonlinear dynamic relationship among environment variables; performing real-time estimation on the cold chain environment state by using a dynamic monitoring algorithm, and predicting the future change of the cold chain environment state; constructing a target function, and optimizing the operation parameters of the cold chain equipment according to the dynamic change of the cold chain environment; constructing a mapping model between the cold chain environment and the aquatic product quality, and dynamically evaluating the quality index of the aquatic product according to the historical change of the environment; and generating a traceability report based on the evaluation result, wherein the traceability report comprises a dynamic record of the cold chain environment and an aquatic product quality change curve. Dynamic monitoring, quality tracing and system optimization of the whole cold-chain transportation process can be realized, and the stability of the cold-chain environment and the aquatic product quality guarantee capability are improved.

The invention relates to the technical field of traffic data analysis, and provides a cold-chain transportation route optimization method and system based on deep learning, and the method comprises the steps: firstly collecting a target data set containing traffic state information, cargo characteristic data and vehicle scheduling information in a cold-chain transportation scene; then carrying out association mapping processing based on the cargo characteristic data and the vehicle scheduling information, and generating a cold chain transportation line network comprising a plurality of transportation nodes and line sides; identifying the traffic condition of the transportation node by using the traffic state information, and inferring and analyzing by combining the influence relationship between the nodes to obtain the congestion distribution characteristics of the line nodes and the cold chain transportation delay label; and finally, in response to an instant transportation demand instruction, processing the original traffic area node network in combination with the above characteristics and a message passing mechanism of the pre-training graph neural network, updating node state information and connection weight, and generating a cold chain transportation optimization line matching the instant transportation demand, and the method can improve the cold chain transportation efficiency and reliability.

The invention discloses a cold chain transportation path optimization method based on intelligent scheduling, and the method comprises the steps: collecting the environmental data of a transportation path, and constructing a directed graph structure containing the temperature and length attributes of a path segment; defining a cooling capacity consumption function, calculating an estimated cooling capacity consumption value of the path section, and forming an environment diagram containing the refrigeration cost; constructing a state vector and an action space, and designing a path selection strategy network and a state value network; setting a multi-objective reward function including cold consumption, delivery completion, cold chain failure and supply behaviors, performing multi-round strategy training by adopting an improved PPO algorithm, and optimizing and updating network parameters through a trust region strategy; and finally, calling an optimal strategy output path selection action in actual transportation to realize dynamic avoidance and supply insertion regulation and control of the cold chain interruption risk. According to the invention, intelligent, self-adaptive and energy-efficient optimization of cold chain path selection can be realized.

The invention discloses a pork quality evaluation method and system oriented to cold-chain transportation, and relates to the technical field of pork quality detection.The method comprises the following steps that multiple types of data reflecting pork quality changes in the whole cold-chain transportation process are collected, a unified time sequence list is established, and the data are stored in a database; correspondingly marking the fluctuation trend and temperature and humidity changes of each type of data to generate a dynamic data manuscript; and performing time difference sorting on the dynamic data manuscript, extracting synchronous change fragments of the pH value and the volatile basic nitrogen, and independently labeling and summarizing the fragments without synchronous characteristics to form a non-synchronous characteristic list. According to the method, the unified time sequence list of multiple types of data is established, so that dynamic association and continuous tracking of the physicochemical indexes and the environmental parameters are realized, and the real-time performance and accuracy of quality evaluation are improved; and through asynchronous feature recognition and a dynamic adjustment mechanism, an asynchronous phenomenon is self-adaptively repaired, so that freshness and corruption degree evaluation is kept stable and reliable under environmental fluctuation.

The invention provides a penaeus vannamei quality prediction and grade evaluation method and system, and belongs to the field of penaeus vannamei cold-chain transportation process quality prediction, and the method comprises the steps: S1, constructing a penaeus vannamei data set, and carrying out the dynamic time-varying coupling correlation graph generation, convection-diffusion cooperative information propagation, multi-task joint optimization and end-to-end MLP prediction to obtain a penaeus vannamei data set; obtaining a prediction result of the quality of the penaeus vannamei; s2, splicing is performed according to the prediction result and the actual quality data, and a feature matrix is constructed; clustering the feature matrix by using a rule fusion K-means + + clustering method to obtain an optimal clustering center set; and S3, based on the prediction result and the clustering center set, carrying out dynamic prediction and evaluation on the quality of the penaeus vannamei in the refrigeration state in a period of time in the future. According to the invention, an intelligent solution is provided for quality monitoring in cold-chain logistics, and economic loss caused by quality deterioration is significantly reduced.

