Cargo dynamic tracking system and method adopting satellite accurate positioning

Abstract

The invention discloses a cargo dynamic tracking system and method adopting satellite precise positioning, and particularly relates to the technical field of cargo dynamic tracking, and the method comprises the following steps: collecting cargo positioning data in real time, carrying out the precision analysis, and judging whether an early sign of insufficient positioning precision exists or not; if an early sign exists, dynamic tracking features are extracted, the monitoring condition is evaluated, a feature data set is formed, a pre-trained machine learning model is input, and low-quality tracking, medium-quality tracking and high-quality tracking are classified according to the signal environment and path stability; for high-quality and low-quality tracking, a positioning mode is optimized, so that the precision is kept, the system efficiency is improved, and the accuracy and stability of dynamic goods tracking are ensured; according to the method, the positioning signal quality and the path stability can be ensured by flexibly adjusting the positioning mode, so that the cargo tracking precision is improved, meanwhile, the use of a redundancy technology is reduced, unnecessary errors and calculation burden are avoided, and the response speed and the real-time performance of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cargo dynamic tracking, and more specifically, to a cargo dynamic tracking system and method using satellite precise positioning. Background Art

[0002] With the increasing development of modern logistics and cargo transportation, accurate dynamic tracking systems have played a vital role in improving cargo transportation efficiency, reducing transportation costs and improving customer satisfaction. Especially in complex environments (such as urban canyons, tunnels, underground parking lots, etc.), how to ensure the positioning accuracy and stability of cargo has become a challenge for modern cargo tracking technology.

[0003] Traditional cargo dynamic tracking methods mainly rely on a single positioning technology, such as the Global Navigation Satellite System (GNSS), Wireless Local Area Network (Wi-Fi), Ultra-Wideband (UWB), etc. These technologies have their own advantages, but a single technology will be limited under certain environmental conditions (such as signal blocking, interference, etc.). In order to solve this problem, the industry has gradually introduced multi-modal fusion positioning technology, which combines the advantages of multiple positioning technologies to improve positioning accuracy and stability.

[0004] However, although multimodal fusion positioning technology can improve positioning accuracy, it also brings problems such as increased computing burden and increased energy consumption. Especially in the scenario of high-quality tracking, when the positioning accuracy is sufficient, redundant positioning technology will lead to unnecessary waste of computing resources. Therefore, how to intelligently optimize the positioning method under different tracking quality conditions has become an urgent problem to be solved in the current cargo dynamic tracking technology. To this end, the present invention adopts a cargo dynamic tracking system and method with satellite precise positioning in order to solve the above problems. Summary of the invention

[0005] To achieve the above object, the present invention provides the following technical solutions: The cargo dynamic tracking method using satellite precise positioning includes the following steps: During the transportation of goods, the positioning data of the goods is collected in real time to obtain a positioning data set; Perform positioning accuracy analysis based on positioning data, and use the positioning accuracy analysis results to determine whether there are early signs of insufficient positioning accuracy during the cargo transportation process; When there are early signs of insufficient positioning accuracy, dynamic tracking feature extraction is performed, and then the monitoring of cargo dynamic tracking is evaluated to provide a feature data set for subsequent current dynamic tracking quality classification. The feature data set is imported into a pre-trained machine learning model, and the monitoring of cargo dynamic tracking is classified into low-quality tracking, medium-quality tracking, or high-quality tracking according to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path; For the monitoring of high-quality tracking and low-quality tracking, the current positioning method is optimized to keep the cargo positioning accuracy at a standard state, and the optimized positioning method is used to dynamically track the cargo, store the tracking data, and achieve traceability and remote monitoring.

[0006] In a preferred embodiment, performing positioning accuracy analysis based on positioning data refers to: In the current time window, the average deviation between the current positioning data and the reference position inferred based on the historical trajectory is calculated to measure the overall positioning accuracy and obtain the average offset error value; the standard deviation of the rate of change of the cargo movement speed at adjacent time points in the current time window is calculated to obtain the speed jump value; the standard deviation of the time interval between two consecutive positioning data in the current time window is calculated to obtain the sampling jump value, and then the average offset error value, speed jump value and sampling jump value are summarized together as the actual positioning vector.

[0007] In a preferred embodiment, judging whether there are early signs of insufficient positioning accuracy during cargo transportation through the positioning accuracy analysis result refers to: The actual positioning vector is compared with the preset standard vector, the Euclidean distance between the actual positioning vector and the preset standard vector is calculated, and the Euclidean distance is compared with the preset warning threshold. If the Euclidean distance is greater than the preset warning threshold, it is determined that there are early signs of insufficient positioning accuracy. If the Euclidean distance is less than or equal to the preset warning threshold, it is determined that there are no early signs of insufficient positioning accuracy.

[0008] In a preferred embodiment, the cargo dynamic tracking feature extraction includes positioning environment information and positioning stability information, and then a spatial signal environment index is generated based on the positioning environment information, and the spatial signal environment index is used to evaluate the degree of influence of the environment in which the cargo is located during transportation on the positioning signal; a signal path integrity index is generated based on the positioning stability information, and the signal path integrity index is used to evaluate the stability of the positioning data path of the cargo during transportation; the spatial signal environment index and the signal path integrity index are used together to reflect the monitoring status of cargo dynamic tracking.

[0009] In a preferred embodiment, the logic for obtaining the spatial signal environment index is: During the cargo transportation process, the following four types of environmental information are collected based on the current positioning method: received signal strength coefficient, available signal quality coefficient, signal interference coefficient and signal drift coefficient. In order to ensure that the data of different sensors can be processed uniformly, the received signal strength coefficient, available signal quality coefficient, signal interference coefficient and signal drift coefficient are normalized respectively: The minimum-maximum normalization method is used for the received signal strength coefficient: ; is the normalized result of the received signal strength coefficient corresponding to the positioning mode type i, is the received signal strength coefficient corresponding to positioning mode type i, , is the minimum and maximum values ​​of the standard received signal strength coefficient corresponding to positioning mode type i; The available signal quality coefficients are normalized using a proportional approach: ; is the normalized result of the available signal quality coefficient corresponding to the positioning mode type i, is the available signal quality coefficient corresponding to positioning mode type i, is the standard available signal quality coefficient value corresponding to positioning mode type i; The signal interference coefficient is processed by exponential decay normalization: ; is the normalized result of the signal interference coefficient corresponding to the positioning mode type i, is the signal interference coefficient corresponding to the positioning mode type i; The signal drift coefficient is normalized using exponential decay: ; is the normalized result of the signal drift coefficient corresponding to the positioning mode type i, is the signal interference coefficient corresponding to the positioning mode type i; The calculation formula of spatial signal environment value is: ; is the spatial signal environment value, is the total number of positioning methods in the current time window, , , and are all preset non-zero scale factors, is the preset exponential weight, with a value range of [0.5, 2]. Finally, all spatial signal environment values ​​in the current time window are averaged to obtain the spatial signal environment index. .

