245 results about "Environment surveillance" patented technology

The invention relates to the technical field of cloud computing, and discloses a resource scheduling method, device and equipment, and the method comprises the steps: generating a multi-dimensional data topological graph of cloud environment monitoring data, decomposing a to-be-scheduled job set into quantum computing unit sets which can be processed in parallel, and adding priority labels for the quantum computing unit sets through a quantum heuristic algorithm, according to priority labels and a multi-dimensional data topological graph, physical resources of a cloud environment are divided into non-uniform resource fragments matched with task requirements, based on the non-uniform resource fragments, resource scheduling is carried out through an improved hybrid particle swarm optimization algorithm, and weighting and fitness evaluation and resource constraint correction are introduced. According to the resource scheduling method and device, the multi-dimensional data topological graph is generated to comprehensively present the resource state and association, then the to-be-scheduled job set is decomposed into the quantum computing unit sets, the priority labels are added, and then the non-uniform resource fragments are divided for resource scheduling, so that the resource adaptation accuracy is improved, and the scheduling efficiency and the resource utilization rate are improved.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to the technical field of environment monitoring, in particular to an intelligent environment monitoring system based on the Internet of Things, which comprises a data quality checking module, a data exception correction module, a data association analysis module, an exception monitoring marking module and an environment early warning transmitting module. According to the method, the checking of data equipment numbers and time labels and the filtering of abnormal or discontinuous data fragments are emphasized, so that the integration of the data is improved, the calling and mode analysis of historical data are carried out, and the abnormity is identified and corrected, and the data better meet the requirements of a time sequence mode; according to the method, key monitoring item combinations can be accurately identified and recorded in data correlation analysis, so that the real-time analysis and response capability of monitoring data is remarkably improved, the adaptive capacity to environmental changes and the early warning accuracy are greatly enhanced through timely marking of abnormal states and rapid release of early warning information, and the early warning efficiency is improved. And the efficiency of environment monitoring and decision support is optimized.

The invention discloses a multi-source environment monitoring data fusion method based on adaptive Bayesian reasoning. The method comprises the following steps: S1, collecting an environment monitoring data set; s2, obtaining a preprocessed environment monitoring data set; s3, carrying out uncertainty evaluation on the preprocessed environment monitoring data set; s4, forming an initial prior probability; s5, calculating the posterior probability distribution of each environment monitoring data source through the Bayesian theorem, and dynamically updating the weight of each data source in the environment monitoring data fusion process according to the posterior probability distribution; s6, performing weighted fusion on each environment monitoring data source according to the dynamically updated weight to form fused environment monitoring data; and S7, carrying out post-processing on the fused environment monitoring data. According to the invention, the data fusion process can adapt to environmental changes in real time, so that the accuracy of data fusion is improved.

The invention relates to the technical field of environment monitoring, in particular to a machine room dynamic environment monitoring and management method. Comprises: collecting machine room environment parameter data; adding a data label to each piece of parameter data and recording a timestamp; creating an environment monitoring data frame, and filling basic information; adding an abnormal event indication field in the data frame, and comparing with a preset threshold to mark an abnormal condition; identifying a sudden change trend in the time sequence data and recording a result; calculating relevance indexes, and analyzing data relevance among different sensors; the signaling modules are mapped to different data processing units, and data processing and circulation services of corresponding levels are provided; the data frames are interpreted step by step, and corresponding monitoring and management strategies are executed; dynamic weight distribution is implemented, and the data frame processing priority is adjusted according to historical weight changes and the current state; data frame specific processing fields are registered and tracked. According to the method, real-time monitoring, comprehensive analysis and accurate management of the dynamic environment of the machine room are realized.