The invention relates to the technical field of logistics monitoring, in particular to a cold-chain logistics monitoring method and system. The method comprises the following steps: calculating a data integrity index value at each moment in a historical time period, namely an accumulated value of a product of an effective data proportion and an importance weight of each sensor; obtaining threshold exceeding degree index values of all moments, and preprocessing data and corresponding set numerical value intervals based on the sensor; obtaining a multi-source consistency index value at each moment according to a redundant sensor data standard deviation, a semantic tag and a data variable quantity at adjacent moments; carrying out weighted summation on the three indexes to obtain a confidence coefficient weight at each moment; constructing an enhanced input vector according to the confidence coefficient weight, the semantic tag and the data of each sensor; based on the enhanced input vector at each moment in the historical time period, the data and the semantic tag preprocessed by each sensor in the future preset time step length are predicted, the cold-chain logistics can be accurately monitored, and the monitoring reliability is improved.

The invention provides an energy efficiency prediction and energy-saving scheduling optimization system and method for a food cold chain system, and relates to the technical field of cold chain system energy-saving scheduling, and the system comprises a data recognition module which is used for recognizing cold chain loading state data, and analyzing the space influence characteristics of the loading state data of a cold chain loading space; the strategy obtaining module is used for carrying out energy efficiency optimization on cold chain energy supply according to the space energy efficiency relation between the cold chain space and the link energy supply parameters and the space influence characteristics; the state chain establishment module is used for carrying out loading state dynamic analysis based on the cold chain vehicle path and predicting a path switching node; and the optimization strategy obtaining module is used for carrying out path state node scheduling optimization according to the cold chain path state chain by taking a space energy efficiency strategy as an initial parameter based on the vehicle energy supply coupling relationship and the space energy efficiency relationship of the cold chain system. The technical problem of energy consumption waste in cold chain transportation in the prior art can be solved, and the technical effect of reducing cold chain energy consumption is achieved.

The invention relates to the technical field of cold-chain logistics transportation and storage temperature control, in particular to an air compressor vehicle-mounted refrigeration and refrigeration house collaborative temperature control system based on AI scheduling, which comprises a multi-dimensional state sensing module used for collecting real-time temperature field data, environmental meteorological parameters, compressor operation states and goods category information in a refrigerator car and a refrigeration house, and sending the real-time temperature field data to a server; a structured multi-dimensional state vector is output; and the thermodynamic evolution prediction module is used for setting a prediction time domain with a finite length based on the multi-dimensional state vector, and calling and constructing a mathematical model of a heat balance relationship between the refrigerating capacity generated by the operation frequency of the compressor and the environment and the cargo heat load. According to the method, the comprehensive cost function fusing the energy consumption, the cargo quality loss and the equipment wear is constructed, and the hidden quality attenuation is converted into the dominant economic cost by using the Arrhenius model, so that the global operation benefit maximization of the system on the premise of ensuring the cargo safety is realized.

The invention relates to the crossing field of supply chain management and block chain technologies, discloses a block chain-driven circulating packaging ownership tracing and reuse optimization system, and solves the problems that existing circulating packaging ownership tracing is not credible, the reuse rate is low, data sharing is barrier and settlement is low in efficiency. The system comprises a sensing layer, a network layer, a block chain layer and an application layer, wherein the sensing layer collects and packages full life cycle data; the network layer carries out preprocessing and 5G data transmission; the block chain layer adopts an alliance chain to deploy ownership change, full life cycle records and privacy protection contracts; the application layer is internally provided with a core unit for XGBoost demand prediction and genetic algorithm scheduling, and functions of ownership tracing, multiplexing scheduling and the like are realized. Implementation shows that the ownership tracing credibility is 100%, the reuse rate is improved to 90% or above, the settlement time is shortened from 3 days to 1.5 hours, and the method is suitable for circulating packaging scenes such as cold chains.

