Intelligent control system for cross-border product distribution

Abstract

The application discloses an intelligent control system for cross-border product distribution, and relates to the technical field of cross-border product data processing. The system comprises a state monitoring module, a channel scheduling module and an intelligent distribution module. The state monitoring module is used for monitoring the product state in real time based on product state parameters, and dynamically adjusting the sensor monitoring frequency based on the product state monitoring condition and the sensor power consumption. The channel scheduling module is used for dynamically adjusting the container position update frequency based on the position joint state parameters, and dynamically allocating wireless channels based on the container position distribution. The intelligent distribution module is used for dynamically distributing the distribution containers based on the liquid content in each transport container and the consumer order. After the distribution is completed, the residual liquid content state of each distributed transport container is updated in real time, and the distribution details are recorded to the system monitoring end, so that the full-link intelligent control and resource dynamic optimization from the transportation guarantee, data transmission to product distribution are realized.

Description

Technical Field

[0001] This invention relates to the field of cross-border product data processing technology, and in particular to an intelligent control system for cross-border product distribution. Background Technology

[0002] The technological evolution of cross-border product distribution is rooted in the information architecture of traditional enterprise resource planning, supply chain management systems, and warehouse management systems. By integrating basic technologies such as GPS, electronic data interchange, and application programming interfaces, it has built basic tracking and data collection capabilities for inventory, orders, and logistics status. Subsequently, it has evolved to introduce big data analytics technology, achieving preliminary integration and trend mining of multi-source heterogeneous data, laying the data foundation for intelligent transformation.

[0003] Current intelligent control methods rely on the integration of artificial intelligence, big data, and the Internet of Things to build data-driven intelligent supply chain systems. Their core architecture achieves dynamic and visualized management across the entire supply chain through an AI-native platform, enables real-time tracking and risk warning of global cargo trajectories based on multi-source data fusion, and utilizes AI algorithms to complete a closed-loop process from anomaly diagnosis to decision execution. Simultaneously, a data intelligence engine enables collaborative computation of demand forecasting, inventory optimization, and precise product selection. Through multi-platform collaboration and real-time data dashboards, supply chain strategies are dynamically adjusted, ultimately achieving the comprehensive goals of improved fulfillment efficiency, optimized operating costs, and enhanced market responsiveness.

[0004] For example, patent application CN109155022A discloses a distribution network for monitoring, controlling, and optimizing the flow of liquid beverage products delivered to customers via containers. This network includes: a liquid product distribution network that monitors, controls, and optimizes the flow of liquid products delivered to consumers via liquid product dispensing containers, such as beer kegs. At least one liquid product dispensing container includes an adaptively attached radio transmitter and microprocessor for sensing and transmitting multiple data measurements related to the state of the liquid product dispensing container. A fixed or mobile radio signal reader transmits the data measurements from the radio transmitter. Data collection functions include liquid product management, liquid product sales, and liquid product consumer management functions. A computer processing server communicates with an internet communication device or cloud interface. Reporting and marketing functions interact with the functions of producing, distributing, selling, and consuming liquid products.

[0005] For example, the product distribution management method and system based on a cross-border e-commerce platform disclosed in patent application CN116911505B includes: obtaining product information transmitted by multiple distributors through communication; modularizing the product information of each distributor according to the product management specifications of the cross-border e-commerce platform to obtain product information modules for multiple distributors; and automatically synchronizing the product information modules of multiple distributors to the corresponding product information setting modules of the cross-border e-commerce platform to obtain cross-border product sales modules for multiple distributors.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, traditional location update strategies employ fixed-frequency mechanisms that are disconnected from real-time communication environments and transportation dynamics. This leads to frequent update failures under adverse channel conditions and unnecessary communication overhead during stable phases. Furthermore, wireless channel allocation fails to detect dynamic density changes in container spatial distribution, resulting in a contradiction between channel contention in high-density areas and idle spectrum resources in low-density areas. Consequently, data transmission reliability is poor, and there are issues with low efficiency in cross-border product distribution control. Summary of the Invention

[0008] This application provides an intelligent control system for cross-border product distribution, which solves the problems in the prior art where the traditional location update strategy uses a fixed frequency mechanism that is out of touch with the real-time communication environment and transportation dynamics. This results in frequent update failures under poor channel conditions and unnecessary communication overhead during stable phases. At the same time, the wireless channel allocation fails to perceive the dynamic density changes of container spatial distribution, causing a contradiction between channel competition conflicts in high-density areas and idle spectrum resources in low-density areas. As a result, the data transmission reliability is poor and the control efficiency of cross-border product distribution is low. This application constructs a highly reliable and efficient cross-border product distribution data transmission system.

[0009] This application provides an intelligent control system for cross-border product distribution, comprising: a status monitoring module, a channel scheduling module, and an intelligent allocation module. The status monitoring module monitors the product status in real time based on product status parameters during cross-border product transportation and dynamically adjusts the sensor monitoring frequency based on the product status monitoring results and sensor power consumption. The channel scheduling module dynamically adjusts the container location update frequency based on location and status parameters during cross-border product data transmission and dynamically allocates wireless channels based on the location distribution of each container. The intelligent allocation module dynamically allocates distribution containers based on the liquid content in each transport container and consumer orders during cross-border product distribution. After allocation, it updates the remaining liquid content status of each allocated transport container in real time and records the allocation details to the system monitoring terminal.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0011] 1. Through the synergistic effect of three modules—status monitoring, channel scheduling, and intelligent allocation—product quality is ensured and energy consumption is optimized in the transportation stage; communication quality is guaranteed and resource utilization is improved in the data transmission stage; and precise matching and operational efficiency are achieved in the distribution stage. This leads to the construction of a closed-loop intelligent management and control system covering the entire supply chain of cross-border products, ultimately achieving the comprehensive management goals of controllable quality, reliable transmission, and optimized allocation.

