Logistics node dynamic monitoring and early warning method, system and device and storage medium

Abstract

The invention relates to a logistics node dynamic monitoring and early warning method, system and device and a storage medium, and the method comprises the steps: obtaining a multi-source equipment signal and real-time ownership tracking data of a logistics node, carrying out the channel distribution construction, and obtaining a node exclusive channel; acquiring space coordinates and customs supervision information of the logistics nodes, and performing dynamic permission construction in combination with the node exclusive channels to obtain cross-border supervision permissions; acquiring cross-border cargo data and node environment states through the node exclusive channels, and performing multi-factor compliance verification with a preset compliance strategy library to obtain logistics node security levels; and performing hierarchical management and control operation based on the cross-border supervision authority and the logistics node security level to obtain node early warning information. According to the invention, the real-time performance and the transmission stability of logistics node monitoring data can be improved, and a data island phenomenon is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics monitoring, in particular to a logistics node dynamic monitoring and early warning method, system, device and storage medium. BACKGROUND

[0002] With the rapid development of global cross-border trade, the safety and efficiency of logistics nodes, which are the core hubs of cargo transfer and supervision, directly affect the smoothness of international trade. Currently, the monitoring system of logistics nodes relies on a single data source or static supervision strategy, which is difficult to cope with complex scenarios such as multi-device collaboration, dynamic ownership change and cross-border compliance verification. Especially in the field of customs supervision, the traditional method lacks dynamic correlation analysis of the spatial coordinates of the logistics node, real-time cargo attributes and environmental state, resulting in lagging supervision or rigid allocation of permissions. In addition, the multi-source data of cross-border goods (such as device signals, customs information, and environmental parameters) cannot be efficiently integrated due to channel conflicts or lack of permissions, further increasing the difficulty of safety risk early warning. SUMMARY

[0003] The main purpose of the present application is to provide a logistics node dynamic monitoring and early warning method, system, device and storage medium, which can improve the real-time performance and transmission stability of logistics node monitoring data and avoid data island phenomenon.

[0004] To achieve the above purpose, the present application provides a logistics node dynamic monitoring and early warning method, comprising: obtaining multi-source device signals and real-time ownership tracking data of a logistics node, performing channel allocation construction, and obtaining a node exclusive channel; obtaining the spatial coordinates and customs supervision information of the logistics node, and combining the node exclusive channel to perform dynamic permission construction, and obtaining cross-border supervision permissions; obtaining cross-border cargo data and node environmental state through the node exclusive channel, and performing multi-factor compliance verification with a preset compliance strategy library, and obtaining a logistics node safety level; based on the cross-border supervision permissions and the logistics node safety level, performing hierarchical control operation, and obtaining node early warning information.

[0005] Further, obtaining multi-source device signals and real-time ownership tracking data of a logistics node, performing channel allocation construction, and obtaining a node exclusive channel, comprising: performing node device monitoring on the logistics node according to a preset node device identifier, and obtaining multi-source device signals; performing ownership identification on the logistics node according to a consignor identification field, and obtaining the real-time ownership tracking data; performing frequency domain extraction and signal strength clustering on the multi-source device signals, and obtaining device frequency band distribution information; According to the real-time ownership tracking data, the equipment frequency band distribution information is mapped for ownership, and an equipment owner association table is obtained; Based on the equipment frequency band distribution information, channel resource identification is performed, and an available channel resource pool is obtained; The equipment owner association table and the available channel resource pool are subjected to channel polling scheduling allocation, and an initial allocation channel is obtained. Based on the real-time ownership tracking data, the initial allocation channel is subjected to frequency hopping encryption configuration, and the node exclusive channel is obtained.

[0006] Further, the spatial coordinates and customs supervision information of the logistics node are obtained, and dynamic permission is constructed in combination with the node exclusive channel, and cross-border supervision permission is obtained, including: The spatial coordinates of the logistics node are obtained by three-mode fusion positioning; Based on the spatial coordinates and the preset customs supervision database, the location of the logistics node is identified, and the customs supervision information is obtained; The spatial coordinates are subjected to geographical boundary identification, and an initial supervision boundary set is obtained; According to the customs supervision information, the initial supervision boundary set is subjected to hierarchical coding, and a supervision fence level is generated; The historical customs clearance records and credit rating data of the owner are extracted through the node exclusive channel, and the supervision fence level is subjected to permission analysis, and an owner permission table is obtained; Based on the customs supervision information, the owner permission table and the supervision fence level are subjected to supervision security matching, and the cross-border supervision permission is obtained.

[0007] Further, the cross-border cargo data and node environment state are obtained through the node exclusive channel, and multi-factor compliance verification is performed with a preset compliance strategy library, and a logistics node safety level is obtained, including: The cargo transmission signal of the node exclusive channel is obtained, and multi-protocol analysis is performed, and the cross-border cargo data and the node environment state are obtained; According to the preset compliance strategy library, the cross-border cargo data is subjected to dynamic index matching, and target cargo compliance information is obtained; According to a preset environment threshold library, the node environment state is subjected to time sequence environment comparison processing, and an environment abnormal sequence is obtained; The cross-border cargo data and the target cargo compliance information are subjected to compliance mapping, and a cargo compliance deviation vector is obtained, and the environment abnormal sequence is subjected to safety evaluation, and the logistics node safety level is obtained.

[0008] Further, the dynamic index matching of the cross-border cargo data according to the preset compliance strategy library obtains target cargo compliance information, including: characteristic extraction processing is performed on the cross-border cargo data to obtain cargo category code, place of origin identification, and transportation level information; The preset compliance strategy library is used for hierarchical index screening of the cargo category code to obtain initial cargo compliance information; The initial cargo compliance information is filtered based on the place of origin identification to obtain cargo regional information; The cargo regional information is sorted according to the transportation level information to obtain cargo transportation information; The cargo transportation information is subjected to rule constraint testing and validity verification to obtain the target cargo compliance information.

[0009] Further, the hierarchical control operation based on the cross-border supervision authority and the logistics node security level obtains node early warning information, including: The cross-border supervision authority is used to generate a cargo supervision fence range and a fence access rule, and the logistics node security level is used for control division to obtain multi-level control parameters; The fence access rule and the multi-level control parameters are used for differential authority setting to generate a hierarchical control instruction set; The hierarchical fence monitoring is performed according to the cargo supervision fence range, the logistics node security level is dynamically adjusted, and the hierarchical control instruction set is executed to obtain the node early warning information.

[0010] Further, the hierarchical fence monitoring according to the cargo supervision fence range, the dynamic adjustment of the logistics node security level, and the execution of the hierarchical control instruction set obtain the node early warning information, including: The cargo supervision fence range is subjected to multi-level fence threshold division processing to obtain a first supervision area, a second supervision area, and a third supervision area; The first supervision area, the second supervision area, and the third supervision area are subjected to dynamic fence state monitoring processing to obtain a fence triggering event; The fence triggering event and the logistics node security level are dynamically corrected, and the hierarchical control instruction set is subjected to instruction matching to obtain an adaptive control instruction; The first supervision area is subjected to forced fence locking according to the adaptive control instruction to obtain first-level execution feedback information; The second supervision area is subjected to dynamic fence contraction according to the adaptive control instruction to obtain second-level execution feedback information; According to the adaptive control instruction, the three-level supervision area is subjected to fence permission degradation, and three-level execution feedback information is obtained; The first-level execution feedback information, the second-level execution feedback information and the third-level execution feedback information are subjected to node early warning aggregation, and the node early warning information is obtained. The collection module is configured to acquire multi-source equipment signals and real-time ownership tracking data of the logistics node, perform channel allocation construction, and obtain a node exclusive channel. The analysis module is configured to acquire spatial coordinates and customs supervision information of the logistics node, and perform dynamic permission construction in combination with the node exclusive channel, to obtain cross-border supervision permission. The correlation module is configured to acquire cross-border cargo data and node environment state through the node exclusive channel, and perform multi-factor compliance verification with a preset compliance strategy library, to obtain a logistics node safety level. The processing module is configured to perform hierarchical control operation based on the cross-border supervision permission and the logistics node safety level, to obtain node early warning information.