The invention discloses a cold-chain logistics temperature control supervision method and system based on GPS linkage, and relates to the related technical field of cold-chain logistics, and the method comprises the steps: installing a vehicle-mounted terminal on a cold-chain logistics transport vehicle, and collecting the vehicle position information and carriage temperature data in real time; encrypting and uploading to a monitoring processing center, and calling cold-chain logistics temperature control analysis dual channels of the target cold-chain goods; obtaining a temperature comparison result with a preset safe temperature threshold value, and matching and determining a target temperature control analysis channel; and performing temperature control analysis, determining a temperature control adjustment strategy parameter, and performing cold-chain logistics temperature control supervision. The technical problems that in the prior art, temperature control supervision lags behind, deep fusion analysis with the vehicle position and the transportation state is not carried out, specific reasons of temperature abnormity cannot be distinguished, and the regulation and control strategy is lack of accuracy and predictability are solved, and real-time, accurate and intelligent cold chain transportation temperature control abnormity recognition and supervision are achieved. And the reliability, the safety and the efficiency of cold-chain logistics are improved.

The invention relates to the technical field of cold-chain logistics, in particular to a digital collaborative tracing method and system for cold-chain logistics based on the Internet of Things. The method comprises the following steps: binding a unique identifier associated with a batch of goods with at least one Internet of Things sensor; in the process that the goods pass through the supply chain, the at least one Internet of Things sensor is used for continuously collecting environment and state data; transmitting the environment and state data to a data processing platform in real time, and processing the environment and state data by using a hybrid prediction model in the data processing platform so as to dynamically calculate the remaining shelf life of the goods; and outputting the dynamically calculated remaining shelf life, and presenting the information to logistics managers and consumers through a customized data visual interface. The invention aims to reduce corruption, enhance food safety and improve operation efficiency by changing passive monitoring to active intelligent prediction.

The invention provides an integrated hospital intelligent monitoring system for cold-chain medicine temperature early warning, and relates to the technical field of medical monitoring. The integrated hospital intelligent monitoring system for cold-chain medicine temperature early warning comprises a sensing layer module which is deployed in a dual-mode temperature sensing unit in cold-chain equipment and comprises a main sensor and an auxiliary sensor, the main sensor is arranged in a central area in the cold-chain equipment, and the transmission layer module is arranged in a central area in the cold-chain equipment. The transmission layer module comprises a hybrid networking unit, the hybrid networking unit integrates a 433MHz wireless ad hoc network subunit and an NB-IoT / 4G remote transmission subunit, the platform layer module comprises a data processing unit and an intelligent early warning decision unit, and the execution layer module. When the box door is opened, the system can accurately identify non-fault temperature fluctuation by analyzing the temperature difference of the double sensors, the false alarm rate is reduced to 5.2% from the average industrial 38.7%, and the interference of invalid alarm to medical staff is greatly reduced.

The invention discloses a cold chain equipment fault detection method based on multi-modal sensor fusion, which relates to the field of fault detection and comprises the steps of multi-source data acquisition, preprocessing, feature extraction, three-level fusion, fault judgment, early warning and verification. The multi-source data acquisition module acquires operation, environment and fault associated data of the cold chain equipment; the preprocessing module processes data through cleaning, standardization and time-space alignment; the feature extraction module generates single-mode sensitive and cross-mode associated features. In the three-level fusion, the data layer adaptively screens weighted features, the feature layer uses an improved graph neural network to strengthen modal association, and the decision-making layer dynamically weights multiple expert models. The fault judgment and early warning module judges faults in combination with the fusion result and constructs third-level early warning; the verification module guarantees effectiveness and economy of the method. According to the method, the fault identification precision and robustness are improved through hierarchical adaptive fusion, the false alarm rate is reduced, the hardware operation and maintenance cost is controlled, and efficient detection and early warning of the cold chain fault are realized.

The invention discloses a loading lap joint device for cold chain transportation and sorting and a using method thereof, and relates to the technical field of cold chain transportation, the loading lap joint device comprises a drawing cold storage module, a cold storage module, a cold storage module, a cold storage module, a cold storage module, a cold storage module and a cold storage module, the drawing cold storage module is slidably mounted on a sliding groove in the bottom of a cold chain vehicle compartment, and a cold storage cavity is formed in the drawing cold storage module; the linkage defrosting mechanism comprises a frame type defrosting scraping plate arranged above the drawing cold storage module, a frost collecting groove is formed in the side edge of the cold storage cavity, and scraped frost water is guided into the frost collecting groove through the defrosting scraping plate; the lap joint interface module is arranged at the front end of the drawing cold storage module and comprises a conveying plate and a temperature adjusting air curtain which are movably hinged, and the temperature adjusting air curtain communicates with the cold storage cavity and the frost collecting groove through an air duct and releases cold air; the control unit is used for monitoring the temperature in the compartment and the platform position and adjusting the air speed of the air curtain to control the cold air circulation amount. The system not only can automatically remove the frost layer in the carriage and effectively reduce the environment temperature during loading, but also can be flexibly butted with a sorting line, so that energy conservation, high efficiency and temperature control guarantee of a cold chain loading link are realized.

