Location server for monitoring goods in logistics process

Abstract

The invention provides a position server for monitoring goods in a logistics process, and relates to the technical field of logistics. The position server for monitoring the goods in the logistics process comprises a monitoring deployment module, the monitoring deployment module is bidirectionally connected with a data communication module, the data communication module is bidirectionally connected with a data processing module, the data processing module is bidirectionally connected with a data storage module, and the data storage module is bidirectionally connected with a server. And the data processing module is bidirectionally connected with an arrival time prediction module. A logistics network inherent node is used as an accurate position anchor point, accurate recording of cargo circulation is carried out only at a key node, and adaptive position deduction is carried out on the basis of an intelligent algorithm and a historical data model in the transportation process, so that the dependence on high-power-consumption and high-cost continuous positioning hardware is thoroughly eliminated; the cost of the terminal equipment and the overall communication energy consumption are greatly reduced, and the economic feasibility of large-scale popularization and application is achieved.

Description

Technical Field

[0001] This invention relates to the field of logistics technology, specifically to a location server for monitoring goods during the logistics process. Background Technology

[0002] Logistics refers to the physical flow of goods from the supply location to the receiving location, including transportation, warehousing, loading and unloading, packaging, distribution processing, and information processing. Its aim is to deliver goods to their destination efficiently and at low cost. Monitoring goods during the logistics process is crucial, primarily because it ensures the safety and integrity of goods; real-time tracking prevents theft, loss, or damage; improves transportation efficiency, as monitoring data helps optimize routes, scheduling, and inventory management, reducing delays; enhances supply chain transparency, allowing customers and managers to accurately grasp the location and status of goods, improving service reliability; and facilitates problem tracing and responsibility determination, enabling timely warnings and handling of anomalies, effectively managing risks, and ultimately ensuring that the entire logistics process is controllable and visible, improving overall operational quality and customer satisfaction.

[0003] Traditional logistics monitoring solutions mostly rely on GPS-equipped positioning terminals installed on goods for continuous tracking. This approach results in high hardware costs and high power consumption, requiring frequent battery replacements, making large-scale deployments uneconomical. Furthermore, continuous wireless communication incurs ongoing data traffic costs, further increasing operational expenses. In terms of signal strength, GPS signals are easily lost indoors, in tunnels, or in remote areas, creating blind spots and interrupting the tracking, failing to accurately reflect the transportation status. Existing solutions often create data silos, with monitoring systems from different logistics carriers not interconnected. When goods are transferred between different companies, monitoring information is broken, preventing complete end-to-end traceability. Regarding anomaly detection, current technologies often use simple rule thresholds, such as triggering alarms by exceeding predetermined geofences or dwell time limits. This mechanical criterion cannot adapt to the complex and ever-changing real-world transportation scenarios, easily misjudging reasonable route adjustments, traffic congestion, or temporary stops as abnormal events, generating numerous false alarms and disrupting operational efficiency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a location server for monitoring goods during the logistics process, solving the problems of high monitoring costs, incomplete monitoring, and susceptibility to false alarms in existing technologies.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a location server for monitoring goods during the logistics process, comprising a monitoring deployment module, wherein the monitoring deployment module is bidirectionally connected to a data communication module, and the data communication module is bidirectionally connected to a data processing module; The data processing module is bidirectionally connected to the data storage module, the data processing module is bidirectionally connected to the arrival time prediction module, and the data processing module is bidirectionally connected to the intelligent alarm suppression module.

[0006] Preferably, the monitoring deployment module includes a logistics node module, which is connected to a node gateway and a cargo electronic tag.

[0007] Preferably, the data communication module includes a network switching module and a data communication component, wherein the network switching module uses a cost function to make network switching decisions.

[0008] Preferably, the data communication component is connected to a satellite communication module and a wireless network communication module.

[0009] Preferably, the data processing module includes a hardware module and a software module, wherein the hardware module includes a processor and a storage medium.

[0010] Preferably, the software module includes a location calculation module, an anomaly detection module, and a monitoring strategy management module.