The invention discloses a computer remote login identification system based on artificial intelligence, and the system comprises a remote login request receiving and processing module which is responsible for receiving a remote login request of a user, and carrying out the preliminary processing and verification; the multi-mode identity authentication module adopts a three-factor verification matrix of biological characteristics, behavior characteristics and equipment fingerprints, and adjusts weights of different authentication dimensions according to security requirements and user requirements; the lock screen state sensing module is responsible for sensing a lock screen state; the dynamic behavior analysis engine is used for monitoring behavior characteristics of the user in real time and comparing the behavior characteristics with a preset abnormal behavior mode so as to find abnormal login behaviors in time; the intelligent unlocking decision tree is used for making an intelligent unlocking decision according to the authentication score, the equipment environment and the behavior abnormality; and the security execution module is responsible for automatically filling a password and executing an unlocking operation after the user identity authentication is passed, and monitoring an abnormal condition in the unlocking process in real time in combination with equipment environment monitoring and an abnormal detection model.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

The invention discloses a server fault monitoring system, which relates to the technical field of servers, and is characterized in that a data acquisition terminal deployed in a machine room acquires environment monitoring data and software monitoring data of a server, and the environment monitoring data and the software monitoring data are used as server monitoring data to be sent to a corresponding edge server; and when the edge server detects that the software / hardware monitoring data is abnormal, extracting related abnormal data from the corresponding server monitoring data, and generating an abnormal report log. And the central server determines that the server has a fault according to the exception report log and the related exception data, and generates fault data information. And the monitoring terminal locates a graphic object corresponding to the fault server in the three-dimensional machine room virtual model according to the equipment identifier, updates the display state of the graphic object of the server into a fault state, and generates a fault data interaction label according to the fault data information. According to the invention, the problem of relatively large fault response delay in related technologies can be solved, and the server fault monitoring efficiency can be effectively improved.

The invention relates to the technical field of dangerous chemical storage and transportation, and discloses an intelligent dangerous chemical storage and transportation system, which comprises a sensing layer provided with an RFID automatic identification module, an environment monitoring module and a video monitoring and behavior analysis module; the data processing layer is configured with a data acquisition gateway, a data storage platform and a data processing and analyzing module; the application layer is configured with a management platform, an intelligent early warning and alarm system, an intelligent decision support system and a report generation and data archiving system; all the layers are connected through a communication network and are used for realizing real-time monitoring and management of the whole storage and transportation process of the hazardous chemical substances. By integrating technologies of RFID automatic identification, environment monitoring, video monitoring, behavior analysis and the like, real-time monitoring and automatic early warning of the whole storage and transportation process of the hazardous chemical substances are realized. Once environmental parameters exceed standards or abnormal behaviors occur, the system gives an alarm immediately and starts emergency response, the safety of the storage and transportation process is remarkably improved, and accidents are prevented and reduced.

Automated, environmentally monitored and controlled storage units and systems for storing, monitoring, and maintaining a supply of products, particularly temperature sensitive pharmaceutical and / or other high-value products, be they temperature sensitive or not. Such automated storage units contain an array of independently addressable holding locations for containers with product in one or more environmentally monitored (e.g., for temperature, humidity, etc.) and controlled zones fitted with appropriate environmental sensors. In preferred embodiments, the automated storage units also include a reader to track product information and status. Product loading, retrieval, and movement within such automated storage units is performed by a computer-controlled robot. A user interface device, preferably in communication with an application service provider to provide remotely managed inventory management and other services, provides users with inventory- and product-specific information.

The invention discloses a base liquor full-life-cycle traceability management method based on a sovereign block chain. The base liquor full-process management is realized by constructing systems such as double-chain cooperation, full-process digital monitoring and credible evidence storage. In the raw material link, a block chain is used for establishing a quality file, and in the production link, the industrial Internet of Things is used as a basis, and a layered chaining and double-code anti-counterfeiting technology is adopted; a dynamic environment monitoring chain is built in a warehouse logistics link, and a multi-source sensing and condition triggering evidence storage strategy is applied; constructing a two-way verification system in a sales link to combine with risk assessment; and a carbon footprint tracking mechanism is introduced in the recovery link. The method can reduce the risk of raw material adulteration, improve the quality inspection efficiency, guarantee the production quality and brand safety, improve the safety and efficiency of warehouse logistics, achieve the guarantee of full-link certified products, improve the full-life-cycle management, improve the reliability of a block chain network, employ the verification mechanism of an oracle machine to verify and guarantee the real chaining of the data of each link, and improve the efficiency of the whole-link certified products. And consistency of online and offline data is ensured.