[0012] 2. By establishing a two-layer judgment mechanism based on the reference range of environmental operating parameters and the reference range of medium state parameters under transportation status, and combining the duration threshold of abnormal parameters for state classification, accurate identification and graded early warning of product quality risks can be achieved. On this basis, the product state level is coupled with sensor energy consumption management, and differentiated dynamic adjustment strategies for sensor monitoring frequency are implemented for different states. This significantly optimizes the overall energy consumption efficiency of the sensor network and extends its continuous working time while ensuring product transportation safety and state controllability, thus constructing an intelligent state monitoring system that takes into account reliability, sensitivity and economy.

[0013] 3. By establishing a joint judgment mechanism based on transportation stage identification, location change rate, and communication environment assessment, the location update frequency is dynamically adjusted. Through location data credibility verification and a channel differentiation allocation strategy based on geographic density, the system can significantly optimize the utilization efficiency of wireless resources and equipment energy consumption while ensuring positioning accuracy and communication link stability during critical stages. This leads to the construction of an adaptive wireless communication scheduling system that integrates location data quality assurance, on-demand allocation of communication resources, and intelligent management of system energy consumption. Attached Figure Description

[0014] Figure 1A schematic diagram of the structure of the intelligent control system for cross-border product distribution provided in the embodiments of this application;

[0015] Figure 2 A flowchart illustrating the monitoring frequency adjustment of an intelligent control system based on cross-border product distribution, provided for embodiments of this application.

[0016] Figure 3 A flowchart illustrating the distribution container allocation process of an intelligent control system for cross-border product distribution provided in this application embodiment. Detailed Implementation

[0017] This application provides an intelligent control system for cross-border product distribution, solving the problems of poor data transmission reliability and low efficiency in cross-border product distribution control. This is due to the use of fixed-frequency mechanisms in traditional location update strategies, which are disconnected from real-time communication environments and transportation dynamics. These mechanisms often fail to adapt to real-time communication environments and transportation dynamics, leading to frequent update failures under poor channel conditions and unnecessary communication overhead during stable periods. Furthermore, the wireless channel allocation fails to detect dynamic density changes in container spatial distribution, resulting in a contradiction between channel contention in high-density areas and idle spectrum resources in low-density areas. The overall approach is as follows:

[0018] Through the coordinated operation of the status monitoring module, channel scheduling module, and intelligent allocation module, intelligent management of the entire cross-border product supply chain is achieved. The status monitoring module collects real-time data on product transportation status and operating parameters during transport, dynamically determines product status based on parameter comparison and duration analysis, and adaptively adjusts the monitoring frequency according to status level and sensor power consumption. During data transmission, the channel scheduling module dynamically adjusts the container location update frequency based on container status parameters and communication environment parameters, and differentiates wireless channel allocation based on container distribution density. During distribution, the intelligent allocation module generates the optimal allocation scheme through a multi-level matching strategy of container attributes and liquid content, and updates container inventory status and allocation records in real time, achieving precise closed-loop control throughout the entire process from transportation support and data transmission to sales distribution.

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] like Figure 1The diagram shows the structure of an intelligent control system for cross-border product distribution provided in this application embodiment. The intelligent control system includes a status monitoring module, a channel scheduling module, and an intelligent allocation module. The status monitoring module monitors the product status in real-time based on product status parameters during cross-border product transportation and dynamically adjusts the sensor monitoring frequency based on the product status monitoring status and sensor power consumption. Product status parameters include product transportation status and transportation operating condition parameters. Product transportation status includes warehousing status, transportation status, and operation status. Transportation operating condition parameters include environmental operating condition parameters and medium status parameters. The channel scheduling module dynamically adjusts the container location update frequency based on location-linked status parameters during cross-border product data transmission and dynamically allocates wireless channels based on the location distribution of each container to ensure the stability and efficiency of the communication link. Containers include transportation containers and distribution containers. Location-linked status parameters include container status parameters and communication environment parameters. The intelligent allocation module dynamically allocates distribution containers based on the liquid content in each transportation container and consumer orders during cross-border product distribution. After allocation, it updates the remaining liquid content status of each allocated transportation container in real-time and records the allocation details to the system monitoring terminal.

[0021] In this embodiment, the present invention constructs an end-to-end intelligent management and control system for cross-border liquid products through the collaborative operation of a status monitoring module, a channel scheduling module, and an intelligent allocation module. The status monitoring module collects and analyzes multi-dimensional environmental and media status parameters during cross-border transportation in real time, establishes a multi-level risk assessment mechanism based on the transportation stage, and dynamically optimizes the monitoring frequency according to product status level and sensor energy consumption, maximizing energy efficiency while ensuring accurate monitoring of liquid quality. The channel scheduling module integrates container transportation stage, location change rate, and cross-border communication environment parameters to construct an adaptive location update strategy and dynamically allocates wireless channels based on container spatial density distribution, effectively overcoming network heterogeneity and resource competition issues in cross-border transmission. The intelligent allocation module uses container attribute matching and liquid content optimization algorithms, combined with cross-border compliance requirements and real-time inventory status, to allocate suitable transportation containers to orders, ensuring that the distribution process simultaneously meets operational efficiency and regulatory compliance requirements. Through data sharing and business linkage, the three modules form a closed-loop management system covering the entire chain from cross-border transportation assurance and heterogeneous network data transmission to intelligent distribution decision-making, ultimately achieving controllable, reliable, and efficient collaborative operation of the cross-border liquid product supply chain.

