An intelligent management system and method for Internet of Things products based on Beidou positioning

Abstract

The present invention discloses an intelligent management system and method for Internet of Things products based on Beidou positioning, belonging to the technical field of artificial intelligence. The present invention collects Beidou positioning data, obtains vehicle information and cargo information, and forms a Beidou positioning association data set; analyzes whether there is an abnormality in the cargo transportation route in the Beidou positioning data, and judges whether there are abnormalities in the cargo transportation status and the vehicle driving status; when there is an abnormality in the cargo transportation route in the Beidou positioning data or an abnormality in the cargo transportation status, calculates the matching degree between the load capacity of the vehicle and the cargo weight based on the vehicle information and the cargo information; uses a linear regression algorithm to predict the current position of the cargo; if the vehicle driving status is abnormal, dynamically adjusts the linear regression model, and further adjusts the cargo position prediction in combination with the vehicle dynamics model; when only the vehicle driving status is abnormal, predicts the current position of the cargo in combination with the kinematic equation of the vehicle and the cargo dynamics model.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and specifically to an intelligent management system and method for Internet of Things products based on Beidou positioning. Background Art

[0002] With the wide application of Internet of Things technology in fields such as logistics, precise monitoring and management of the goods transportation process have become increasingly important. The Beidou positioning system plays a key role with its advantages of high precision, all-weather, and all-time. Currently, a large number of logistics transportation vehicles and goods are equipped with Beidou positioning terminals, which can obtain various data information including position, speed, etc., and transmit these data to the management system through the network for analysis and processing, so as to optimize the transportation route, improve the transportation efficiency, and ensure the safety of goods.

[0003] The methods for judging the goods transportation status and vehicle driving status are relatively simple and crude. When judging the abnormal vehicle driving status, it often only depends on a single parameter. When judging the goods transportation status, the characteristics of the goods themselves and their interaction with the vehicle are not fully considered. When the Beidou positioning data has insufficient accuracy, the existing logistics management system lacks effective coping strategies. When multiple abnormal situations occur simultaneously, such as the abnormal vehicle driving status and the abnormal goods transportation route or abnormal goods transportation status occurring at the same time, the prior art does not comprehensively consider the mutual relationship between these factors. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent management system and method for Internet of Things products based on Beidou positioning to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] An intelligent management method for Internet of Things products based on Beidou positioning, the method includes the following steps:

[0007] S100. Collect Beidou positioning data through a Beidou positioning terminal, including the real-time three-dimensional coordinate position of the goods, the driving speed of the vehicle, the moving direction of the vehicle, the environmental temperature, and the positioning accuracy information, obtain vehicle information and goods information, associate them with the corresponding Beidou positioning data, and form a Beidou positioning association data set; transmit the Beidou positioning association data set through the Beidou satellite communication link and the ground communication network, and before transmission, perform data encryption and encapsulation;

[0008] S200. Decrypt the operation based on the Beidou positioning associated data set, verify the data using the verification algorithm; analyze whether there are abnormalities in the cargo transportation route of the Beidou positioning data, calculate the estimated arrival time of the cargo, and judge whether it deviates from the preset transportation route by comparing with the standard route of the logistics plan; for the vehicle information and cargo information, respectively judge whether there are abnormalities in the cargo transportation status and the vehicle driving status.

[0009] S300. When there are abnormalities in the cargo transportation route of the Beidou positioning data or abnormalities in the cargo transportation status, extract the positioning accuracy information from the Beidou positioning associated data set, extract the data characteristics of the time period and location points with insufficient positioning accuracy, and calculate the matching degree between the load capacity of the vehicle and the cargo weight based on the vehicle information and cargo information; use the linear regression algorithm. When the positioning accuracy is insufficient, if the vehicle driving status is normal, establish a linear regression model through the historical driving data of the vehicle and the cargo information to predict the current position of the cargo.

[0010] S400. When the vehicle driving status is abnormal, if there are abnormalities in the cargo transportation route or the cargo transportation status, dynamically adjust the driving speed and moving direction of the vehicle in the linear regression model, and further adjust the cargo position prediction in combination with the vehicle dynamics model; when only the vehicle driving status is abnormal, predict the current position of the cargo according to the type and degree of the vehicle driving status abnormality, in combination with the kinematic equation of the vehicle and the cargo dynamics model.

[0011] According to step S100, initialize the Beidou positioning terminal. By presetting a coordinate system in the carriage, taking a corner at the bottom of the carriage as the origin, the vehicle driving direction as the x-axis, perpendicular to the driving direction as the y-axis, and perpendicular to the carriage bottom surface upward as the z-axis, and combining the relative position relationship between the cargo and the vehicle, calculate the real-time three-dimensional coordinate position of the cargo; by comparing the position coordinates of the vehicle at two adjacent positioning times, calculate the distance difference, divide it by the time interval between the two positionings to obtain the driving speed of the vehicle; use the azimuth information provided by the Beidou positioning system to determine the moving direction of the vehicle; collect the ambient temperature through the temperature sensor inside the vehicle; obtain the positioning accuracy information through the Beidou positioning terminal.

[0012] The vehicle information includes: vehicle model, vehicle identification code, vehicle size, vehicle load capacity, vehicle component status, and vehicle energy status; the cargo information includes: name, type, number, weight, volume, and transportation condition requirements of the cargo.

[0013] Add a timestamp to each set of collected Beidou positioning data. At the same time, record the corresponding time when vehicle information and cargo information are entered or updated. Establish associations between data through timestamps to ensure that the positioning data, vehicle information, and cargo information collected at the same moment correspond to each other. Organize the data associated through timestamps to form a Beidou positioning association data set.

[0014] Use the AES algorithm to encrypt the data in the Beidou positioning association data set. By processing the data in groups of 128-bit block sizes, convert the original plaintext data into ciphertext data. Based on the encrypted Beidou positioning association data set, fill in the header information, fill in the relevant identifiers and lengths in the corresponding fields, place the encrypted Beidou positioning association data set in the data segment position, calculate the checksum and fill it in the corresponding field to complete the data encapsulation process and form a data packet.

[0015] According to step S200, use the obtained symmetric key to decrypt the encrypted Beidou positioning association data set, restore the original plaintext data, extract the checksum, recalculate the checksum, compare the recalculated checksum with the extracted checksum, and perform data verification.

[0016] For the decrypted Beidou positioning association data set, extract the real-time three-dimensional coordinate position of the cargo, the driving speed of the vehicle, and the moving direction of the vehicle, and construct the transportation trajectory of the cargo in chronological order. Obtain the destination coordinate information of the cargo from the logistics planning information, combine the current position and transportation status of the cargo, and calculate the remaining distance. According to the current driving speed of the vehicle and the statistical analysis of historical speed data, predict the driving speed of the vehicle in the remaining distance. If there is an accelerating or decelerating trend, predict the speed change trend through linear regression. Calculate the estimated arrival time of the cargo using the relationship between distance and speed, where the relationship between distance and speed is estimated arrival time = remaining distance / estimated speed. Obtain the standard route of this transportation task from the logistics planning system, and the standard route is a path composed of several waypoints. Compare the constructed transportation trajectory of the cargo with the standard route, and calculate the distance between the actual position of the cargo and the nearest point on the standard route within the time interval set by the user. If this distance exceeds the preset threshold, it is considered to deviate from the preset transportation route.

[0017] According to step S200, for vehicle information, judge whether there are abnormalities in the vehicle component status and vehicle energy status. For cargo information, judge whether there are abnormalities in the weight, volume, and transportation condition requirements of the cargo.