The invention discloses a multi-source environment monitoring and intelligent alarm system and method based on a Palladium platform. The method comprises the following steps: S1, obtaining preprocessed environment monitoring data; s2, forming transmission environment monitoring data; s3, carrying out distributed aggregation and fusion on the transmission environment monitoring data on the central Palladium platform; s4, identifying abnormal fluctuation in the data; s5, generating adjusted alarm threshold data by using a self-learning mechanism; s6, according to the adjusted alarm threshold data, carrying out grading evaluation on the detected environment abnormal events, forming multi-grade risk determination, and generating corresponding intelligent alarm signals; and S7, transmitting the intelligent alarm signal to each edge computing node and a related monitoring terminal, issuing alarm information in a short message, mail, voice broadcast or system interface display mode, and starting a corresponding emergency processing plan. According to the invention, the transmission efficiency of the alarm information can be effectively improved, and the emergency response system can make proper response according to different risk levels.

The invention discloses an intelligent research and judgment and power recovery method and system based on a low-voltage power distribution network, and relates to the technical field of intelligent power grid and power system automation, and the method comprises the steps: collecting monitoring data, carrying out information fusion check based on geography and power management, dynamically generating a topological file, and constructing a hierarchical visual power grid model. Analyzing the states of a circuit breaker and protection equipment, calculating main and backup protection deviation, analyzing electrical quantity change in combination with wavelet transform and singular value decomposition, and identifying a fault section; a whole-network sensing system is constructed, three-phase imbalance and zero-sequence current are monitored in real time, and rapid power restoration and active research and judgment are carried out by combining a dichotomy and an intelligent switch optimization power restoration strategy. According to the intelligent research and judgment and power recovery method based on the low-voltage power distribution network, the reliability and safety of the power distribution network are improved by constructing an environment monitoring system, the accuracy of fault analysis of a low-voltage transformer area is improved through topology accurate checking and intelligent fusion, and the fault analysis accuracy of the low-voltage transformer area is improved through a dichotomy fault isolation and intelligent power recovery strategy. The fault recovery efficiency is improved, and better effects are achieved in the aspects of safety, accuracy and efficiency.

The invention relates to the technical field of engineering monitoring, measurement and control, in particular to an intelligent acquisition instrument based on AI analysis and self-adaptive sampling and a self-adaptive interrogation method. According to the technical scheme, the device comprises a multi-source sensing interface module, an edge intelligent processing module, a self-adaptive sampling module, a communication module, a storage module, a key interrogation module, a power supply protection module and a structure packaging module, multi-dimensional feature fusion and edge decision making are achieved, the self-adaptive sampling module automatically adjusts sampling frequency according to an AI analysis result and an environment threshold value and has a dynamic threshold value adjusting function, the communication module supports various wireless protocols, and the key interrogation module can achieve automatic sensor recognition and instant data acquisition and uploading. The system can be widely applied to the fields of industrial monitoring, environment monitoring and equipment health management, and efficient and intelligent data acquisition and management are realized.

The invention discloses a coal storage and transportation intelligent management method and system, relates to the technical field of intelligent management systems, and aims to solve the problems of lack of environment monitoring, scheduling decision lag, extensive safety response and serious data island in the prior art. The method comprises the following steps: collecting the physical state, environmental parameters and operation behavior data of a coal storage yard in real time through a distributed sensor network; constructing a three-dimensional dynamic digital twinborn model and inputting the model into a time sequence neural network to predict the risk of spontaneous combustion, collapse or dust explosion in the future; and generating a dynamic scheduling instruction in combination with the task priority and the equipment state, and triggering a multi-level security response mechanism. The system comprises a sensor network module, a data fusion modeling module, a risk prediction module, a scheduling optimization module, and a closed-loop control and safety response module. According to the scheme, safety early warning preposition, scheduling decision intelligent optimization and resource energy efficiency collaborative improvement in the storage and transportation process are achieved, and the response capability and operation reliability of the coal supply chain are remarkably enhanced.

The invention relates to the technical field of building construction monitoring, and discloses a building construction safety environment monitoring system and a monitoring data analysis method. The system comprises a data acquisition unit, a risk judgment unit, an adjustment execution unit, an anomaly analysis unit, a strategy correction unit and a failure early warning unit. The data acquisition unit acquires construction site environment and structure parameters at fixed intervals; the risk judgment unit carries out threshold judgment on the parameters and generates abnormal type signals; the adjustment execution unit executes parameter adjustment according to the priority of the exception type; the anomaly analysis unit calculates a parameter offset proportion and an adjustment efficiency value based on the anomaly signal and the adjustment data; the strategy correction unit adjusts the data acquisition frequency and optimizes the adjustment intensity; the failure early warning unit counts the total amount of abnormities and generates a failure signal in combination with the construction duration. According to the system, comprehensive monitoring, exception handling, system optimization and risk early warning of a construction safety environment are realized.