[0022] like Figure 2The diagram shows a flowchart of the monitoring frequency adjustment process for an intelligent control system based on cross-border product distribution provided in this application embodiment. Specifically, the steps for real-time monitoring of product status based on product status parameters include: matching the product transportation status with a preset state environment parameter mapping table to obtain an environmental condition parameter reference range. The state environment parameter mapping table is a preset set of rules that defines reasonable ranges for various environmental condition parameters under different transportation states; comparing the environmental condition parameters in each monitoring time period with the environmental condition parameter reference range; if an environmental condition parameter in any monitoring time period is not within its corresponding environmental condition parameter reference range, then the environmental condition parameter is recorded as abnormal. The system monitors environmental parameters in real time, and if the duration of an abnormal environmental parameter outside its corresponding reference range exceeds a preset duration threshold, the product status is marked as a warning state; if the duration of the abnormal environmental parameter outside its corresponding reference range does not exceed the preset duration threshold, the product status is marked as an observation state; if all environmental operating parameters are within their corresponding environmental operating parameter reference ranges, the medium status parameter reference range is obtained based on the environmental operating parameters; if the medium status parameters are all within their corresponding medium status parameter reference ranges, the product status is marked as a normal state; otherwise, the product status is marked as an abnormal state.

[0023] The reference range for media state parameters is obtained as follows: The average value of environmental operating parameters within each monitoring period is statistically analyzed and recorded as the average environmental operating parameter value. The ratio of the average environmental operating parameter value to its respective preset environmental operating parameter reference value is calculated to obtain the corresponding environmental operating parameter factor. Then, based on the preset contribution of environmental operating parameters, the environmental operating parameter factors are weighted. The weighted results are then added and merged to obtain the environmental operating condition evaluation value. The environmental operating condition evaluation value is a numerical indicator that comprehensively quantifies the degree of environmental condition superiority or inferiority based on the environmental operating parameters within each monitoring period. The environmental operating condition evaluation value is matched with a preset operating condition media parameter mapping table to obtain the reference range for media state parameters. The operating condition media parameter mapping table is a set of rules that defines the reasonable range of each media state parameter under different environmental operating conditions.

[0024] In this embodiment, environmental operating condition parameters are directly collected by various sensors deployed inside the cross-border transport container. These include sensors for monitoring temperature, humidity, pressure, and light intensity under normal physical conditions, and additional sensors for coping with complex cross-border environments, such as vibration intensity sensors (monitoring transport bumps and loading / unloading impacts), salt spray concentration sensors (assessing the risk of corrosion during maritime transport), and microbial concentration sensors (preventing cross-border biological contamination). The corresponding reference values ​​for these environmental operating condition parameters are based on thresholds pre-set according to the characteristics of cross-border products (such as precision liquids sensitive to vibration and metal container packaging products susceptible to salt spray) and international transport standards. The contribution weight of each parameter is determined based on a quantitative analysis of the impact of historical cross-border logistics data and expert experience on product quality. The medium state parameters are obtained through professional sensing and model fusion. In addition to using immersion temperature sensors, vibration density meters, and visual image recognition systems to monitor liquid temperature, density, and foam sedimentation status, volatile organic compound sensors are introduced to track the evaporation of common liquid components during cross-border transport. Furthermore, a high-precision weight sensor is used in conjunction with temperature and humidity parameters and multi-country evaporation standard models to dynamically calculate the evaporation rate, thereby constructing a precise end-to-end state perception system adapted to the characteristics of cross-border products.

[0025] This invention constructs a hierarchical, adaptive real-time product status monitoring and early warning system. By initially matching the product's transportation status with environmental operating parameters, it achieves preliminary screening and continuous monitoring of transportation environment compliance. It innovatively introduces a duration-based status grading mechanism (early warning status and observation status), effectively distinguishing between transient interference and persistent anomalies, avoiding misjudgments. Furthermore, when environmental conditions meet standards, the system further integrates multiple environmental parameters into a comprehensive environmental condition assessment value, dynamically generating a more accurate reference range for media status parameters. This achieves a seamless shift and accurate determination of the monitoring focus from the external environment to the internal media status of the product. Ultimately, this invention forms a comprehensive, closed-loop status assessment system from external triggers to internal status, capable of promptly identifying potential product deterioration risks caused by environmental anomalies. This significantly improves the accuracy and foresight of status judgment, providing precise and reliable data support and decision-making basis for quality assurance and risk intervention of cross-border products in complex logistics chains.

