1734 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 relates to the technical field of warehouse management, in particular to a logistics warehouse management method and system based on digital twinning. The method comprises the following steps: deploying a multi-source sensor array and constructing a cold chain storage standard data set; performing timestamp alignment on the cold chain storage standard data set to generate cold chain storage timestamp alignment data; performing three-dimensional space mapping on the cold chain storage timestamp alignment data, and establishing a feature tensor matrix to generate a cold chain storage fusion feature matrix; therefore, through space-time fusion of multi-source data and dynamic optimization of a digital twinborn technology, the problems of data islands and inaccurate prediction in traditional cold chain storage are solved, the real-time performance of storage environment monitoring and the precision of temperature regulation and control are improved, and safety and efficiency management of cold chain logistics are effectively supported.

The invention relates to the technical field of cold chain management, and discloses a multi-modal AIGC cold chain logistics abnormal event early warning method and system, and the method comprises the steps: collecting the multi-modal data of a cold chain node through an Internet of Things sensing system, carrying out the data mapping through a cross-modal alignment algorithm, generating a multi-modal monitoring feature sequence, and carrying out the early warning of the abnormal event of the cold chain logistics. According to the method, cold chain network evaluation feature distribution is obtained through high-dimensional feature analysis, local and global abnormal features are extracted by using a space-time diagram convolutional network, an abnormal probability information set is generated, the information set is subjected to multi-strategy deduction and diffusion simulation, prediction data are corrected, and finally accurate abnormal event early warning information is generated. According to the method, the abnormal early warning precision and response speed of cold-chain logistics can be improved, the risk and loss are reduced, the spatial topology and time evolution of a cold-chain network are captured, the method is superior to traditional isolated node detection, and the problem that in the prior art, single monitoring data prediction has the limitation of recognition capacity is solved.

The invention discloses a product logistics management scheduling method based on a block chain, a medium and equipment, and the method comprises the steps: generating an initial transportation path scheme through collecting logistics transportation demand information and supply chain node data; constructing a decentralized logistics resource pool, and collecting and verifying transportation fluctuation parameters to generate a credible state matrix; inputting the initial transportation path scheme and the logistics resource credible state matrix into a graph neural network scheduling model, and outputting a dynamic scheduling scheme; compliance verification of task allocation and scheduling instructions is automatically executed through the smart contract; and finally, updating the scheduling execution result and the logistics resource credible state matrix to a distributed account book, and outputting a logistics management report. Through deep cooperation of the intelligent contract and dynamic path optimization, real-time credible scheduling of the transportation process is realized, the problems of insufficient dynamic response and multi-party cooperation trust deficiency of an existing logistics system are solved, and the reliability and efficiency of cold chain and other sensitive commodity transportation are improved.

The invention discloses an unmanned forklift dynamic task scheduling method and system based on deep reinforcement learning of a cold chain warehouse, and belongs to the field of unmanned forklift scheduling. The method comprises the steps that a sensor is arranged in a target cold chain warehouse, environment data of the target cold chain warehouse is collected, and the environment data of the target cold chain warehouse is acquired; constructing a digital twinborn model of the target cold chain warehouse at the cloud based on a digital twinborn technology; a dynamic environment state of the target cold chain warehouse is constructed according to data fed back by the sensor, an initial unmanned forklift track is generated based on a path generation algorithm, the initial unmanned forklift track is optimized by using a multi-feature coupled space-time joint path planning algorithm deployed at a cloud end, and an optimal path and scheduling plan are generated; and the cloud end issues the instruction to the edge gateway, and the edge gateway decomposes the instruction and issues the decomposed instruction to each unmanned forklift. According to the invention, the optimization effect of the scheduling scheme 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 discloses a man-machine exoskeleton cooperation method based on multi-modal signal fusion, and relates to the technical field of exoskeleton program control. The method comprises the following steps that man-machine exoskeleton driving pre-pressure cold and hot mutation analysis is conducted in combination with man-machine exoskeleton man-machine cooperation components; and performing memory alloy spring pre-pressure optimization adjustment in combination with a man-machine exoskeleton driving pre-pressure cold and hot mutation analysis result. According to the invention, cold chain exoskeleton cooperative control is realized through multi-mode signal fusion, and three-level regulation and control are implemented based on a current threshold value and a myoelectricity state: graded current rising / falling and safety early warning are carried out in case of abnormal current; dynamic gain compensation or joint retardation protection is carried out when myoelectricity is abnormal; according to the man-machine exoskeleton cooperative control method and the man-machine exoskeleton cooperative control system, the effect of improving the accuracy of man-machine exoskeleton cooperative control for coping with cold-chain logistics is achieved, and the problem that the accuracy of man-machine exoskeleton cooperative control for coping with cold-chain logistics is disabled in the prior art is solved.