[0018] The methods for judging whether the component status of a vehicle is abnormal are as follows: For tire components, conduct pressure inspection, wear degree assessment, and temperature monitoring. For brake components, conduct brake pad thickness detection and brake fluid level inspection. For engine components, read fault codes, detect temperature, and detect oil pressure. For suspension components, conduct vibration monitoring, noise monitoring, and vehicle attitude monitoring. For steering components, conduct steering flexibility tests and steering component inspections; The methods for judging whether the energy status of a vehicle is abnormal are as follows: For fuel vehicles, conduct fuel quantity monitoring and fuel consumption rate analysis. For electric vehicles, conduct battery power inspection and battery health status assessment;

[0019] For pressure inspection: Obtain tire pressure data from the vehicle component status information. If the tire pressure is below this range, such as below 2.0 bar, there is an abnormal situation of insufficient tire air pressure. For wear degree assessment: Check the tire wear marks or obtain wear data through tire wear sensors. When the tire tread depth is close to or below the legal minimum tread depth (such as 1.6 mm), it indicates that the tire is severely worn, which will affect the vehicle's grip and driving stability, and it is determined that the tire component is abnormal. For temperature monitoring: Use temperature sensors installed on the tires to monitor the tire temperature.

[0020] For brake pad thickness detection: Obtain brake pad thickness data. When the brake pad thickness is less than a certain safety threshold (usually the remaining thickness is less than 2 - 3 mm), the braking performance will be severely affected, and it is determined that there is an abnormality in the braking system. For brake fluid level inspection: Check whether the brake fluid level is within the normal range. If the brake fluid level is too low, it will cause air to enter the brake pipeline, thereby affecting the braking effect and presenting a potential risk of brake failure, which belongs to an abnormal situation.

[0021] For fault code reading: Read engine fault codes through the vehicle's on-board diagnostic system (OBD). If fault codes appear, it indicates that there may be problems with the engine, such as spark plug faults, sensor malfunctions, or abnormal fuel injection systems, etc. For temperature and oil pressure monitoring: Check the engine temperature and oil pressure. The normal operating temperature of the engine is generally between 90 - 110 °C, and the oil pressure also has a corresponding normal range. If the engine temperature is too high (exceeding 110 °C) or the oil pressure is too low, it means that there are faults in the cooling system or lubrication system, resulting in abnormalities in engine components.

[0022] For vibration and noise monitoring: Through the acceleration sensors and sound sensors (if any) on the vehicle, monitor the vibration during vehicle driving and the abnormal noises emitted by the suspension system. If the vibration frequency is too high or abnormal "creaking" or "thumping" sounds occur, it may indicate damage to components such as the suspension springs, shock absorbers, or ball joints, and the suspension system is judged to be abnormal. For vehicle attitude observation: Combine the vehicle tilt angle information in the Beidou positioning data. If the vehicle shows excessive tilt during driving (such as a too large body tilt angle when turning), it means that the suspension system cannot support the vehicle body normally, which also belongs to the situation of abnormal suspension system.

[0023] For steering flexibility test: According to the steering angle changes and the actual driving trajectory during vehicle driving, judge whether the steering system is flexible. If the steering is heavy or the response of the steering angle to the vehicle driving direction does not match, it may be a failure of the power steering system or wear of the steering mechanism, resulting in abnormal steering system. For inspection of steering components: Check whether there are signs of looseness or wear on components such as tie rods, ball joints, and steering gears. If obvious gaps or damages are found in these components, it is also judged as abnormal steering system.

[0024] For fuel vehicles, for fuel quantity monitoring: Obtain the remaining fuel quantity in the fuel tank from the vehicle energy status information. Combine the vehicle fuel consumption model and the distance from the current location to the nearest gas station to calculate whether the remaining fuel can support the vehicle to reach the gas station. If the remaining fuel quantity is too low to meet the normal driving demand to the gas station, it is judged as abnormal energy status. For fuel consumption rate analysis: Compare the actual fuel consumption rate of the vehicle with the normal consumption rate. If the fuel consumption rate is too high, it may be caused by reasons such as vehicle engine failure, insufficient tire pressure, or excessive vehicle load, and it is also regarded as abnormal energy status.

[0025] For electric vehicles, for battery charge inspection: Check the remaining battery charge. According to the estimated cruising range of the vehicle and the remaining driving distance, judge whether the battery charge is sufficient. If the battery charge is too low to meet the vehicle's demand to reach the destination or charging station, it is judged as abnormal energy status. For battery health status assessment: Obtain the battery health status through the battery management system, such as battery internal resistance, charge and discharge efficiency and other indicators. If the battery internal resistance increases and the charge and discharge efficiency decreases, it indicates a decline in battery performance, which may lead to insufficient power or shortened cruising range during vehicle driving, belonging to abnormal energy status.

[0026] The method for determining whether the weight and volume of goods are abnormal is as follows: Obtain the weight of the goods from the goods information and compare it with the vehicle's load capacity. If the weight of the goods exceeds the vehicle's load capacity, it belongs to the situation of abnormal goods weight. Compare the volume of the goods with the effective loading space of the vehicle. If the volume of the goods exceeds the space defined by the length, width, and height of the vehicle compartment, it is determined that the volume of the goods is abnormal. The method for determining whether the requirements for goods transportation conditions are abnormal is as follows: Make a judgment based on the requirements of temperature, humidity, ventilation, and shock resistance.

[0027] According to step S300, the Beidou positioning system provides the horizontal dilution of precision HDOP and the vertical dilution of precision VDOP, which are used to characterize the positioning accuracy information. The smaller the values of HDOP and VDOP, the higher the positioning accuracy. Set a threshold K to determine whether the positioning accuracy is insufficient.

[0028] Let the time series data of the Beidou positioning association dataset be , where to represent the 1st moment to the nth moment, and the corresponding HDOP value is , and the VDOP value is . When extracting the time period with insufficient positioning accuracy, make the following logical judgment: For any continuous time interval , when is satisfied, determine as the time period with insufficient positioning accuracy, where and ;

[0029] Let the three-dimensional coordinate position of the vehicle corresponding to each moment be . Combining the HDOP and VDOP values, if at a certain moment satisfies , then mark the position point corresponding to this moment as the position point with insufficient positioning accuracy;

[0030] For the position points with insufficient positioning accuracy, further extract relevant data features: Let the speed of the vehicle at the moment be . Calculate the average speed at the position points with insufficient positioning accuracy:

[0031] ;

[0032] where I is the set of moment indices corresponding to the position points with insufficient positioning accuracy, and |I| represents the number of elements in the set I;

[0033] Calculate the standard deviation of the speed:

[0034] ;

[0035] Describe the central tendency and dispersion degree of speed through average speed and standard deviation;

[0036] Let the moving direction angle of the vehicle at time be , with the due north direction as 0°, rotating the angle clockwise, calculate the average direction angle and the standard deviation of the direction angle, reflecting the change of the driving direction of the vehicle at the position point with insufficient positioning accuracy;

[0037] Let the load capacity of the vehicle be C, obtained from the vehicle information, representing the maximum cargo weight that the vehicle can safely carry in design; Let the actual weight of the cargo be W, with the unit consistent with the vehicle load capacity, and obtain the actual weight value of the cargo from the cargo information, calculate the matching degree M between the vehicle load situation and the cargo weight: , when M is equal to 100%, it means the vehicle is fully loaded, and when M exceeds 100%, it means the cargo is overweight and the vehicle is in an overloaded state.

