Cargo position detection system for cold-chain logistics

Abstract

The invention relates to the technical field of logistics transportation, in particular to a cargo position detection system for cold-chain logistics, and the system comprises a data obtaining module which is used for obtaining cargo position and state data in real time; the position prediction module is used for determining a moving track and a predicted position coordinate of the cargo based on the position data and determining a qualified position according to a matching degree; the position early warning module is used for determining an early warning value according to the unqualified matching degree; the strategy determination module is used for determining the path deviation degree to determine an early warning strategy and determining whether the cargo transportation path is compliant or not according to the data credibility; the cargo state module responds to the condition that the cargo transportation path is not compliant and determines the cargo state based on the cargo collision times; the position calibration module determines a coincidence rate based on the cargo status disqualification to determine an update to the position coordinates. According to the invention, the problems of opaque transportation process and risk early warning lag caused by single cold-chain logistics monitoring means and lack of prediction capability are overcome.

Description

Technical Field

[0001] This invention relates to the field of logistics and transportation technology, and in particular to a cargo location detection system for cold chain logistics. Background Technology

[0002] With the rapid development of fresh food e-commerce and pharmaceutical cold chain logistics, and the increasing demand from consumers for high-quality goods, cold chain logistics has become a crucial link in ensuring food safety and drug efficacy. The location and status of goods in cold chain logistics not only directly affect transportation efficiency and supply chain transparency, but are also the core basis for assessing product quality and tracing liability for damage. Traditional methods of monitoring cargo location, such as manual recording or simple GPS tracking, are insufficient for real-time, accurate, and intelligent monitoring of cargo location, transportation trajectory, and physical condition, and cannot effectively warn of route deviations, abnormal delays, or potential physical damage during transportation.

[0003] Chinese Patent Publication No. CN118485367A discloses a railway cold chain logistics transportation system based on blockchain technology, relating to the field of logistics technology. The system includes a cold chain information acquisition module, a data communication module, a processor module, a blockchain infrastructure module, and a blockchain traceability module. The cold chain information acquisition module is used to collect first information about the carriages containing goods within railway vehicles; the first information includes temperature and humidity information and location information. The blockchain infrastructure module is used to acquire second information about the goods; the second information includes production environment information, storage status information, production process information, and transportation location information. The processor module is used to send target information, including the first and second information, to the blockchain traceability module. The blockchain traceability module is used to store the hash value of the target information and the corresponding cargo identifier in the blockchain.

[0004] Therefore, the aforementioned railway cold chain logistics transportation system based on blockchain technology has the following problems: 1. The lack of a real-time prediction and dynamic verification mechanism for cargo transportation trajectories makes it impossible to provide timely warnings of transportation risks such as route deviation, abnormal stagnation, or potential physical damage, resulting in delayed risk response and making it difficult to guarantee the timeliness and safety of cold chain logistics.

[0005] 2. Relying on a single location data source and static information records, without introducing multi-source redundant positioning and data reliability assessment, it is prone to monitoring blind spots or false alarms when the positioning signal is lost, drifts, or there is environmental interference, which reduces the reliability and accuracy of the system. Summary of the Invention

[0006] To address this, the present invention provides a cargo location detection system for cold chain logistics, which overcomes the problems of opaque transportation processes and delayed risk warnings caused by the single monitoring method and lack of predictive capabilities in the prior art.

[0007] To achieve the above objectives, the present invention provides a cargo location detection system for cold chain logistics, comprising: The data acquisition module is used to acquire the location and status data of the goods in real time. The location prediction module is used to determine the trajectory of the cargo based on the cargo's location data, predict its location coordinates for the next stage, and calculate the matching degree between the predicted location coordinates and the real-time location coordinates to determine whether the matching degree is qualified. A location warning module is used to determine a location warning value based on the unqualified matching degree; The strategy determination module responds to the warning value and determines the path deviation based on the running trajectory and the preset standard transportation path to determine the warning strategy. It also obtains the data credibility based on the signal source of the location data and the current refresh frequency to determine whether the cargo transportation path is compliant. The cargo status module determines whether the cargo status is acceptable based on the comparison between the number of cargo collisions and the preset number of collisions, based on the status data, if the cargo transportation route is non-compliant. The position calibration module, in response to the cargo status being unqualified, activates a redundant positioning source to obtain several position coordinates for the next stage, so as to form a cargo trajectory based on the several position coordinates, calculate the overlap rate between the several position coordinate points of the redundant positioning source and the trajectory, compare it with a preset overlap rate to determine whether the overlap rate meets the standard, and determine the position coordinate update amount and update it based on the overlap rate not meeting the standard.