[0011] The present application also provides a logistics node dynamic monitoring and early warning device, comprising: A memory is configured to store a program. A processor is configured to execute the program, to implement each step of the logistics node dynamic monitoring and early warning method.

[0012] The present application also provides a storage medium storing computer instructions, which are configured to enable a computer to execute the method.

[0013] The logistics node dynamic monitoring and early warning method, system, device and storage medium provided by the present application have the following advantages: The node exclusive channel is established through the dynamic frequency band allocation strategy, the multi-source device signal interference problem is effectively solved, the real-time performance and transmission stability of the logistics node monitoring data are improved, and the data island phenomenon is avoided. The authority is dynamically adjusted in combination with the spatial coordinates of the logistics node and the customs supervision information, the limitations of the traditional static supervision are overcome, the customs fence can accurately adapt to the changes of the cargo flow, and the flexibility and accuracy of the supervision are improved. Through integrating the cross-border cargo data and the node environment state, and comparing with a preset compliance strategy library, multi-dimensional safety verification is realized, the possibility of risk misjudgment or omission is reduced, and the safety control capability of the logistics node is enhanced. Based on the cross-border supervision authority and the safety level of the logistics node, hierarchical control is performed, early warning information is generated more timely, active intervention measures are supported, the supervision lag problem is reduced, and the overall safety and operation efficiency of the cross-border logistics are improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a logistics node dynamic monitoring and early warning method flow chart provided by the present application; Figure 2 is a logistics node dynamic monitoring and early warning system structure diagram provided by the present application; Figure 3 is a logistics node dynamic monitoring and early warning device structure diagram provided by the present application.

[0015] The implementation of the object, functional characteristics and advantages of the present application will be further described with reference to the drawings. DETAILED DESCRIPTION

[0016] In order to make the object, technical scheme and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0017] Below, the present application will be further described in combination with the drawings and specific embodiments.

[0018] Referring to Figure 1 , the present application provides a logistics node dynamic monitoring and early warning method, comprising: Step S1: acquiring multi-source device signals and real-time ownership tracking data of a logistics node, performing channel allocation and construction, and obtaining a node exclusive channel; Step S2: acquiring spatial coordinates of the logistics node and customs supervision information, and combining the node exclusive channel to perform dynamic authority construction, and obtaining cross-border supervision authority; Step S3: acquiring cross-border cargo data and node environment state through the node exclusive channel, and performing multi-factor compliance verification with a preset compliance strategy library, and obtaining a safety level of the logistics node; Step S4: Hierarchical control operation based on cross-border regulatory authority and logistics node security level, get node warning information.

[0019] Based on the above steps, the detailed step process is as follows: Step S1: The dynamic monitoring of the logistics node relies on the cooperative work of multiple source heterogeneous devices, including RFID reader, GPS positioning module, temperature and humidity sensor, video monitoring device, etc. The signals generated by these devices have protocol differences and frequency band conflicts in the physical layer, which need to be unified scheduled through adaptive channel allocation algorithm. In specific implementation, the spectrum sensing technology based on software defined network (SDN) is adopted to dynamically scan the channel occupation of 2.4GHz / 5GHz frequency band, and the non-overlapping channel is allocated combined with the device priority label (such as the customs supervision device marked as the highest priority). For the ownership tracking data (such as the electronic waybill of goods and the enterprise record code), it is mapped to the metadata layer of the communication channel through the cross-chain gateway of the block chain, forming a special data pipeline with identity authentication. After the channel is constructed, the lightweight MQTT protocol is deployed to realize low latency transmission, and the channel key pair is generated on the edge computing node to ensure the end-to-end encryption of subsequent data transmission. This step provides a standardized data entry for the subsequent regulatory authority construction, while avoiding the supervision blind area caused by multi-device signal interference.

[0020] Step S2: The geographic fence supervision of the logistics node needs to integrate static geographic information and dynamic permission strategy. The real-time longitude and latitude coordinates of the node are obtained through the Beidou / GNSS high-precision positioning module, the vector map data is superimposed, and the three-dimensional space index is established. The customs supervision information (such as the boundary coordinates of the bonded area and the storage rules of prohibited goods) is injected into the spatial database, and the real-time spatial relationship calculation is carried out with the node coordinates. When the spatial topological relationship between the goods coordinates and the supervision area changes (such as entering the 500m buffer area of the bonded warehouse), the role-based access control (RBAC) model reconstruction is triggered. The model inputs the variables such as transportation enterprise qualification, goods HS code and current customs clearance status into the fuzzy logic decision maker, and outputs the dynamic permission token (such as temporary opening of customs declaration modification permission). The permission data is synchronized to the supervision sandbox of the General Administration of Customs through the special channel, forming a traceable permission change log.

[0021] Step S3: Cross-border cargo data (such as X-ray scan images, chemical safety data sheets) are matched with distributed compliance policy libraries through a dedicated channel. The policy library adopts a multi-level index structure, with the top layer being the red channel rules of the World Customs Organization (WCO), and the lower layer embedding special regulatory requirements of the target country (such as the EU REACH regulation). The verification process is divided into three stages: first, the integrity of the cargo outer packaging is detected using computer vision, then the key fields of the attached documents (such as the certificate of origin number) are parsed by the NLP engine, and finally the multi-factor weighted score is calculated based on the environmental sensor data (such as the temperature of the cold chain container). The score result is input into the prediction model to dynamically correct the safety level (0-5 levels, 5 being the highest risk). For example, lithium battery cargo automatically triggers level promotion in high temperature environment, and generates a verification code attached to the cargo digital twin.

[0022] Step S4: Cross-border regulatory permissions and logistics node safety levels are matrixed in the decision engine to generate a set of graded response instructions. The permission-level combination is encoded as a six-dimensional vector (such as [bond access permission, 3-level risk, A-class enterprise…]), which is mapped to 107 standard disposal plans. For low-risk scenarios (level 1-2), an electronic fence status update is automatically sent to the shipper's APP; for medium-high risk scenarios (level 3-4), a UAV inspection network is triggered, and real-time thermal imaging data is transmitted to the customs command center; for the highest risk (level 5), the intelligent container locking system is directly linked. The warning information is packaged in EPCIS event standards, including timestamp, geographic hash value, disposal suggestion, etc., and is pushed back to all related parties (such as carriers, inspection agencies) through the dedicated channel established in S1. At the same time, an anti-interference communication mode is started, and the system is forced to switch to a maritime satellite backup channel.

[0023] The logistics node dynamic monitoring and early warning method, system, device and storage medium provided by the application have the following beneficial effects: By establishing a node-specific channel through a dynamic frequency allocation strategy, the problem of signal interference from multiple source devices is effectively solved, the real-time performance and transmission stability of logistics node monitoring data are improved, and the phenomenon of data island is avoided. By dynamically adjusting permissions based on the spatial coordinates of the logistics node and the customs supervision information, the limitations of traditional static supervision are overcome, making the customs fence accurately adapt to changes in cargo flow and improving the flexibility and accuracy of supervision. By integrating cross-border cargo data and node environmental status and comparing them with the pre-set compliance policy library, multi-dimensional safety verification is achieved, reducing the possibility of risk misjudgment or omission, and enhancing the safety control capability of the logistics node. Based on the cross-border regulatory permissions and the safety level of the logistics node, the graded control is carried out, making the generation of early warning information more timely, supporting proactive intervention measures, reducing the problem of supervision lag, and improving the overall safety and operational efficiency of cross-border logistics.