[0010] In a preferred embodiment, the logic for obtaining the signal path integrity index is: In the current time window, the difference between the data receiving time corresponding to time t and the expected receiving time is calculated to obtain the signal delay value corresponding to time t; the time proportion of signal loss in the sub-time period to which time t belongs is calculated to obtain the signal loss degree corresponding to time t; the ratio of the deviation between two adjacent positioning points at time t and time t-1 to the maximum allowable deviation is calculated to obtain the data consistency corresponding to time t; The signal path integrity index is calculated as: ; i represents the number of the positioning method type, is the total number of positioning methods in the current time window, It represents the standard deviation of all signal delay values ​​corresponding to positioning mode type i in the current time window. It represents the standard deviation of all signal loss corresponding to positioning mode type i in the current time window. It represents the standard deviation of the consistency of all data corresponding to the positioning method type i in the current time window. , These are the preset influence coefficients. Represents the signal path integrity index.

[0011] In a preferred embodiment, the machine learning model is a convolutional neural network model, and the feature data set, namely the spatial signal environment index and the signal path integrity index, is imported into a pre-trained convolutional neural network model. According to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path, the convolutional neural network model divides the monitoring status of dynamic tracking of goods into low-quality tracking, medium-quality tracking or high-quality tracking.

[0012] In a preferred embodiment, for the monitoring of high-quality tracking and low-quality tracking, fuzzy reasoning is used to optimize the current positioning method, and the spatial signal environment index, the signal path integrity index, and the positioning method under the current time window are used as input variables of fuzzy logic. The optimized positioning method is used as the output variable of fuzzy logic, the input variables are fuzzified, and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the adaptability of each positioning method type under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the optimized positioning method and apply it.

[0013] In a preferred embodiment, a cargo dynamic tracking system using satellite precise positioning includes: The data collection module collects the positioning data of the goods in real time during the transportation of the goods to obtain the positioning data set; The positioning accuracy analysis module performs positioning accuracy analysis based on positioning data and determines whether there are early signs of insufficient positioning accuracy during cargo transportation through the positioning accuracy analysis results; The dynamic tracking feature extraction module performs dynamic tracking feature extraction when there are early signs of insufficient positioning accuracy, and then evaluates the monitoring status of cargo dynamic tracking to provide a feature data set for subsequent current dynamic tracking quality classification; The dynamic tracking quality classification module imports the feature data group into the pre-trained machine learning model, and classifies the monitoring status of cargo dynamic tracking into low-quality tracking, medium-quality tracking or high-quality tracking according to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path; The positioning method optimization module optimizes the current positioning method under the monitoring conditions of high-quality tracking and low-quality tracking, so that the cargo positioning accuracy remains at the standard state, and uses the optimized positioning method to dynamically track the cargo.

[0014] Technical effects and advantages of the present invention: The present invention combines GNSS, Wi-Fi, UWB and other positioning technologies to provide accurate positioning services in different environments by taking advantage of their respective advantages. For high-quality tracking, sufficient accuracy can be achieved by using a single or a few technologies, avoiding the use of redundant technologies, thereby avoiding unnecessary errors and computational burdens. In complex environments (such as tunnels, underground parking lots, urban canyon effects, etc.), the system can ensure the quality of positioning signals and path stability by flexibly adjusting the positioning method, thereby improving the accuracy of cargo tracking.

[0015] The present invention can optimize computing resources and reduce energy consumption, reduce redundant positioning methods, and use intelligent optimization methods to streamline positioning methods when the system determines that the current positioning accuracy has met the requirements. For example, in the case of high-quality tracking, efficient combinations such as GNSS+INS are used to replace UWB and Wi-Fi, thereby reducing unnecessary computing burden and power consumption. Through this optimization, the system can not only maintain high-precision positioning, but also significantly reduce energy consumption and extend the service life of the equipment. Intelligent adjustment of positioning method: Through fuzzy reasoning, the system can dynamically adjust the positioning method so that each positioning technology can be used when necessary, avoiding the system from opening multiple positioning methods at the same time when it is not necessary, thereby optimizing the computing burden and resource consumption.

[0016] The present invention improves the system response speed and real-time performance, reduces the computational burden, and can significantly reduce the computational burden and improve the system response speed by reducing redundant technologies and optimizing positioning methods. For high-quality tracking, the optimization of positioning methods can be adjusted through real-time data, so that the system can make the best decision in a shorter time and provide fast and efficient dynamic tracking services.

[0017] By combining the spatial signal environment index and the signal path integrity index, the present invention can monitor changes in the environment where the goods are located in real time and automatically adjust the positioning strategy according to these changes. When the environmental signal changes, the system can quickly switch to the most appropriate combination of positioning technologies to adapt to fluctuations in environmental conditions and ensure that cargo tracking is always in the best state. Since the system can dynamically adjust the positioning technology according to environmental changes, it has stronger robustness against various interference sources (such as signal shielding, electromagnetic interference, etc.) and can maintain stable and reliable positioning performance in complex and uncertain environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a schematic diagram of the method for dynamic cargo tracking using satellite precise positioning in the present invention.

[0019] Figure 2 This is a schematic diagram of the cargo dynamic tracking system using satellite precise positioning in the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 - Figure 2 The following embodiments are obtained: Embodiment 1: In the process of modern cargo transportation, especially in complex environments, positioning technology is widely used in cargo tracking and management. Traditional cargo tracking systems mainly rely on a single positioning technology, such as GNSS (Global Navigation Satellite System) or Wi-Fi. However, these single technologies often show insufficient accuracy when facing signal interference, obstruction (such as tunnels, basements, urban canyon effects, etc.) and environmental changes. In addition, with the diversification of transportation environments and types of goods, existing tracking technologies have the following problems: Fluctuation of positioning accuracy: In complex environments, positioning signals may be affected by multipath effects or signal obstruction, resulting in fluctuations in positioning accuracy and affecting cargo tracking. Signal loss: Signal loss or instability leads to missing positioning data, reducing the reliability of the tracking system. Poor path stability: Even if multiple positioning methods are used, large path deviations may still occur without an effective path optimization mechanism.