[0008] Furthermore, the location prediction module determines the predicted position coordinates of the cargo in the next stage as the sum of the predicted displacement vector and the latest acquired position coordinate vector, wherein, The predicted displacement vector is the product of the moving velocity vector and the acquisition time interval, and the moving velocity vector is the ratio of the displacement vector to the acquisition time interval.

[0009] Furthermore, the location prediction module determines that the matching degree is unqualified based on the matching degree being less than a preset matching degree threshold, wherein, The matching degree is the straight-line distance between the predicted location coordinates and the real-time location coordinates.

[0010] Furthermore, the strategy determination module determines that the cargo transportation route is non-compliant based on the data credibility being greater than a preset data credibility threshold, provided that the path deviation is greater than a preset path deviation.

[0011] Furthermore, the strategy determination module determines the data reliability as the product of a preset baseline reliability and a frequency ratio factor, wherein, The preset benchmark reliability is determined based on the acquired satellite positioning signal; The frequency ratio factor is determined based on the ratio of the current refresh frequency to the standard refresh frequency of the preset benchmark confidence level, and the minimum value compared with the preset frequency ratio is determined.

[0012] Furthermore, the cargo status module determines that the cargo status is unqualified based on the non-compliance of the cargo transportation route and the fact that the number of cargo collisions exceeds the preset number of collisions.

[0013] Furthermore, in response to a cargo status defect, the position calibration module uses the cargo movement trajectory with the highest data quality score in the cargo movement trajectory as the cargo trajectory, wherein... Enable all available redundant positioning sources and collect the position coordinate data of several positioning sources in parallel at a standard refresh rate within the preset data acquisition time window; By connecting the location coordinates collected by several positioning sources in a continuous time series in chronological order, the cargo movement trajectory corresponding to several positioning sources is constructed.

[0014] Furthermore, the position calibration module determines that the overlap rate is substandard based on the fact that the overlap rate is less than the preset overlap rate.

[0015] Furthermore, in response to the overlap rate failing to meet the standard, the position calibration module updates the position coordinates by the difference between the latest position coordinates acquired by the reference source at the end of the data acquisition time window and the currently used position coordinates. From several redundant positioning sources, the positioning source with the highest overlap rate is selected as the reference source.

[0016] Furthermore, the position calibration module updates the currently used position coordinates to the latest position coordinates of the reference source.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention achieves intelligent closed-loop monitoring of the cold chain logistics transportation process through multi-module collaboration. The multi-source data acquisition and redundant positioning mechanism avoids the signal loss and drift problems of traditional single-point positioning. The trajectory prediction and matching degree judgment can not only identify transportation risks such as path deviation and abnormal stagnation in advance, but also simultaneously verify the reliability and continuity of positioning data. By calculating the collision frequency through the state parameters of the goods during transportation, the path anomalies and the safety status of the goods are accurately correlated, and potential cargo damage risks are warned in advance to reduce economic losses. Furthermore, the positioning calibration mechanism based on multi-source overlap rate improves positioning reliability and reduces monitoring blind spots by switching reference sources and updating coordinates to improve the traceability and transparency of the entire cold chain logistics process, thereby improving the quality assurance and risk management capabilities of high-value cargo transportation.

[0018] Furthermore, this invention establishes a reliable method for real-time, quantitative verification of cargo transportation trajectories through a matching degree calculation and judgment mechanism introduced by the location prediction module. It transforms the isolated, static location reports in traditional systems into continuous, verifiable, dynamic path reliability assessments, accurately quantifying the reliability of the current trajectory. This allows for the keen identification of trajectory anomalies caused by various reasons such as signal drift, unplanned path deviation, abnormal vehicle stagnation, or data transmission failures. This matching degree judgment based on precise calculation provides an objective and quantitative basis for decision-making for the entire system, achieving a leap from fuzzy judgments relying on experience to precise control based on data. This ensures the timeliness and accuracy of early warnings in the cold chain logistics monitoring system and improves the safety and transparency of the transportation process.