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 a food fresh-keeping method, device and equipment and a storage medium, and the method comprises the steps: collecting respiration intensity data and decay gas concentration of target food in a cold chain carriage, carrying out fresh-keeping threshold matching, and generating food fresh-keeping information; real-time environment parameters of the cold chain compartment are collected, a fresh-keeping instruction is generated according to the food fresh-keeping information and the real-time environment parameters, and an air adjusting valve opening instruction and a compressor frequency instruction are obtained; dynamic regulation and control operation is executed based on the air regulating valve opening instruction and the compressor frequency instruction, and putrefying bacterium comprehensive evaluation indexes and compressor power consumption data are obtained in real time; and according to the putrefying bacterium comprehensive evaluation index and the compressor power consumption data, instruction feedback analysis is carried out, strategy construction is carried out, and a fresh-keeping regulation and control strategy is output. The air conditioning and refrigeration strategy can be dynamically adjusted according to the breathing characteristics of different foods and the growth dynamic state of putrefying bacteria.

The invention discloses a fresh cold chain quality early warning regulation and control method based on the Internet of Things, and belongs to the technical field of detection and analysis of fresh goods, and the method comprises the steps: obtaining the original quality state of goods, and obtaining the initial data of the goods; acquiring temperature, humidity, gas concentration and vibration intensity in the cold chain environment, and generating multi-source environmental parameters; based on the commodity initial data and the multi-source environment parameters, calculating to obtain a corruption activity index; generating a graded early warning signal according to the decay activity index; in response to the graded early warning signal, generating an equipment cooperation instruction; the device cooperation instruction is sent to the cold chain device cluster to execute regulation and control; and acquiring environment feedback parameters after the cold chain equipment cluster executes regulation and control, and updating the corruption activity index based on the environment feedback parameters. According to the method, the fresh food quality degradation risk can be accurately predicted, the targeting and effectiveness of regulation and control are improved, and the goods damage in the cold chain transportation process is remarkably reduced.

The invention provides a cold chain transportation method and device, electronic equipment and a storage medium. The method comprises the steps that the cargo type of cargo transported by a target cold chain transportation vehicle is acquired; determining at least one vehicle operation parameter and a standard parameter range corresponding to each vehicle operation parameter according to the cargo type; in the running process of the target cold-chain transport vehicle, real-time measurement values of all the vehicle running parameters are monitored in real time, and environment data of the environment where the target cold-chain transport vehicle is located are obtained; according to the environment data, the real-time measurement value of each vehicle operation parameter and the standard parameter range, the driving parameter and the refrigeration parameter of the target cold-chain transport vehicle are adjusted; and acquiring environment monitoring data in the cargo hold of the target cold-chain transport vehicle, and generating a three-dimensional visual interface including temperature field distribution and cargo placement state according to the environment monitoring data.

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 discloses an intelligent control system and method for a multi-unit parallel water-cooling refrigerator, and belongs to the technical field of refrigeration equipment. The control system comprises a data acquisition module, a load prediction module, an energy efficiency optimization module and a cooperative control module. The data acquisition module is used for acquiring the running state and environmental parameters of the refrigerator in real time; the load prediction module is used for predicting the refrigerator thermal load based on the operation state and the environmental parameters; the energy efficiency optimization module is used for optimizing operation parameters of each unit according to the predicted thermal load of the refrigerator; and the cooperative control module is used for cooperatively controlling the compressor frequency and the cooling water flow of each unit according to the optimized operation parameters. According to the intelligent control system and method for the multi-unit parallel water-cooling refrigerator, precise temperature control and energy efficiency optimization of the multi-unit parallel water-cooling refrigerator can be achieved, load and water flow distribution is uniform, temperature fluctuation is small, system energy consumption is low, and the temperature control and energy saving requirements of a high-quality cold chain can be met.

