2687 results about "Cold chain" patented technology

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

The invention relates to the technical field of cold-chain logistics monitoring, and discloses a temperature prediction method and system for a multi-modal AIGC cold-chain logistics monitoring platform, and the method comprises the steps: carrying out the multi-modal data collection and data fusion of a cold-chain logistics transportation process based on a cold-chain logistics monitoring platform, and obtaining a multi-modal fusion data matrix; inputting an AIGC model to carry out feature extraction and inter-modal feature weight calculation to obtain a multi-modal feature importance vector; performing cross-modal weighted fusion calculation and associated feature map construction to obtain a temperature state feature sequence; inputting a multi-modal deep belief network to carry out probability distribution modeling to obtain a temperature prediction probability distribution sequence; performing time sequence characteristic analysis to obtain a temperature prediction interval sequence; and performing dynamic threshold judgment to obtain a multi-stage temperature early warning trigger signal. According to the invention, the temperature prediction accuracy of the cold-chain logistics monitoring platform is improved.

The invention relates to the technical field of AI big data, and discloses an intelligent cold chain integrated management method and system based on AI big data, and the method comprises the steps: carrying out the feature data collection of nodes in a cold chain system, obtaining the original data of the nodes, and constructing a cold chain network initial adjacency matrix; inputting a temperature control parameter identification model to carry out graph convolution operation to obtain a temperature control parameter identification result; performing task decomposition and resource allocation calculation to obtain a multi-region collaborative task allocation scheme; performing dynamic load calculation and inter-region task allocation based on a multi-region collaborative task allocation scheme to obtain real-time task execution state data; performing grading processing on the real-time task execution state data and collecting health assessment data of the cold chain system; according to the method, early warning of temperature control abnormity and equipment-level and system-level cooperative control are realized, and a set of complete intelligent cold chain integrated management solution is formed.

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 cold-chain logistics information, and discloses a multi-mode AIGC cold-chain logistics information processing method and system, and the method comprises the steps: carrying out the feature extraction and feature enhancement of video data, temperature data and position data in a cold-chain logistics system based on a logistics information AIGC model, and obtaining an enhanced feature matrix; carrying out digital twinborn modeling to obtain a digital twinborn model of the logistics system, and constructing a state parameter space; performing deep reinforcement learning and information delay index calculation to generate a real-time data synchronization scheme; constructing a distribution network graph structure, and performing depth map network coding on the distribution network graph structure to obtain a distribution network representation vector; and performing multi-agent collaborative decision-making on the distribution network representation vector to obtain a cold chain distribution scheduling scheme, and updating the cold chain distribution scheduling scheme to the logistics system digital twinborn model. Therefore, intelligent scheduling and accurate control of the cold-chain logistics system in a complex scene are realized.

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 data processing, in particular to an intelligent logistics platform management system and method based on multi-source data. The method comprises the following steps: acquiring cold-chain logistics order data; performing multi-type cargo temperature control demand processing on the cold-chain logistics order data to generate cargo temperature control demand data; performing multi-node temperature control task decomposition on the cold-chain logistics order data through the cargo temperature control demand data to generate multi-node fine temperature control task data; performing cold chain abnormal feature analysis according to the multi-node fine temperature control task data to generate abnormal temperature control processing scheme data; and carrying out transportation-junction point temperature control correction according to the abnormal temperature control processing scheme data, and carrying out cold chain quality weighted scoring to generate logistics quality scoring data. According to the invention, through cold chain task decomposition and cooperation of multiple transportation nodes, intelligent cold chain logistics management is realized, and the temperature control management efficiency and cargo quality guarantee of cold chain logistics are improved.