[0019] Furthermore, this invention uses a location warning module to determine the location warning value based on the mismatch, thereby achieving graded and quantitative early warning of transportation anomalies. The system can trigger different levels of response strategies based on the location warning value, thus avoiding the coarseness of the traditional binary (yes / no) alarm method. This refined early warning mechanism not only improves the sensitivity of risk identification but also optimizes resource allocation, ensuring that monitoring personnel can prioritize high-risk events, significantly reducing the risk of cargo damage caused by delayed or missed warnings, and enhancing the system's proactive protection capabilities.

[0020] Furthermore, this invention constructs a path compliance judgment mechanism based on data credibility through a strategy determination module, achieving a leap from relying on single location data to comprehensively evaluating data quality and path status. By quantitatively analyzing the inherent reliability of signal sources and the timeliness of data updates, it provides key data quality evidence for core decisions, enabling the system to clearly distinguish between high-confidence path deviations and suspected deviations caused by unreliable data. This improves the accuracy and credibility of system alarms, effectively avoiding false alarms caused by common problems such as temporary drift or loss of positioning signals. At the same time, it ensures that any real path anomalies will not be ignored by the system due to data quality issues, thereby improving the level of automation and providing dual protection for the safe transportation of cold chain logistics.

[0021] Furthermore, this invention precisely correlates physical anomalies of the transportation route with the physical state of the goods themselves through a cargo status module, achieving multi-dimensional and comprehensive monitoring of transportation safety. When the system determines that the route is non-compliant, it initiates cargo status diagnosis, analyzing historical collision data to determine whether non-compliant transportation behavior has posed a substantial physical risk to the goods. This correlation mechanism from route anomaly to status assessment overcomes the limitations of traditional systems that only focus on location while ignoring the safety of the goods themselves. It can effectively distinguish between normal bumps and abnormal loading / unloading, rough transportation, and other behaviors that may cause cargo damage, thereby extending the monitoring perspective from single location management to comprehensive quality assurance. For cases where the route deviates but the cargo is in good condition, the focus can be on route correction. For cases where cargo status is also abnormal, it is beneficial to initiate cargo damage inspection and claims procedures. This provides crucial core data support for ensuring the transportation quality of high-value goods (such as precision instruments and cold chain fresh produce), improving the level of precision in logistics management and risk control capabilities.

[0022] Furthermore, this invention constructs a multi-source positioning data fusion and verification mechanism through a position calibration module, effectively solving the position monitoring blind spots caused by the failure or accuracy degradation of a single positioning source signal during cold chain logistics transportation. When an abnormal cargo status is detected, redundant positioning sources can be activated to collect position data in parallel. Through multi-track comparison and data integrity assessment, the most reliable benchmark trajectory is selected, ensuring the continuity and accuracy of position monitoring data. By introducing a quantitative evaluation system for overlap rate, the signal quality of each positioning source can be objectively identified, and when the overlap rate is found to be substandard, the system switches to the positioning source with the best performance, achieving accurate calibration and real-time updates of position coordinates. This intelligent calibration strategy based on multi-source data cross-validation improves the system's adaptability and reliability in complex transportation environments, effectively avoiding monitoring failures caused by positioning signal drift, obstruction, or equipment malfunction. It provides a solid technical guarantee for the full traceability and risk warning of cold chain logistics, thereby significantly improving the safety and management efficiency of cargo transportation. Attached Figure Description

[0023] Figure 1 This is a module connection diagram of a cargo location detection system for cold chain logistics according to an embodiment of the present invention; Figure 2 This is a logic diagram for determining whether the matching degree is qualified in an embodiment of the present invention; Figure 3 This is a logic diagram illustrating the determination of whether a cargo transportation route is compliant in an embodiment of the present invention. Figure 4 This is a logic diagram illustrating the determination of whether the goods are in good condition according to an embodiment of the present invention. Figure 5 This is a logic diagram for determining whether the overlap rate meets the standard in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0025] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1 The diagram shown is a flowchart of the steps of the cold chain logistics cargo location detection system according to an embodiment of the present invention.