The invention relates to the technical field of cold-chain transportation, in particular to a cold-chain transportation safety remote supervision system, which not only can evaluate the vibration acceleration of goods in the transportation process in real time, but also can identify abnormities in advance under the condition of sudden vibration by using a sequential probability ratio method, thereby improving the safety of the goods. Therefore, goods damage caused by sudden turning, collision and the like of the vehicle is avoided. In addition, by combining historical data, the system can accurately set normal characteristic parameters of vibration and compare real-time data, and the sensitivity to abnormal vibration is further enhanced. According to the monitoring mode based on the sequential probability ratio test method, the defects of a traditional fixed threshold value are avoided, and whether vibration is abnormal or not can be judged more accurately. In the aspect of environment monitoring, compared with traditional static threshold judgment, a prediction method based on a Hort linear trend method can reflect environment changes more dynamically, environment adjustment measures are started in advance, and goods damage caused by too large fluctuation of temperature and humidity is avoided.

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 the technical field of transfer robots, in particular to an intelligent warehouse logistics automatic transfer robot. The robot comprises a scraper and a suction cup which are arranged on a robot body, the scraper is provided with specially-designed groove pressing teeth, an arc-shaped groove with a guiding function can be formed in the surface of a box body, and it is ensured that a frost layer effectively falls off along a preset path through the design. The system collects temperature gradient, surface appearance and contact stress data in real time through the integrated multi-mode environment sensing unit, the intelligent decision-making unit establishes a dynamic control model based on the data, and accurate evaluation and self-adaptive processing of the frost layer state are achieved. The whole system constructs a complete closed-loop control chain, the limitation of a traditional defrosting method is overcome, the adsorption stability is kept, meanwhile, the operation efficiency is improved, the system is particularly suitable for the low-temperature storage environment of-25 DEG C and below, and a reliable solution is provided for cold-chain logistics automatic operation.

The invention relates to the technical field of cold chain product traceability, and discloses a cold chain product whole-process wireless traceability system based on the Internet of Things, and the system comprises an acquisition module which is used for collecting environment data of a cold chain product; the state matrix construction module is used for constructing an observation state matrix; the feature extraction module is used for extracting observation state matrix features and constructing transportation abnormity and risk grade indexes; the risk evaluation module is used for evaluating whether environment abnormity and state mutation exist or not at present; the data uploading control module is used for sending alarm information to the cloud; a state graph modeling and visualization module; and constructing a state graph based on the alarm information. State estimation is carried out through the state estimation unit, reconstruction compensation can still be carried out on key data when the nodes are in the dormant state, the integrity and continuity of a data chain are guaranteed, a data blind area is effectively filled, the integrity of data detection is improved, and the accuracy of cold chain product traceability is improved.

The invention discloses a fresh commodity after-ripening regulation and control planning method oriented to maturity grading, and relates to the technical field of food science and biologication.The method comprises the steps that physiological parameters of fresh commodities are collected in real time based on a multi-modal sensor, and the physiological parameters comprise the epidermis pigment content, the ethylene release rate, the fruit hardness and the respiration intensity; the collected physiological parameters are input into a multi-objective optimization model based on deep learning, the model fuses a metabolic kinetic equation and an LSTM time sequence prediction algorithm, and a regulation and control parameter combination adaptive to the current mature stage is output; according to the regulation and control parameter combination, the temperature, the relative humidity and the O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously regulated through a distributed control system, and a plant hormone antagonist is injected through an ultrasonic atomization device; and constructing a supply chain collaborative network based on a block chain technology, binding a grading result with logistics path planning, and adjusting a temperature control strategy and a shelf life countdown parameter of the cold chain transport vehicle in real time through an edge computing node.

The invention relates to the technical field of intelligent logistics scheduling optimization, and discloses a logistics resource intelligent scheduling optimization system based on big data analysis, and the system comprises a data collection module which is used for collecting basic data, and the basic data comprise temperature sensing data, transportation load data, path parameter data, and vehicle operation state information; the data fusion module is used for fusing the temperature sensing data and the transportation load data and outputting a temperature zone label and a temperature control load characteristic; the index construction module is used for calculating thermal exposure time and generating a thermal exposure intensity index and a quality risk index; the energy efficiency modeling module is used for calculating path cost values corresponding to the plurality of paths and vehicle combinations; and the path energy efficiency optimization module is used for constructing a multi-target path optimization model and outputting an optimal transportation path combination. According to the invention, efficient energy consumption control and quality risk collaborative optimization of resource scheduling in a cold-chain logistics scene are realized.