The invention provides a multi-region independent temperature control intelligent frequency conversion refrigeration and preservation method and system, and relates to the technical field of refrigeration and preservation. A region division module, a temperature control module, an environment monitoring module, a data analysis module, an intelligent frequency conversion control module and a user interface module work cooperatively; fine temperature control management of different areas in the refrigeration equipment is realized, the system adopts three-dimensional laser scanning and multispectral image acquisition technologies to automatically divide temperature areas, a quantum-level precision thermistor array and an intelligent fuzzy PID controller ensure accurate temperature control, and meanwhile, humidity and air flow are monitored to optimize a storage environment, so that the temperature control efficiency is improved. The data analysis module is combined with deep learning and an association rule mining algorithm to provide decision support for frequency conversion control and realize energy consumption optimization, the user interface module provides visual operation experience, and the whole scheme improves the intelligence, accuracy and energy efficiency of refrigeration and preservation, and is suitable for various cold chain scenes.

The invention discloses a cargo transportation state monitoring system based on the Internet of Things, and relates to the technical field of logistics management. Electromagnetic signal intensity of each frequency band in a cargo box is recorded in real time through an electromagnetic interference monitoring module, and multi-point temperature data and temperature deviation information collected by a redundant sensor module are combined; the data processing and transmission module is used for preprocessing the signals and uploading the signals to the cloud platform, and the comprehensive analysis module extracts spectrum interference intensity abnormal features and temperature difference fluctuation features and accurately evaluates the influence of electromagnetic interference on the sensor data precision; then, the interference division module divides the interference event into instantaneous interference and continuous interference, the early warning processing module delays the instantaneous interference to trigger the temperature control equipment to adjust and send low-level early warning, and the continuous interference is deeply analyzed to generate a graded early warning signal and guide a coping strategy; and the data reliability, the equipment stability and the cargo protection capability of the cold chain transportation system in a complex electromagnetic environment are effectively improved.

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 PUF (Physical Unclonable Function) key generation and equipment identity authentication system for a cold chain environment, and particularly relates to the technical field of information security, comprising temperature change data perception, drift modeling, dynamic error correction and closed-loop optimization, and collecting original response data of PUF hardware and temperature sensor readings by simulating the cold chain environment in a temperature-controllable experiment module; learning a nonlinear mapping relation between the dynamic temperature change and PUF response drift, and outputting a drift compensation matrix; constructing a two-stage error correction mechanism based on the drift compensation matrix; the first-stage error correction unit carries out nonlinear calibration on the original PUF response sequence according to the drift compensation matrix; the second-stage error correction unit adopts a temperature sensing type reconfigurable LDPC coding architecture, an LDPC check matrix is obtained based on a temperature-drift joint constraint model, the error correction performance of a high-drift scene is optimized through the LDPC check matrix, and the problem of key instability caused by PUF hardware response drift in a cold chain environment is solved.

The invention relates to the technical field of cargo state analysis, in particular to a multi-mode AIGC cold chain cargo state tracking and tracing method and system. The method comprises the following steps: acquiring real-time state parameters of multi-dimensional cold chain goods; abnormal data cleaning is carried out on the real-time state parameters of the multi-dimensional cold chain goods, heterogeneous state fusion is carried out, and therefore goods heterogeneous state features are obtained; carrying out cargo vibration time-frequency mining on the multi-dimensional cold chain cargo real-time state parameters, and carrying out transportation scene identification so as to obtain a cargo transportation scene; carrying out multi-time-point position calculation according to a cargo transportation scene, carrying out real-time scene modeling, and constructing a dynamic scene transportation track map; and carrying out one-by-one state position matching on the dynamic scene transportation track map based on the cargo heterogeneous state features, carrying out dynamic state rendering, and constructing a dynamic cargo transportation twin model. According to the method, the safety of cold chain cargo transportation is improved, and the real-time state of the cargo is efficiently analyzed.