[0028] The present invention provides a cargo location detection system for cold chain logistics, comprising: The data acquisition module is used to acquire the location and status data of the goods in real time. The location prediction module, which is connected to the data acquisition module, is used to determine the trajectory of the cargo based on the cargo's location data, predict its location coordinates in the next stage, and calculate the matching degree between the predicted location coordinates and the real-time location coordinates to determine whether the matching degree is qualified. A location warning module, which is connected to the location prediction module, is used to determine a location warning value based on the unqualified matching degree; The strategy determination module is connected to the data acquisition module and the location warning module. It responds to the warning value and determines the path deviation based on the running trajectory and the preset standard transportation path to determine the warning strategy. It also obtains the data credibility based on the signal source of the location data and the current refresh frequency to determine whether the cargo transportation path is compliant. The cargo status module, which is connected to the data acquisition module and the strategy determination module, determines whether the cargo status is qualified based on the comparison between the cargo collision count and the preset collision count, based on the non-compliance of the cargo transportation route. The position calibration module, which is connected to the data acquisition module and the cargo status module, responds to the cargo status being unqualified by activating a redundant positioning source to acquire several position coordinates for the next stage, so as to form a cargo trajectory based on the several position coordinates, calculate the overlap rate between the several position coordinates of the redundant positioning source and the trajectory, compare it with a preset overlap rate to determine whether the overlap rate meets the standard, and determine the position coordinate update amount and update it based on the overlap rate not meeting the standard.

[0029] Specifically, this invention achieves intelligent closed-loop monitoring of the cold chain logistics transportation process through multi-module collaboration. The multi-source data acquisition and redundant positioning mechanism avoids the signal loss and drift problems of traditional single-point positioning. The trajectory prediction and matching degree judgment can not only identify transportation risks such as path deviation and abnormal stagnation in advance, but also simultaneously verify the reliability and continuity of positioning data. By calculating the collision frequency through the state parameters of the goods during transportation, the path anomalies and the safety status of the goods are accurately correlated, and potential cargo damage risks are warned in advance to reduce economic losses. The positioning calibration mechanism based on the multi-source overlap rate improves positioning reliability and reduces monitoring blind spots by switching the reference source and updating the coordinates, thereby improving the traceability and transparency of the entire cold chain logistics process and enhancing the quality assurance and risk management capabilities of high-value cargo transportation.

[0030] In this embodiment of the invention, the location data is obtained through at least one of a GPS positioning module, a Beidou positioning module, and a cellular network base station positioning module; the status data is obtained through one or more of a triaxial accelerometer, a temperature sensor, a humidity sensor, and a packaging integrity detection sensor installed on the transport container.

[0031] Please see Figure 2 As shown, it is a logic judgment diagram for determining whether the matching degree is qualified in an embodiment of the present invention.

[0032] Specifically, the location prediction module determines the trajectory of the cargo based on the cargo's location data and predicts its location coordinates for the next stage. Based on the cargo position coordinates of multiple consecutive acquisition cycles, calculate the displacement vector between adjacent coordinate points; The predicted position coordinates of the cargo in the next stage are the sum of the predicted displacement vector and the latest acquired position coordinate vector. Wherein, the predicted displacement vector is the product of the moving velocity vector and the acquisition time interval, and the moving velocity vector is the ratio of the displacement vector to the acquisition time interval.

[0033] In this embodiment of the invention, the displacement vector (△x, △y) represents the change in longitude and the change in latitude. The trajectory of the cargo is a spatial path curve connecting multiple consecutive position coordinate points in chronological order. The change in longitude is the difference between the longitude coordinates of the current cycle and the longitude coordinates of the previous cycle, and the change in latitude is the difference between the latitude coordinates of the current cycle and the latitude coordinates of the previous cycle.

[0034] In this embodiment of the invention, the range of the acquisition time interval is [5s, 60s], preferably 15s, and the range of the consecutive acquisition cycles is [3, 7], preferably 5. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0035] Specifically, the location prediction module calculates the matching degree between the predicted location coordinates and the real-time location coordinates to determine whether the matching degree is qualified; If the matching degree is greater than or equal to the preset matching degree threshold, then the matching degree is determined to be qualified; If the matching degree is less than the preset matching degree threshold, then the matching degree is determined to be unqualified.