[0022] In order to solve the above problems and improve the accuracy and stability of the cargo dynamic tracking system, the present invention proposes a dynamic tracking optimization system based on multimodal positioning. The system automatically adjusts the positioning mode by comprehensively considering the characteristics of different positioning methods, the influence of environmental signals and path stability, and optimizes the positioning mode under tracking conditions of different qualities. Specifically, the reasons for proposing the present invention include: Dealing with positioning problems in complex environments: Different positioning methods have different performances in different environments. For example, GNSS signals may be lost in tunnels or dense urban environments, while Wi-Fi and UWB perform better in these environments. By optimizing multi-modal positioning methods, positioning errors can be minimized.

[0023] Improve system flexibility and reliability: Dynamically adjust positioning methods based on environmental impact and path stability, so that the system can adjust its working mode according to real-time positioning data, avoiding the problem that a single positioning method cannot cope with complex situations.

[0024] Automated tracking optimization: Traditional cargo tracking systems often rely on manual intervention to adjust equipment or strategies, while the present invention uses machine learning and fuzzy reasoning technology to automatically optimize positioning methods, reduce manual intervention, and improve system efficiency and accuracy.

[0025] The present invention aims to improve the performance of the cargo dynamic tracking system through the following aspects: Achieve the integration of multi-modal positioning methods: In scenes such as tunnels, underground parking lots, urban high-rise dense areas (urban canyon effect), and warehouse interiors, GNSS signals are easily blocked or interfered by multipath, resulting in reduced positioning accuracy or even signal loss. Combine multi-modal fusion positioning technologies such as inertial navigation (INS), Wi-Fi positioning, Bluetooth beacons, and UWB (ultra-wideband) to enhance tracking capabilities in complex environments. Integrate multiple positioning technologies such as GNSS, INS, Wi-Fi, and UWB, and use their respective advantages to improve positioning accuracy and stability.

[0026] Provide dynamic tracking quality classification: Through machine learning models and fuzzy reasoning to optimize positioning methods, the system can automatically judge the tracking quality according to environmental changes and classify it into low-quality, medium-quality or high-quality tracking.

[0027] Optimize positioning: Whether it is high-quality or low-quality tracking, the system can optimize the positioning method in real time based on the environmental signal quality and path stability to ensure that the cargo positioning accuracy remains at the standard state.

[0028] Achieve traceability and remote monitoring: The optimized positioning method can track goods in real time and store data on the cloud or blockchain to ensure data traceability and security.

[0029] Optimization of low-quality tracking: When the monitoring results of the cargo dynamic tracking system are of low quality, it indicates that there are large errors in the positioning signal or the path is unstable, which may affect subsequent transportation management and cargo tracking. In this case, it is necessary to optimize the positioning method. For example, if a single GNSS positioning is currently used and the signal is unstable or lost, the system needs to switch to a multi-modal positioning method (such as GNSS+INS+UWB) to reduce errors and improve stability.

[0030] In the case of high-quality tracking, the positioning accuracy usually meets the requirements. In this case, there is no need to enable multiple positioning methods at the same time, but the positioning methods should be streamlined to avoid unnecessary calculations and resource consumption. For example, if GNSS+INS already provides sufficiently accurate positioning, there is no need to enable UWB or Wi-Fi, which may bring additional computing burden and energy consumption.

[0031] Improve energy efficiency: Multiple positioning methods (such as UWB, Wi-Fi, GNSS, etc.) may require high power and computing resources. If high-quality tracking is already achieved through a single or a few technologies, it is necessary to reduce energy consumption and extend the battery life of the device by reducing redundant technologies.

[0032] Reduce computational burden: Although multimodal fusion positioning can improve accuracy, it will also increase the computational burden, especially in real-time tracking systems. Optimizing the selection of positioning methods for high-quality tracking can reduce the computational burden while maintaining accuracy and improve system response speed.

[0033] Based on this, the present invention proposes the following content: A cargo dynamic tracking method using satellite precise positioning includes the following steps: During the transportation of goods, the positioning data of the goods is collected in real time to obtain the positioning data set; real-time collection of positioning data of goods is the basis of dynamic tracking. By collecting positioning information containing timestamps, location coordinates, speed and other data, the location changes of the goods can be understood in real time to ensure accurate tracking of the transportation process.

[0034] Perform positioning accuracy analysis based on positioning data. Use the positioning accuracy analysis results to determine whether there are early signs of insufficient positioning accuracy during cargo transportation. By analyzing positioning data, evaluate the current positioning accuracy and ensure the accuracy of positioning information. Accuracy analysis helps determine whether positioning is interfered with by environmental factors (such as signal loss, occlusion, etc.) and whether the system can provide reliable positioning results. If the accuracy analysis shows that the positioning data has large deviations or instability, by determining whether there are signs of insufficient accuracy, potential system problems can be warned in advance. Early detection of positioning problems can help take measures to avoid tracking failures or reduced accuracy.

[0035] When there are early signs of insufficient positioning accuracy, dynamic tracking feature extraction is performed, and then the monitoring of cargo dynamic tracking is evaluated to provide a feature data group for the subsequent current dynamic tracking quality classification. The feature data group is imported into a pre-trained machine learning model. According to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path, the monitoring of cargo dynamic tracking is divided into low-quality tracking, medium-quality tracking or high-quality tracking; when it is determined that the positioning accuracy is insufficient, the current tracking status is further analyzed by extracting dynamic tracking features. The extracted features provide data support for subsequent quality evaluation and optimization. The quality of cargo dynamic tracking is understood by evaluating the extracted features. This evaluation helps to judge the current tracking status (low quality, poor stability, or high quality, strong stability) and provide a basis for subsequent optimization decisions. Based on the feature data group (such as positioning accuracy, path stability), the cargo dynamic tracking situation is classified as low quality, medium quality or high quality. The classification results help the system identify and solve positioning problems and ensure the accuracy of cargo tracking. Data classification through machine learning models can automatically evaluate tracking quality. The pre-trained model can judge the quality of cargo tracking based on historical data and patterns and give reasonable classification results.

