1189 results about "Cold chain" patented technology

The invention relates to the field of transportation visualization, in particular to a cold chain cargo transportation management visualization method and system. The method comprises the following steps of collecting cold chain cargo state parameters, performing cargo quality trend evaluation, and generating state data streams of a plurality of cargoes; performing dynamic positioning and tracking on the cold-chain transport vehicle, performing space-time mapping processing according to the state data flow, and constructing a real-time tracking trajectory flow; performing future aging deviation prediction on the real-time tracking trajectory flow to generate an aging deviation prediction result; carrying out transportation risk comprehensive assessment according to the aging deviation prediction result, and generating a transportation risk assessment report; and carrying out path planning calculation and client notification pushing according to the transportation risk assessment report so as to execute intelligent transportation management. According to the invention, the transportation energy consumption and time cost are reduced, the resource allocation efficiency is improved, and the cargo real-time tracking visualization effect is improved.

The invention discloses an agricultural product cold chain carriage temperature control method based on space equalization, and relates to the technical field of intelligent cold chain logistics, and the method comprises the steps: collecting carriage three-dimensional point cloud data and agricultural product electronic tag information, building a carriage loading three-dimensional model, carrying out the space grid division, and generating a partition topological feature vector; collecting temperature, humidity and carbon dioxide concentration data, calculating average temperature and average gas concentration observation values, and generating a breathing heat estimation value in combination with the partition topological feature vector; inputting the breathing heat estimation value into a dynamic prediction model, generating a subarea cold load curve and a priority weight by combining the subarea temperature deviation, and outputting a subarea cold load sequence; and according to the partition cold load sequence, generating a local execution proposal, constructing a global optimization problem taking the space temperature balance degree as an optimization target, and generating a global control plan. According to the method, global temperature equalization and energy consumption collaborative optimization are realized through network reasoning and dynamic optimization of the meta-reinforcement learning strategy.

The invention belongs to the technical field of cold chain warehouse management, and particularly relates to a cold storage dynamic scheduling cooperative control method and system based on the Internet of Things, and the method comprises the steps: obtaining operation parameters and environment parameters; according to the operation parameters and the environment parameters, an E-LSTM model is used for analyzing the operation state of the refrigeration equipment, an analysis result is generated, and through a TFT neural network model, the environment parameters and the current time and production plan are combined, and the refrigeration demand change trend is predicted; based on the analysis result and the refrigeration demand change trend, a dynamic scheduling strategy of the refrigeration equipment is generated by combining a scheduling rule and an optimization target and utilizing a PPO algorithm; the scheduling strategy is sent to the PLC control end of the refrigeration equipment through the Internet of Things communication network, the PLC control end performs cooperative control on the refrigeration equipment according to the scheduling strategy, and meanwhile, the execution state of the equipment is fed back to the cloud server. Therefore, the problems of rigid scheduling mode, serious energy waste, insufficient emergency capability and the like in the prior art are solved.

The invention relates to the technical field of cold-chain commodity transportation, in particular to a temperature monitoring and control method in a cold-chain commodity transportation process, which comprises the following steps: acquiring real-time temperature data, and generating a thermal response digital file of a cold-chain commodity according to the real-time temperature data and a cold-chain commodity category database; real-time positions and external air temperature data of the cold chain commodities are collected, and a temperature threshold value of the next time period is predicted according to the heat capacity characteristic parameters reflected by the thermal response digital archive; a three-dimensional temperature field distribution diagram is constructed based on the temperature threshold value of the next time period and the vibration data of the compartment where the cold-chain commodities are located; generating a control instruction according to the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution diagram; after the control weight coefficient is calculated according to the control instruction and the preset energy consumption optimization objective function, the temperature control operation is executed according to the control weight coefficient and the control instruction, and balance temperature monitoring and control and energy consumption optimization are effectively achieved.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

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 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.