[0036] In this embodiment of the invention, the matching degree is the straight-line distance between the predicted location coordinates and the real-time location coordinates.

[0037] In this embodiment of the invention, the preset matching degree threshold ranges from [50m, 200m], preferably set to 100m. However, the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0038] Specifically, this invention establishes a reliable method for real-time, quantitative verification of cargo transportation trajectories through a matching degree calculation and judgment mechanism introduced by the location prediction module. It transforms the isolated, static location reports in traditional systems into continuous, verifiable, dynamic path reliability assessments, which can accurately quantify the reliability of the current trajectory. This allows for the keen identification of trajectory anomalies caused by various reasons such as signal drift, unplanned path deviation, abnormal vehicle stagnation, or data transmission failures. This matching degree judgment based on precise calculation provides an objective and quantitative basis for decision-making for the entire system, achieving a leap from fuzzy judgments relying on experience to precise control based on data. This ensures the timeliness and accuracy of early warnings from the cold chain logistics monitoring system and improves the safety and transparency of the transportation process.

[0039] Specifically, the location warning module determines a location warning value based on the unqualified matching degree, wherein the location warning value is the ratio of the preset matching degree threshold to the matching degree.

[0040] Specifically, this invention uses the location warning module to determine the location warning value based on the mismatch, thereby realizing graded and quantitative early warning of transportation anomalies. The system can trigger different levels of response strategies according to the location warning value, thus avoiding the coarseness of the traditional binary (yes / no) alarm method. This refined early warning mechanism not only improves the sensitivity of risk identification, but also optimizes resource allocation, ensuring that monitoring personnel can prioritize high-risk events, significantly reducing the risk of cargo damage caused by delayed or missed warnings, and enhancing the system's proactive protection capabilities.

[0041] Specifically, the strategy determination module responds to the warning value and determines the warning strategy based on the path deviation between the running trajectory and the preset standard transportation path; Spatial overlay comparison is performed based on the actual trajectory of the goods and the preset standard path; Calculate the maximum distance by which the running trajectory deviates from the preset standard path; The path deviation is determined based on the ratio of a preset distance threshold to the maximum distance; If the location warning value is greater than the preset location warning value, or the path deviation is greater than the preset path deviation, then a first frequency sound alarm is triggered.

[0042] If the location warning value is less than or equal to the preset location warning value, and the path deviation is greater than the preset path deviation, then it is determined to trigger a second frequency sound alarm. If the location warning value is greater than the preset location warning value, and the path deviation is less than or equal to the preset path deviation, then a third frequency sound alarm is triggered. If the location warning value is less than or equal to the preset location warning value, and the path deviation is less than or equal to the preset path deviation, then it is determined that no sound alarm will be triggered.

[0043] In this embodiment of the invention, the calculation process of the maximum distance is to represent the actual running trajectory of the goods and the preset standard path as discrete position coordinate sequences respectively. For each position coordinate point on the running trajectory, the shortest vertical distance from it to all line segments on the preset standard path is calculated, and the distance with the largest value is selected from several vertical distances.

[0044] In this embodiment of the invention, the preset standard route is pre-set by the transportation plan and includes the starting point, the destination, and the coordinates of key path points along the route.

[0045] In this embodiment of the invention, the preset distance threshold ranges from [500m, 2000m], preferably 1000m; the first frequency sound alarm is a continuous short beep (e.g., 2Hz); the second frequency sound alarm is an intermittent long beep (e.g., 0.5Hz); the third frequency sound alarm is a single short prompt tone; the preset location warning value ranges from [1.0, 1.5], preferably 1.2; and the preset path deviation ranges from [10%, 50%], preferably 25%. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0046] Please see Figure 3 As shown, it is a logic diagram for determining whether a cargo transportation route is compliant according to an embodiment of the present invention.