The invention relates to the technical field of data processing, and discloses a vehicle-mounted cold chain temperature and humidity intelligent supervision and remote control method, system and device. The method comprises the steps of collecting environment data through a temperature and humidity sensor and adding time codes, dividing priority data packets and encrypting and compressing the priority data packets, performing cleaning and abnormity correction on the data, dividing an early warning threshold value based on feature data and generating an early warning instruction, converting the early warning instruction into control parameters and issuing a remote regulation and control command, and finally, generating a monitoring report in combination with a terminal execution result to form an intelligent supervision scheme. According to the method, a multi-level data processing mechanism is introduced, cleaning, correction and interpolation compensation are carried out on the data in combination with an intelligent algorithm, and the accuracy and reliability of a monitoring result are remarkably improved.

The invention relates to the technical field of vehicle-mounted temperature control, and discloses a method and system for intelligently controlling the temperature and humidity of a vehicle-mounted cold chain and a storage medium. The method comprises the following steps: acquiring and preprocessing temperature and humidity data through a sensor; performing state evaluation based on a three-way decision model; constructing a temperature and humidity trend analysis model to predict a change trend; constructing a control decision system by adopting multi-agent reinforcement learning; executing a multi-mode control strategy; and adaptively updating the control system according to the execution effect. According to the application, the future temperature and humidity change trend is predicted according to the historical data and the real-time state, collaborative optimization control of temperature and humidity is realized, self-learning and continuous optimization capabilities are provided, and the accuracy, stability and energy utilization efficiency of cold chain temperature and humidity control are effectively improved.

The invention provides a cold-chain transportation demand analysis method and system based on big data prediction, and the method comprises the steps: firstly obtaining a historical cold-chain order data set which comprises temperature-sensitive cargo transportation records of multiple time periods and corresponding environment parameters in a target region, and then extracting a transportation demand feature set from the historical cold-chain order data set; the characteristics of correlation between cargo types and temperature sensitivity, balance between transportation aging and equipment energy consumption, matching between regional climate and storage conditions and the like are covered. And then inputting the feature set into a pre-trained space-time prediction model, and generating multi-dimensional cold chain demand parameters, such as a temperature fluctuation tolerance threshold value and the like, of the target area in a specified period in the future. And finally, based on the multi-dimensional cold chain demand parameters, generating a cold chain transportation dynamic scheduling scheme with a timestamp. According to the method, the cold chain transportation requirements can be accurately analyzed by integrating multi-dimensional factors, scientific dynamic scheduling is realized, and the cold chain transportation efficiency and quality are improved.

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 provides an intelligent cold chain transportation information management method, system and device and a storage medium, and the method comprises the steps: recognizing the type of transported goods and environment demands through a task recognition module, and determining and configuring a target sensor of a cold chain vehicle through a sensor configuration module; the target sensor collects environment data, the vehicle-mounted GPS module collects position information and a driving state, the RFID tag records a cargo loading state, and all data are sent to the central management platform. The platform constructs a multi-dimensional feature vector, the multi-dimensional feature vector is compressed into a low-dimensional feature representation through an encoder, a reconstruction data vector is obtained through reconstruction of a decoder, and a reconstruction error is calculated. And by analyzing the reconstruction error, the platform generates an analysis result, and the environment control equipment in the vehicle is automatically adjusted according to the result, so that the stability of cargo transportation is ensured.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

A central air-conditioning system energy consumption simulation method based on a multi-region coupling model comprises the steps that basic data and initial parameters of a cold-chain logistics center site are obtained, and the basic data and the initial parameters are preprocessed; and constructing a multi-region coupling physical mechanism model based on a heat and mass transfer equation, and outputting a physical model state through a finite difference method by taking the basic data and the initial parameters as boundary conditions. And taking the physical model state as the input of a convolutional neural network, extracting short-period penetration load characteristics through time sequence convolution, and outputting a short-period penetration load prediction result. And combining the short-term penetration load prediction result with the cargo thermal parameters and the partition state as the input of a Transform model, and outputting multi-region energy consumption distribution prediction data. And performing strategy optimization processing on the multi-region energy consumption distribution prediction data through MPC, and calculating a preliminary control strategy. And searching a global optimal solution in the high-dimensional nonlinear parameter space through a myxomycete optimization algorithm, and outputting an optimal control strategy.