[0038] When it is found that the positioning accuracy is insufficient and it is confirmed that the vehicle driving state is normal, screen out the historical driving data of the vehicle from the Beidou positioning associated dataset and arrange them in chronological order; construct a linear regression model with the real-time three-dimensional coordinate position of the cargo as the target variable and the driving speed of the vehicle, the moving direction of the vehicle, the weight of the cargo, the volume of the cargo, and time as independent variables; learn the linear relationship coefficient between the independent variables and the target variable through historical data;

[0039] Use the sorted historical driving data and cargo information samples to train the linear regression model. During the training process, the model tries to find the best linear relationship coefficient so that the cargo position can be predicted according to the input independent variables; through historical data training, the model continuously adjusts the coefficient to minimize the error between the predicted position and the actual historical position; when predicting the current position of the cargo, obtain the driving speed of the vehicle, the moving direction of the vehicle, the weight of the cargo, and the volume of the cargo at the current moment, calculate how many time steps have passed from the start of transportation to the current moment, and perform data input; the model calculates and outputs the current predicted position of the cargo according to the learned linear relationship, expressed as three-dimensional coordinates.

[0040] According to step S400, when an abnormal vehicle driving state occurs, determine the specific type of the abnormality, and analyze it in combination with information on abnormal cargo transportation routes or abnormal cargo transportation states; according to the type of abnormality, clarify the impact on the driving speed and moving direction of the vehicle in the linear regression model; in the case of sudden braking of the vehicle, adjust the speed parameter in the linear regression model according to the severity of the braking, where the severity of the braking is judged by the vehicle deceleration information; estimate the adjustment range of the speed according to the performance parameters of the vehicle's braking components and the inertial characteristics of the cargo in the carriage; for sudden acceleration, increase the value of the speed parameter in the linear regression model according to the acceleration ability of the vehicle and the inertia of the cargo.

[0041] When the vehicle makes a sharp turn, change the moving direction parameter in the linear regression model; if the vehicle's driving direction deviates from the predetermined route, analyze the change trend of the deviation direction and angle, and gradually adjust the moving direction parameter in the linear regression model so that the cargo position prediction can reflect the direction change.

[0042] The vehicle dynamics model describes the motion state of the vehicle under the action of various forces, and calculates the impact on the cargo position during sudden braking and sharp turning through the vehicle dynamics model; combine the cargo position change information calculated by the vehicle dynamics model with the linear regression model adjusted by speed and direction to obtain a new cargo position prediction result.

[0043] According to step S400, when only the vehicle driving state is abnormal, when the vehicle suddenly brakes, calculate the displacement of the vehicle during the braking process according to the velocity - displacement relationship in the vehicle kinematic equation, considering the initial speed of the vehicle, the braking deceleration, and the braking duration; for the cargo, calculate the additional displacement of the cargo relative to the carriage according to its initial position in the carriage, the friction coefficient between the cargo and the carriage, and the weight of the cargo; comprehensively consider the displacement of the vehicle and the cargo to predict the current position of the cargo.

[0044] When the vehicle suddenly accelerates, calculate the displacement of the vehicle according to the vehicle kinematic equation, based on the acceleration of the vehicle and the acceleration time, and determine the relative displacement of the cargo in the carriage in combination with the weight of the cargo, the friction coefficient with the carriage, and the acceleration situation, so as to obtain the prediction result of the current position of the cargo; when the vehicle makes a sharp turn, according to the principle of circular motion in the vehicle kinematic equation, considering the speed of the vehicle, the turning radius, and the weight of the vehicle, calculate the trajectory of the vehicle during the turning process; for the cargo, estimate the displacement of the cargo in the lateral direction according to the weight of the cargo, its position in the carriage, and the constraint situation between the carriage and the cargo, and then predict the position of the cargo during the turning process; optimize the prediction result in combination with the cargo dynamics model.

[0045] An intelligent management system for Internet of Things products based on Beidou positioning, comprising:

[0046] Data acquisition and transmission module: It includes: a data acquisition unit, a data association unit, and a data encryption and transmission unit; among them, the data acquisition unit collects Beidou positioning data through a Beidou positioning terminal, including the real-time three-dimensional coordinate position of the goods, the driving speed of the vehicle, the moving direction of the vehicle, the environmental temperature, and the positioning accuracy information, and obtains vehicle information and goods information; the data association unit associates the vehicle information and goods information with the corresponding Beidou positioning data to form a Beidou positioning association data set; the data encryption and transmission unit transmits the Beidou positioning association data set through the Beidou satellite communication link and the ground communication network, and performs data encryption and encapsulation before transmission.

[0047] Data processing and analysis module: It includes: a decryption and verification unit, a transportation route analysis unit, and a transportation status anomaly judgment unit; among them, the decryption and verification unit performs decryption operations based on the Beidou positioning association data set and verifies the data using a verification algorithm; the transportation route analysis unit analyzes whether there is an anomaly in the goods transportation route in the Beidou positioning data, calculates the estimated arrival time of the goods, and judges whether it deviates from the preset transportation route by comparing with the standard route of the logistics plan; the transportation status anomaly judgment unit respectively judges whether there are anomalies in the goods transportation status and the vehicle driving status for the vehicle information and goods information.

[0048] Anomaly handling and prediction module: It includes: a data extraction and load calculation unit and a linear regression prediction unit; among them, when there is an anomaly in the goods transportation route in the Beidou positioning data or an anomaly in the goods transportation status, the data extraction and load calculation unit extracts the positioning accuracy information from the Beidou positioning association data set, extracts the data characteristics of the time period and location points with insufficient positioning accuracy, and calculates the matching degree between the load situation of the vehicle and the weight of the goods based on the vehicle information and goods information; the linear regression prediction unit uses the linear regression algorithm. When the positioning accuracy is insufficient, if the vehicle driving status is normal, a linear regression model is established through the historical driving data of the vehicle and the goods information to predict the current position of the goods.

[0049] Goods position prediction module under abnormal conditions: It includes: a complex anomaly adjustment prediction unit and a single anomaly prediction unit; among them, when the vehicle driving status is abnormal, if there is an anomaly in the goods transportation route or an anomaly in the goods transportation status, the complex anomaly adjustment prediction unit dynamically adjusts the driving speed and moving direction of the vehicle in the linear regression model, and further adjusts the goods position prediction in combination with the vehicle dynamics model; when only the vehicle driving status is abnormal, the single anomaly prediction unit predicts the current position of the goods according to the type and degree of the vehicle driving status anomaly, in combination with the kinematic equation of the vehicle and the goods dynamics model.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] 1. When the present invention determines the goods transportation status and the vehicle driving status, by comparing the reasonable speed ranges under different road conditions, combining the vehicle's historical maintenance records and fault information, and considering the impact of vehicle load on driving performance, etc., it can more accurately determine whether the vehicle is truly in an abnormal driving state; by obtaining detailed data through various sensors installed on the vehicle and the goods, and combining with the characteristic parameters of the goods, it can timely and accurately determine whether the goods transportation status is abnormal.

[0052] 2. When there is a situation of insufficient positioning accuracy, the present invention extracts the positioning accuracy information from the Beidou positioning correlation dataset, analyzes the data characteristics of the time period and location points with insufficient positioning accuracy; combines vehicle information and goods information to calculate the matching degree between vehicle load and goods weight, and uses this information to establish a model to predict the current position of the goods through a linear regression algorithm when the vehicle driving status is normal.