[0047] Specifically, the strategy determination module obtains data credibility based on the signal source and current refresh frequency of the location data in order to determine whether the cargo transportation route is compliant; The reliability of the preset benchmark determined by the satellite positioning signal is obtained; The frequency ratio is determined based on the ratio of the current refresh frequency to the standard refresh frequency with the preset benchmark confidence level, and the minimum value is determined by comparing it with the preset frequency ratio to determine the frequency ratio factor. The data reliability is the product of a preset benchmark reliability and a frequency ratio factor; If the path deviation is less than or equal to the preset path deviation, the cargo transportation path is determined to be compliant. Under the condition that the path deviation is greater than the preset path deviation, If the data credibility is greater than a preset data credibility threshold, then the cargo transportation route is determined to be non-compliant. If the data credibility is less than or equal to the preset data credibility threshold, it is determined that the compliance of the cargo transportation route cannot be confirmed and manual verification will be carried out.

[0048] In this embodiment of the invention, the preset benchmark confidence level ranges from [0.7, 0.9], preferably 0.8, and the preset frequency ratio is preferably 1.0. However, the above values ​​are not limited to these, and those skilled in the art can adjust them according to actual needs.

[0049] In this embodiment of the invention, the preset data reliability threshold ranges from [0.6, 0.8], preferably set to 0.7. The standard refresh frequency is 1Hz, which means that location data is collected once per second. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the values ​​according to actual needs.

[0050] In this embodiment of the invention, the manual verification involves the system generating a verification report that includes the real-time location of the goods, the predicted path, the path deviation, and the data reliability, and then pushing the report to the administrator terminal of the monitoring center.

[0051] Specifically, this invention constructs a path compliance judgment mechanism based on data credibility through a strategy determination module, achieving a leap from relying on single location data to comprehensively evaluating data quality and path status. By quantitatively analyzing the inherent reliability of signal sources and the timeliness of data updates, it provides key data quality evidence for core decisions, enabling the system to clearly distinguish between high-confidence path deviations and suspected deviations caused by unreliable data. This improves the accuracy and credibility of system alarms, effectively avoiding false alarms caused by common problems such as temporary drift or loss of positioning signals. At the same time, it ensures that any real path anomalies will not be ignored by the system due to data quality issues, thereby improving the level of automation and providing dual protection for the safe transportation of cold chain logistics.

[0052] Please see Figure 4 As shown, it is a logic diagram for determining whether the condition of goods is qualified according to an embodiment of the present invention.

[0053] Specifically, the cargo status module determines whether the cargo status is qualified based on the comparison between the number of cargo collisions and the preset number of collisions, based on the status data, since the cargo transportation route is non-compliant. If the number of impacts on the goods exceeds the preset number of impacts, the goods are determined to be in an unqualified state. If the number of impacts on the goods is less than or equal to the preset number of impacts, then the goods are deemed to be in a qualified state.

[0054] In this embodiment of the invention, the preset number of impacts ranges from [3 times, 10 times], preferably set to 5 times, but the above value is not limited to this, and those skilled in the art can also adjust the value according to actual needs.

[0055] In this embodiment of the invention, the number of cargo impacts in the cargo status data is obtained by a triaxial accelerometer installed on the transport container. The sensor monitors the acceleration of the cargo in real time. When the acceleration value in any axis is greater than a preset impact acceleration threshold, it is recorded as one cargo impact.

[0056] Specifically, this invention uses a cargo status module to precisely correlate physical anomalies in the transportation route with the physical state of the cargo itself, achieving multi-dimensional and comprehensive monitoring of transportation safety. When the system determines that the route is non-compliant, it initiates cargo status diagnosis, analyzing historical collision data to determine whether non-compliant transportation behavior has posed a substantial physical risk to the cargo. This correlation mechanism from route anomaly to status assessment overcomes the limitations of traditional systems that only focus on location while ignoring the safety of the cargo itself. It can effectively distinguish between normal bumps and abnormal loading / unloading, rough transportation, and other behaviors that may cause cargo damage, thereby extending the monitoring perspective from single location management to comprehensive quality assurance. For cases where the route deviates but the cargo is in good condition, the focus can be on route correction. For cases accompanied by cargo status anomalies, it is beneficial to initiate cargo damage inspection and claims procedures. This provides crucial core data support for ensuring the transportation quality of high-value goods (such as precision instruments and cold chain fresh produce), improving the level of precision in logistics management and risk control capabilities.