The invention discloses a highway service area intelligent environment monitoring and management system based on the Internet of Things, and relates to the field of environment monitoring and management, and the system comprises an acquisition module which is used for uploading the carbon emission source data of each energy consumption unit of a target service area, synchronously acquiring the carbon concentration in the environment of the target service area and the associated meteorological parameters of the same time interval, and transmitting the acquired data to a cloud server; constructing a carbon footprint basic data set; the traceability module is used for receiving the carbon footprint basic data set, constructing a traceability link based on the data set, analyzing carbon emission key nodes, and identifying a carbon emission ratio and a generation path of each node; according to the method, carbon emission related data and associated meteorological information are collected, a complete carbon footprint data basis is constructed, carbon emission key links and formation paths are carded out through reasonable analysis, the emission reduction potential of each link is calculated in combination with the actual operation condition of a service area, and links needing to be regulated and controlled are determined according to double judgment standards.

Systems and methods for managing asset risk in computer environment can include receiving, by one or more processors, data indicative of attributes of an asset of the computer environment. The one or more processors can determine a risk context based on the data indicative of the attributes of the asset. The one or more processors can update a risk score of the asset based at least on the risk context. The one or more processors can adjust, responsive to the risk score of the asset, a configuration parameter of at least one of the asset or another asset to mitigate a security risk associated with the asset.

The invention discloses an intelligent agricultural big data system, and belongs to the field of agricultural big data, and the system comprises an environment monitoring module, a sensing layer deploys a sensor to collect original data, and a transmission layer is provided with an edge gateway which is connected with the sensor and carries out the transmission of the original data; the equipment layer is used for regulating and controlling irrigation, ventilation and light supplement schemes, and the control layer is used for optimizing MQTT message middleware to control the agricultural Internet of Things system; according to the data management module, a data collection layer obtains original data, a data processing layer processes the original data according to a three-layer data cleaning mechanism, and a data storage layer stores the processed data; according to the system management module, the data application layer constructs an interface covering Web, a mobile terminal and AR equipment, the data display layer generates a soil moisture thermodynamic diagram, a crop growth distribution diagram and an equipment operation state diagram, and intelligent management of agricultural production is ensured. The advanced Internet of Things technology is adopted, and the accuracy and the real-time performance of data acquisition are improved.

The invention relates to the technical field of multi-cloud resource scheduling, in particular to a reinforcement learning system and method for resource scheduling in a multi-cloud environment, and the method comprises the steps: collecting first state performance monitoring data of a plurality of heterogeneous cloud platforms in the multi-cloud environment in real time through an environment monitoring module, and constructing a dynamic graph structure; performing multiple rounds of message passing and node aggregation on the dynamic graph based on a graph neural network to generate a structured state vector fusing topological features; and analyzing the state vector based on a strategy network, outputting an action parameter containing a resource selection probability and a configuration parameter, analyzing the action parameter through an autoregression decoder, generating a cross-cloud resource scheduling instruction sequence, and converting the cross-cloud resource scheduling instruction sequence into an application program interface instruction for execution. And the strategy adjustment module constructs a multi-cloud environment resource scheduling score and drives dynamic optimization of a strategy network. According to the method, the global optimization capability of multi-cloud environment resource scheduling can be effectively improved.

The invention relates to the technical field of building environment monitoring, and discloses an intelligent building environment monitoring system and method, and the method comprises the steps: S1, collecting the related environment information of each preset monitoring point in a warehouse through a first environment collection module; s2, comparing each acquired environment information parameter with a corresponding preset early warning threshold value, and if any environment information parameter exceeds the corresponding preset early warning threshold value, generating an abnormal early warning signal of the monitoring point; according to the method, warehouse monitoring point environment parameters are collected in real time through distributed sensors, single-point threshold comparison is performed firstly to trigger preliminary early warning, then environment characteristic values are calculated through multi-parameter weighting and compared with a historical abnormal range, high-density sampling analysis is performed on a characteristic value abnormal region through an inspection robot, and the abnormal region is subjected to early warning. The method overcomes the defects that a traditional warehouse environment monitoring mode is high in false alarm rate, lacks comprehensive evaluation, cannot perform dynamic inspection and the like.