The invention provides an intelligent temperature control method, device and equipment for cold-chain transportation of fruits and vegetables and a medium, which are used for reducing the transportation loss rate of the fruits and vegetables and saving the energy consumption of fresh-keeping transportation. The method comprises the following steps: carrying out space-time alignment and weighted fusion on a multi-source asynchronous sensing data stream to generate an environment state data set, and carrying out dynamic thermodynamic parameter joint estimation according to a refrigeration system power utilization parameter and the environment state data set to obtain a real-time system thermal parameter set, performing working condition identification on the real-time system thermal parameter set and the environment state data set to obtain a current working condition mode label, and performing multi-mode temperature trajectory prediction on the real-time system thermal parameter set and the environment state data set according to the current working condition mode label to generate a future temperature change curve; and performing control decision prediction on the future temperature change curve according to the equipment physical constraint set to generate a temperature control instruction sequence.

The invention discloses an intelligent cold-chain logistics monitoring method and system, and relates to the technical field of cold-chain logistics monitoring, the system comprises a data acquisition module, a data processing module, a monitoring evaluation module and a management analysis module, and the monitoring evaluation module comprises an external influence sub-module, a comprehensive influence sub-module and a cargo comprehensive risk analysis sub-module. Temperature and humidity, vibration strength and loading and unloading times are included in environmental influence indexes output by the external influence sub-module, potential influence of a transportation environment on cargo quality can be evaluated in real time, and on the basis of the environmental influence indexes, comprehensive effects of cargo release gas concentration, pressure values and the like in various cold chain transportation processes are combined, so that the cargo quality can be evaluated in real time. According to the method, the gas pressure influence factors are output through the environmental influence indexes, sensitive goods are accurately monitored, the gas pressure influence factors and the environmental influence indexes are combined, the goods comprehensive risk coefficient is output in combination with factors such as moisture loss, comprehensive risk indexes are provided for enterprises, and a cold-chain logistics risk assessment system is perfected.

The invention relates to the technical field of risk management and control, in particular to a distributed security risk management and control platform fusing situation awareness and an intelligent contract, and the platform comprises an acquisition module, a situation awareness module, a prediction module, an identification module, and an intelligent contract execution module. Multi-dimensional dynamic parameters related to a port cold chain are comprehensively collected and calculated, temperature difference fluctuation is used for reflecting environment stability, frequency jitter is used for reflecting equipment operation states, queue density and emergency brake behaviors are used for reflecting operation intensity and operation risks, trend judgment is carried out based on different situation index occurrence frequencies in a prediction module, and a prediction result is obtained. And in combination with the time continuity and the space aggregation of the abnormal nodes, the transportation speed is finally automatically adjusted through the intelligent contract, so that the problems of risk early warning lagging and difficulty in effective implementation of field control caused by response delay and insufficient prediction timeliness due to dependence on centralized processing are effectively solved.

The invention relates to the technical field of electric energy dispatching, in particular to an electric energy dispatching method based on an energy storage system. The method comprises the following steps: deploying a distributed sensor array in a battery module to collect multi-source heterogeneous data, and carrying out standardization processing to generate a standardized energy storage data set; performing tensor completion on the standardized energy storage data set to generate energy storage tensor completion data; time-space dimension analysis is carried out on the energy storage tensor complemented data, cross-modal feature fusion is carried out, and an energy storage cross-modal feature space is generated; therefore, through structural processing of multi-dimensional fusion data and dynamic feedback optimization of the digital twinborn model, the problems that traditional cold chain storage environment prediction lags behind and the model adaptability is poor are solved, and the precision of temperature prediction and the intelligent level of storage management are improved.