[0057] Specifically, in response to an unqualified cargo status, the position calibration module activates a redundant positioning source to obtain several position coordinates for the next stage, so as to form a cargo trajectory based on the several position coordinates. Enable all available redundant positioning sources and collect the position coordinate data of several positioning sources in parallel at a standard refresh rate within the preset data acquisition time window; Based on the location coordinates collected by several positioning sources in a continuous time series, the cargo movement trajectory corresponding to several positioning sources is constructed by connecting them in time sequence. The cargo movement trajectory with the highest data quality score is used as the cargo trajectory.

[0058] In this embodiment of the invention, the available redundant positioning sources include any two or more of the following: primary GPS positioning module, backup BeiDou positioning module, and cellular network base station positioning module.

[0059] In this embodiment of the invention, the data quality score is determined by filtering out cargo movement trajectories with a data missing rate less than a preset missing rate threshold. Based on the filtered cargo movement trajectories, the cargo movement trajectory with the minimum positional fluctuation value is selected as the maximum data quality score. The preset missing rate threshold ranges from [5%, 20%], preferably set to 10%, but the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0060] In this embodiment of the invention, within the data acquisition time window, the data missing rate is calculated as (1 - the actual number of coordinate points acquired / the theoretical number of coordinate points to be acquired) × 100%.

[0061] In this embodiment of the invention, the location fluctuation is determined as the standard deviation of the straight-line distance between all adjacent location coordinate points on the cargo movement trajectory within the data acquisition time window.

[0062] In this embodiment of the invention, the data acquisition time window ranges from [5 seconds to 30 seconds], preferably 10 seconds. The data missing rate is the ratio of the number of actual acquired coordinate points to the preset number of acquired coordinate points. The preset number of acquired coordinate points ranges from [5 to 60], preferably 10. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the values ​​according to actual needs.

[0063] Please see Figure 5 As shown, it is a logic judgment diagram for determining whether the overlap rate meets the standard in an embodiment of the present invention.

[0064] Specifically, the position calibration module calculates the overlap rate between several position coordinate points of the redundant positioning source and the trajectory, compares it with a preset overlap rate to determine whether the overlap rate meets the standard, and determines the position coordinate update amount and updates it based on the overlap rate not meeting the standard. If the overlap rate is greater than or equal to the preset overlap rate, then the overlap rate is determined to meet the standard. If the overlap rate is less than the preset overlap rate, then the overlap rate is determined to be substandard. In response to the overlap rate not meeting the standard, the location source with the highest overlap rate is selected as the reference source from a number of redundant location sources. The update amount of the location coordinates is the difference between the latest location coordinates obtained by the reference source at the end of the data acquisition time window and the currently used location coordinates; Then update the currently used position coordinates to the latest position coordinates of the reference source.

[0065] In this embodiment of the invention, the overlap rate is calculated by several redundant positioning sources based on several vertical distances from each location coordinate point provided within the data acquisition time window to the cargo trajectory, the number of location coordinate points with vertical distances less than a preset distance is counted, and the percentage of the number of location coordinate points in the total number of coordinate points provided by several redundant positioning sources is calculated.

[0066] In this embodiment of the invention, the preset overlap rate is set to [80%, 95%], preferably 85%, and the preset distance is set to [10m, 50m], preferably 20m. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the values ​​according to actual needs.

[0067] Specifically, this invention constructs a multi-source positioning data fusion and verification mechanism through a position calibration module. This effectively solves the problem of blind spots in position monitoring caused by the failure or accuracy degradation of a single positioning source signal during cold chain logistics transportation. When an abnormal cargo status is detected, redundant positioning sources can be activated to collect position data in parallel. Through multi-track comparison and data integrity assessment, the most reliable baseline trajectory is selected, ensuring the continuity and accuracy of position monitoring data. By introducing a quantitative evaluation system for overlap rate, the signal quality of each positioning source can be objectively identified. When the overlap rate is found to be substandard, the system switches to the positioning source with the best performance, achieving accurate calibration and real-time updates of position coordinates. This intelligent calibration strategy based on multi-source data cross-validation improves the system's adaptability and reliability in complex transportation environments, effectively avoiding monitoring failures caused by positioning signal drift, obstruction, or equipment malfunction. It provides a solid technical guarantee for the full traceability and risk warning of cold chain logistics, thereby significantly improving the safety and management efficiency of cargo transportation.