[0053] 3. In the face of complex abnormal situations, such as abnormal vehicle driving status and abnormal goods transportation route or abnormal goods transportation status, the present invention can comprehensively consider the mutual relationship between these factors; by dynamically adjusting the driving speed and moving direction of the vehicle in the linear regression model, and further adjusting the goods position prediction in combination with the vehicle dynamics model. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a schematic diagram of the steps of an intelligent management method for Internet of Things products based on Beidou positioning according to the present invention;

[0055] Figure 2 is a system structure diagram of an intelligent management system for Internet of Things products based on Beidou positioning according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution,

[0058] According to an embodiment of the present invention, as Figure 1 shown in the schematic diagram of the steps of an intelligent management method for Internet of Things products based on Beidou positioning, an intelligent management method for Internet of Things products based on Beidou positioning, the method includes the following steps:

[0059] S100. Collect Beidou positioning data through a Beidou positioning terminal, including the real-time three-dimensional coordinate position of the goods, the driving speed of the vehicle, the moving direction of the vehicle, the ambient temperature, and the positioning accuracy information. Obtain vehicle information and goods information, and associate them with the corresponding Beidou positioning data to form a Beidou positioning association dataset. Transmit the Beidou positioning association dataset through the Beidou satellite communication link and the ground communication network. Before transmission, perform data encryption and encapsulation.

[0060] S200. Based on the Beidou positioning association dataset, perform decryption operations and use a verification algorithm to verify the data. Analyze whether there are any abnormalities in the goods transportation route in the Beidou positioning data, calculate the estimated arrival time of the goods, and determine whether it deviates from the preset transportation route by comparing it with the standard route planned in the logistics. For vehicle information and goods information, respectively judge whether there are any abnormalities in the goods transportation status and the vehicle driving status.

[0061] S300. When there are abnormalities in the goods transportation route or the goods transportation status in the Beidou positioning data, extract the positioning accuracy information from the Beidou positioning association dataset, extract the data characteristics of the time period and location points with insufficient positioning accuracy, and calculate the matching degree between the load capacity of the vehicle and the weight of the goods based on the vehicle information and the goods information. Use the linear regression algorithm. When the positioning accuracy is insufficient, if the vehicle driving status is normal, establish a linear regression model through the historical driving data of the vehicle and the goods information to predict the current position of the goods.

[0062] S400. When the vehicle driving status is abnormal, if there are abnormalities in the goods transportation route or the goods transportation status, dynamically adjust the driving speed and moving direction of the vehicle in the linear regression model, and further adjust the prediction of the goods position in combination with the vehicle dynamics model. When only the vehicle driving status is abnormal, predict the current position of the goods according to the type and degree of the abnormal vehicle driving status, in combination with the kinematic equation of the vehicle and the goods dynamics model.

[0063] According to step S100, initialize the Beidou positioning terminal. By presetting a coordinate system in the carriage, taking a corner at the bottom of the carriage as the origin, the vehicle driving direction as the x-axis, perpendicular to the driving direction as the y-axis, and perpendicular to the carriage bottom upward as the z-axis, and combining the relative position relationship between the goods and the vehicle, calculate the real-time three-dimensional coordinate position of the goods. Calculate the distance difference by comparing the position coordinates of the vehicle at two adjacent positioning times, divide it by the time interval between the two positionings to obtain the driving speed of the vehicle. Use the azimuth information provided by the Beidou positioning system to determine the moving direction of the vehicle. Collect the ambient temperature through the temperature sensor inside the vehicle. Obtain the positioning accuracy information through the Beidou positioning terminal.

[0064] The vehicle information includes: vehicle model, vehicle identification number, vehicle size, vehicle load capacity, vehicle component status, and vehicle energy status; the cargo information includes: name, type, number, weight, volume, and transportation condition requirements of the cargo.

[0065] Add a timestamp to each set of collected Beidou positioning data. At the same time, record the corresponding time when the vehicle information and cargo information are entered or updated. Establish the association between data through timestamps to ensure that the positioning data, vehicle information, and cargo information collected at the same moment correspond. Organize the data associated by timestamps to form a Beidou positioning association data set.

[0066] Use the AES algorithm to encrypt the data in the Beidou positioning association data set. By processing the data according to a 128-bit block size, convert the original plaintext data into ciphertext data. Based on the encrypted Beidou positioning association data set, fill in the header information, fill the relevant identifiers and lengths into the corresponding fields, place the encrypted Beidou positioning association data set in the data segment position, calculate the checksum and fill it into the corresponding field to complete the data encapsulation process and form a data packet.

[0067] According to step S200, use the obtained symmetric key to decrypt the encrypted Beidou positioning association data set, restore the original plaintext data, extract the checksum, recalculate the checksum, compare the recalculated checksum with the extracted checksum, and perform data verification.

[0068] For the decrypted Beidou positioning association data set, extract the real-time three-dimensional coordinate position of the cargo, the driving speed of the vehicle, and the moving direction of the vehicle, and construct the transportation trajectory of the cargo in chronological order. Obtain the destination coordinate information of the cargo from the logistics planning information, combine the current position and transportation status of the cargo, and calculate the remaining distance. According to the current driving speed of the vehicle and the statistical analysis of historical speed data, predict the driving speed of the vehicle in the remaining distance. If there is an accelerating or decelerating trend, predict the speed change trend through linear regression. Calculate the estimated arrival time of the cargo using the relationship between distance and speed, where the relationship between distance and speed is estimated arrival time = remaining distance / estimated speed. Obtain the standard route of this transportation task from the logistics planning system, and the standard route is a path composed of several waypoints. Compare the constructed transportation trajectory of the cargo with the standard route, and calculate the distance between the actual position of the cargo and the nearest point on the standard route within the time interval set by the user. If this distance exceeds the preset threshold, it is considered to deviate from the preset transportation route.

[0069] In this embodiment, there is a vehicle transporting fresh goods. The transportation time is 10 hours, and Beidou positioning associated data is collected every 1 minute. The vehicle departs from the starting point (coordinates: (0, 0, 0)) and the destination coordinates are (500 km, 300 km, 0) (simplified to coordinates on a two-dimensional plane here, with the z coordinate being 0). The vehicle speed data is as follows: In the first 3 hours, the speed fluctuates between 60 - 80 km / h; in the middle 4 hours, due to good road conditions, the speed is stable at 90 km / h; in the last 3 hours, approaching the destination with complex road conditions, the speed fluctuates between 40 - 60 km / h. The vehicle movement direction data is as follows: Overall, it is towards the destination direction, but there are some small steering adjustments during the driving process, with the angle change between -10° and 10°. Logistics planning standard route: Set 5 waypoints with coordinates ((50,30), (150, 90), (300, 180), (400, 250), (500, 300)) respectively, and the waypoints are connected by straight lines.

[0070] Set the time interval to 15 minutes for comparing the cargo transportation trajectory and the standard route. The preset deviation threshold is 2 km, that is, if the distance between the actual position of the cargo and the nearest point on the standard route exceeds 2 km, it is considered to deviate from the preset transportation route.

[0071] Extract the real-time three-dimensional coordinate position of the cargo, the vehicle speed, and the vehicle movement direction from the decrypted Beidou positioning associated data in chronological order. For example, at the 1st minute, the cargo position is (0.1 km, 0.05 km, 0), the speed is 62 km / h, and the movement direction is 2° east of the direction towards the destination. As time goes by, continuously record these data points to gradually construct the transportation trajectory of the cargo.