[0026] Furthermore, the steps for dynamically adjusting the sensor monitoring frequency based on product status monitoring and sensor power consumption include: if the product status is in an early warning state, the sensor monitoring frequency for abnormal environmental parameters is directly adjusted to the maximum value; if the product status is in an observation state, the value by which the abnormal environmental parameter deviates from its reference range is recorded as the abnormal environmental deviation value, and the duration for which the abnormal environmental parameter is not within its corresponding reference range is recorded as the abnormal environmental duration, and the sensor monitoring frequency for abnormal environmental parameters is dynamically adjusted based on the abnormal environmental deviation value and the abnormal environmental duration; if the product status is in a normal state, it is determined whether the power consumption rate of the sensors under each environmental condition at the monitoring frequency exceeds a preset power consumption rate threshold, where the power consumption rate is the ratio of the power consumption of the environmental condition sensors in the previous monitoring period to the power consumption of the same environmental condition sensors in the current monitoring period. The difference in sensor power consumption is considered. If not, no further processing is performed. If so, the difference between the power consumption rate threshold and the power consumption rate of the corresponding environmental condition sensor is recorded as the deviation power consumption rate. The deviation power consumption rate is matched with a preset first monitoring frequency correction value mapping table to obtain the corresponding first monitoring frequency correction value. The difference between the sensor monitoring frequency and the first monitoring frequency correction value is recorded as the corrected sensor monitoring frequency. The first monitoring frequency correction value mapping table is a preset correspondence table between different environmental condition sensors in different deviation power consumption rate ranges and monitoring frequency correction values. If the corrected sensor monitoring frequency is lower than the preset minimum sensor monitoring frequency, the corrected sensor monitoring frequency is directly set to the minimum sensor monitoring frequency. If the product status is abnormal, a product abnormality prompt is issued.

[0027] Specifically, the steps for dynamically adjusting the sensor monitoring frequency of abnormal environmental parameters based on abnormal environmental deviation values ​​and durations include: matching the duration of the abnormal environment with a preset adjustment factor mapping table to obtain the corresponding monitoring frequency adjustment factor; the adjustment factor mapping table defines the correspondence between different abnormal environmental duration intervals and monitoring frequency adjustment factors; comparing the abnormal environmental deviation value with a unit environmental deviation value, rounding the comparison result to obtain a monitoring frequency adjustment multiple, and multiplying the monitoring frequency adjustment multiple by the monitoring frequency adjustment unit to obtain a monitoring frequency adjustment value; the unit environmental deviation value is a preset basic unit of measurement for quantifying the degree of deviation of abnormal environmental parameters, and the monitoring frequency adjustment unit is a preset basic frequency unit used to calculate the monitoring frequency adjustment amount; obtaining a second monitoring frequency correction value by multiplying the monitoring frequency adjustment factor by the monitoring frequency adjustment value, and recording the sum of the sensor monitoring frequency and the second monitoring frequency correction value as the corrected monitoring frequency of the abnormal environmental parameter sensor; if the corrected monitoring frequency of the abnormal environmental parameter sensor is higher than the preset maximum value of the sensor monitoring frequency, then the corrected monitoring frequency of the abnormal environmental parameter sensor is directly taken as the maximum value of the sensor monitoring frequency.

[0028] In this embodiment, the present invention constructs an adaptive monitoring frequency dynamic adjustment system deeply coupled with product status and sensor energy consumption. By establishing a response mechanism that prioritizes status while considering energy efficiency, it achieves efficient and precise allocation of monitoring resources: when the product is in an early warning state, the system operates at the highest response level, forcibly maximizing the monitoring frequency of abnormal parameters to ensure close monitoring of high-risk situations; when in an observation state, it introduces the deviation value and duration of abnormal environments as core variables, dynamically increasing the monitoring frequency through relevant adjustment factors and adjustment multiples, making the monitoring intensity positively correlated with the severity and urgency of potential risks, achieving intelligent gradient response; when the product is in a normal state, the system shifts its optimization focus to energy consumption management, tracking the power consumption rate of sensors in real time, and deriving the lowerable monitoring frequency based on its deviation from threshold values, maximizing sensor energy savings and extending their service life while ensuring basic monitoring needs are met. Ultimately, this invention integrates product status, risk level, and equipment power consumption into a unified decision-making process, forming a closed-loop intelligent control system that can accurately exert force and respond quickly during abnormal situations, while also being efficient and energy-saving during normal situations. This significantly improves the resource utilization efficiency and sustainable operation capability of the entire monitoring system while ensuring the effectiveness of product status monitoring.

[0029] Furthermore, the container state parameters include the rate of position change and the transportation stage; the steps for dynamically adjusting the container position update frequency based on the joint position state parameters include: if the container's transportation stage matches a preset key stage in the update strategy library, then the container position update frequency is dynamically adjusted based on the update strategy corresponding to that key stage; if the container's transportation stage does not match a preset key stage in the update strategy library, then a position change threshold is obtained based on the current container position update frequency; if the average rate of position change of the container within a certain monitoring period exceeds the position change threshold, then the difference between the average rate of position change of the container and the position change threshold is recorded as the deviation rate of position change, and the deviation rate of position change is matched with a preset first update frequency correction value mapping table to obtain... The corresponding first update frequency correction value is obtained, and the container position update frequency is adjusted based on the first update frequency correction value. The first update frequency correction value mapping table is a preset correspondence table between each deviation position change rate interval and the position update frequency correction value. If the position change rate of the container in a certain monitoring period does not exceed the preset position change threshold, the communication environment evaluation value is obtained based on the communication environment parameters. The communication environment evaluation value is matched with the preset second update frequency correction value mapping table to obtain the corresponding second update frequency correction value. The container position update frequency is adjusted based on the second update frequency correction value. The second update frequency correction value mapping table is a preset correspondence table between each communication environment evaluation interval and the position update frequency correction value.