[0011] A method for monitoring logistics goods using a location server, characterized by comprising the following steps: S1. Initialize the monitoring system The monitoring deployment module assigns a unique identifier to the goods, determines the initial monitoring intensity based on the goods' attributes, and plans the expected transportation route and key nodes. S2. Execution Node Relay Monitoring When goods arrive at the logistics node module, the node gateway scans the goods' electronic tag to record the precise location and time, updates the goods' location information on the server, and predicts the arrival time range of the next node. S3. Implement transportation process monitoring The cargo electronic tag activates the data communication module according to the monitoring strategy. The server executes the logistics network adaptive positioning algorithm through the location calculation module of the data processing module to calculate the location reliability score. S4. Anomaly Detection and Handling The anomaly detection module executes the anomaly detection algorithm, triggers the corresponding processing flow according to the anomaly level, and records the anomaly processing results for algorithm optimization. S5. Destination Confirmation The final node scan confirms the arrival of the goods, generates a complete transportation trajectory report, updates the historical transportation pattern database, and stores the data through the data storage module.

[0012] This invention provides a location server for monitoring goods during the logistics process. It has the following advantages: 1. This invention provides a location server for monitoring goods during the logistics process. By using the inherent nodes of the logistics network as precise location anchors, it accurately records the flow of goods only at key nodes. During transportation, it relies on intelligent algorithms and historical data models to perform adaptive location inference, thereby completely eliminating the dependence on high-power, high-cost continuous positioning hardware. This significantly reduces the cost of terminal equipment and overall communication energy consumption, making it economically feasible for large-scale application. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the overall workflow of the present invention; Figure 2 This is a flowchart illustrating the monitoring deployment module of the present invention; Figure 3 This is a flowchart illustrating the data communication module of the present invention; Figure 4 This is a flowchart illustrating the data processing module of the present invention.

[0014] The module comprises: 1. Monitoring and deployment module; 2. Data communication module; 3. Data processing module; 4. Data storage module; 5. Arrival time prediction module; 6. Intelligent alarm suppression module; 101. Logistics node module; 102. Node gateway; 103. Cargo electronic tag; 201. Network switching module; 202. Data communication module; 203. Satellite communication module; 204. Wireless network communication module; 301. Hardware module; 302. Processor; 303. Storage medium; 304. Software module; 305. Location calculation module; 306. Anomaly detection module; and 307. Monitoring strategy management module. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figure 1-4 As shown, this embodiment of the invention provides a location server for monitoring goods during the logistics process, including a monitoring deployment module 1, a data communication module 2 bidirectionally connected to the monitoring deployment module 1, a data processing module 3 bidirectionally connected to the data communication module 2, a data storage module 4 bidirectionally connected to the data processing module 3, an arrival time prediction module 5 bidirectionally connected to the data processing module 3, and an intelligent alarm suppression module 6 bidirectionally connected to the data processing module 3.

[0017] The monitoring deployment module 1 includes a logistics node module 101, which is connected to a node gateway 102 and a cargo electronic tag 103.

[0018] Specifically, the communication module 2 is used to communicate with the node gateway 102 and the cargo electronic tag 103 deployed in the logistics node module 101, receive cargo scanning information from the logistics node module 101, receive base station positioning information and sensor data from the cargo electronic tag 103, execute the logistics network adaptive positioning algorithm to calculate the current location of the cargo, execute the anomaly detection algorithm to identify transportation anomalies, and dynamically adjust the monitoring strategy according to the cargo value and transportation stage.

[0019] Data storage module 4 is used to store cargo location data, logistics network topology data, and historical transportation pattern data.

[0020] Arrival Time Prediction Module 5 calculates the arrival time using the following formula: (1) Basic forecast time:

[0021] in, Let i be the distance of the remaining i-th road segment. This represents the historical average speed of this road section.

[0022] (2) Time range prediction:

[0023] in, The time uncertainty range is calculated as follows:

[0024] in, Let i be the speed variation coefficient of road segment i. is the confidence coefficient.

[0025] The intelligent alarm suppression module 6 performs the alarm suppression process through the following steps: (1) The same goods within the time window Merging similar anomalies within:

[0026] in, This is an abnormal event. This is an abnormal location. This is the merging distance threshold.

[0027] (2) Adaptive alarm threshold adjustment based on transportation mode:

[0028] in, For similarity of transportation modes: , Let be the position of the j-th historical transport at time t.

[0029] The data communication module 2 includes a network switching module 201 and a data communication component 202. The network switching module 201 uses a cost function to make network switching decisions, and the data communication component 202 is connected to a satellite communication module 203 and a wireless network communication module 204.

[0030] Specifically, the cost function is as follows:

[0031] in, For communication costs, For network latency costs, Cost of network reliability (based on historical packet loss rate). For power consumption cost, to This is a weighting coefficient that is dynamically adjusted based on cargo priority and message urgency.