The invention provides a fresh food quality degradation pre-judgment method and system based on multi-modal dynamic coupling, and aims to solve the problem of prediction lag caused by data isolation and dynamic coupling deficiency in the traditional storage and transportation process. Specifically, sensory layer data, environment layer data and operation layer data are synchronously collected through a multi-source sensor, chromatic aberration quantification, chemical bond feature extraction and spatial chemical fusion processing are carried out on the sensory data, and a quality degradation feature tensor is generated; based on a graph network model and frequency domain analysis, constructing a dynamic coupling parameter set of quality degradation characteristics and environmental parameters; fusing the loading and unloading pulse trajectory tensor, the cold chain stability distribution and the coupling parameter set, and generating a degradation constraint distribution matrix through multivariable weight mapping; and finally, combining acoustic resonance mode coherence analysis and constraint matrix evolution, and outputting a four-stage quality label. According to the method, through multi-dimensional real-time sensing, dynamic coupling modeling and hierarchical management and control, the fresh food storage and transportation loss is remarkably reduced, and the cold chain management efficiency is improved.

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 intelligent warehousing, and discloses an intelligent warehousing management system and method based on the Internet of Things, and the method comprises the following steps: monitoring a cold chain warehousing environment and the state of each agent in real time, and collecting related data; wherein each agent is responsible for controlling one independent physical device, and the types of the independent physical devices comprise a generator, a motor and a sensor; each agent evaluates own state and shares state information so as to enhance a global view angle; and identifying potential strategy conflicts among all the paired agents by adopting a conflict detection algorithm. According to the method, synchronous sampling is carried out through a multi-source sensor, a global collaborative view angle is enhanced based on conflict function modeling and dynamic influence weight distribution, and a multi-agent strategy conflict is solved by combining conflict threshold updating of Q function iteration and action optimization driven by coordination factors; the collaborative efficiency of all subsystems in cold chain storage can be remarkably improved, energy consumption and cargo loss are reduced, and continuous and stable operation of the system is guaranteed.

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 is suitable for the field of data processing and analysis, and provides a medicine cold chain on-line fast verification system and a real-time data processing and intelligent analysis method thereof.The medicine cold chain on-line fast verification system has the beneficial effects that the medicine cold chain on-line fast verification system comprises a wireless temperature and humidity recorder, an intelligent management host and upper computer server platform software, and the wireless temperature and humidity recorder adopts an SHT40 sensor; the wireless management host receives encrypted data of the recorder, then transfers the encrypted data to the W25Q256JVEIQ storage chip, and encrypts and uploads collected environment parameters and equipment state information to the upper computer server, an online real-time verification technology is introduced, instant monitoring and response to overtemperature early warning can be achieved, rapid deviation correction can be achieved, repeated verification of traditional offline verification is avoided, and the verification efficiency is improved. And the verification period is greatly shortened. The automatic data processing system realizes full-chain unmanned intervention, reduces manual operation time, guarantees data authenticity, and improves verification efficiency and result objectivity.

The invention relates to a cold-chain food distribution analysis method and system based on big data, and the method comprises the steps: carrying out the dynamic feature extraction of multi-source monitoring data of a cold-chain food distribution vehicle, and obtaining and generating transportation state parameters; carrying out correlation matching on the transportation state parameters and real-time environment data of the cold-chain food distribution vehicle to obtain an environment influence evaluation result; carrying out distribution efficiency analysis on the vehicle path transportation data of the cold-chain food distribution vehicle based on the environmental influence evaluation result to form initial distribution efficiency data; road condition diagnosis is carried out on the route transportation data, route pre-judgment is carried out in combination with the environmental influence evaluation result, and a route prediction result is generated; and performing cross analysis on the initial delivery efficiency data and the path prediction result, and formulating a global delivery scheme of the cold-chain food delivery vehicle. According to the invention, logistics resource configuration can be optimized, and the overall operation efficiency of cold-chain logistics is improved.

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 discloses a blockchain traceability catering supply chain risk early warning method and system, and the method comprises the steps: deploying a multi-source sensor in a cold chain transportation link, collecting the temperature, humidity and vibration intensity data of food materials in real time, and carrying out the real-time binding with the GPS track of a transportation vehicle, and forming a space-time related transportation environment data set; setting a dynamic threshold based on the type of the transported food material, starting redundant sensor cross validation when sensor data in the transportation environment data set continuously exceeds the dynamic threshold for N times, and if the deviation between the validation data and the transportation environment data set exceeds a preset tolerance, marking the transportation environment data set as an abnormal data segment and triggering block chain evidence storage; and compressing the abnormal data segment and the GPS track fragment associated with the abnormal data segment into a data block, writing the data block into a lightweight alliance chain after the data block is subjected to double signature by a transportation party node and a receiving party node, and generating a traceability code containing a timestamp. According to the method, the supply chain risk disposal efficiency is improved through fusion of space-time gridding multi-source data and risk multi-dimensional assessment.