[0036] For the monitoring of high-quality and low-quality tracking, the current positioning method is optimized to keep the positioning accuracy of the goods at the standard state, and the optimized positioning method is used to dynamically track the goods, and the tracking data is stored to achieve traceability and remote monitoring. According to the classification results, the low-quality or high-quality tracking is optimized to ensure the positioning accuracy. If low-quality tracking is found, the system will automatically switch to a more stable positioning method. If it is high-quality tracking, unnecessary positioning methods can be reduced to save resources. With the support of the optimized positioning method, the cargo tracking is continued to ensure the tracking accuracy and stability. The optimized positioning method can avoid excessive consumption of resources while maintaining high accuracy and improve system efficiency. The tracking data of the goods is stored in the cloud or blockchain to ensure the security, integrity and traceability of the data. The stored tracking data can not only ensure real-time monitoring, but also provide historical data support for subsequent cargo transportation analysis, problem tracing and optimization.

[0037] Positioning accuracy analysis based on positioning data means: within the current time window, calculating the average deviation between the current positioning data and the reference position inferred based on the historical trajectory, measuring the overall positioning accuracy, and obtaining the average offset error value; calculating the standard deviation of the rate of change of the cargo movement speed at adjacent time points within the current time window, and obtaining the speed jump value; calculating the standard deviation of the time interval between two consecutive positioning data within the current time window, and obtaining the sampling jump value, and then summarizing the average offset error value, speed jump value, and sampling jump value into the actual positioning vector.

[0038] The overall accuracy of the positioning system can be evaluated by calculating the average deviation between the current positioning data and the reference position inferred from the historical trajectory. If the deviation is large, it means that the error of the current positioning system is high, which may affect the accurate tracking of the goods. This value reflects the long-term stability of the system and helps evaluate the effectiveness of the current positioning method.

[0039] Calculate the speed jump value: The standard deviation of the rate of change of the cargo movement speed at adjacent time points can reveal the degree of change in the acceleration or deceleration of the cargo during transportation. Excessive speed fluctuations may indicate signal problems, environmental interference, or sensor failure. The speed jump value helps to determine whether there are abnormal positioning data fluctuations, which in turn affects the stability of positioning accuracy.

[0040] Calculate the sampling jump value: The standard deviation of the time interval between two consecutive positioning data can reflect the discontinuity in the sampling process. Excessive time interval changes may indicate discontinuous data collection, resulting in positioning errors. This value helps to determine whether the sampling process is stable, whether there is data loss or the sampling interval is too long.

[0041] Aggregating the above three indicators (average offset error, speed jump value, sampling jump value) into an actual positioning vector can more comprehensively reflect the current status of the cargo positioning system. The actual positioning vector provides a comprehensive evaluation in multiple dimensions to help the system comprehensively judge the quality of positioning accuracy.

[0042] Judging whether there are early signs of insufficient positioning accuracy during cargo transportation through positioning accuracy analysis results means: comparing the actual positioning vector with a preset standard vector, calculating the Euclidean distance between the actual positioning vector and the preset standard vector, and comparing the Euclidean distance with a preset warning threshold. If the Euclidean distance is greater than the preset warning threshold, it is determined that there are early signs of insufficient positioning accuracy. If the Euclidean distance is less than or equal to the preset warning threshold, it is determined that there are no early signs of insufficient positioning accuracy.

[0043] By calculating the Euclidean distance between the actual positioning vector and the preset standard vector, the system can quantify the difference between the current positioning accuracy and the standard. The Euclidean distance provides a mathematical measurement method that makes the judgment of positioning accuracy more objective and intuitive. The preset warning threshold defines the maximum deviation range tolerated by the system. When the Euclidean distance exceeds the warning threshold, the system will consider that the positioning accuracy is insufficient. At this time, warnings and subsequent measures (such as switching positioning methods, enhancing positioning signals, etc.) are required. If the Euclidean distance is less than or equal to the warning threshold, it means that the positioning accuracy is good and the system does not need to intervene. By comparing the Euclidean distance between the actual positioning vector and the standard vector in real time, the system can promptly identify whether there are early signs of insufficient positioning accuracy. It is conducive to taking corresponding optimization measures in advance, such as adjusting the positioning method, to ensure the accuracy and reliability of cargo positioning.

[0044] The feature extraction of cargo dynamic tracking includes positioning environment information and positioning stability information. Then, a spatial signal environment index is generated based on the positioning environment information. The spatial signal environment index is used to evaluate the influence of the environment in which the cargo is located during transportation on the positioning signal. A signal path integrity index is generated based on the positioning stability information. The signal path integrity index is used to evaluate the stability of the positioning data path of the cargo during transportation. The spatial signal environment index and the signal path integrity index are used together to reflect the monitoring situation of cargo dynamic tracking.

[0045] Cargo dynamic tracking feature extraction is a core step in the entire cargo tracking system. Its significance lies in evaluating the positioning signal quality and path stability of cargo during transportation through comprehensive analysis of positioning environment information and positioning stability information. This process provides key information for subsequent tracking quality evaluation and positioning method optimization.

[0046] Positioning environment information refers to the impact of the environment in which the goods are transported on the positioning signal, such as signal strength, available signal quality, interference sources, etc. This information helps the system evaluate how the external environment affects positioning accuracy. For example, in complex environments such as tunnels and urban canyons, the signal may be blocked, affecting positioning accuracy.

[0047] Positioning stability information refers to the data consistency during the dynamic tracking of goods, including the stability of positioning signals, path stability, etc. Stability information evaluates whether the path is continuous and the signal is stable, which helps to identify unstable positioning data and thus improve tracking quality.

[0048] The Spatial Signal Environment Index (SSEI) is a key indicator generated based on positioning environment information, which is used to quantify the impact of the environment on the quality of positioning signals. Assess the extent to which the signal is affected by the environment: By integrating multi-dimensional environmental information such as signal strength, available signal quality, interference conditions and signal drift, SSEI provides an intuitive quantitative result to measure the stability and quality of the signal under environmental conditions. Decision support: The higher the SSEI value, the less impact the environment in which the goods are located has on the positioning signal, and the higher the positioning accuracy. Conversely, it means that environmental factors have a greater impact on the positioning signal, and it may be necessary to adjust the positioning method or optimize the sensor configuration. Optimize positioning solutions: According to the SSEI, the system can dynamically adjust the positioning method and use multi-modal positioning (such as GNSS+INS+UWB) in unfavorable environments. In good environments, the system can reduce the use of redundant positioning technologies and save computing resources.