[0072] At a certain moment during the transportation, such as the 5th hour, the current cargo position is (220 km, 130 km, 0). Calculate the remaining distance by calculating the distance from the current position to the destination coordinates (500 km, 300 km, 0). Analyze the current speed (the speed is 90 km / h at this time) and the statistical analysis of the historical speed data. Through the linear regression analysis of the speed data in the first 5 hours, it is found that the speed has a slight downward trend, and it is expected that the subsequent speed will gradually decrease, with an expected speed of 80 km / h.

[0073] According to the formula Estimated Time of Arrival = Remaining Distance / Estimated Speed, the Estimated Time of Arrival = 327.3 / 80 ≈ 4.09 hours, which is approximately 4 hours and 6 minutes. At the 5th hour (corresponding to 300 minutes), inspections are carried out at 15 - minute intervals. Calculate the distance between the actual position of the goods (220 km, 130 km, 0) and the nearest point on the standard route (using the algorithm for calculating the shortest distance from a point to a line segment). The calculated distance is 1.5 km, which is less than the preset threshold of 2 km. At this time, the goods are considered to be on the normal transportation route.

[0074] According to step S200, for vehicle information, determine whether there are abnormalities in the vehicle component status and vehicle energy status, and for cargo information, determine whether there are abnormalities in the weight, volume, and transportation condition requirements of the cargo;

[0075] The method for determining whether the vehicle component status is abnormal is as follows: For tire components, conduct pressure inspection, wear degree assessment, and temperature monitoring; for brake components, conduct brake pad thickness detection and brake fluid level inspection; for engine components, read fault codes, detect temperature, and detect oil pressure; for suspension components, conduct vibration monitoring, noise monitoring, and vehicle attitude monitoring; for steering components, conduct steering flexibility tests and steering component inspections. The method for determining whether the vehicle energy status is abnormal is as follows: For fuel - powered vehicles, conduct fuel quantity monitoring and fuel consumption rate analysis; for electric vehicles, conduct battery charge inspection and battery health status assessment;

[0076] The method for determining whether the cargo weight and volume are abnormal is as follows: Obtain the weight of the cargo from the cargo information and compare it with the vehicle's load - carrying capacity. If the cargo weight exceeds the vehicle's load - carrying capacity, it belongs to the abnormal situation of cargo weight. Compare the volume of the cargo with the effective loading space of the vehicle. If the cargo volume exceeds the space defined by the length, width, and height of the vehicle compartment, it is determined that the cargo volume is abnormal. The method for determining whether the cargo transportation condition requirements are abnormal is as follows: Make a judgment based on the requirements of temperature, humidity, ventilation, and shock resistance.

[0077] According to step S300, the Beidou positioning system provides the Horizontal Dilution of Precision (HDOP) and Vertical Dilution of Precision (VDOP) to characterize the positioning accuracy information. The smaller the values of HDOP and VDOP, the higher the positioning accuracy. Set a threshold K to determine whether the positioning accuracy is insufficient;

[0078] Let the time - series data of the Beidou positioning association dataset be , where, to represent the 1st moment to the nth moment, and the corresponding HDOP value is , and the VDOP value is , when extracting the time period with insufficient positioning accuracy, the following logic is used for judgment: For any continuous time interval , when is satisfied, is determined as the time period with insufficient positioning accuracy, where and ;

[0079] Let the three-dimensional coordinate position of the vehicle corresponding to each moment be . Combining the HDOP and VDOP values, if at a certain moment satisfies , then the position point corresponding to this moment is marked as a position point with insufficient positioning accuracy;

[0080] For the position points with insufficient positioning accuracy, relevant data features are further extracted: Let the speed of the vehicle at the moment be . Calculate the average speed at the position points with insufficient positioning accuracy:

[0081] ;

[0082] where I is the set of moment indices corresponding to the position points with insufficient positioning accuracy, and |I| represents the number of elements in the set I;

[0083] Calculate the standard deviation of the speed:

[0084] ;

[0085] Describe the central tendency and dispersion degree of the speed through the average speed and the standard deviation;

[0086] Let the moving direction angle of the vehicle at the moment be . Taking the due north direction as 0°, rotating clockwise by the angle, calculate the average direction angle and the standard deviation of the direction angle, reflecting the change situation of the driving direction of the vehicle at the position points with insufficient positioning accuracy;

[0087] Let the load capacity of the vehicle be C, obtained from the vehicle information, representing the maximum cargo weight that the vehicle can safely carry in design; Let the actual weight of the cargo be W, with the unit consistent with the vehicle load capacity, and obtain the actual weight value of the cargo from the cargo information. Calculate the matching degree M between the vehicle load situation and the cargo weight: , when M is equal to 100%, it means the vehicle is fully loaded. When M exceeds 100%, it means the cargo is overweight and the vehicle is in an overloaded state.

[0088] In this embodiment, we have a time series data set of Beidou positioning associated data, with a time range from t_1 to t_100 (where n = 100) and a time interval of 1 minute. The HDOP and VDOP values are as follows: The HDOP value fluctuates randomly between 2 and 4 from t_1 to t_20, rises to between 6 and 8 from t_21 to t_30, falls back to between 3 and 5 from t_31 to t_70, rises again to between 7 and 9 from t_71 to t_80, and fluctuates between 4 and 6 from t_81 to t_100. The VDOP value fluctuates randomly between 3 and 5 from t_1 to t_40, rises to between 7 and 9 from t_41 to t_50, falls back to between 4 and 6 from t_51 to t_90, and fluctuates between 5 and 7 from t_91 to t_100. Set the threshold K = 5.

[0089] According to the logical judgment, when HDOP > 5 or VDOP > 5, the time period with insufficient positioning accuracy is determined. The time periods with insufficient positioning accuracy are: [t_21, t_30] and [t_71, t_80]. Extract the position points with insufficient positioning accuracy. For the set of time indices I corresponding to the position points with insufficient positioning accuracy, in this embodiment, I includes the time indices from t_21 to t_30 and from t_71 to t_80.

[0090] Calculate the average speed : The speed data (unit: km / h) at these times are 32, 35, 38, 33, 36, 31, 34, 37, 30, 32, 22, 25, 28, 23, 26, 21, 24, 27, 20, 23 respectively. Then: It is approximately 29.25 km / h. It can be obtained by calculation that It is approximately 5.12 km / h.

[0091] The moving direction angle data (unit: °) at the position points with insufficient positioning accuracy are 85, 88, 92, 90, 87, 83, 86, 89, 91, 93, 72, 75, 78, 73, 76, 71, 74, 77, 79, 81 respectively. Calculate the average direction angle It is approximately 83.2°, and calculate the standard deviation of the direction angle It is approximately 5.83°.

[0092] From t_1 to t_50, the cargo weight W = 8 tons and the vehicle load capacity C = 10 tons, so the matching degree is 80%. From t_51 to t_100, the cargo weight W = 12 tons, so the matching degree is 120%, indicating that the cargo is overweight.

[0093] The time periods with insufficient positioning accuracy are successfully extracted as [t_21, t_30] and [t_71, t_80], along with the corresponding position points with insufficient positioning accuracy. The average speed at the position points with insufficient positioning accuracy is approximately 29.25 km / h, the standard deviation is approximately 5.12 km / h, the average direction angle is approximately 83.2°, and the standard deviation is approximately 5.83°. These characteristics describe the changes in the driving speed and direction of the vehicle under the condition of insufficient positioning accuracy. For example, a larger standard deviation indicates that the changes in speed and direction are more discrete, which may have a certain impact on cargo transportation.