The invention discloses a low-temperature plasma cold sterilization and fresh-keeping method for a chilled poultry meat product, and particularly relates to a low-temperature plasma cold sterilization and fresh-keeping method for the chilled poultry meat product by virtue of low-temperature plasma activated water (PAW) and dielectric barrier (DBD). The method comprises the following steps: spraying and sterilizing the whole slaughtered and eviscerated poultry meat by virtue of PAW with the temperature close to the freezing point temperature of-1.5 to-2.5 DEG C of the product; the fresh-keeping method comprises the following steps: soaking the fresh-keeping materials in PAW at the temperature of less than or equal to-1.50 DEG C, cooling to the temperature of-1.5 + / -0.5 DEG C, cutting and packaging, carrying out DBD low-temperature plasma cold sterilization under the working voltage of 120-160 kV, and carrying out cold-chain logistics under the condition of the fresh-keeping temperature of-1.0 to-1.5 DEG C, so that the fresh-keeping period is prolonged from 4 days to 15 days. According to the method, PAW and DBD plasma cold sterilization and preservation are adopted at the temperature of the chilled products, the shelf life of the poultry meat chilled products is effectively prolonged on the premise of ensuring the freshness, safety and high quality of the poultry meat chilled products, and industrial innovation and sustainable development are promoted.

The invention discloses a cold chain temperature control remote monitoring and early warning system and method driven by the Internet of Things, and the system comprises a monitoring terminal module which is disposed in a cold chain environment and is used for collecting temperature and environment state signals and generating original sensing data signals; the wireless communication module is connected with the monitoring terminal module, receives an original sensing data signal and packages the original sensing data signal into an Internet of Things transmission frame signal; the cloud platform processing module receives the Internet of Things transmission frame signal, analyzes and stores the data, and generates a temperature abnormity early warning signal through an integrated artificial intelligence analysis algorithm; the early warning service module receives the temperature abnormity early warning signal and generates a multi-stage early warning notification signal according to a predefined warning strategy; the terminal execution module is connected with the cloud platform processing module and the early warning service module. According to the cold chain temperature control remote monitoring and early warning system and method driven by the internet of things, the problems of temperature control information lag, inaccurate early warning and difficulty in tracing in the cold chain transportation process can be solved.

The invention relates to the technical field of cold-chain logistics, in particular to a digital collaborative tracing method and system for cold-chain logistics based on artificial intelligence. The method comprises the following steps: acquiring cargo image data and environmental parameters in real time by using vehicle-mounted image acquisition equipment and a sensor; fusing the multi-modal data, inputting the fused data into a pre-trained artificial intelligence model to identify and quantify visual state information of the goods in real time, and generating goods state parameters; when the cargo state parameters reach a preset abnormal threshold value, dynamic path planning is triggered, the cargo state parameters serve as core optimization variables, and an optimal new transportation route is generated in combination with real-time road conditions, weather and other external environment data; and sending the new route to the cold chain vehicle for adjustment. According to the invention, the state and quality of the goods are taken as decision basis, and the conversion from passive monitoring to active intervention is realized, so that the quality of the goods is guaranteed to the maximum extent, and the loss is reduced.

The invention discloses a cold-chain logistics energy consumption optimization method and system based on digital twinning, relates to the technical field of cold-chain logistics, and provides the following scheme that the method comprises the steps that an environment data distribution field in a cold-chain carrier and space coordinates of goods units are obtained and synchronized to a virtual body instantiated based on an existing digital twinning platform, and the space coordinates of the goods units are obtained; local environment information vectors are extracted from the environment data distribution field according to the space coordinates of all the goods units, and the equivalent heat exchange flux value of the surface of each goods unit is calculated through a heat balance equation in combination with the physical parameters of all the goods units. According to the method, the digital twin virtual body is constructed, the continuous environment field is synthesized, and the metamorphic reaction rate field is iteratively calculated in combination with the thermal penetration-metamorphic dynamics coupling model, so that the defects that a traditional monitoring system depends on scattered temperature and humidity point data, lacks the three-dimensional environment field modeling capability and cannot quantitatively predict the metamorphic rate are overcome; the full-dimension continuous reconstruction of the environment field in the cold chain carrier and the dynamic quantitative prediction of the corruption rate of the goods are realized.