[0024] In one embodiment, multi-source device signals and real-time ownership tracking data of the logistics node are acquired, channel allocation construction is performed, and node-specific channels are obtained, including: Multi-source device signals refer to wireless signals generated by various sensors, RFID readers, GPS modules, and other devices within the logistics node. The wireless signals generated by various sensors, RFID readers, GPS modules, and other devices are identified and extracted through pre-set node device identifiers, obtaining corresponding time-domain signals, and converting the time-domain signals into frequency-domain representation through Fourier transform to identify the main frequency components of the signals. Signal strength clustering uses an unsupervised learning method to group signal strength values, distinguishing the signal coverage of different devices. The device frequency band distribution information is a two-dimensional structure, with rows representing device identifiers and columns representing frequency band ranges, and the matrix element values are signal strength averages. The association of frequency domain extraction and clustering lies in the fact that frequency band division relies on signal strength distribution, and signals with similar strength are classified into the same frequency band. When implementing, the frequency band width threshold needs to be pre-set to avoid frequency band overlap or fragmentation. After the matrix is generated, it provides a quantitative basis for the frequency band occupation situation for subsequent ownership mapping.

[0025] According to the owner identification field, the ownership of the goods and devices in the logistics node is identified, and the corresponding real-time ownership tracking data is obtained, which includes goods ID, owner information, and bound device identifier fields. Ownership mapping associates the frequency band allocation records in the matrix with the owner information through the device identifier. The structure of the device owner association table is a three-tuple of frequency band number, device identifier, and owner ID, and the data in the table is sorted by frequency band number. The mapping process needs to match the consistency of the device identifier, and the frequency band of the device not bound to the owner is marked as a public resource. The core role of the association table is to establish the ownership relationship of the frequency band resources and provide ownership constraints for channel allocation. When implementing, device identifier conflicts need to be handled to ensure that the same device is not mapped repeatedly. The table and the device frequency band distribution information share the frequency band number index to ensure data consistency.

[0026] Channel resource identification filters the unoccupied frequency bands or frequency bands with signal strength lower than the interference threshold from the matrix. The available channel resource pool is a collection of discrete frequency band intervals, each interval marked with a center frequency and bandwidth. The identification process needs to exclude the allocated frequency bands in the matrix and the protection interval of adjacent frequency bands to avoid co-frequency interference. The resource pool dynamic update mechanism adjusts the list of available frequency bands according to the real-time changes of signal strength in the matrix. When implementing, the frequency band merging algorithm is used to integrate fragmented idle frequency bands into continuous channels. The association of the resource pool and the device frequency band distribution information is reflected in the fact that they share the same frequency band division standard, ensuring the compatibility of resource allocation.

[0027] The channel polling scheduling allocation adopts a time slice rotation strategy, and selects frequency band requirements from the association table according to the priority of the consignor. The initial allocation channel is a temporary allocation scheme of the consignor and the frequency band, which records the consignor ID, the allocated frequency band and the validity period. The allocation process gives priority to meeting the exclusive frequency band request of the consignor with high priority, and the remaining resources enter the public pool for cyclic allocation. The polling scheduling relies on the ownership relationship in the association table and the real-time state of the resource pool to ensure that the allocation result meets the frequency band isolation requirement. When implemented, an allocation timeout mechanism needs to be set to prevent low-priority consignors from occupying resources for a long time. The initial allocation channel needs to retain the flexibility of frequency band switching as the input of frequency hopping encryption.

[0028] The frequency hopping encryption configuration generates a pseudo-random sequence according to the consignor ID to dynamically adjust the frequency band switching mode of the initial allocation channel. The node exclusive channel contains three elements: the basic frequency band, the frequency hopping pattern and the encryption key. The configuration process uses the consignor's identity information as a random seed to ensure that the frequency hopping sequences of different consignors are orthogonal. Real-time ownership tracking data triggers key updates, and the frequency hopping parameters are regenerated when the consignor ownership changes. The association between the exclusive channel and the initial allocation channel is that the frequency hopping range is limited to the allocated frequency band to avoid out-of-bound interference. When implemented, a lightweight encryption algorithm (such as AES-128) is used to ensure communication security, and frequency hopping synchronization information is transmitted through the control channel.

[0029] In addition, the generation of the node exclusive channel can also include: performing time domain segmentation and hash coding on the real-time ownership tracking data to generate an ownership timestamp sequence; calculating the frequency point offset of the initial allocation channel according to the ownership timestamp sequence to generate a dynamic frequency hopping parameter table; performing orthogonal frequency division multiplexing mapping on the dynamic frequency hopping parameter table and the initial allocation channel to construct a frequency hopping channel set; performing chaotic encryption on the frequency hopping channel set based on the ownership timestamp sequence to generate an encryption key stream; performing channel-by-channel XOR mask processing on the frequency hopping channel set and the encryption key stream to obtain the node exclusive channel.

[0030] The device frequency band distribution information is constructed by frequency domain extraction and signal strength clustering, which can accurately identify the signal distribution characteristics of each device in the logistics node, avoid frequency band conflicts, and improve the utilization rate of frequency spectrum resources. Based on the ownership mapping of real-time ownership tracking data, the frequency band allocation and consignor information are strictly bound, the traceability of logistics data is enhanced, and unauthorized devices are prevented from occupying channel resources. Channel resource identification and polling scheduling allocation are combined to dynamically optimize the frequency band allocation strategy, reduce signal interference and improve communication stability. The frequency hopping encryption configuration generates exclusive communication channels based on the consignor's identity to effectively prevent data eavesdropping and malicious interference, and ensures the security of logistics information transmission.

[0031] In one embodiment, the spatial coordinates of the logistics nodes are obtained together with the customs supervision information, and combined with the node-specific channel to construct dynamic permissions to obtain cross-border supervision permissions, including: The spatial coordinates of the logistics nodes are basic data for dynamic monitoring. The coordinates are obtained by acquiring spatial information of GPS, Beidou positioning system, and base station and performing three-mode information fusion. These coordinates exist in the form of discrete points and need to be converted into continuous geographic fence boundaries. Geographical boundary recognition connects discrete coordinate points into a closed polygon through geometric algorithms to form an initial supervision boundary. The fitting process needs to ensure that the polygon covers the actual activity range of the logistics node while avoiding excessive fitting that leads to boundary redundancy. The initial supervision boundary set contains multiple polygons, each corresponding to the supervision range of a logistics node. The fitting accuracy directly affects the accuracy of subsequent supervision, so the rationality of the boundary needs to be verified in combination with a geographic information system. The initial supervision boundary set provides a spatial basis for subsequent hierarchical coding to ensure that the supervision range is consistent with the actual logistics activity area.

[0032] The spatial coordinates identified are matched with the customs supervision database to obtain the corresponding customs supervision information locally. The customs supervision information includes the supervision level of the logistics node, the type of goods, the customs clearance requirements, and other key data. The hierarchical coding of the initial supervision boundary set is based on the priority rules in the customs supervision information. Higher supervision level areas (such as bonded areas and high-risk goods storage areas) are assigned higher levels of coding, and lower supervision level areas (such as ordinary warehouse areas) have lower coding levels. The coding process divides the initial supervision boundary set into multiple levels, each corresponding to a different supervision intensity. The generation of supervision fence levels makes dynamic permission allocation have a structured feature, facilitating subsequent permission analysis. The hierarchical coding is closely related to customs supervision policies, ensuring that the supervision logic is consistent with administrative requirements. The division of supervision fence levels also provides a hierarchical basis for the generation of the consignee permission table.