[0030] In this embodiment, the rate of location change can be obtained by periodically acquiring the container's latitude and longitude coordinates using a GPS positioning module, combined with a high-precision clock, by calculating the ratio of the straight-line distance to the time interval between two consecutive location data points; the transportation stage can be obtained through a combination of logical judgment and geofencing technology. The system compares the container's real-time GPS location with preset key areas (including geofences such as ports, customs, and transit stations) in the electronic map. When the container enters or leaves a certain geofence, its transportation stage is updated. Key stages refer to special links in the logistics transportation process that have higher requirements for the real-time and accuracy of location tracking. These stages are usually preset in the update strategy library, such as port / customs loading and unloading areas, cross-border border crossings, transit stations of transportation vehicles, and the final delivery segment. The received signal strength can be directly provided by the wireless communication module on the container (such as a 4G / 5G module or a LoRa module). This module can measure and receive the signal power from the base station or gateway in real time. The channel utilization is statistically obtained and fed back by the network infrastructure (such as base stations and gateways). The link budget margin is obtained by querying the device specifications or configuration parameters to obtain known fixed values, including the transmit power and transmit antenna gain of the wireless signal transmitter, and the receive sensitivity of the receiver. Then, based on the free space path loss formula and combined with parameters such as the real-time distance between the two communicating parties and the signal frequency, the path loss of the signal in the spatial transmission process is calculated. Finally, the sum of the transmit power and the transmit and receive antenna gains is subtracted from the calculated path loss and receive sensitivity. The remaining value is the link budget margin. This margin directly reflects the degree of margin in the current communication link where the signal strength is higher than the minimum requirement (i.e., receive sensitivity) that the receiving device can demodulate.

[0031] This invention constructs a multi-factor driven, scenario-adaptive intelligent dynamic adjustment system for container position update frequency. Its core objective is to achieve the optimal balance between positioning accuracy, communication efficiency, and equipment energy consumption. This invention achieves precise control through a triple-judgment logic: First, it introduces a transportation stage perception mechanism, automatically switching to the corresponding high-frequency update strategy at preset key logistics nodes (such as port loading / unloading and customs clearance), ensuring comprehensive tracking capabilities during error-prone "critical stages." When in a non-critical stage, the system initiates dual-modal adaptive adjustment: if the container moves rapidly (high rate of position change), the update frequency is increased proportionally based on its deviation rate exceeding a threshold, ensuring no distortion during rapid movement; if the container moves slowly or remains stationary, the optimization focus shifts to communication quality. By fusing three core indicators—received signal strength, channel utilization, and link budget margin—a comprehensive communication environment assessment value is calculated. Based on this, updates are increased when channel quality is good to utilize idle resources, and updates are decisively reduced when channel congestion or poor quality to avoid communication failures and save power. Ultimately, this invention deeply integrates business logic (transportation phase), physical motion state (location change), and communication link status (environmental parameters) to form a closed-loop control system that can intelligently perceive the context and dynamically allocate resources accordingly. This ensures transparency throughout the supply chain while minimizing network signaling overhead and sensor power consumption, significantly improving the intelligence level and operational economy of the entire system.

[0032] Furthermore, the communication environment parameters include received signal strength, channel utilization, and link budget margin. The communication environment evaluation value is obtained as follows: the ratio of received signal strength and link budget margin to their respective preset reference values ​​for received signal strength and link budget margin is calculated to obtain the received signal strength evaluation parameter and the link budget margin evaluation parameter; the ratio of the preset channel utilization reference value to the channel utilization is calculated to obtain the channel utilization evaluation parameter; based on the preset contribution of communication environment parameters, the received signal strength evaluation parameter, the channel utilization evaluation parameter, and the link budget margin evaluation parameter are weighted respectively, and then the weighted results are added and fused to obtain the communication environment evaluation value. The communication environment evaluation value is a numerical index that comprehensively quantifies the quality of communication conditions based on the communication environment parameters within the monitoring period; the contribution of communication environment parameters includes the contribution of received signal strength, the contribution of channel utilization, and the contribution of link budget margin.

[0033] The steps for dynamically allocating wireless channels based on the location distribution of each container include: generating a location set based on container location data after dynamically correcting the container location update frequency; generating historical location trajectories based on the location set; comparing the container location data with the historical location trajectories; and counting the number of abnormal data based on preset jump thresholds and distribution thresholds. Abnormal location jumps refer to changes in the distance or speed of container location between consecutive time points exceeding the jump threshold, indicating that the movement trajectory is discontinuous or unreasonable. Unreasonable location distribution refers to the spatial distribution of container locations deviating from the historical distribution pattern (such as concentration or dispersion) by more than the distribution threshold, indicating that the distribution pattern is abnormal. If the number of abnormal data exceeds the preset number threshold, an abnormal container location sensor warning is issued. If the number of abnormal data does not exceed the preset number threshold, the entire monitoring area is divided into multiple sub-regions based on a preset geographic grid or dynamic clustering algorithm. The ratio of the number of containers in each region to the area of ​​the corresponding region is recorded as the container density of each region based on the real-time location information of each container. Wireless channels are dynamically allocated based on the container density of each region.

[0034] Specifically, the steps for dynamically allocating wireless channels based on container density in each region include: designating regions where container density exceeds a preset container density threshold as the first region and regions where container density does not exceed the preset container density threshold as the second region; allocating dedicated wireless channels to each container in the first region to ensure that the channel is used independently by the container and is not shared with other containers; allocating reusable and shared wireless channels to each container in the second region so that multiple containers can communicate by sharing the same channel resources through time division, frequency division, or code division.

[0035] In this embodiment, since the risk of communication conflicts is significantly higher in the high-density container area (first area), allocating a dedicated channel to each container can completely eliminate mutual interference, ensuring data transmission reliability and low latency in critical areas. Conversely, the communication demand in the low-density container area (second area) is relatively sparse; reusing shared channels can greatly improve the utilization efficiency of scarce spectrum resources and avoid wasting channel resources. This invention dynamically allocates wireless channels based on container density, achieving an optimal balance between overall network performance and resource utilization efficiency: ensuring communication quality in high-load areas while economically supporting the communication needs of low-load areas, thus achieving the best trade-off between system capacity and cost-effectiveness.