The invention discloses a cold chain warehouse unmanned aerial vehicle obstacle avoidance and path planning checking method and system based on AI intelligence, and belongs to the field of intelligent obstacle avoidance, and the method comprises the steps: arranging different types of sensors used for sensing the environment change of a cold chain warehouse in the cold chain warehouse, the edge gateway integrates different types of sensors, preprocesses the collected data, and sends the processed data to a cloud database; constructing a digital twin model of the target cold chain warehouse and an intelligent inventory path planning algorithm in the cloud database; the cloud sends a path scheme output by the intelligent inventory path planning algorithm to the unmanned aerial vehicle terminal and the digital twin model at the same time; the unmanned aerial vehicle terminal completes a checking task according to the path scheme, and the digital twin model is updated in real time according to the path scheme and data fed back by the unmanned aerial vehicle terminal. According to the invention, it can be ensured that the unmanned aerial vehicle efficiently and safely performs inventory tasks in a complex cold chain warehouse environment.

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 a goat milk product cold-chain logistics system risk evaluation method. The method comprises the steps of S1, collecting a plurality of key parameters in a goat milk product cold-chain logistics process in real time; s2, historical cold-chain logistics data are collected, risk event labeling is carried out, and a historical data set is generated; s3, preprocessing the real-time data set and the historical data set, and generating a feature vector for risk assessment by applying a feature engineering technology; s4, based on the feature vectors, a support vector machine algorithm is adopted to construct a multi-level risk assessment model through a multi-dimensional feature fusion technology; s5, performing dynamic risk assessment according to the real-time data set and the multi-level risk assessment model; and S6, according to a dynamic risk assessment result, by setting an intelligent threshold value, automatically identifying a potential risk and triggering an early warning mechanism, forming an early warning signal, and according to an early warning level, automatically starting an intervention measure. The method has the advantages of being accurate, real-time and dynamic in response, and the safety and reliability in the cold-chain logistics process are effectively 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 relates to the technical field of dynamic weighing, and discloses a dynamic weighing error self-correction system based on multi-modal edge coordination, which comprises a multi-modal data acquisition module, a space-time alignment module, a dynamic error propagation modeling module and a coordination calibration and suppression module. According to the system, accurate positioning of an error source and whole-course tracking of a propagation path are realized through real-time construction of a vibration energy transfer topological graph and dynamic judgment of path priority, error diffusion is directly blocked by combining a piezoelectric ceramic reverse vibration wave neutralization technology, and the industrial problems of fuzzy error traceability and compensation lag in a traditional scheme are solved; through collaborative design of vibration wave velocity correction and temperature signal dynamic interpolation, space-time consistency compensation of cross-modal data is realized, and compensation logic conflicts are avoided in a cold-chain logistics temperature sudden change scene; based on an edge node intelligent dormancy and hidden fault self-sensing mechanism, the long-term stable operation of the system under the conditions of highway continuous weighing and industrial high load is ensured.

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 relates to the technical field of cold-chain medicine transportation, in particular to an intelligent monitoring and early warning system for a cold-chain medicine transportation environment. Comprising a multi-source heterogeneous data acquisition module, a time-space synchronization data fusion module, a three-dimensional thermodynamic distribution modeling module, a drug property degradation risk assessment module, a dynamic grading early warning module and a path self-optimization execution module, the multi-source heterogeneous data acquisition module is used for generating multi-source heterogeneous data; the space-time synchronization data fusion module generates an environment parameter matrix; the three-dimensional thermodynamic distribution modeling module calculates an equivalent thermal load value of each grid node; the drug property degradation risk assessment module predicts residual effective time; the dynamic grading early warning module generates a dynamic early warning signal; and the path self-optimization execution module generates optimized transportation path parameters. According to the invention, the medicine is prevented from losing efficacy due to environment change, and meanwhile, the response speed of cold-chain transportation and the medicine quality guarantee are improved.

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.