[0033] The node-specific channel is a secure transmission link between the logistics node and the customs data platform, ensuring data real-time and confidentiality. The consignee's historical customs clearance records reflect their past compliance, and credit rating data quantify their credibility. These data are obtained in real time through the communication channel and matched with the supervision fence levels for analysis. High credit rating consignees may have more relaxed fence permissions (such as entering high supervision level areas), and low credit rating consignees have limited permissions. The permission analysis process generates a consignee permission table that clearly defines each consignee's operating permissions at each supervision level. The mapping table is the core output of dynamic permission construction, directly determining the consignee's activity range within the logistics node. The combination of supervision fence level division and consignee data makes permission allocation targeted and flexible.

[0034] The customs supervision information is used as the final verification basis to match the cargo owner's authority table and the supervision fence level. The customs supervision information includes key data such as policies and regulations, cargo type restrictions, risk level division, and special supervision requirements. These information are used as matching criteria to perform security verification on the cargo owner's authority table and the supervision fence level. The matching process ensures that the cargo owner's authority strictly corresponds to the customs supervision rules, avoiding illegal operations or security vulnerabilities. The supervision security matching may trigger authority adjustment, such as temporarily tightening the authority of high-risk cargo owners. The cross-border supervision authority is the final result of dynamic authority construction, integrating spatial coordinates, supervision levels, cargo owner data, and other multi-dimensional information. This authority takes effect in real time and is synchronized to the monitoring system of the logistics node through a node-specific channel, realizing dynamic fence supervision. Supervision security matching is the last step of authority construction, ensuring compliance and security throughout the process.

[0035] Specifically, the supervision security matching process includes: The rules in the customs supervision information, such as the prohibition of certain goods entering the bonded area and the additional approval required for high-risk cargo owners, are structured and stored to form a supervision rule library. The authority entries in the cargo owner's authority table, such as a certain cargo owner being allowed to enter an A-level supervision area, are compared with the supervision rule library. For example, if a cargo owner has a low credit rating but the mapping table allows it to enter a high supervision level area, the supervision rule library will trigger authority downgrade or additional review.

[0036] The risk level of the supervision fence level, such as the first-level supervision area only allowing high-credit cargo owners, is matched with the cargo owner's credit rating. If the cargo owner's credit rating is lower than the supervision level requirement, the authority is automatically adjusted to restrict its entry into high-risk areas. At the same time, dynamic risk data in the customs supervision information, such as recent high incidence of smuggling cases, may temporarily tighten the authority of certain levels, affecting the matching results.

[0037] The cargo type declared by the cargo owner, such as dangerous goods and tax-free goods, is matched with the cargo access rules of the supervision fence level. For example, dangerous goods are only allowed to enter specific supervision areas, and if the cargo owner's authority mapping table incorrectly assigns the authority of an ordinary warehouse area, the authority will be corrected or blocked.

[0038] The customs supervision information may be dynamically updated with policy changes (such as temporary bans and new supervision requirements), and the matching process needs to respond in real time. For example, if a certain type of goods is temporarily added to the control list, all cargo owner authorities related to this type of goods will be re-matched, which may revoke the original authority or add additional supervision conditions.

[0039] When there is a conflict between the cargo owner's authority mapping table and the supervision fence level (such as a cargo owner being assigned mutually exclusive authorities), the authorities are corrected according to the priority rules of the customs supervision information. For example, if a cargo owner has full authority in area A, but area A is recently listed as a high-risk area, the authority may be downgraded or require manual review.

[0040] The present embodiment can accurately demarcate the supervision boundary of the logistics node through geographical boundary recognition, ensure that the supervision range is highly consistent with the actual logistics activity area, and effectively improve the accuracy of supervision. The hierarchical coding method is used to structure the supervision boundary, making the division of areas with different supervision intensities more clear and reasonable, and facilitating differentiated management. Combined with the historical customs clearance records and credit rating data of the cargo owner, the permission analysis is performed to realize the personalized configuration of the supervision permission, which ensures the strictness of the supervision and improves the customs clearance efficiency of the legal cargo owner. Through the supervision safety matching mechanism, the permission setting can be dynamically adjusted to respond to policy changes and risk warnings in a timely manner, significantly enhancing the real-time and adaptability of the customs supervision. The node-specific channel is used to realize real-time data transmission, ensuring the timely update of supervision information and the rapid effectiveness of permission, and greatly improving the response speed of the overall supervision system.

[0041] In one embodiment, the cross-border cargo data and the node environment state are obtained through the node-specific channel, and multi-factor compliance verification is performed with the preset compliance strategy library to obtain the logistics node safety level, including: The node-specific channel is the core data entry of the dynamic monitoring and early warning method of the logistics node, and the cargo transmission signal contains the original attribute information of the cross-border cargo and the node environment state. The transmission signal can use multiple communication protocols (such as HTTP, MQTT, TCP / IP, FTP, WebSocket, etc.), and the multi-protocol parsing process decodes and standardizes the signal through a protocol adapter. The parsing engine first identifies the protocol type of the signal source and matches the corresponding parsing rules, such as extracting JSON or binary data in the Payload for MQTT protocol parsing, and parsing the Header and Body parts for HTTP protocol. In the data standardization stage, the data of different protocols is uniformly converted to a structured format (such as XML or JSON Schema) to ensure consistency in subsequent processing.

[0042] The parsed data is divided into two parts: cross-border cargo data includes structured fields such as cargo type, weight, volume, source, destination, customs declaration number, HS code, and transportation batch number, and may also include extended attributes such as hazardous material identification and special storage requirements; the node environment state covers real-time environmental parameters collected by sensors such as temperature, humidity, light, vibration, air pressure, and geographic location, as well as node device status (such as cold storage cabinet operating status, camera monitoring data). During the parsing process, data integrity needs to be verified, and methods such as CRC check and hash check are used to detect data tampering or loss, and invalid or duplicate signals are removed. The time stamp or batch number is used to associate the cargo attribute and environment state data to ensure the time sequence consistency of subsequent processing.

[0043] The preset compliance policy library stores regulations, trade agreements, customs regulations, industry standards, and other rules related to cross-border logistics, and establishes multi-level indexes according to the dimensions of goods type, transportation route, trade partner, and time limit. The dynamic index matching process first extracts the key fields in the cross-border goods data (such as HS code, country of origin, transportation method, and goods category), and triggers the hierarchical retrieval of the policy library. For example, the transportation of certain medical equipment needs to match the medical device transportation specification, the import permit requirements of the target country, and the special packaging standards.

[0044] The matching process uses a rule engine (such as Drools, Aviator) for efficient query, supporting fuzzy matching (such as HS code prefix matching) and priority sorting. When there is a conflict in attribute data or multiple rules overlap, the most recently effective rule or the highest priority rule is used as the target goods compliance information. For example, if a certain goods matches both the general commodity transportation rule and the dangerous goods transportation rule, the dangerous goods rule is applied first. The constraint information output in this step includes allowed transportation conditions (such as temperature control range), declaration requirements (such as documents that must be provided), and prohibited transportation clauses (such as certain categories of goods prohibited for import in some countries), providing a basis for subsequent compliance mapping.

[0045] The preset environmental threshold library defines the safety range of environmental parameters corresponding to different goods categories (such as cold chain goods temperature threshold -18℃ to -22℃, precision instrument vibration threshold <0.5G). The time series environment comparison process divides the real-time collected node environment status into fixed time windows (such as 5 minutes) or event trigger windows (such as when the goods arrive at the node), and compares each piece of data with the standard value of the matching goods type in the threshold library.