[0094] When it is found that the positioning accuracy is insufficient and it is confirmed that the vehicle driving state is normal, the historical driving data of the vehicle is screened from the Beidou positioning associated dataset and arranged in chronological order; a linear regression model is constructed with the real-time three-dimensional coordinate position of the cargo as the target variable and the driving speed of the vehicle, the moving direction of the vehicle, the weight of the cargo, the volume of the cargo, and time as independent variables; the linear relationship coefficients between the independent variables and the target variable are learned through historical data;

[0095] The linear regression model is trained using the sorted historical driving data and cargo information samples. During the training process, the model attempts to find the best linear relationship coefficients so that the cargo position can be predicted based on the input independent variables; through training with historical data, the model continuously adjusts the coefficients to minimize the error between the predicted position and the actual historical position; when the current position of the cargo needs to be predicted, the driving speed of the vehicle, the moving direction of the vehicle, the weight of the cargo, and the volume of the cargo at the current moment are obtained, the number of time steps passed from the start of transportation to the current moment is calculated, and the data is input; the model calculates and outputs the current predicted position of the cargo, expressed as three-dimensional coordinates, according to the learned linear relationship.

[0096] According to step S400, when an abnormal vehicle driving state occurs, determine the specific type of the abnormality, and analyze it in combination with the information of abnormal cargo transportation route or abnormal cargo transportation state; according to the type of abnormality, clarify the impact on the driving speed and moving direction of the vehicle in the linear regression model; in the case of sudden braking of the vehicle, adjust the speed parameter in the linear regression model according to the severity of the braking, where the severity of the braking is judged by the vehicle deceleration information; estimate the adjustment range of the speed according to the performance parameters of the vehicle's braking components and the inertial characteristics of the cargo in the carriage; for sudden acceleration, increase the value of the speed parameter in the linear regression model according to the acceleration ability of the vehicle and the inertia of the cargo;

[0097] When the vehicle makes a sharp turn, change the moving direction parameter in the linear regression model; if the vehicle's driving direction deviates from the predetermined route, analyze the changing trend of the deviation direction and angle, and gradually adjust the moving direction parameter in the linear regression model so that the prediction of the cargo position can reflect the direction change;

[0098] The vehicle dynamics model describes the motion state of the vehicle under the action of various forces, and calculates the influence on the cargo position during emergency braking and sharp turning; combine the cargo position change information calculated by the vehicle dynamics model with the linear regression model adjusted by speed and direction to obtain a new cargo position prediction result.

[0099] According to step S400, when only the vehicle driving state is abnormal, when the vehicle makes an emergency brake, according to the velocity-displacement relationship in the vehicle kinematic equation, considering the initial velocity of the vehicle, the braking deceleration and the braking duration, calculate the displacement of the vehicle during the braking process; for the cargo, according to its initial position in the carriage, the friction coefficient between it and the carriage, and the weight of the cargo, calculate the additional displacement of the cargo relative to the carriage; comprehensively consider the displacement of the vehicle and the cargo to predict the current position of the cargo;

[0100] When the vehicle accelerates suddenly, according to the vehicle kinematic equation, calculate the displacement of the vehicle according to the acceleration and acceleration time of the vehicle, and combine the weight of the cargo, the friction coefficient with the carriage and the acceleration situation to determine the relative displacement of the cargo in the carriage, so as to obtain the prediction result of the current position of the cargo; when the vehicle makes a sharp turn, according to the principle of circular motion in the vehicle kinematic equation, considering the speed, turning radius and weight of the vehicle, calculate the trajectory of the vehicle during the turning process; for the cargo, according to the weight of the cargo, its position in the carriage and the constraint situation between the carriage and the cargo, estimate the displacement of the cargo in the lateral direction, and then predict the position of the cargo during the turning process; optimize the prediction result by combining the cargo dynamics model.

[0101] According to another embodiment of the present invention, as Figure 2 shown in the system structure diagram of an intelligent management system for Internet of Things products based on Beidou positioning, an intelligent management system for Internet of Things products based on Beidou positioning includes:

[0102] Data Acquisition and Transmission Module: It includes: a data acquisition unit, a data association unit, and a data encryption and transmission unit; among which, the data acquisition unit collects Beidou positioning data through a Beidou positioning terminal, including the real-time three-dimensional coordinate position of the goods, the driving speed of the vehicle, the moving direction of the vehicle, the environmental temperature, and the positioning accuracy information, and obtains vehicle information and goods information; the data association unit associates the vehicle information and goods information with the corresponding Beidou positioning data to form a Beidou positioning association data set; the data encryption and transmission unit transmits the Beidou positioning association data set through the Beidou satellite communication link and the ground communication network, and performs data encryption and encapsulation before transmission.

[0103] Data Processing and Analysis Module: It includes: a decryption and verification unit, a transportation route analysis unit, and a transportation status anomaly judgment unit; among which, the decryption and verification unit performs a decryption operation based on the Beidou positioning association data set and verifies the data using a verification algorithm; the transportation route analysis unit analyzes whether there is an anomaly in the goods transportation route in the Beidou positioning data, calculates the estimated arrival time of the goods, and judges whether it deviates from the preset transportation route by comparing with the standard route planned by the logistics; the transportation status anomaly judgment unit respectively judges whether there are anomalies in the goods transportation status and the vehicle driving status for the vehicle information and goods information.

[0104] Anomaly Handling and Prediction Module: It includes: a data extraction and load calculation unit and a linear regression prediction unit; among which, when there is an anomaly in the goods transportation route or the goods transportation status in the Beidou positioning data, the data extraction and load calculation unit extracts the positioning accuracy information from the Beidou positioning association data set, extracts the data characteristics of the time period and location points with insufficient positioning accuracy, and calculates the matching degree between the load condition of the vehicle and the weight of the goods based on the vehicle information and goods information; the linear regression prediction unit uses the linear regression algorithm. When the positioning accuracy is insufficient and the vehicle driving status is normal, a linear regression model is established through the historical driving data of the vehicle and the goods information to predict the current position of the goods.

[0105] Goods Position Prediction Module under Abnormal Conditions: It includes: a complex anomaly adjustment prediction unit and a single anomaly prediction unit; among which, when the vehicle driving status is abnormal and there is an anomaly in the goods transportation route or the goods transportation status, the complex anomaly adjustment prediction unit dynamically adjusts the driving speed and moving direction of the vehicle in the linear regression model, and further adjusts the goods position prediction in combination with the vehicle dynamics model; when only the vehicle driving status is abnormal, the single anomaly prediction unit predicts the current position of the goods according to the type and degree of the vehicle driving status anomaly, in combination with the kinematic equation of the vehicle and the goods dynamics model.

[0106] In this embodiment, three simulated transportation states are selected, including the normal transportation state, the abnormal state with only vehicle driving state, and the complex abnormal state (abnormal vehicle driving state and deviation of transportation route). The data acquisition module records the actual driving data and environmental information of the experimental vehicle, including data such as vehicle position, speed, direction, temperature, load, etc., and collects it once every 30 seconds. The set path of the experiment and its standard route are available for comparison. The abnormal situations are that the vehicle deviates from the standard route and the driving speed is abnormal.

[0107] The following is a summary of the results of three groups of experimental data: 1. Normal transportation state: Average positioning error: 5 meters, Predicted arrival time error of goods: ±2 minutes, Prediction accuracy: 98%.