The invention relates to the technical field of medicine cold chain logistics information, in particular to a medical intravenous distribution code scanning logistics tracking method which comprises the following steps: attaching a self-energized label on the outer side of a preparation, reading magnetic tunnel junction physical fingerprints and nanocrystalline point-deoxyribonucleic acid information to generate a reference hash value, and collecting humiture recursion environment hash chains at fixed intervals; generating a challenge by using a satellite high-entropy random number during handover, obtaining a second fingerprint, constructing a zero-knowledge event proof in combination with a reference hash value and an environment chain, and writing a plurality of proof into a distributed account book after dynamic planning aggregation; and synchronously capturing wireless channel state information to construct a dynamic graph, and outputting a next node instruction and a risk score by a graph neural network and near-end strategy optimization. According to the method, battery-free anti-cloning and anti-tampering cold chain monitoring and network disconnection path decision are realized, the chain cost is low, and the robustness is high.

The invention discloses a cold-chain pork quality dynamic evaluation method and system based on multi-source data fusion, and relates to the technical field of data processing and intelligent evaluation, and the method comprises the following steps: collecting multi-source signal information of a whole cold-chain transportation process, sorting according to a unified time sequence, continuously arranging all signals on a time axis, and carrying out data processing on all the signals; a complete dynamic record is formed; and extracting each time interval and signal amplitude change track from the formed dynamic record, analyzing a signal section with repeated wave crests in a short time, and generating a preliminary data manuscript for reflection interference judgment. According to the invention, through time sequence arrangement and dynamic association of multi-source signals, a wave crest track analysis mechanism is constructed, real-time identification and correction of reflection interference are realized, and continuity and accuracy of a gas concentration curve are ensured; and by generating a reflection source position list and adjusting numerical value distribution of repeated fragments, false fluctuation is eliminated, and the stability and reliability of cold-chain pork quality evaluation are improved.

The invention belongs to the technical field of fresh cold-chain logistics distribution, and particularly relates to a path optimization method and path optimization system for fresh cold-chain logistics distribution, and the method comprises the steps: planning a first path connected with a plurality of first nodes, and calculating a first deviation between the total transportation time of the first path and a preset standard transportation time; when the first deviation is greater than a first preset threshold value, generating a deviation information set serving as a first optimization result by calculating predicted sampling time of a plurality of first nodes and acquiring actual sampling time of the first nodes; updating the average deviation parameter based on the first optimization result to form a second deviation, and further generating a to-be-executed path based on the second deviation; according to the method, the deviation of the actual transportation time and the preset standard transportation time caused by accident factors in the distribution process can be responded in real time, and meanwhile, the punctuality rate of fresh cold-chain logistics distribution can be improved by continuously monitoring and adjusting and correcting the subsequent distribution plan.

The invention belongs to the technical field of refrigerator van plates, and particularly relates to a high-strength low-heat-bridge refrigerator van plate which comprises a plate body of a multi-layer composite structure, and the plate body sequentially comprises an outer skin layer, a first bonding layer, a polyurethane rigid foam heat preservation layer, a second bonding layer and an inner skin layer from outside to inside. The polyurethane rigid foam thermal insulation layer is filled with polyurethane glue; a plurality of hollow reinforcing channels extending longitudinally are formed in the polyurethane rigid foam heat preservation layer; the first bonding layer and the second bonding layer are fiber reinforced composite material layers, and the fiber reinforced composite material layers are formed by compounding and curing fiber materials and polyurethane foam. A metal framework is completely replaced by the integrally-formed hollow reinforced channel, a heat conduction path is cut off fundamentally, the heat preservation performance is remarkably improved, and cold chain transportation energy consumption is reduced.

The invention provides a cold-chain transportation tracing method and system, and is suitable for the technical field of data processing, and the method comprises the steps: carrying out the prediction calculation according to cold-chain transportation environment information, cold-chain transportation process vibration information and cold-chain transportation cargo state monitoring information, and obtaining cold-chain transportation state monitoring prediction information; obtaining a cold-chain transportation association feature matrix according to the cold-chain transportation environment information, the cold-chain transportation monitoring equipment state information, the cold-chain transportation communication node position information and the cold-chain transportation state monitoring prediction information; according to the cold chain transportation association feature matrix, generating initial cold chain transportation communication transmission path information; iteratively optimizing the initial cold chain transportation communication transmission path information to obtain target cold chain transportation communication transmission path information; and according to the target cold chain transportation communication transmission path information, carrying out tracing processing on the cold chain transportation information. According to the invention, high efficiency and reliability of communication transmission in the cold-chain transportation process are guaranteed, so that timeliness and effectiveness of cold-chain transportation tracing are improved.