[0046] The comparison process uses a sliding window algorithm to smooth transient fluctuations, avoiding false positives caused by temporary data anomalies. For example, occasional transient jumps in temperature sensors do not immediately trigger an abnormality flag, and only when multiple consecutive sampling points exceed the threshold will an abnormality be recorded. The abnormality flag contains detailed information such as abnormality type (such as temperature exceeding the standard, humidity being too high), duration, deviation amplitude (such as temperature +3℃), and location (such as a certain cold storage area). Abnormalities are classified by severity (such as warning, serious, and urgent), and are stored in a time series database (such as InfluxDB), forming an environmental anomaly sequence, providing a quantitative basis for subsequent safety evaluation.

[0047] Compliance mapping compares each item of goods attribute data with the target goods compliance information through a rule engine, generating a goods compliance deviation vector. For example, if the constraint requires a certain type of goods to provide a quality inspection report, and the attribute data is missing the file, the deviation vector records the "missing quality inspection report" item; if the weight of the goods exceeds the declared value by 10%, the "weight exceeds the limit" item is recorded. The deviation vector uses a structured format (such as JSON), containing information such as deviation type, deviation value, and constraint clause reference.

[0048] The safety assessment of the environmental anomaly sequence is based on the environmental risk coefficient calculated based on the anomaly type, frequency, and duration. For example, the more times the temperature exceeds the standard and the longer the duration, the higher the risk coefficient. The risk calculation introduces a weighting mechanism, such as perishable goods being more sensitive to temperature, with a higher weight for temperature anomalies than for vibration anomalies. The cargo compliance deviation vector and the environmental risk coefficient are input into the level assessment model, which outputs a comprehensive safety level (such as normal, attention, warning, danger) using a multi-dimensional scorecard. For example, if the cargo compliance deviation is severe and the environmental risk coefficient is high, the level is determined as "danger", triggering emergency measures such as automatically suspending the flow of goods at the node. The safety level is fed back to the monitoring system in real time and recorded in the log database for subsequent audit and optimization.

[0049] The compliance mapping process includes: The core of compliance mapping is to compare cross-border cargo data with target cargo compliance information, identify potential deviations, and quantify their severity. This process uses a structured matching mechanism to ensure that each cargo attribute is accurately compared with compliance constraints.

[0050] (1) Attribute-constraint matching Cross-border cargo data includes key fields such as cargo category, weight, volume, HS code, transportation conditions, and customs declaration documents. Target cargo compliance information covers regulatory requirements, trade agreement provisions, and customs declaration rules in target countries / regions. The matching process is based on a pre-defined rule engine that checks each cargo attribute against constraint conditions. For example, if a certain type of chemical requires specific packaging standards for transportation, and the actual cargo attribute does not indicate compliance packaging, it is recorded as a deviation item.

[0051] (2) Deviation quantification and classification Each deviation is assigned a specific weight reflecting its impact on compliance. For example, the weight of a missing key customs declaration document is higher than that of a slight overweight condition. The deviation vector uses a structured data format (such as JSON or XML) to record information such as deviation type (such as "missing document", "overweight", "contraband"), deviation value (such as 5% overweight), constraint reference (such as specific regulatory provisions), etc. This vector provides standardized input for subsequent safety assessment.

[0052] The safety assessment process includes: The environmental anomaly sequence includes abnormal records of node environmental parameters (such as temperature exceeding the standard, vibration exceeding the limit, etc.). The safety assessment combines the cargo compliance deviation vector and environmental anomaly data to comprehensively determine the risk level of the logistics node.

[0053] (1) Environmental anomaly risk calculation: Each anomaly in the environmental anomaly sequence is associated with a severity score, such as a brief temperature fluctuation may be classified as low risk, while sustained over-temperature is considered high risk. The evaluation model uses a weighted cumulative approach to calculate the environmental risk coefficient, different anomaly types (temperature and humidity, vibration, air pressure, etc.) are given different weights to ensure that key environmental factors have a greater impact on the final safety level.

[0054] (2) Comprehensive safety level determination The cargo compliance deviation vector and the environmental risk coefficient are input into a multi-dimensional evaluation model, which may use a scorecard or decision logic, combined with industry standards and historical data to set thresholds. For example: Low risk (normal): no major compliance deviations and few environmental anomalies; Medium risk (attention): there are minor compliance deviations or occasional environmental anomalies; High risk (warning): key compliance is missing or frequent environmental anomalies; Severe risk (danger): serious violations (such as prohibited goods) or sustained environmental out-of-control.

[0055] This embodiment can real-time identify various compliance deviations in the process of cross-border cargo transportation by establishing a precise mapping mechanism of cross-border cargo data and compliance constraint information, significantly improving the accuracy and timeliness of cross-border logistics supervision. Using structured deviation vector quantification evaluation method, it can grade different severity of violations for subsequent risk disposal to provide scientific basis. Combined with the safety evaluation model of environmental anomaly sequence, it realizes the dynamic monitoring of the running state of logistics nodes, effectively preventing the risk of cargo loss caused by environmental anomalies. The safety level output by multi-dimensional comprehensive evaluation can automatically trigger the corresponding early warning and disposal mechanism, greatly improving the intelligent level of cross-border logistics management.

[0056] In one embodiment, according to the preset compliance strategy library, the cross-border cargo data is dynamically indexed and matched to obtain target cargo compliance information, including: Cross-border cargo data is the basis for dynamic monitoring and early warning of logistics nodes, and its structuring and standardization directly affects the accuracy of subsequent compliance matching. The core of feature extraction processing is to identify key fields from the original declaration data or logistics documents and convert them into indexable standardized identifiers. Cargo category coding usually uses the internationally recognized HS coding system, which maps to the corresponding tariff classification code by analyzing text information such as cargo description, material, and purpose. The origin identifier needs to be combined with cargo production certificates, processing technology, etc., to extract the country or region code to ensure compliance with free trade agreements or preferential tariff rules. Transportation level information is based on the physical and chemical properties of the goods (such as hazardous material categories, temperature control requirements) or logistics requirements (such as time priority), to divide transportation priority or special handling labels. The output of this step provides structured input for subsequent hierarchical indexing, ensuring the matching efficiency of the compliance policy library.

[0057] The preset compliance policy library is a multi-level rule set, usually organized by cargo category, trade terms, regulatory requirements, etc. Hierarchical indexing filters use cargo category coding as the first key value to quickly locate the corresponding rule branch in the policy library. For example, the first four digits of the coordination system code may be associated with import and export bans or license requirements for a certain type of goods, and the last few digits may refine to specific product inspection standards. During indexing, the tree structure of the policy library allows for layer-by-layer drilling, excluding irrelevant rule branches and narrowing the matching range. Initial cargo compliance information includes basic regulatory requirements (such as whether quarantine is required), customs duty rates, technical standards, and preliminary screening results. The accuracy of this step depends on the timeliness of the policy library and the completeness of the coding mapping to avoid subsequent filtering failure due to classification bias.

[0058] Regional compliance filtering cross-verifies the initial compliance information with the origin identifier against trade agreements, sanctions lists, or differentiated regulatory policies. For example, some countries may impose anti-dumping duties on goods from certain origins or require additional certificates of origin. Regional rules in the policy library usually use origin identifiers as indexes to dynamically load corresponding additional terms or restrictions. The filtering process may involve multiple levels of judgment: first exclude countries with comprehensive bans, then match the list of preferential tariff agreement members, and finally check the additional requirements of special regulatory areas. The cargo region information further eliminates rules in the initial results that conflict with the origin, ensuring that subsequent transportation sequencing is based only on valid constraints.