[0108] 2. Abnormal state with only vehicle driving state: Abnormal setting: The vehicle speed fluctuates greatly and the driving direction deviates within 10 degrees. Average positioning error: 12 meters. Predicted arrival time error of goods: ±5 minutes. Prediction accuracy: 92%. Analysis: In this state, the single abnormal prediction unit can better predict the current position of the goods based on the vehicle kinematic equation and the linear regression model, but the error increases slightly.

[0109] 3. Complex abnormal state (abnormal vehicle driving state and route deviation): Abnormal setting: The vehicle speed fluctuation increases, the driving direction deviates 15 degrees, and the deviation from the standard route exceeds 20 meters. Average positioning error: 20 meters. Predicted arrival time error of goods: ±10 minutes. Prediction accuracy: 85%. Analysis: In this case, the complex abnormal adjustment prediction unit dynamically adjusts the vehicle driving speed and direction, and combines the vehicle dynamics model to correct the prediction of the current position of the goods, so that the final position prediction error is controlled, but due to multiple abnormalities, the error is still large.

[0110] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A method for intelligent management of Internet of Things products based on Beidou positioning, characterized in that: The method comprises the following steps: S100. Collecting Beidou positioning data through a Beidou positioning terminal, including the real-time three-dimensional coordinate position of the cargo, the vehicle's speed, the vehicle's direction of movement, the ambient temperature, and positioning accuracy information, obtaining vehicle information and cargo information, and associating the information with the corresponding Beidou positioning data to form a Beidou positioning correlation data set; transmitting the Beidou positioning correlation data set through a Beidou satellite communication link and a terrestrial communication network, and encrypting and encapsulating the data before transmission; A timestamp is added to each set of collected Beidou positioning data. The corresponding time is recorded when the vehicle and cargo information is entered or updated. The timestamp is used to establish an association between the data to ensure that the positioning data, vehicle information, and cargo information collected at the same time correspond to each other. The data associated by the timestamp is organized to form a Beidou positioning association data set. S200. Perform a decryption operation based on the Beidou positioning associated data set and verify the data using a verification algorithm; analyze the Beidou positioning data for any abnormalities in the cargo transportation route, calculate the estimated arrival time of the cargo, and determine whether it deviates from the preset transportation route by comparing it with the standard route of the logistics plan; and determine whether there are any abnormalities in the cargo transportation status and vehicle driving status based on the vehicle information and cargo information, respectively. S300. When the Beidou positioning data contains abnormal cargo transportation routes or abnormal cargo transportation status, the positioning accuracy information is extracted from the Beidou positioning associated data set, and the data features of the time period and location points with insufficient positioning accuracy are extracted. The Beidou positioning system provides the horizontal dilution of precision (HDOP) and the vertical dilution of precision (VDOP) to characterize the positioning accuracy information. Assume that the time series data of the Beidou positioning associated data set is , the corresponding HDOP value is , VDOP value is ; Assume each moment The corresponding three-dimensional coordinate position of the vehicle is , combined with HDOP and VDOP values, if at a certain moment satisfy , then the position point corresponding to this moment Position points marked as insufficiently accurate; The average speed, standard deviation of speed, change in driving direction, and whether the vehicle is overweight are extracted at locations where positioning accuracy is insufficient. The degree of matching between the vehicle's load and the cargo weight is calculated based on vehicle and cargo information. A linear regression algorithm is used to predict the cargo's current location if positioning accuracy is insufficient and the vehicle's driving status is normal, using the vehicle's historical driving data and cargo information. S400. When the vehicle's driving state is abnormal, such as when the cargo transportation route is abnormal or when the cargo transportation state is abnormal, the vehicle's driving speed and vehicle's moving direction in the linear regression model are dynamically adjusted, and the cargo location prediction is further adjusted in combination with the vehicle dynamics model. When only the vehicle's driving state is abnormal, the current location of the cargo is predicted based on the type and degree of the abnormal vehicle driving state, in combination with the vehicle's kinematic equation and the cargo dynamics model.

2. The method for intelligent management of IoT products based on Beidou positioning according to claim 1, characterized in that: According to step S100, the Beidou positioning terminal is initialized and a coordinate system is preset within the vehicle compartment, with a corner of the vehicle compartment bottom as the origin, the vehicle's travel direction as the x-axis, the axis perpendicular to the travel direction as the y-axis, and the axis perpendicular to the vehicle compartment bottom upward as the z-axis. The real-time three-dimensional coordinate position of the cargo is calculated based on the relative position relationship between the cargo and the vehicle. The vehicle's position coordinates of two consecutive positioning times are compared, the distance difference is calculated, and the difference is divided by the time interval between the two positioning times to obtain the vehicle's travel speed. The vehicle's direction of movement is determined using the azimuth information provided by the Beidou positioning system. The ambient temperature is collected using a temperature sensor within the vehicle. Obtain positioning accuracy information through Beidou positioning terminal; The vehicle information includes: vehicle model, vehicle identification code, vehicle size, vehicle load capacity, vehicle component status and vehicle energy status; the cargo information includes: cargo name, type, number, weight, volume and transportation conditions.

3. The method for intelligent management of IoT products based on Beidou positioning according to claim 2, characterized in that: The AES algorithm is used to encrypt the Beidou positioning associated data set. The original plaintext data is converted into ciphertext data by processing the data according to the 128-bit group size. Based on the encrypted Beidou positioning associated data set, the header information is filled in, the relevant identifier and length are filled in the corresponding fields, the encrypted Beidou positioning associated data set is placed in the data segment position, the checksum is calculated and filled in the corresponding fields, and the data encapsulation process is completed to form a data packet.

4. The method for intelligent management of IoT products based on Beidou positioning according to claim 1, characterized in that: According to step S200, the encrypted Beidou positioning associated data set is decrypted using the obtained symmetric key to restore the original plaintext data, extract the checksum, recalculate the checksum, and compare the recalculated checksum with the extracted checksum to perform data verification; For the decrypted Beidou positioning association data set, the real-time three-dimensional coordinate position of the goods, the vehicle's driving speed and the vehicle's movement direction are extracted, and the goods' transportation trajectory is constructed in chronological order; the destination coordinate information of the goods is obtained from the logistics planning information, and the remaining distance is calculated based on the current location and transportation status of the goods; the vehicle's driving speed in the remaining distance is predicted based on the statistical analysis of the vehicle's current driving speed and historical speed data. If there is an acceleration or deceleration trend, the speed change trend is predicted through linear regression; the estimated arrival time of the goods is calculated using the relationship between distance and speed, and the relationship between distance and speed is estimated arrival time = remaining distance / expected speed; the standard route of this transportation task is obtained from the logistics planning system, and the standard route is a path composed of several waypoints; the constructed goods transportation trajectory is compared with the standard route, and the distance between the actual location of the goods and the nearest point on the standard route is calculated within the time interval set by the user. If the distance exceeds the preset threshold, it is considered to have deviated from the preset transportation route.