[0068] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A cargo location detection system for cold chain logistics, characterized in that, include, The data acquisition module is used to acquire the location and status data of the goods in real time. The location prediction module is used to determine the trajectory of the cargo based on the cargo's location data, predict its location coordinates for the next stage, and calculate the matching degree between the predicted location coordinates and the real-time location coordinates to determine whether the matching degree is qualified. A location warning module is used to determine a location warning value based on the unqualified matching degree; The strategy determination module responds to the warning value and determines the path deviation based on the running trajectory and the preset standard transportation path to determine the warning strategy. It also obtains the data credibility based on the signal source of the location data and the current refresh frequency to determine whether the cargo transportation path is compliant. The cargo status module determines whether the cargo status is acceptable based on the comparison between the number of cargo collisions and the preset number of collisions, based on the status data, if the cargo transportation route is non-compliant. The position calibration module, in response to the cargo status being unqualified, activates a redundant positioning source to obtain several position coordinates for the next stage, so as to form a cargo trajectory based on the several position coordinates, calculate the overlap rate between the several position coordinate points of the redundant positioning source and the trajectory, compare it with a preset overlap rate to determine whether the overlap rate meets the standard, and determine the position coordinate update amount and update it based on the overlap rate not meeting the standard.

2. The cargo location detection system for cold chain logistics according to claim 1, characterized in that, The location prediction module determines the predicted position coordinates of the cargo in the next stage as the sum of the predicted displacement vector and the latest acquired position coordinate vector, wherein, The predicted displacement vector is the product of the moving velocity vector and the acquisition time interval, and the moving velocity vector is the ratio of the displacement vector to the acquisition time interval.

3. The cargo location detection system for cold chain logistics according to claim 2, characterized in that, The location prediction module determines that the matching degree is unqualified based on the matching degree being less than a preset matching degree threshold. The matching degree is the straight-line distance between the predicted location coordinates and the real-time location coordinates.

4. The cargo location detection system for cold chain logistics according to claim 1, characterized in that, The strategy determination module determines that the cargo transportation route is non-compliant based on the fact that the path deviation is greater than a preset path deviation and the data credibility is greater than a preset data credibility threshold.

5. The cargo location detection system for cold chain logistics according to claim 4, characterized in that, The strategy determination module determines the data credibility as the product of a preset baseline credibility and a frequency ratio factor, wherein, The preset benchmark reliability is determined based on the acquired satellite positioning signal; The frequency ratio factor is determined based on the ratio of the current refresh frequency to the standard refresh frequency of the preset benchmark confidence level, and the minimum value compared with the preset frequency ratio is determined.

6. The cargo location detection system for cold chain logistics according to claim 4, characterized in that, The cargo status module determines that the cargo status is unqualified based on the non-compliance of the cargo transportation route and the fact that the number of cargo collisions exceeds the preset number of collisions.

7. The cargo location detection system for cold chain logistics according to claim 6, characterized in that, In response to a cargo status defect, the position calibration module uses the cargo movement trajectory with the highest data quality score in the cargo movement trajectory as the cargo trajectory, wherein... Enable all available redundant positioning sources and collect the position coordinate data of several positioning sources in parallel at a standard refresh rate within the preset data acquisition time window; By connecting the location coordinates collected by several positioning sources in a continuous time series in chronological order, the cargo movement trajectory corresponding to several positioning sources is constructed.

8. The cargo location detection system for cold chain logistics according to claim 1, characterized in that, The position calibration module determines that the overlap rate is not up to standard based on the fact that the overlap rate is less than the preset overlap rate.

9. The cargo location detection system for cold chain logistics according to claim 1, characterized in that, In response to the overlap rate not meeting the standard, the position calibration module updates the position coordinates by the difference between the latest position coordinates acquired by the reference source at the end of the data acquisition time window and the currently used position coordinates. From several redundant positioning sources, the positioning source with the highest overlap rate is selected as the reference source.

10. The cargo location detection system for cold chain logistics according to claim 9, characterized in that, The position calibration module updates the currently used position coordinates to the latest position coordinates of the reference source.

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