[0032] The data processing module 3 includes a hardware module 301 and a software module 304. The hardware module 301 includes a processor 302 and a storage medium 303. The software module 304 includes a location calculation module 305, an anomaly detection module 306, and a monitoring strategy management module 307.

[0033] Specifically, processor 302 and storage medium 303 provide the basic hardware foundation for the operation of data processing module 3. Location calculation module 305 is used to execute the Logistics Network Adaptive Positioning Algorithm (LNAA) and calculate the location confidence score. The Logistics Network Adaptive Positioning Algorithm (LNAA) calculates the estimated location of goods on transport segment k according to the following formula:

[0034] in, This is the estimated position coordinate vector at time t; The precise location coordinate vector of the starting point of road segment k is obtained through node gateway scanning; Let be the precise coordinate vector of the endpoint of road segment k; The departure time of the goods at the starting point of road segment k; This refers to the predicted time for goods to arrive at the destination of route segment k based on historical data. The historical average speed coefficient for road segment k is calculated as follows: For standard transport speed, Let k be the distance of road segment k. The time taken for the i-th historical transport on this road segment; This is the time decay factor, reflecting the accumulation of prediction error over time: This is the attenuation coefficient, with a value ranging from 0.1 to 0.3. External factor adjustment coefficients, taking into account the impact of weather and traffic conditions: The weight of factor j The degree of influence of factor j at time t; The random error term follows a normal distribution. ; The variance of the error as it increases over time: The initial error variance, This is the variance growth factor.

[0035] The formula for location credibility scoring is:

[0036] in: The location credibility score at time t, ranging from [0,1]; Reliability weights for the positioning method: ; For the time freshness factor: This is the last time the location was updated. It is the attenuation constant; Historical accuracy factor: For indicator functions, For accurate thresholding, K represents the number of historical data points; Let be the weighting coefficient, satisfying .

[0037] The anomaly detection module 306 is used to detect transportation anomalies based on pattern recognition, specifically through the following steps: (1) Calculate the current position deviation

[0038] in, The actual reported location coordinates; (2) Determine the dynamic threshold

[0039] in: Based on the threshold, The transportation stage coefficient is calculated using the following piecewise function: ; For the value coefficient of goods: For the value of goods, The maximum value of goods in the system; The time factor reflects the increase of expected error over time: This represents the time-related influence coefficient.

[0040] (3) Determine the level of abnormality .

[0041] The monitoring strategy management module 307 is used to dynamically adjust the monitoring intensity based on the cargo attributes, which is achieved through the following steps: (1) Regarding the value of goods and monitoring intensity Mapping:

[0042] in, , For minimum and maximum monitoring intensity, To adjust the index.

[0043] (2) The intensity of monitoring is specifically reflected in: Location update frequency Anomaly detection sensitivity Data transmission priority ; (3) For goods with low monitoring intensity ( ),use Node relay model: The system records precise locations only at logistics nodes, uses path estimation for transportation between nodes, and sends signals only 1-2 times per day.

[0044] A method for monitoring logistics goods using a location server, specifically including the following steps: S1. Initialize the monitoring system The monitoring deployment module 1 assigns a unique identifier to the goods, determines the initial monitoring intensity based on the goods attributes, and plans the expected transportation route and key nodes. S2. Execution Node Relay Monitoring When the goods arrive at the logistics node module 101, the node gateway 102 scans the goods' electronic tag 103 to record the precise location and time, updates the goods' location information on the server, and predicts the arrival time range of the next node. S3. Implement transportation process monitoring The cargo electronic tag 102 activates the data communication module 2 according to the monitoring strategy. The server executes the logistics network adaptive positioning algorithm through the location calculation module 305 of the data processing module 3 to calculate the location reliability score. S4. Anomaly Detection and Handling The anomaly detection module 306 executes the anomaly detection algorithm, triggers the corresponding processing flow according to the anomaly level, and records the anomaly processing results for algorithm optimization. S5. Destination Confirmation The final node scan confirms the arrival of the goods, generates a complete transportation trajectory report, updates the historical transportation pattern database, and stores it through data storage module 4.