The invention discloses a heat preservation box and processing equipment thereof, and belongs to the technical field of heat preservation boxes. The heat preservation box comprises a box body and a box cover, the box body is formed by wrapping a heat preservation layer with an inner shell and an outer shell, and the heat preservation layer is wrapped with a box cover shell. An aerogel groove and a vacuum plate groove which are overlapped are formed in the heat preservation layer and filled with aerogel particles and a vacuum heat insulation plate respectively, and the surface of the heat preservation layer is sprayed with a nanometer aerogel coating and provided with a reflecting film. The box body is connected with the box cover through a supporting hinge, and is provided with a supporting piece, a roller, a limiting sinking groove, a flow guide channel and a residual volume space measuring system. The processing equipment comprises a bracket, a rotary power source, a rotary platform, a vibration unit and a filling device, and can realize uniform filling and compaction of aerogel particles. The problems that a traditional heat preservation box is poor in heat preservation performance, pressure is accumulated, and transportation is inconvenient are solved, and the heat preservation box is suitable for cold-chain transportation and other scenes.

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 food traceability, in particular to a food safety online supervision platform and traceability method based on the Internet of Things and a block chain, and the platform comprises an environment abnormity recognition module, an internal circulation failure recognition module, a track offset recognition module, an abnormal time period cross judgment module and a transportation process traceability data construction module. According to the method, a time sequence structure is constructed through multi-point temperature and humidity information in a cold chain transportation scene, the collaborative abnormal state of a temperature control environment can be recognized, then in combination with response lag between cold machine starting time and temperature and humidity fluctuation, a GPS track in a transportation path is aligned with distance counting sensing information, the difference value change between the track and the physical mileage is extracted, and the real-time monitoring of the temperature and humidity environment is achieved. And carrying out fitting analysis on the continuous offset interval to identify potential path abnormity, and carrying out combined judgment on the offset degree and the cold control response ratio to realize identification, verification and task traceability of abnormal behaviors in the cold chain transportation process.

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 discloses a natural eutectic solvent enhanced konjac glucomannan film as well as a preparation method and application thereof, and belongs to the technical field of food packaging. The konjac glucomannan film enhanced by the natural deep eutectic solvent, provided by the invention, comprises konjac glucomannan, the natural deep eutectic solvent and gallic acid modified MXene nanoparticles; the mass ratio of the konjac glucomannan to the natural eutectic solvent to the gallic acid modified MXene nanoparticles is (850 to 950) to (50 to 250) to (10 to 40). Through the synergistic effect of all the components, the defects that KGM is insufficient in intrinsic mechanical strength, weak in antibacterial and antioxidant performance and poor in moisture resistance are overcome, the comprehensive performance of the film is remarkably improved, and the film can be used for cold-chain packaging of high-value fresh food and has environment friendliness and functional practicability.

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 discloses a cold chain temperature and humidity dynamic early warning method and system based on neural network prediction. The method comprises the following steps: collecting state data and environmental parameter data of equipment operation in real time; inputting the data into the trained multi-task prediction model, and outputting a future prediction value; evaluating the credibility of the predicted value, if the credibility is greater than a threshold value, performing prediction after correcting the multi-task prediction model, and if the credibility is less than or equal to the threshold value, performing the next step; and comparing the prediction result with a current actual threshold interval through a dynamic risk scoring algorithm to generate a dynamic risk score, and if the risk score exceeds a dynamic threshold, pushing a grading early warning to a management platform or a user. Compared with a traditional method, the method has the advantages that more time is brought forward to find potential overrun risks, sufficient time is reserved for manual intervention, data sequences are collected regularly based on sensor data collection, and meanwhile, the working conditions of equipment and external public opinion information are comprehensively analyzed to give intelligent early warning information.

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 provides a sensor-based cold chain equipment fault detection method and system. Belongs to the technical field of cold chain equipment fault detection. The method comprises the following steps: collecting multi-dimensional data of the operation state of the cold chain equipment in real time through a multi-dimensional sensor; performing dimension reduction processing on the standardized multi-dimensional data set by adopting a principal component analysis algorithm, extracting main feature components in the data, and constructing a high-dimensional feature vector set; performing time sequence feature extraction on the multi-dimensional data set by using an LSTM model to obtain a multi-dimensional time sequence feature matrix; fusing the high-dimensional feature vector set with the multi-dimensional time sequence feature matrix to obtain a fused feature matrix; a method of combining principal component analysis and a long short-term memory network is adopted, main features and time sequence features of data are extracted respectively, and the main features and the time sequence features are organically combined through a feature fusion strategy, so that the operation state of the cold chain equipment is described more comprehensively, and the accuracy of fault feature extraction is 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.