[0036] This invention constructs an intelligent wireless channel allocation system integrating data credibility verification and adaptive optimization of communication resources. First, it continuously analyzes container location data after dynamic frequency correction, generates historical trajectories, and performs anomaly detection based on jump and distribution thresholds. This effectively identifies and warns of unreliable location information caused by sensor malfunctions or data interference at the source, ensuring the reliability of data upon which subsequent decisions are based. Based on data credibility, the system divides the monitored area into multiple sub-regions using a geographic grid or dynamic clustering algorithm and accurately calculates the container density in each region, achieving precise perception of the logistics spatial situation. Finally, the system implements a differentiated channel allocation strategy based on container density: a dedicated wireless channel is allocated to the high-density first region to completely avoid communication conflicts and ensure the real-time and reliable data transmission in critical congested areas; simultaneously, a reusable shared channel is allocated to the low-density second region, greatly improving the overall utilization efficiency of spectrum resources through multiple access technology. This closed-loop process achieves intelligent management of the entire chain, from data quality assurance to spatial density perception, and then to on-demand allocation of communication resources, thereby simultaneously optimizing communication service quality and efficiently utilizing wireless channel resources in a complex and dynamic logistics environment.

[0037] like Figure 3The diagram shows a flowchart of the distribution container allocation process for an intelligent control system based on cross-border product distribution provided in this application embodiment. The steps for dynamically allocating distribution containers based on the liquid content within each transport container and consumer orders include: Step 1: Obtaining liquid content data and container attribute data for each transport container, and obtaining the liquid demand and required container attributes for each consumer order based on the consumer order; Step 2: Matching the container attribute data of each transport container with the required container attributes of each consumer order, and marking transport containers with matching attributes and meeting national import standards as candidate containers for the corresponding consumer order; Step 3: If candidate containers exist, proceed directly to Step 4; if no candidate containers exist, proceed directly to Step 5; Step 4: Selecting the transport container with the smallest difference between liquid content and order demand from the candidate containers and allocating it to the corresponding order; Step 5: If no candidate containers exist but there are non-compliant containers that meet basic physical attributes, a special approval process is initiated, and after approval, the transport container with the smallest difference between its liquid content and order demand is allocated; if no candidate containers exist but there are non-compliant containers that meet basic physical attributes, a special approval process is initiated, and after approval, the transport container with the smallest difference between its liquid content and order demand is allocated; if no candidate containers exist, the transport container with the smallest difference between its liquid content and order demand is allocated; if no candidate containers exist, the transport container with the smallest difference between its liquid content and order demand is allocated; if no candidate containers exist, the transport container with the smallest difference between its liquid content and order demand is allocated; if no candidate containers exist, the transport container with the smallest difference between its liquid content and order demand is allocated. For containers with specific liquid content, an emergency procurement process is initiated, sending a procurement request to a certified supplier. Step Six: If the liquid content of a single transport container is less than the order requirement, the remaining liquid requirement for the order is calculated, and candidate containers with liquid content greater than the remaining liquid requirement are selected from the available transport containers. Step Seven: If candidate containers exist, the order is additionally allocated to the candidate container with the smallest difference between its liquid content and remaining liquid requirement. Step Eight: If no candidate containers exist, multiple containers are selected from the available transport containers in descending order of liquid content for combination and allocation until the cumulative liquid content of the selected container combination meets the order requirement. Step Nine: During the combination and allocation process, the difference between the cumulative liquid content of the currently selected container combination and the order requirement is calculated in real time, and the container combination is locked as the final allocation scheme when the difference is smallest. Step Ten: When the total liquid content of all available transport containers is less than the order requirement, an insufficient inventory warning is generated and sent to the system monitoring terminal, while providing alternative solutions, including transferring compliant containers from an overseas bonded warehouse or initiating a phased shipment process.

[0038] In this embodiment, the present invention constructs a dynamic intelligent distribution container allocation system aimed at minimizing liquid residue and maximizing allocation efficiency. By precisely matching the liquid content in the transport containers with consumer order demands, the system prioritizes the allocation of containers with contents closest to the demand, thereby significantly reducing liquid residue in containers and improving material utilization while fulfilling orders. When a single container cannot meet the demand, the system automatically calculates the remaining demand and intelligently selects the optimal supplementary containers or combination schemes: adding candidate containers based on the principle of minimum difference, or using a strategy of sorting from largest to smallest combined with real-time difference monitoring to lock in the optimal container combination, ensuring that orders are fulfilled with the fewest containers and the most accurate total liquid volume, effectively avoiding resource waste. The entire allocation process achieves fully automated decision-making and real-time optimization, and proactively issues warnings when inventory is insufficient, ultimately achieving efficient allocation of transport container resources and precise management of liquid inventory while ensuring order fulfillment rates.