[0059] Transportation level information determines the priority of logistics resource allocation and the strictness of operation process. The sorting logic reorganizes the filtered compliance rules by transportation level, for example, dangerous goods need to be matched with emergency disposal plans first, and high-value goods are associated with enhanced security clauses. Transportation-related rules in the strategy library are usually divided into weights by level, and high-priority clauses (such as cold chain temperature control range) cover low-priority clauses (such as ordinary packaging requirements). The sorting result may be reflected in the optimized selection of transportation path (avoiding prohibited transit ports), differentiated allocation of warehouse conditions (constant temperature warehouse priority), or simplification of customs procedures (fast lane applicable). The goods transportation information integrates compliance constraints and logistics operation needs, providing context for final verification.

[0060] Rule constraint testing checks the consistency of clauses in goods transportation information through a logic engine, such as whether the dangerous goods transportation level matches the packaging standard, and whether the origin preferential tax rate provides valid proof documents. Validity verification ensures that all constraint conditions still have legal effect at the current time point, excluding rules that have been abolished or temporarily adjusted. The verification process may call external databases (such as customs regulation update logs) for real-time comparison, or trigger manual review processes to handle edge cases. The target goods compliance information output is a structured instruction set that directly guides the operation of logistics nodes (such as automatic filling of customs declaration forms, inspection focus prompts), while generating monitoring indicators for early warning threshold calculation. This step closes the whole process from data to decision, ensuring the real-time nature of dynamic monitoring and the accuracy of early warning.

[0061] This embodiment can quickly and accurately identify the compliance constraint conditions of goods by dynamically indexing and matching cross-border goods data based on a pre-set compliance strategy library, avoiding the risk of regulatory violations caused by human judgment errors, and significantly improving the compliance of cross-border logistics. Hierarchical index screening combined with goods category coding, origin identification, and transportation level information enables multi-dimensional rule matching, ensuring that differentiated regulatory requirements in different countries and regions are accurately adapted, reducing logistics delays caused by rule omissions or conflicts. Regional compliance filtering based on origin identification can automatically avoid trade sanctions or special regulatory restrictions, reducing the risk of fines or return due to unfamiliarity with policies. Dynamic sorting of transportation level information optimizes logistics resource allocation, improves the processing efficiency of high-priority goods, and ensures the safe transportation of special goods (such as dangerous goods and temperature-controlled goods).

[0062] In one step, based on cross-border regulatory authority and logistics node security level, hierarchical control operation is performed to obtain node early warning information, including: The cross-border regulatory authority generates a cargo regulatory fence range by analyzing the geographic coordinate range, cargo type restrictions, and operation time limit rules in the customs regulatory information. The cargo regulatory fence range takes a three-dimensional geographic coordinate set as the boundary, and converts the physical regulatory area defined by the customs into a digital fence model through a geographic information system (GIS). The model contains fence boundary vertex coordinates, height constraints, and valid time intervals. The fence access rules dynamically generate access conditions based on the cargo clearance status, cargo owner qualifications, and transportation tool types, including cargo type white list, cargo owner digital certificate validity verification, and transportation tool real-time positioning matching rules.

[0063] The logistics node security level is determined by a preset node risk scoring system, which generates a quantitative security value based on historical violation records, environmental safety parameters, and equipment operation stability. The security value is divided into high, medium, and low levels. The multi-level control parameters are generated by cross-matching the fence access rules with the logistics node security level. When the security level is high, a full-dimensional access verification strategy is used. When the security level is medium, a dynamic sampling verification strategy is used. When the security level is low, a minimal verification strategy is used. Different strategies correspond to different cargo inspection frequencies, equipment operation permission ranges, and data transmission encryption strengths.

[0064] The differential permission setting is achieved by establishing a mapping relationship between the access rules and the equipment operation permissions. Under the full-dimensional access verification strategy, the equipment needs to verify the cargo electronic tag, the cargo owner digital signature, and the transportation tool positioning trajectory simultaneously, generating an operation permission set that includes cargo full-attribute verification, equipment operation log forced recording, and data transmission end-to-end encryption. Under the dynamic sampling verification strategy, the equipment randomly selects a certain proportion of cargo for tag verification, generating a permission set that allows some equipment to perform non-critical operations (such as cargo position fine-tuning).

[0065] Under the minimal verification strategy, the equipment only verifies the cargo basic attributes and the cargo owner identity, generating a permission set that allows the equipment to perform regular warehouse operations. The hierarchical control instruction set is generated by encoding the operation permission set into machine executable instructions. The instructions include device control instructions, data acquisition instructions, and alarm triggering instructions, such as "prohibit unverified devices from accessing core databases", "collect cargo temperature and humidity data every 30 minutes", and "trigger sound and light alarms when fence boundary is crossed". The instruction set is sent to the Internet of Things terminal devices in the logistics node through the cargo owner exclusive communication channel.

[0066] The hierarchical fence monitoring is realized by deploying positioning base stations and RFID readers at logistics nodes. The base stations collect the coordinates of goods in real time and calculate the spatial inclusion relationship with the regulatory fence range. When the coordinates of the goods deviate from the preset fence boundary by more than a threshold distance, it is marked as a fence crossing event. The dynamic adjustment of the security level of the logistics node is based on real-time monitoring data and historical behavior pattern analysis. If the same consignor's goods are detected to have crossed the fence for three consecutive times, the security level will be upgraded from low to medium, and the automatic switching mechanism of the control strategy will be triggered.

[0067] The execution of the hierarchical control instruction set is realized through the device control protocol stack. The protocol stack analyzes the instruction content and calls the device bottom interface, such as turning off the power of unauthorized devices through the Modbus protocol or pushing alarm messages to the monitoring center through the MQTT protocol. The node warning information is generated by aggregating device state data, alarm event list and security level change record, and is transmitted to the customs supervision platform and the consignor terminal through an encrypted tunnel, forming a standardized warning report containing alarm level, event location coordinates and processing suggestions.

[0068] The embodiment generates the goods supervision fence range and access rules based on cross-border supervision authority, and divides the multi-level control parameters combined with the security level of the logistics node, realizes the fine classification management of the dynamic risk of cross-border logistics node, solves the resource mismatch problem caused by the rigid strategy in traditional supervision, and reduces the risk of missed detection in high security level area and excessive verification in low level area. By generating a hierarchical control instruction set through differentiated authority settings, the device operation authority and the goods security state are dynamically bound to ensure the precise isolation and cooperation of the authorities of different subjects in cross-border transportation, and to avoid data leakage or abnormal retention of goods caused by unauthorized operation. Through the hierarchical fence monitoring and the dynamic adjustment mechanism of the security level, combined with the encryption communication protocol and the standardized data format, an early warning network of real-time linkage of multiple nodes across countries is constructed, which meets the data sovereignty and compliance requirements of each country, shortens the response cycle of cross-border abnormal events, and improves the collaborative disposal efficiency of customs, logistics enterprises and consignors.

[0069] In one embodiment, hierarchical fence monitoring is performed according to the goods supervision fence range, the security level of the logistics node is dynamically adjusted, and a hierarchical control instruction set is executed to obtain node warning information, including: The multi-level fence threshold division of the cargo supervision fence range is completed by superimposing a preset supervision weight parameter through a geographic information system (GIS). The first-level supervision area takes the high-value cargo storage point designated by the customs and the customs inspection area as the core, takes a radius of 50 meters as the initial threshold range, adjusts the actual coverage area through a dynamic weight algorithm (such as cargo value density, historical violation frequency), and generates a polygon geographic fence. The second-level supervision area expands outward around the first-level supervision area, and the boundary threshold is dynamically calculated according to the maximum moving speed of the transportation tool (such as a truck, a ship) and a preset response time (such as 10 minutes), for example, the truck fence expansion radius is 200 meters, and the ship fence expansion radius is 500 meters. The third-level supervision area covers the entire logistics node, and the boundary threshold is automatically filled to generate the remaining space after excluding the core and secondary areas. The coordinate data and threshold parameters of the multi-level fence are written into the logistics node database for subsequent monitoring module calling.