[0049] The Signal Path Integrity Index (SPII) is an important indicator generated based on positioning stability information, which is used to evaluate the stability of the positioning data path. Evaluating data path stability: SPII reflects the continuity and consistency of data during the positioning process. A higher SPII value indicates that the path stability of the positioning data is better, there is no data loss or significant drift, and the positioning system can maintain a high level of accuracy. Detecting abnormal fluctuations: If the SPII value is low, it means that there are large fluctuations, data loss or inconsistencies in the path, and the system needs to take timely measures (such as switching positioning methods, compensating for missing data, etc.) to maintain tracking quality. Ensuring tracking quality: The analysis results of SPII directly affect the system's judgment on the quality of the positioning signal, and thus affect the subsequent positioning method selection and optimization strategy. A stable path can ensure the efficiency and accuracy of cargo tracking.

[0050] The Space Signal Environment Index (SSEI) and the Signal Path Integrity Index (SPII) together reflect the monitoring of cargo dynamic tracking: Comprehensive assessment of tracking quality: SSEI mainly assesses the impact of environmental factors on positioning signals, while SPII assesses the stability and continuity of positioning signals. The combination of the two can comprehensively assess the quality of cargo tracking. SSEI provides environmental judgments, while SPII provides data stability judgments. The combination of the two can help the system better classify quality and optimize decisions. Through the evaluation of SSEI and SPII, the system can more accurately classify the quality of current tracking (low quality, medium quality, high quality) and take corresponding optimization measures based on these indicators. For example, if both SSEI and SPII indicate poor signal quality, the system will give priority to high-precision positioning methods, such as combining multiple positioning technologies (GNSS+INS+UWB) to improve tracking accuracy.

[0051] The logic for obtaining the spatial signal environment index is: During the cargo transportation process, the following four types of environmental information are collected based on the current positioning method: received signal strength coefficient, available signal quality coefficient, signal interference coefficient and signal drift coefficient. In order to ensure that the data of different sensors can be processed uniformly, the received signal strength coefficient, available signal quality coefficient, signal interference coefficient and signal drift coefficient are normalized respectively: Different positioning methods (such as GNSS, Wi-Fi, UWB, etc.) may have different ranges, units, and accuracy when collecting data in different environments. Through normalization, various data can be converted to a unified scale for comprehensive evaluation and comparison. The measurement of different signals (for example, the units of signal strength may be different) may affect the final result. Normalization can remove these unit differences so that various environmental information can be calculated under the same standard. After normalization, each type of coefficient will contribute to the final spatial signal environment index in proportion, so that the impact of each factor on the final index is under the same standard.

[0052] The minimum-maximum normalization method is used for the received signal strength coefficient: ; is the normalized result of the received signal strength coefficient corresponding to the positioning mode type i, is the received signal strength coefficient corresponding to positioning mode type i, , is the minimum and maximum values ​​of the standard received signal strength coefficient corresponding to positioning mode type i; The available signal quality coefficients are normalized using a proportional approach: ; is the normalized result of the available signal quality coefficient corresponding to the positioning mode type i, is the available signal quality coefficient corresponding to positioning mode type i, is the standard available signal quality coefficient value corresponding to positioning mode type i; The signal interference coefficient is processed by exponential decay normalization: ; is the normalized result of the signal interference coefficient corresponding to the positioning mode type i, is the signal interference coefficient corresponding to the positioning mode type i; The signal drift coefficient is normalized using exponential decay: ; is the normalized result of the signal drift coefficient corresponding to the positioning mode type i, is the signal interference coefficient corresponding to the positioning mode type i; The calculation formula of spatial signal environment value is: ; is the spatial signal environment value, is the total number of positioning methods in the current time window, , , and are all preset non-zero scale factors, is the preset exponential weight, with a value range of [0.5, 2]. Finally, all spatial signal environment values ​​in the current time window are averaged to obtain the spatial signal environment index. . Different weight coefficients are assigned to different environmental information (received signal strength, available signal quality, interference, and drift), and the contribution of each factor to the environmental impact can be adjusted according to actual conditions. For example, signal interference may have a greater impact on positioning accuracy in some environments, while received signal strength may have a greater impact in areas with weak signals. The introduction of exponential weights (such as a range of 0.5 to 2) further enhances the nonlinear performance of the model, so that the impact of environmental factors on signal quality can be more consistent with the sensitivity in actual applications. For example, the changes in signal strength and interference may not be linear, and the use of exponential functions can better capture such changes.

[0053] The received signal strength coefficient is used to measure the strength of the signal received by each positioning method. In GNSS, Wi-Fi and UWB, the calculation method of signal strength is slightly different. The following is the calculation method of example positioning method: GNSS (Global Navigation Satellite System): In the GNSS system, the strength of the received satellite signal is usually expressed by the signal-to-noise ratio (SNR) or the received signal strength indication (RSSI). The signal strength can be obtained by measuring the ratio of the signal strength from the satellite to the noise signal (signal-to-noise ratio) from the satellite to the receiver. Calculation of the received signal strength coefficient: Signal strength coefficient = (received signal strength) / (standard signal strength of this positioning method).

[0054] Wi-Fi (Wireless Local Area Network): In Wi-Fi systems, received signal strength is usually evaluated by received signal strength indication (RSSI), and the RSSI value is usually proportional to the signal strength received by the device. Calculation of received signal strength coefficient: Signal strength coefficient = (Wi-Fi received signal strength) / (standard signal strength of the Wi-Fi positioning method) UWB (Ultra-Wideband): In UWB positioning systems, the measurement of signal strength is usually based on the strength of the received electromagnetic waves. Calculation of received signal strength coefficient: Signal strength coefficient = (received UWB signal strength) / (standard UWB signal strength).

[0055] The signal interference coefficient is used to evaluate the degree to which the signal is interfered with by external factors during transmission. In different positioning methods, the interference sources may be different, such as multipath effect, electromagnetic interference, etc.

[0056] In GNSS systems, signal interference is usually related to multipath effects (signal reflections) and electromagnetic interference. Interference coefficient calculation: signal interference coefficient = (interference strength of received signal) / (standard interference strength). Wi-Fi signal interference coefficient is usually affected by the surrounding environment, such as electromagnetic wave interference, the presence of physical obstacles such as walls. Interference coefficient calculation: signal interference coefficient = (interference of Wi-Fi received signal) / (standard Wi-Fi interference). UWB signal interference usually comes from interference or signal reflection from neighboring wireless devices. Interference coefficient calculation: signal interference coefficient = (interference strength of UWB signal) / (standard UWB interference).