[0039] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0043] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0044] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent control system for cross-border product distribution, characterized in that, It includes a status monitoring module, a channel scheduling module, and an intelligent allocation module: The status monitoring module is used to monitor the product status in real time based on product status parameters during cross-border product transportation, and to dynamically adjust the sensor monitoring frequency based on the product status monitoring and sensor power consumption. The product status parameters include product transportation status and transportation operating condition parameters. The product transportation status includes warehousing status, transportation status and operation status, and the transportation operating condition parameters include environmental operating condition parameters and medium status parameters. The channel scheduling module is used to dynamically adjust the container location update frequency based on the location joint state parameters and dynamically allocate wireless channels based on the location distribution of each container in cross-border product data transmission. The containers include transportation containers and distribution containers. The location joint state parameters include container state parameters and communication environment parameters. The container state parameters include the location change rate and transportation stage. The step of dynamically adjusting the container position update frequency based on the joint position state parameters includes: If the transportation stage of the container matches the preset key stage in the update strategy library, the container position update frequency will be dynamically adjusted based on the update strategy corresponding to the key stage. If the transportation stage of the container does not match the preset key stage in the update strategy library, the location change threshold is obtained based on the current container location update frequency. If the average position change rate of the container exceeds the position change threshold during a certain monitoring period, the difference between the average position change rate of the container and the position change threshold is recorded as the deviation position change rate. The deviation position change rate is matched with a preset first update frequency correction value mapping table to obtain the corresponding first update frequency correction value. The container position update frequency is adjusted based on the first update frequency correction value. The first update frequency correction value mapping table is a preset correspondence table between each deviation position change rate interval and the position update frequency correction value. If the rate of change of the container's position does not exceed the preset position change threshold during a certain monitoring period, a communication environment evaluation value is obtained based on the communication environment parameters. The communication environment evaluation value is matched with a preset second update frequency correction value mapping table to obtain the corresponding second update frequency correction value. The container's position update frequency is adjusted based on the second update frequency correction value. The second update frequency correction value mapping table is a preset correspondence table between each communication environment evaluation interval and the position update frequency correction value. The intelligent allocation module is used to dynamically allocate distribution containers based on the liquid content in each transport container and consumer orders during the cross-border product distribution process. After the allocation is completed, the remaining liquid content status of each allocated transport container is updated in real time, and the allocation details are recorded to the system monitoring terminal.

2. The intelligent control system for cross-border product distribution as described in claim 1, characterized in that, The steps for real-time monitoring of product status based on product status parameters include: The environmental condition parameter reference range is obtained by matching the product transportation status with a preset state environment parameter mapping table. The state environment parameter mapping table is a set of rules that defines the reasonable range of each environmental condition parameter under different transportation statuses. The environmental operating parameters in each monitoring time period are compared with the reference range of the environmental operating parameters. If the environmental operating parameter is not in the reference range of its corresponding environmental operating parameter in any monitoring time period, the environmental operating parameter is recorded as an abnormal environmental parameter and the abnormal environmental parameter is monitored in real time. If the duration of the abnormal environmental parameter being outside its corresponding reference range exceeds the preset duration threshold, the product status is marked as a warning status. If the duration of the abnormal environmental parameter being outside its corresponding reference range does not exceed the preset duration threshold, the product status will be marked as observation status. If all environmental operating parameters are within their corresponding environmental operating parameter reference range, then the reference range of medium state parameters is obtained based on the environmental operating parameters. Determine whether all media status parameters are within the corresponding media status parameter reference range. If so, mark the product status as normal. Otherwise, mark the product status as abnormal.

3. The intelligent control system for cross-border product distribution as described in claim 2, characterized in that, The reference range for the medium state parameters is obtained as follows: The average value of environmental operating parameters within each monitoring period is recorded as the average value of environmental operating parameters. The ratio of the average value of each environmental condition parameter to its respective preset reference value is calculated to obtain the corresponding environmental condition parameter factor. Then, the environmental condition parameter factors are weighted based on the preset contribution of the environmental condition parameters. The weighted results are then merged to obtain the environmental condition evaluation value. The environmental condition evaluation value is a numerical indicator that comprehensively quantifies the degree of environmental condition quality based on the environmental condition parameters in each monitoring period. The environmental condition assessment value is matched with a preset working condition medium parameter mapping table to obtain the reference range of medium state parameters. The working condition medium parameter mapping table is a set of rules that defines the reasonable range of each medium state parameter under different environmental conditions.

4. The intelligent control system for cross-border product distribution as described in claim 2, characterized in that, The step of dynamically adjusting the sensor monitoring frequency based on product status monitoring and sensor power consumption includes: If the product status is in a warning state, then directly adjust the sensor monitoring frequency of abnormal environmental parameters to the maximum value; If the product status is observation status, the value of the abnormal environmental parameter deviating from its reference range is recorded as the abnormal environmental deviation value, and the duration of the abnormal environmental parameter not being within its corresponding reference range is recorded as the abnormal environmental duration. The sensor monitoring frequency of the abnormal environmental parameter is dynamically adjusted based on the abnormal environmental deviation value and the abnormal environmental duration. If the product status is normal, then determine whether the power consumption rate of each environmental condition sensor under the monitoring frequency exceeds the preset power consumption rate threshold. The power consumption rate is the difference between the power consumption of the environmental condition sensor in the previous monitoring period and the power consumption of the same environmental condition sensor in the current monitoring period. If not, no further processing will be performed; If so, the difference between the power consumption rate threshold and the power consumption rate of the corresponding environmental condition sensor is recorded as the deviation power consumption rate. The deviation power consumption rate is matched with the preset first monitoring frequency correction value mapping table to obtain the corresponding first monitoring frequency correction value. The difference between the sensor monitoring frequency and the first monitoring frequency correction value is recorded as the corrected sensor monitoring frequency. The first monitoring frequency correction value mapping table is a preset correspondence table between different environmental condition sensors in different deviation power consumption rate ranges and monitoring frequency correction values. If the corrected sensor monitoring frequency is lower than the preset minimum sensor monitoring frequency, then the corrected sensor monitoring frequency will be directly set to the minimum sensor monitoring frequency. If the product status is abnormal, a product abnormality prompt will be issued.