[0070] The dynamic fence state monitoring is realized by deploying positioning base stations, RFID readers and cameras in the logistics node. The first-level supervision area uses millimeter wave radar and UWB high-precision positioning technology to track the coordinates of the goods in real time. The coordinate data is compared with the fence range every 5 seconds. If the goods stay for more than a preset time (such as 30 minutes without moving) or the coordinates deviate from the fence boundary by more than 1 meter, it is marked as a core fence trigger event. The second-level supervision area monitors the speed and direction of the transportation tool through a sensor network, and predicts its trajectory combined with a Kalman filter algorithm. When the tool moving trajectory deviates from the preset path and the speed suddenly changes (such as deceleration to 0 for more than 5 minutes), it is marked as a secondary fence trigger event. The third-level supervision area uses a video analysis engine to identify abnormal gathering behavior of personnel and vehicles, for example, more than 5 unauthorized personnel appear in the same area within 10 minutes, which is marked as a peripheral fence trigger event. All event data are stored in the event log with time stamp and location label.

[0071] The dynamic correction module adjusts the safety level of the logistics node through the association rule base of event type and safety level. If the first-level supervision area triggers a goods boundary crossing event, the safety level is immediately upgraded to the highest level; if the second-level supervision area triggers two trajectory anomaly events in succession, the safety level is upgraded from medium to high; if the third-level supervision area triggers three abnormal gathering events, the safety level is upgraded from low to medium. The hierarchical control instruction set matches the instructions according to the updated safety level: the highest safety level matches the core area forced locking instruction, the high level matches the secondary area contraction instruction, and the medium level matches the peripheral area permission downgrade instruction. The instruction matching process is realized through a pre-defined strategy mapping table, for example, the safety level "high" is mapped to the instruction code "SEC_CONTRACT", and the instruction parameters include the contraction speed (such as 0.5 meters per second) and the contraction termination radius (such as 100 meters outside the core area).

[0072] The first-level regulatory area is forcibly locked by physical gate machines, electronic access control, and unmanned aerial vehicle patrol. After the adaptive control instruction is triggered, the gate machine control protocol (such as KNX) closes all access doors to the core area, the electronic fence activates the high-voltage pulse mode, and the unmanned aerial vehicle flies at a height of 10 meters along the boundary of the core area and broadcasts warning audio. The execution feedback information data includes the gate machine state (such as “locked”), the electronic fence voltage value (such as 5000V), and the unmanned aerial vehicle positioning coordinates, which are uploaded to the monitoring platform through the OPCUA protocol and marked as first-level execution feedback information.

[0073] The second-level regulatory area dynamic fence contraction is realized by variable radio frequency beacons and mobile gate machines. The adaptive control instruction drives the radio frequency beacon to move towards the core first-level regulatory area at a preset contraction rate (such as 5 meters per minute), and the mobile gate machine adjusts the position to reduce the secondary area range. During the contraction process, the RFID reader performs a second verification on the goods in the area, and unauthorized goods trigger a red LED warning. The execution feedback information data includes the contracted fence radius (such as from 200 meters to 150 meters), the number of unauthorized goods, and the warning state, which are pushed to the monitoring terminal through the MQTT protocol and marked as second-level execution feedback information.

[0074] The third-level regulatory area permission downgrade is realized by turning off unnecessary sensors and limiting personnel access. After the adaptive control instruction is issued, the video monitoring resolution is reduced from 4K to 720P to save bandwidth, and the personnel access control system only allows authorized personnel with high-level digital certificates to enter. The execution feedback information data includes the sensor power consumption (such as from 100W to 60W) and the number of access denied times, which are compressed by the edge computing node and uploaded, marked as third-level execution feedback information.

[0075] The node early warning aggregation information integrates multi-level feedback data through time window alignment technology (such as a 5-minute sliding window). The unmanned aerial vehicle coordinates in the first-level execution feedback information and the contraction radius in the second-level execution feedback information are superimposed to generate a dynamic heat map, and the permission downgrade data in the third-level execution feedback information is associated with the core area lock state to generate a comprehensive risk score. The aggregated data is packaged as an early warning information package, including the event level (such as “urgent”), the affected area list (such as “first-level core area A1, second-level secondary area B2, and third-level peripheral area C3”), and the disposal suggestion (such as “start joint customs inspection”), which is synchronized to the customs supervision platform and the consignor terminal through the TLS encrypted channel.

[0076] The application realizes fine hierarchical management of the space of the logistics node by generating a core supervision area, a secondary supervision area and a peripheral supervision area through multi-level fence threshold division, solves the problem of scattered or insufficient coverage of supervision resources in the traditional single fence mode, ensures that high-value goods and key areas are preferentially controlled, and at the same time reduces the redundant supervision cost of low-risk areas. Through dynamic correction of the fence trigger event and the security level and matching of the hierarchical control instruction, the abnormal event level and the control strength are dynamically bound, the differentiated response of core area forced locking, secondary area contraction and peripheral area permission degradation is realized, which not only guarantees the rapid isolation and disposal of high-risk events, but also reduces the operation interference in the low-risk scene. Through early warning aggregation and standardized packaging of multi-level execution feedback information, an early warning information network is constructed across regions and multiple levels.

[0077] Referring to Figure 2 The application also provides a logistics node dynamic monitoring and early warning system, which is applied to the logistics node dynamic monitoring and early warning method. The acquisition module is used for acquiring multi-source device signals and real-time ownership tracking data of the logistics node, performing channel allocation and construction, and obtaining a node exclusive channel; The analysis module is used for acquiring spatial coordinates and customs supervision information of the logistics node, and combining the node exclusive channel to perform dynamic permission construction and obtain cross-border supervision permission; The correlation module is used for acquiring cross-border cargo data and node environment state through the node exclusive channel, and performing multi-factor compliance verification with a preset compliance strategy library to obtain a logistics node security level; The processing module is used for performing hierarchical control operation based on the cross-border supervision permission and the logistics node security level to obtain node early warning information.

[0078] The logistics node dynamic monitoring and early warning system provided by the application establishes a node exclusive channel through a dynamic frequency band allocation strategy, effectively solves the multi-source device signal interference problem, improves the real-time performance and transmission stability of the logistics node monitoring data, and avoids the data island phenomenon. The dynamic adjustment of the permissions in combination with the spatial coordinates and customs supervision information of the logistics node overcomes the limitations of the traditional static supervision, so that the customs fence can accurately adapt to the changes of the cargo flow and improve the flexibility and accuracy of the supervision. By integrating the cross-border cargo data and the node environment state and comparing them with the preset compliance strategy library, multi-dimensional safety verification is realized, the possibility of risk misjudgment or omission is reduced, and the safety control capability of the logistics node is enhanced. Based on the cross-border supervision permission and the logistics node security level, hierarchical control is performed, the generation of early warning information is more timely, active intervention measures are supported, the problem of supervision lag is reduced, and the overall safety and operation efficiency of cross-border logistics are improved.

[0079] Referring to Figure 3As shown, the application also provides a logistics node dynamic monitoring and early warning device, comprising: a memory for storing programs; a processor for executing programs to realize each step of the above-mentioned any one kind of logistics node dynamic monitoring and early warning method.