The invention discloses a communication machine room environment monitoring system and device, and relates to the technical field of machine room environment monitoring, and the communication machine room environment monitoring system comprises a fusion alarm module, a fault root cause analysis module and a dynamic air duct optimization energy saving module. The fusion alarm module significantly reduces false alarms through heterogeneous sensor space-time alignment, confidence weighting and sound vibration interference filtering; the fault root cause analysis module constructs an equipment dependence graph based on CAD topology and CFD wind resistance, and uses a Bayesian network to lock root cause equipment in a second level and perform 3D visual positioning; the dynamic air duct optimizing and energy-saving module controls a fan and a flow guide plate in a closed-loop mode through a high-resolution thermodynamic diagram and reinforcement learning, cold energy is precisely put according to needs, energy is saved, and consumption is reduced. The system effectively improves the alarm reliability, shortens the fault positioning time, and reduces the PUE of a machine room.

According to the locatable basement area environment monitoring method based on the Internet of Things, Bluetooth mobile beacons are built in a plurality of monitoring modules, communication between receiving base stations in a basement is combined, and the angle between the Bluetooth mobile beacons and a receiving antenna of a data all-in-one machine can be accurately obtained through an AOA positioning algorithm; the position information of the Bluetooth mobile beacon is trimmed through an Atmann-Z model algorithm, the position information of the monitoring module can be accurately obtained, matching of environment monitoring of the monitoring module and the position information can be accurately obtained by combining the position of the monitoring module and data information collected by the monitoring module, and precise environment monitoring in a basement area is achieved.

The invention relates to a field programmable gate array (FPGA)-based intelligent environment monitoring system in the field of environment monitoring systems, which comprises an FPGA master control module used for processing and analyzing monitoring data of each partition to obtain a partition state and an environment parameter change trend of the partition in the next one hour, and selecting a corresponding processing strategy according to the partition state; the partition management module is used for setting the type of each partition and judging and associating similar partitions; the control module is used for controlling related equipment in each partition to execute a preset processing strategy; the data acquisition module is used for data acquisition of each independent partition; the self-adaptive processing module is used for adjusting the range of the basic threshold value, the scene threshold value and the alarm threshold value; and the abnormity evaluation module is used for evaluating the abnormity probability according to the environment change trend and the threshold range, and the system can associate similar areas by monitoring the environment parameters of each area in real time and comprehensively evaluating the similarity of the areas so as to realize efficient environment monitoring and management.

The invention relates to the technical field of tree information acquisition, in particular to an urban tree information rapid acquisition method and system, and the method comprises the following steps: obtaining the geographic coordinates of each tree in a city through a GNSS receiver, recording the accuracy and timestamp information of each position, executing data synchronization, and generating urban tree coordinate data. According to the method, the GNSS receiver and the microwave remote sensing equipment are introduced, high-precision and multi-dimensional analysis of tree data is ensured, the GNSS receiver provides accurate coordinates and timestamps, the real-time performance and accuracy of geographic data are enhanced, and the microwave remote sensing technology accurately evaluates the age and health condition of the tree by analyzing the dielectric constant of the tree, so that the reliability of the tree is improved. The image sensor capable of automatically adjusting the focal length and the exposure parameters can capture the color and the shape of the leaves in detail, the detail degree and the accuracy of disease and insect pest diagnosis are improved, tree information mapping is updated in real time, the efficiency of environment monitoring and urban planning is improved, and powerful support is provided for management of an urban ecological system.