[0057] The signal drift coefficient measures the degree of signal drift during transmission and is usually used for calculations in inertial navigation systems (INS), affecting positioning accuracy. In GNSS systems, drift may occur due to changes in satellite positions or equipment errors. Calculation of drift coefficient: Signal drift coefficient = (signal position change) / (standard drift value). The drift coefficient of a Wi-Fi system may be related to factors such as equipment error and moving speed. Calculation of drift coefficient: Signal drift coefficient = (change in Wi-Fi positioning data) / (standard Wi-Fi drift value). The drift coefficient of a UWB positioning system is related to sensor errors and signal propagation characteristics. Calculation of drift coefficient: Signal drift coefficient = (drift value of UWB signal) / (standard UWB drift value) The available signal quality coefficient reflects the quality of the signal available in the current environment, which is usually determined by factors such as signal stability, signal interference, and signal reliability. In GNSS systems, signal quality is usually related to the number of satellites received, signal stability, and the geometric distribution of satellites (such as DOP value). Calculation of signal quality coefficient: Available signal quality coefficient = (number of satellites received) / (maximum number of standard satellites). The signal quality of Wi-Fi systems is usually evaluated by signal stability and link quality. Link quality is calculated based on the signal strength of Wi-Fi access points, interference between access points, and link stability. Calculation of signal quality coefficient: Signal quality coefficient = (Wi-Fi connection stability) / (standard Wi-Fi stability). The quality of UWB positioning signals is usually related to signal integrity and reliability. The quality of UWB signals is affected by the transmission distance between devices, propagation obstacles, and interference sources. Calculation of signal quality coefficient: Signal quality coefficient = (effective reception time of UWB signals) / (standard UWB signal reception time).

[0058] It should be noted that, since it is difficult to calculate the instantaneous value when calculating some parameters, in the actual calculation process, the current time window can be divided into multiple sub-time windows of the same length, the parameter values ​​of the corresponding sub-time windows are calculated, and the instantaneous values ​​corresponding to the time points are replaced so that all calculation formulas are valid. For example, when calculating the signal drift coefficient, it is usually necessary to estimate it based on the continuity of the device (such as the integral error of the inertial sensor) or the long-term offset of the signal. If the instantaneous drift coefficient is calculated directly, it is easily affected by noise and may not reflect the actual long-term trend. Therefore, using the time window for smoothing and calculating the drift coefficient in each sub-time window can effectively reduce these errors. Specific steps: Divide the time window: divide the current time window into multiple sub-time windows of equal length, for example, each sub-time window is 1 second or 5 seconds. Calculate the drift coefficient of each sub-time window: calculate the signal drift in each sub-time window. Signal drift can be integrated based on the data output by the inertial sensor (INS) or calculated based on the offset of the positioning data. For example, the drift coefficient within a sub-time window can be obtained by averaging or weighting the signal deviation within the time period. Replace the instantaneous value: Use the average drift coefficient calculated in each sub-time window to replace the instantaneous drift value at that moment. In this way, the drift coefficient at each moment can more accurately reflect the long-term drift trend of the device, rather than relying solely on instantaneous data. In fact, the drift coefficients of all sub-time windows can also be smoothed (for example, by weighted averaging) to finally obtain a stable drift coefficient value. This process effectively reduces the error that may be caused by the instantaneous drift value and improves positioning accuracy.

[0059] The logic for obtaining the signal path integrity index is: In the current time window, the difference between the data receiving time corresponding to time t and the expected receiving time is calculated to obtain the signal delay value corresponding to time t; the time proportion of signal loss in the sub-time period to which time t belongs is calculated to obtain the signal loss degree corresponding to time t; the ratio of the deviation between two adjacent positioning points at time t and time t-1 to the maximum allowable deviation is calculated to obtain the data consistency corresponding to time t; The signal path integrity index is calculated as: ; i represents the number of the positioning method type, is the total number of positioning methods in the current time window, It represents the standard deviation of all signal delay values ​​corresponding to positioning mode type i in the current time window. It represents the standard deviation of all signal loss corresponding to positioning mode type i in the current time window. It represents the standard deviation of the consistency of all data corresponding to the positioning method type i in the current time window. , These are the preset influence coefficients. Represents the signal path integrity index. Signal path integrity analysis: Signal delay: Measures the delay from sending to receiving the signal, reflecting the time difference in signal transmission. When the delay is too large, the timeliness of the positioning data may be reduced, thereby affecting the accuracy of the entire positioning system. Signal loss: In actual applications, the signal may be lost or interrupted due to various factors (such as obstruction or interference). The lost signal will affect the stability and continuity of positioning. Data consistency: reflects the ratio of the deviation between adjacent positioning data points to the maximum allowable deviation. If the data deviation is too large, it indicates that the positioning path is highly instable.

[0060] , These are all preset influence coefficients, and the weight design reflects the relative importance of different factors to positioning accuracy and signal path stability. For example, signal loss and delay may have a greater impact on positioning accuracy, while data consistency affects the continuity and stability of the path. The standardized calculation of signal delay, signal loss, and data consistency allows these original indicators to be converted into a unified standard range, thereby avoiding calculation errors caused by different dimensions between different indicators and effectively quantifying the stability of the signal path.

[0061] The exponential part of the formula is the key to balancing the factors affecting the signal. The design of the exponential function enhances the nonlinear response of the signal path stability to the signal quality. It can make certain factors (such as large signal delay or signal loss) have a more significant impact on the final index, avoiding the excessive amplification of small interference. By summing and normalizing each weighted factor, it ensures that the weight of each factor is reasonable, and finally obtains a single value reflecting the stability of the signal path. In this formula, the impact of various parts of the signal (such as delay, loss, data consistency) is weighted and summed and then processed exponentially, reflecting the nonlinear impact. That is: the greater the factors such as signal delay and signal loss, the lower the final SPII value, indicating a higher degree of path instability. Through the comprehensive evaluation of different factors, the SPII indicator can accurately reflect the stability and reliability of the signal path during cargo positioning.

[0062] The machine learning model is a convolutional neural network model. The feature data set, namely the spatial signal environment index and the signal path integrity index, is imported into the pre-trained convolutional neural network model. According to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path, the convolutional neural network model divides the monitoring status of cargo dynamic tracking into low-quality tracking, medium-quality tracking or high-quality tracking.