[0080] In this embodiment, the processor and the memory can be connected through a bus or other means. The memory can include volatile memory, such as random access memory; the memory can also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid state disk. The processor can be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the application.

[0081] The application also provides a storage medium storing computer instructions for causing a computer to execute the above-mentioned any one method.

[0082] It should be noted that the skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system and each module can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0083] The above is only the preferred embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the application.

Claims

1. A method for dynamic monitoring and early warning of logistics nodes, characterized in that, include: Acquire signals from multiple sources of equipment and real-time ownership tracking data at logistics nodes, construct channel allocation, and obtain dedicated channels for each node; The spatial coordinates and customs supervision information of the logistics node are obtained, and dynamic permissions are constructed in combination with the node's dedicated channel to obtain cross-border supervision permissions; The cross-border cargo data and node environment status are obtained through the node's dedicated channel, and multi-factor compliance verification is performed against the preset compliance policy library to obtain the logistics node security level. Based on the aforementioned cross-border regulatory authority and the security level of the logistics nodes, a tiered management and control operation is performed to obtain node early warning information.

2. The method for dynamic monitoring and early warning of logistics nodes according to claim 1, characterized in that, Acquire multi-source device signals and real-time ownership tracking data from logistics nodes, construct channel allocation, and obtain node-specific channels, including: Based on preset node device identifiers, the logistics nodes are monitored to obtain multi-source device signals; The ownership of the logistics nodes is identified based on the owner identification field to obtain the real-time ownership tracking data; Frequency domain extraction and signal strength clustering are performed on the signals from the multi-source devices to obtain the device frequency band distribution information; Based on the real-time ownership tracking data, the ownership mapping of the equipment frequency band distribution information is performed to obtain the equipment owner association table; Based on the device frequency band distribution information, channel resources are identified to obtain an available channel resource pool; The equipment owner association table and the available channel resource pool are used for round-robin channel scheduling and allocation to obtain the initial allocated channel; Based on the real-time ownership tracking data, the initial allocated channel is configured with frequency hopping encryption to obtain the node's dedicated channel.

3. The method for dynamic monitoring and early warning of logistics nodes according to claim 1, characterized in that, The process involves acquiring the spatial coordinates and customs supervision information of the logistics node, and combining this with the node's dedicated channel to dynamically construct cross-border supervision permissions, including: The spatial coordinates are obtained by performing three-mode fusion positioning on the logistics node; Based on the spatial coordinates and a preset customs supervision database, the location of the logistics node is identified and monitored to obtain the customs supervision information. Geographic boundary identification is performed on the spatial coordinates to obtain an initial set of regulatory boundaries; Based on the customs supervision information, the initial set of regulatory boundaries is hierarchically encoded to generate a regulatory fence hierarchy; The cargo owner's historical customs clearance records and credit rating data are extracted through the node's dedicated channel, and permission analysis is performed in conjunction with the regulatory fence level to obtain the cargo owner permission table. Based on the customs supervision information, the cargo owner permission table and the supervision fence level are matched for supervision security to obtain the cross-border supervision permissions.

4. The method for dynamic monitoring and early warning of logistics nodes according to claim 1, characterized in that, The process involves acquiring cross-border cargo data and node environmental status through the node's dedicated channel, and performing multi-factor compliance verification against a pre-set compliance policy database to obtain the logistics node's security level, including: The cargo transmission signal of the node's dedicated channel is obtained, and multi-protocol parsing is performed to obtain the cross-border cargo data and the node's environmental status. The cross-border cargo data is dynamically indexed and matched according to the preset compliance strategy library to obtain the compliance information of the target cargo. Based on a preset environmental threshold library, the environmental status of the node is subjected to time-series environmental comparison processing to obtain an environmental anomaly sequence. The cross-border cargo data is mapped to the target cargo compliance information to obtain a cargo compliance deviation vector, and the environmental anomaly sequence is assessed for security to obtain the security level of the logistics node.

5. The method for dynamic monitoring and early warning of logistics nodes according to claim 4, characterized in that, The step of dynamically indexing and matching the cross-border cargo data according to the preset compliance strategy library to obtain the compliance information of the target cargo includes: The cross-border cargo data is processed by feature extraction to obtain cargo category codes, country of origin markings, and transport class information; Based on the preset compliance strategy library, the cargo category codes are hierarchically indexed and filtered to obtain initial cargo compliance information; Based on the country of origin label, the initial goods compliance information is filtered for regional compliance to obtain goods regional information; Based on the transport level information, the cargo geographical information is sorted for transport to obtain cargo transport information; The cargo transportation information is subjected to rule constraint testing and validity verification to obtain the compliance information of the target cargo.

6. The method for dynamic monitoring and early warning of logistics nodes according to claim 1, characterized in that, The hierarchical control operation based on the cross-border regulatory authority and the security level of the logistics node, resulting in node early warning information, includes: Based on the aforementioned cross-border regulatory authority, the scope of the cargo regulatory fence and the fence access rules are generated, and the control is divided in conjunction with the security level of the logistics node to obtain multi-level control parameters; Based on the fence access rules and the multi-level control parameters, differentiated permission settings are performed to generate a hierarchical control instruction set. Based on the scope of the cargo supervision fence, graded fence monitoring is carried out to dynamically adjust the security level of the logistics node, and the graded control instruction set is executed to obtain the node early warning information.

7. The method for dynamic monitoring and early warning of logistics nodes according to claim 6, characterized in that, The step involves performing tiered fencing monitoring based on the cargo supervision fencing range, dynamically adjusting the security level of the logistics node, executing the tiered control instruction set, and obtaining node early warning information, including: The cargo supervision fence area is divided into multi-level fence thresholds to obtain a first-level supervision zone, a second-level supervision zone, and a third-level supervision zone. Dynamic fence status monitoring is performed on the first-level, second-level, and third-level regulatory zones to obtain fence trigger events; The fence triggering event and the security level of the logistics node are dynamically corrected, and the hierarchical control instruction set is matched to obtain the adapted control instruction. The first-level regulatory area is forcibly fenced off according to the adaptive control command, and first-level execution feedback information is obtained; The secondary monitoring zone is dynamically fenced down according to the adaptive control command, and secondary execution feedback information is obtained; According to the adaptive control instructions, the fence access of the third-level supervision area is downgraded, and third-level execution feedback information is obtained; The node warning information is obtained by aggregating the first-level execution feedback information, the second-level execution feedback information, and the third-level execution feedback information.

8. A dynamic monitoring and early warning system for logistics nodes, characterized in that, The logistics node dynamic monitoring and early warning method applied to any one of claims 1-7 includes: The acquisition module is used to acquire multi-source device signals and real-time ownership tracking data of logistics nodes, and to construct channel allocation to obtain node-specific channels. The analysis module is used to obtain the spatial coordinates and customs supervision information of the logistics node, and to construct dynamic permissions by combining the node's dedicated channel to obtain cross-border supervision permissions. The association module is used to obtain cross-border cargo data and node environmental status through the node's dedicated channel, and perform multi-factor compliance verification with the preset compliance strategy library to obtain the security level of the logistics node. The processing module is used to perform hierarchical control operations based on the cross-border regulatory authority and the security level of the logistics node, and to obtain node early warning information.

9. A dynamic monitoring and early warning device for logistics nodes, characterized in that, include: Memory, used to store programs; A processor is used to execute the program to implement the various steps of the dynamic monitoring and early warning method for logistics nodes as described in any one of claims 1-7.

10. A storage medium, characterized in that, The computer contains computer instructions for causing the computer to perform the method according to any one of claims 1 to 7.

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