5. The intelligent control system for cross-border product distribution as described in claim 4, characterized in that, The step of dynamically adjusting the sensor monitoring frequency based on the abnormal environment deviation value and the duration of the abnormal environment includes: The duration of the abnormal environment is matched with a preset adjustment factor mapping table to obtain the corresponding monitoring frequency adjustment factor. The adjustment factor mapping table defines the correspondence between different abnormal environment duration intervals and monitoring frequency adjustment factors. The abnormal environmental deviation value is compared with the unit environmental deviation value. Based on the comparison result, the monitoring frequency adjustment multiple is obtained. Based on the monitoring frequency adjustment multiple and the monitoring frequency adjustment unit, the monitoring frequency adjustment value is obtained. The unit environmental deviation value is a preset basic unit of measurement for quantifying the degree of deviation of abnormal environmental parameters. The monitoring frequency adjustment unit is a preset basic frequency unit for calculating the monitoring frequency adjustment amount. The second monitoring frequency correction value is obtained based on the monitoring frequency adjustment factor and the monitoring frequency adjustment value. The sum of the sensor monitoring frequency and the second monitoring frequency correction value is recorded as the corrected monitoring frequency of the abnormal environment parameter sensor. If the corrected monitoring frequency of the abnormal environmental parameter sensor is higher than the preset maximum value of the sensor monitoring frequency, then the corrected monitoring frequency of the abnormal environmental parameter sensor will be directly set to the maximum value of the sensor monitoring frequency.

6. The intelligent control system for cross-border product distribution as described in claim 1, characterized in that, The communication environment parameters include received signal strength, channel utilization, and link budget margin. The communication environment evaluation value is obtained in the following way: Calculate the ratios of the received signal strength and link budget margin to their respective preset reference values ​​for received signal strength and link budget margin, and obtain the received signal strength evaluation parameters and link budget margin evaluation parameters. The channel utilization evaluation parameter is obtained by calculating the ratio of the preset channel utilization reference value to the channel utilization. Based on the contribution of preset communication environment parameters, the received signal strength evaluation parameter, channel utilization evaluation parameter, and link budget margin evaluation parameter are weighted respectively, and then the weighted processing results are fused to obtain the communication environment evaluation value. The communication environment evaluation value is a numerical index that comprehensively quantifies the quality of communication conditions based on the communication environment parameters within the monitoring period. The contribution of the communication environment parameters includes the contribution of received signal strength, the contribution of channel utilization, and the contribution of link budget margin.

7. The intelligent control system for cross-border product distribution as described in claim 6, characterized in that, The step of dynamically allocating wireless channels based on the location distribution of each container includes: A location set is generated based on the container location data after dynamically correcting the container location update frequency, and a historical location trajectory is generated based on the location set. The container location data is compared with historical location trajectories. Based on preset jump thresholds and distribution thresholds, the number of abnormal data is counted. If the number of abnormal data exceeds the preset number threshold, an abnormal container location sensor prompt is issued. If the number of abnormal data does not exceed the preset threshold, the entire monitoring area is divided into multiple sub-areas. Based on the real-time location information of each container, the ratio of the number of containers in each area to the area of ​​the corresponding area is recorded as the container density of each area. Wireless channels are dynamically allocated based on the container density of each area.

8. The intelligent control system for cross-border product distribution as described in claim 7, characterized in that, The steps for dynamically allocating wireless channels based on container density in each region include: The region where the container density exceeds the preset container density threshold is designated as the first region, and the region where the container density does not exceed the preset container density threshold is designated as the second region. Dedicated wireless channels are allocated to each container in the first area, and reusable shared wireless channels are allocated to each container in the second area.

9. The intelligent control system for cross-border product distribution as described in claim 8, characterized in that, The step of dynamically allocating distribution containers based on the liquid content in each transport container and consumer orders includes: Step 1: Obtain liquid content data and container attribute data for each transport container, and obtain the liquid demand and required container attributes for each consumer order based on the consumer orders; Step 2: Match the container attribute data of each transport container with the required container attributes of each consumer order, and mark the transport containers with matching attributes as candidate containers for the corresponding consumer orders; Step 3: If a candidate container exists, proceed directly to Step 4; if no candidate container exists, proceed directly to Step 5. Step 4: Select the transport container with the smallest difference between the liquid content and the order demand from the candidate containers and assign it to the corresponding order; Step 5: Prioritize assigning shipping containers to orders with the smallest difference between their liquid content and the order's required quantity; Step 6: If the liquid content of a single transport container is less than the order requirement, calculate the remaining liquid requirement of the order and select candidate containers with a liquid content greater than the remaining liquid requirement from the available transport containers. Step 7: If candidate containers exist, then add the candidate container with the smallest difference between its liquid content and remaining liquid requirement to the order; Step 8: If no candidate containers are available, select multiple containers from the available transport containers in descending order of liquid content and combine them until the cumulative liquid content of the selected container combinations meets the order requirements. Step 9: During the combination allocation process, calculate in real time the difference between the cumulative liquid content of the currently selected container combination and the order demand, and lock the container combination as the final allocation scheme when the difference is the smallest; Step 10: When the total liquid content of all available transport containers is less than the order demand, generate an insufficient inventory warning and send it to the system monitoring terminal.

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