[0063] The structural principle of convolutional neural network: Input layer: The input data is the spatial signal environment index and the signal path integrity index. These two feature data groups are the input of the convolutional neural network. The spatial signal environment index reflects the impact of the environment in which the goods are located on the positioning signal, and the signal path integrity index evaluates the stability of the positioning data. These two indexes can provide enough information to distinguish different tracking qualities. Convolutional layer: The convolutional layer is responsible for extracting useful features from the input data. Through the sliding window operation, the convolutional neural network can capture local features in the data, such as signal quality fluctuations, changes in path stability, etc. These local features help the convolutional neural network to identify and classify different types of cargo tracking quality, especially for those complex signal patterns that are difficult to simply describe with traditional methods. Pooling layer: The pooling layer is usually located after the convolutional layer. Its function is to reduce the dimensionality of the features output by the convolutional layer, reduce the amount of data and calculation, and retain important feature information. This helps to improve the generalization ability of the model and avoid overfitting problems. Fully connected layer: After the convolutional layer and the pooling layer have extracted enough features, the network passes these features to the fully connected layer. The fully connected layer combines the various feature information through multiple neurons and finally generates an output for classification. Output layer: The task of the output layer is to classify the tracking quality of the goods into low-quality tracking, medium-quality tracking, or high-quality tracking based on the input feature data. By learning the feature combination of different signal environments and path stability, the network can predict the current state based on historical data and give a classification of tracking quality.

[0064] For the monitoring of high-quality tracking and low-quality tracking, fuzzy reasoning is used to optimize the current positioning method. The spatial signal environment index, signal path integrity index, and positioning method under the current time window are used as input variables of fuzzy logic. The optimized positioning method is used as the output variable of fuzzy logic. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the adaptability of each positioning method under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the optimized positioning method and apply it.

[0065] Fuzzification of input variables: The spatial signal environment index and the signal path integrity index are important indicators that affect the positioning quality. Since these indices are usually continuous variables, we need to convert them into fuzzy sets, such as "low", "medium", and "high", so that the system can reason based on the fuzzy rule base.

[0066] Definition of fuzzy rules: Fuzzy rules are formulated based on system experience or data analysis to describe decisions under different input conditions. For example: If the spatial signal environment index is "high" and the signal path integrity index is "high", select "standard positioning method". If the spatial signal environment index is "low" and the signal path integrity index is "low", select "optimize the current positioning method". If the spatial signal environment index is "medium" and the signal path integrity index is "medium", select "maintain the current positioning method".

[0067] Outputs are derived based on fuzzy rules, and decisions are finally made through defuzzification. In the process of fuzzy reasoning, existing technologies such as trigonometric functions and maximum and minimum operations are often needed to complete specific operations. For example, trigonometric functions are mainly used to deal with nonlinear relationships between input variables and output variables in fuzzy reasoning. In some cases, the relationship between input variables may not be linear, and trigonometric functions provide a way to capture these changes. In fuzzy reasoning, maximum and minimum operations are important techniques for synthesizing the inference results of multiple fuzzy rules. The reasoning process in a fuzzy logic system usually involves multiple rules, and the output of each rule is a fuzzy set. In order to synthesize these multiple fuzzy outputs into the final decision result, maximum and minimum operations are usually used. Input variables (such as spatial signal environment index and signal path integrity index) are converted into fuzzy sets, and maximum and minimum operations can help determine the most appropriate membership values ​​during fuzzification. The final output fuzzy set (such as the optimized positioning method) needs to be converted into specific operations through the defuzzification process. The maximum and minimum method is usually used to aggregate the outputs of multiple fuzzy rules to ensure the most appropriate output results.

[0068] Assume that the system is tracking the location of a batch of goods and currently uses multimodal positioning (such as GNSS+INS+UWB). At this time, the system evaluation found that the spatial signal environment index is "high" and the signal path integrity index is "high". According to the optimization principle, the use of redundant technologies can be reduced by the following steps: Current positioning method: GNSS+INS+UWB: GNSS provides high-precision location data; INS (inertial navigation system) provides supplementary, especially in the case of unstable signals, to improve positioning accuracy; UWB (ultra-wideband) is used for more accurate short-range positioning, especially indoors or in blocked areas, but the accuracy improvement it brings is no longer obvious at this time. Current computing burden: Using three technologies to work simultaneously will increase the computing burden, especially when real-time data processing, the system needs to synchronously process data from multiple sensors. Optimized positioning method: GNSS+INS, you can turn off UWB to reduce unnecessary computing and resource consumption. This not only improves the response speed of the system, but also reduces battery consumption and extends the working time of the device.

[0069] Embodiment 2: A cargo dynamic tracking system using satellite precise positioning, comprising: The data collection module collects the positioning data of the goods in real time during the transportation of the goods to obtain the positioning data set; The positioning accuracy analysis module performs positioning accuracy analysis based on positioning data and determines whether there are early signs of insufficient positioning accuracy during cargo transportation through the positioning accuracy analysis results; The dynamic tracking feature extraction module performs dynamic tracking feature extraction when there are early signs of insufficient positioning accuracy, and then evaluates the monitoring status of cargo dynamic tracking to provide a feature data set for subsequent current dynamic tracking quality classification; The dynamic tracking quality classification module imports the feature data group into the pre-trained machine learning model, and classifies the monitoring status of cargo dynamic tracking into low-quality tracking, medium-quality tracking or high-quality tracking according to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path; The positioning method optimization module optimizes the current positioning method under the monitoring conditions of high-quality tracking and low-quality tracking, so that the cargo positioning accuracy remains at the standard state, and uses the optimized positioning method to dynamically track the cargo.

[0070] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0071] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A cargo dynamic tracking method using satellite precise positioning, characterized in that: The following steps are involved: During the transportation of goods, the positioning data of the goods is collected in real time to obtain a positioning data set; Perform positioning accuracy analysis based on positioning data, and use the positioning accuracy analysis results to determine whether there are early signs of insufficient positioning accuracy during the cargo transportation process; When there are early signs of insufficient positioning accuracy, dynamic tracking feature extraction is performed, and then the monitoring of cargo dynamic tracking is evaluated to provide a feature data set for subsequent current dynamic tracking quality classification. The feature data set is imported into a pre-trained machine learning model, and the monitoring of cargo dynamic tracking is classified into low-quality tracking, medium-quality tracking, or high-quality tracking according to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path; For the monitoring of high-quality tracking and low-quality tracking, the current positioning method is optimized to keep the cargo positioning accuracy at a standard state, and the optimized positioning method is used for dynamic tracking of cargo.