The invention discloses an intelligent robot access management and control system for an unmanned archive, and the system comprises a central controller which is used for coordinating the functions of all modules, and a high-precision positioning module which is used for calculating the real-time position of a robot through a fusion positioning algorithm; the dynamic path planning module is used for outputting the optimal access path of the robot; the intelligent storage distribution module is used for optimizing storage rack space based on a greedy algorithm, inputting a file size parameter set and outputting three-dimensional coordinates and shelf numbers; the multi-robot cooperation module is used for distributing transportation tasks by applying an improved contract network protocol; the self-calibration mechanical arm module is used for controlling a mechanical arm to accurately execute a grabbing task; the state detection module is used for detecting a system state; and the environment monitoring module is used for monitoring the environment. According to the invention, intelligent access and management of archives in a full-automatic unmanned archive are realized, the problem of inaccurate positioning is solved, the storage efficiency is improved, and the safety and stability of the system are enhanced.

The embodiment of the invention relates to the field of cold chain transportation, and discloses an environment monitoring method, an environment detection method, electronic equipment and a storage medium. In the embodiment of the invention, when the refrigeration function of the refrigeration equipment is started, the temperature and the relative humidity of the temperature and humidity sensor are monitored in real time; calculating and detecting a temperature change rate in real time based on the temperature; when the temperature change rate is larger than or equal to a preset first temperature change rate threshold value and the relative humidity is larger than or equal to a preset first humidity threshold value, first warning information is sent, and the temperature and humidity sensor is heated; wherein the first warning information is used for indicating that condensate water is generated. By monitoring the temperature change rate in the environment, the condensate water is treated in time, so that the detection precision of the temperature and humidity sensor is not influenced by the condensate water, and the efficiency of the whole cold chain system is ensured.

The invention relates to an outdoor intelligent management method integrating electric power communication and living facilities, and belongs to the technical field of outdoor facility intelligent control. According to the method, a diversified power supply system, satellite and emergency communication guarantee, real-time environment monitoring, energy-saving living facilities and an intelligent conference system are integrated, air quality monitoring, ventilation regulation and control and modular drawable maintenance design are combined, and efficient utilization and comprehensive management of resources in the outdoor environment are achieved. The core of the system comprises a power supply mode automatic switching algorithm based on electric quantity monitoring, a remote communication relay optimization protocol, a life facility water-saving circulation control process, a conference equipment follow-up tracking and multi-screen cooperation technology, and a fault early warning and remote diagnosis mechanism. The method improves the power stability, communication reliability, life comfort and emergency response capability through the centralized control of the cloud platform, is suitable for disaster rescue, field operation and other scenes, and has the advantages of low maintenance cost and high environmental adaptability.

The invention relates to the technical field of cold-chain logistics and information processing, and provides a cold-chain logistics information distribution and tracing system for data management, which comprises a server, a distributed verification module, a node sampling prediction module, a logistics data push module and an environment monitoring module, the distributed verification module is used for collecting data port states of a plurality of logistics nodes and performing consistency verification on node data so as to form an inter-node trusted data set; the node sampling prediction module is used for carrying out periodic sampling on a freight vehicle, storage equipment and a temperature control device, calculating a freight priority index Freight Priority Index and generating prediction data; the logistics data pushing module is used for dynamically pushing node data according to a freight transport priority index and a system load index (Network Load), and generating a delivery feedback index (Delivery Feedback) on the basis of feedback information; the environment monitoring module is used for monitoring cold chain transportation and storage environment parameters in real time, and recording and tracing temperature control states and safety alarm information of all nodes.

The invention discloses a cross-border logistics intelligent tracking and anti-counterfeiting system based on a block chain, and relates to the field of cross-border logistics intelligent tracking and anti-counterfeiting, and the method comprises the steps: collecting logistics package feature information, generating a feature identifier, submitting the feature identifier to a block chain network, obtaining a digital anti-counterfeiting code through segmented encryption processing, and transmitting the digital anti-counterfeiting code to the block chain network; and in the transportation process, parcel feature information is collected for comparison and verification. Meanwhile, in-transit environment data are collected, an environment monitoring threshold value is determined according to the parcel feature identifier, and an adjustment strategy is executed. And carrying out fusion analysis on the anti-counterfeiting verification result and the environment regulation result to obtain a logistics risk level, and determining an optimal logistics path according to the logistics risk level. And planning a node verification strategy according to the optimal logistics path, collecting verification data to generate node proof, and updating the logistics track in the smart contract after block chain network verification. According to the invention, credible tracking and intelligent anti-counterfeiting of cross-border logistics are realized, and the logistics safety is improved.