5. The method for intelligent management of IoT products based on Beidou positioning according to claim 4, characterized in that: According to step S200, for the vehicle information, it is determined whether there are any abnormalities in the vehicle component status and the vehicle energy status; for the cargo information, it is determined whether there are any abnormalities in the cargo weight, volume, and transportation condition requirements; The following methods are used to determine whether the vehicle's component status is abnormal: for tire components, pressure inspection, wear assessment, and temperature monitoring are performed; for brake components, brake pad thickness inspection and brake fluid level inspection are performed; for engine components, fault code reading, temperature inspection, and oil pressure inspection are performed; for suspension components, vibration monitoring, noise monitoring, and vehicle posture monitoring are performed; for steering components, steering flexibility testing and steering component inspection are performed; the following methods are used to determine whether the vehicle's energy status is abnormal: for fuel vehicles, fuel quantity monitoring and fuel consumption rate analysis are performed; for electric vehicles, battery power inspection and battery health status assessment are performed; The method for determining whether the weight and volume of the cargo are abnormal is as follows: obtain the weight of the cargo from the cargo information and compare it with the vehicle's load capacity. If the cargo weight exceeds the vehicle's load capacity, it is considered an abnormal cargo weight situation; compare the volume of the cargo with the vehicle's effective loading space. If the cargo volume exceeds the space limited by the length, width and height of the vehicle compartment, it is determined that the cargo volume is abnormal; the method for determining whether the cargo transportation conditions are abnormal is as follows: judge based on temperature, humidity, ventilation and shockproof requirements.

6. The method for intelligent management of IoT products based on Beidou positioning according to claim 1, characterized in that: When positioning accuracy is insufficient and the vehicle's driving status is confirmed to be normal, the vehicle's historical driving data is filtered from the Beidou positioning related data set and arranged in chronological order. A linear regression model is constructed with the real-time three-dimensional coordinate position of the cargo as the target variable and the vehicle's driving speed, vehicle direction, cargo weight, cargo volume, and time as independent variables. The linear relationship coefficient between the independent variable and the target variable is learned using historical data. The linear regression model is trained using collated historical driving data and cargo information samples. During the training process, the model attempts to find the optimal linear relationship coefficient so that the cargo location can be predicted based on the input independent variables. Through historical data training, the model continuously adjusts the coefficient to minimize the error between the predicted location and the actual historical location. When predicting the current location of the cargo, the current vehicle speed, vehicle direction, cargo weight, and cargo volume are obtained, and the number of time steps from the start of transportation to the current moment is calculated for data input. Based on the learned linear relationship, the model calculates and outputs the current predicted location of the goods, expressed as three-dimensional coordinates.

7. The method for intelligent management of IoT products based on Beidou positioning according to claim 1, characterized in that: According to step S400, when an abnormality in vehicle driving state occurs, the specific type of abnormality is determined and analyzed in combination with information on abnormal cargo transportation route or abnormal cargo transportation state; based on the type of abnormality, the impact on the vehicle's driving speed and vehicle movement direction in the linear regression model is clarified; in the event of sudden braking of the vehicle, the speed parameter in the linear regression model is adjusted based on the severity of the braking, wherein the severity of the braking is determined by the vehicle deceleration information; the speed adjustment range is estimated based on the performance parameters of the vehicle's brake components and the inertia characteristics of the cargo in the vehicle compartment; in the event of sudden acceleration, the speed parameter value in the linear regression model is increased based on the acceleration capability of the vehicle and the inertia of the cargo; When the vehicle makes a sharp turn, the moving direction parameters in the linear regression model are changed. If the vehicle's direction deviates from the planned route, the trend of the deviation direction and angle is analyzed, and the moving direction parameters in the linear regression model are gradually adjusted so that the cargo location prediction can reflect the direction change. The vehicle dynamics model describes the motion state of a vehicle under the influence of various forces. The vehicle dynamics model is used to calculate the impact of sudden braking and sharp turns on the position of cargo. The cargo position change information calculated by the vehicle dynamics model is combined with the linear regression model after speed and direction adjustment to obtain a new cargo position prediction result.

8. The method for intelligent management of IoT products based on Beidou positioning according to claim 7, characterized in that: According to step S400, when only the vehicle's driving state is abnormal, such as when the vehicle brakes suddenly, the vehicle's displacement during the braking process is calculated based on the velocity-displacement relationship in the vehicle's kinematic equation, taking into account the vehicle's initial velocity, braking deceleration, and braking duration. For the cargo, the additional displacement of the cargo relative to the vehicle compartment is calculated based on its initial position within the compartment, the friction coefficient between the cargo and the compartment, and the cargo's weight. The current position of the cargo is predicted based on the combined displacement of the vehicle and cargo. When the vehicle accelerates suddenly, the vehicle's displacement is calculated based on the vehicle's kinematic equation, the vehicle's acceleration, and the acceleration time. The relative displacement of the cargo within the car is determined by combining the cargo weight, the coefficient of friction with the car, and the acceleration, thereby obtaining a prediction of the cargo's current position. When the vehicle makes a sharp turn, the trajectory of the vehicle during the turn is calculated based on the circular motion principle in the vehicle's kinematic equation, taking into account the vehicle's speed, turning radius, and weight. For the cargo, the lateral displacement of the cargo is estimated based on its weight, its position within the car, and the constraints between the car and the cargo, thereby predicting the cargo's position during the turn. The prediction results are optimized using the cargo dynamics model.

9. An intelligent management system for Internet of Things products based on Beidou positioning, using the intelligent management method for Internet of Things products based on Beidou positioning according to any one of claims 1 to 8, characterized in that: include: Data collection and transmission module: includes: a data collection unit, a data association unit and a data encryption and transmission unit; wherein, the data collection unit collects Beidou positioning data through the Beidou positioning terminal, including the real-time three-dimensional coordinate position of the cargo, the vehicle's driving speed, the vehicle's moving direction, the ambient temperature and positioning accuracy information, and obtains vehicle information and cargo information; the data association unit associates the vehicle information and cargo information with the corresponding Beidou positioning data to form a Beidou positioning association data set; the data encryption and transmission unit transmits the Beidou positioning association data set through the Beidou satellite communication link and the ground communication network, and encrypts and encapsulates the data before transmission; Data processing and analysis module: includes: decryption and verification unit, transportation route analysis unit and transportation status abnormality judgment unit; the decryption and verification unit performs decryption operations based on the Beidou positioning related data set and verifies the data using a verification algorithm; the transportation route analysis unit analyzes the Beidou positioning data to see if there are any abnormalities in the cargo transportation route, calculates the estimated arrival time of the cargo, and determines whether it deviates from the preset transportation route by comparing it with the standard route of the logistics planning; the transportation status abnormality judgment unit determines whether there are any abnormalities in the cargo transportation status and vehicle driving status based on the vehicle information and cargo information respectively; Abnormal processing and prediction module: includes: data extraction and load calculation unit and linear regression prediction unit; among them, when the Beidou positioning data shows abnormalities in the cargo transportation route or cargo transportation status, the data extraction and load calculation unit extracts positioning accuracy information from the Beidou positioning associated data set, extracts data features of time periods and locations where positioning accuracy is insufficient, and calculates the degree of match between the vehicle load and cargo weight based on vehicle information and cargo information; the linear regression prediction unit uses a linear regression algorithm. When positioning accuracy is insufficient, if the vehicle driving status is normal, a linear regression model is established based on the vehicle's historical driving data and cargo information to predict the current location of the cargo; Cargo location prediction module under abnormal conditions: includes: complex abnormality adjustment prediction unit and single abnormality prediction unit; among them, the complex abnormality adjustment prediction unit dynamically adjusts the vehicle's driving speed and vehicle's moving direction in the linear regression model when the vehicle's driving state is abnormal, if the cargo transportation route is abnormal or the cargo transportation state is abnormal, and further adjusts the cargo location prediction in combination with the vehicle dynamics model; the single abnormality prediction unit predicts the current location of the cargo according to the type and degree of the abnormal vehicle driving state, combined with the vehicle's kinematic equation and the cargo dynamics model when only the vehicle's driving state is abnormal.

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