[0045] Specifically, this method allows for rapid access to the monitoring network through simple tag binding and gateway configuration, making the technical solution highly feasible and easily scalable. The daily operation phase uses a "node relay" monitoring model as its core. Goods depart from the origin. When they arrive at the first logistics node module 101, the node gateway 102 automatically senses and scans the goods' electronic tag 103, recording the precise location and time of "goods arrived" with millisecond accuracy and uploading it to the location server. At this moment, the server updates the goods' location status to "at node XX," which is the most accurate location point throughout the entire monitoring process. Subsequently, based on the logistics network adaptive positioning algorithm of the location calculation module 305, combined with historical transportation data for that segment, the current transportation vehicle type, and macro-traffic information, the system calculates the estimated time window for the goods to reach the next node. During the journey from the node to the next station, the goods' electronic tag 103 does not continuously transmit location information but enters a low-power sleep state, only reporting signals a very small number of times per day according to its monitoring intensity level, or being awakened only when abnormal vibrations are detected. This greatly saves communication costs and battery power. By replacing expensive and energy-intensive continuous tracking with "precise check-in at key nodes," the system achieves maximum cost and energy savings while ensuring that key milestones in the cargo journey are tracked.

[0046] While goods are en route between nodes, the location calculation module 305 and the anomaly detection module 306 operate continuously. The server receives base station location information incidentally reported by tags or coarse locations shared by transportation vehicles, but this data is not directly used as a location conclusion. The location calculation module 305 integrates this scattered information with the logistics network model, using algorithms to "constrain" the goods onto known transportation routes, and combines departure time and average speed for intelligent route estimation, thus providing a current location estimate with the highest probability and a reasonably expanding range of location confidence over time. Simultaneously, the anomaly detection module 306 compares the current location estimate and movement status with the expected transportation pattern in real time. The system's dynamic threshold technology can automatically adjust the tightness of the judgment based on different stages of transportation and the value of the goods. For example, it allows for larger location deviations in long-haul transportation, while significantly increasing sensitivity during last-mile delivery. Once the system detects that the goods' movement trajectory significantly deviates from the expected pattern, lingers at a certain location for too long, or experiences unexpected route changes, it triggers tiered alarms. This monitoring method effectively distinguishes between normal scheduling and real anomalies in complex and realistic logistics environments, significantly reducing false alarms common in traditional monitoring methods and improving monitoring efficiency.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A location server for monitoring goods during logistics, comprising a monitoring deployment module (1), characterized in that: The monitoring deployment module (1) is bidirectionally connected to the data communication module (2), and the data communication module (2) is bidirectionally connected to the data processing module (3). The data processing module (3) is bidirectionally connected to the data storage module (4), the data processing module (3) is bidirectionally connected to the arrival time prediction module (5), and the data processing module (3) is bidirectionally connected to the intelligent alarm suppression module (6).

2. A location server for monitoring goods during the logistics process according to claim 1, characterized in that: The monitoring deployment module (1) includes a logistics node module (101), which is connected to a node gateway (102) and a cargo electronic tag (103).

3. A location server for monitoring goods during the logistics process according to claim 1, characterized in that: The data communication module (2) includes a network switching module (201) and a data communication component (202). The network switching module (201) uses a cost function to make network switching decisions.

4. A location server for monitoring goods during the logistics process according to claim 3, characterized in that: The data communication component (202) is connected to a satellite communication module (203) and a wireless network communication module (204).

5. A location server for monitoring goods during the logistics process according to claim 1, characterized in that: The data processing module (3) includes a hardware module (301) and a software module (304). The hardware module (301) includes a processor (302) and a storage medium (303).

6. A location server for monitoring goods during the logistics process according to claim 5, characterized in that: The software module (304) includes a location calculation module (305), an anomaly detection module (306), and a monitoring strategy management module (307).

7. A method for monitoring logistics goods using a location server according to any one of claims 1-6, characterized in that, Specifically, the following steps are included: S1. Initialize the monitoring system The monitoring deployment module (1) assigns a unique identifier to the goods, determines the initial monitoring intensity based on the goods attributes, and plans the expected transportation route and key nodes. S2. Execution Node Relay Monitoring When the goods arrive at the logistics node module (101), the node gateway (102) scans the goods' electronic tag (103) to record the precise location and time, updates the goods' location information on the server, and predicts the arrival time range of the next node; S3. Implement transportation process monitoring The cargo electronic tag (102) activates the data communication module (2) according to the monitoring strategy. The server executes the logistics network adaptive positioning algorithm through the location calculation module (305) of the data processing module (3) to calculate the location reliability score. S4. Anomaly Detection and Handling The anomaly detection module (306) executes the anomaly detection algorithm, triggers the corresponding processing flow according to the anomaly level, and records the anomaly processing results for algorithm optimization. S5. Destination Confirmation The final node scan confirms the arrival of the goods, generates a complete transportation trajectory report, updates the historical transportation mode database, and stores it through the data storage module (4).

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