2. The cargo dynamic tracking method using satellite precise positioning according to claim 1 is characterized in that: Positioning accuracy analysis based on positioning data refers to: In the current time window, the average deviation between the current positioning data and the reference position inferred based on the historical trajectory is calculated to measure the overall positioning accuracy and obtain the average offset error value; the standard deviation of the rate of change of the cargo movement speed at adjacent time points in the current time window is calculated to obtain the speed jump value; the standard deviation of the time interval between two consecutive positioning data in the current time window is calculated to obtain the sampling jump value, and then the average offset error value, speed jump value and sampling jump value are summarized together as the actual positioning vector.

3. The cargo dynamic tracking method using satellite precise positioning according to claim 2 is characterized in that: The early signs of insufficient positioning accuracy during cargo transportation can be determined through the positioning accuracy analysis results: The actual positioning vector is compared with the preset standard vector, the Euclidean distance between the actual positioning vector and the preset standard vector is calculated, and the Euclidean distance is compared with the preset warning threshold. If the Euclidean distance is greater than the preset warning threshold, it is determined that there are early signs of insufficient positioning accuracy. If the Euclidean distance is less than or equal to the preset warning threshold, it is determined that there are no early signs of insufficient positioning accuracy.

4. The cargo dynamic tracking method using satellite precise positioning according to claim 3 is characterized in that: The feature extraction of cargo dynamic tracking includes positioning environment information and positioning stability information. Then, a spatial signal environment index is generated based on the positioning environment information. The spatial signal environment index is used to evaluate the influence of the environment in which the cargo is located during transportation on the positioning signal. A signal path integrity index is generated based on the positioning stability information. The signal path integrity index is used to evaluate the stability of the positioning data path of the cargo during transportation. The spatial signal environment index and the signal path integrity index are used together to reflect the monitoring situation of cargo dynamic tracking.

5. The cargo dynamic tracking method using satellite precise positioning according to claim 4 is characterized in that: The logic for obtaining the spatial signal environment index is: During the cargo transportation process, the following four types of environmental information are collected based on the current positioning method: received signal strength coefficient, available signal quality coefficient, signal interference coefficient and signal drift coefficient. In order to ensure that the data of different sensors can be processed uniformly, the received signal strength coefficient, available signal quality coefficient, signal interference coefficient and signal drift coefficient are normalized respectively: The minimum-maximum normalization method is used for the received signal strength coefficient: ; is the normalized result of the received signal strength coefficient corresponding to the positioning mode type i, is the received signal strength coefficient corresponding to positioning mode type i, , is the minimum and maximum values ​​of the standard received signal strength coefficient corresponding to positioning mode type i; The available signal quality coefficients are normalized using a proportional approach: ; is the normalized result of the available signal quality coefficient corresponding to the positioning mode type i, is the available signal quality coefficient corresponding to positioning mode type i, is the standard available signal quality coefficient value corresponding to positioning mode type i; The signal interference coefficient is processed by exponential decay normalization: ; is the normalized result of the signal interference coefficient corresponding to the positioning mode type i, is the signal interference coefficient corresponding to the positioning mode type i; The signal drift coefficient is normalized using exponential decay: ; is the normalized result of the signal drift coefficient corresponding to the positioning mode type i, is the signal interference coefficient corresponding to the positioning mode type i; The calculation formula of spatial signal environment value is: ; is the spatial signal environment value, is the total number of positioning methods in the current time window, , , and are all preset non-zero scale factors, is the preset exponential weight, with a value range of [0.5, 2]. Finally, all spatial signal environment values ​​in the current time window are averaged to obtain the spatial signal environment index. .

6. The cargo dynamic tracking method using satellite precise positioning according to claim 5 is characterized in that: The logic for obtaining the signal path integrity index is: In the current time window, the difference between the data receiving time corresponding to time t and the expected receiving time is calculated to obtain the signal delay value corresponding to time t; the time proportion of signal loss in the sub-time period to which time t belongs is calculated to obtain the signal loss degree corresponding to time t; Calculate the ratio of the deviation between two adjacent positioning points at time t and time t-1 to the maximum allowable deviation to obtain the data consistency corresponding to time t; The signal path integrity index is calculated as: ； i represents the number of the positioning method type. is the total number of positioning methods in the current time window, It represents the standard deviation of all signal delay values ​​corresponding to positioning mode type i in the current time window. It represents the standard deviation of all signal loss corresponding to positioning mode type i in the current time window. It represents the standard deviation of the consistency of all data corresponding to the positioning method type i in the current time window. , These are the preset influence coefficients. Represents the signal path integrity index.

7. The cargo dynamic tracking method using satellite precise positioning according to claim 6 is characterized in that: The machine learning model is a convolutional neural network model. The feature data set, namely the spatial signal environment index and the signal path integrity index, is imported into the pre-trained convolutional neural network model. According to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path, the convolutional neural network model divides the monitoring status of cargo dynamic tracking into low-quality tracking, medium-quality tracking or high-quality tracking.

8. The cargo dynamic tracking method using satellite precise positioning according to claim 7 is characterized in that: For the monitoring of high-quality tracking and low-quality tracking, fuzzy reasoning is used to optimize the current positioning method. The spatial signal environment index, signal path integrity index, and positioning method under the current time window are used as input variables of fuzzy logic. The optimized positioning method is used as the output variable of fuzzy logic. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the adaptability of each positioning method under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the optimized positioning method and apply it.

9. A cargo dynamic tracking system using satellite precise positioning, used to implement a cargo dynamic tracking method using satellite precise positioning as claimed in any one of claims 1 to 8, characterized in that: include: The data collection module collects the positioning data of the goods in real time during the transportation of the goods to obtain the positioning data set; The positioning accuracy analysis module performs positioning accuracy analysis based on positioning data and determines whether there are early signs of insufficient positioning accuracy during cargo transportation through the positioning accuracy analysis results; The dynamic tracking feature extraction module performs dynamic tracking feature extraction when there are early signs of insufficient positioning accuracy, and then evaluates the monitoring status of cargo dynamic tracking to provide a feature data set for subsequent current dynamic tracking quality classification; The dynamic tracking quality classification module imports the feature data group into the pre-trained machine learning model, and classifies the monitoring status of cargo dynamic tracking into low-quality tracking, medium-quality tracking or high-quality tracking according to the degree to which the positioning signal is affected by the environment and the stability of the positioning data path; The positioning method optimization module optimizes the current positioning method under the monitoring conditions of high-quality tracking and low-quality tracking, so that the cargo positioning accuracy remains at the standard state, and uses the optimized positioning method to dynamically track the cargo.

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