Cold chain transportation system applying Beidou system

Abstract

The invention discloses a cold chain transportation system applying a Beidou system, relates to the field of cold chain transportation, and particularly aims to solve the problems of information asymmetry, multiple monitoring blind areas, low response speed and the like in the traditional cold chain transportation and refrigeration process. In the prior art, the requirements of the modern logistics industry for high efficiency, transparency, traceability, real-time positioning, temperature monitoring, data management and other functions are difficult to meet, the cold chain transportation system applying the Beidou system is arranged, and the system architecture can be divided into a sensing layer, a network layer and an application layer; real-time monitoring and intelligent regulation and control of key parameters such as temperature and humidity in the cold chain transportation process are achieved, the system can provide accurate temperature control, safety and quality of goods in the transportation process are ensured, meanwhile, a scientific decision basis is provided through data analysis, an enterprise is helped to optimize resource allocation, and market competitiveness is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cold chain transportation, and specifically relates to a cold chain transportation system using the Beidou system. Background Art

[0002] The invention of modern refrigeration technology began in the mid-18th century, and food transportation gradually used this technology. Because the freezing temperature is not low enough, it is impossible to freeze quickly and keep the freshness and hygiene of food for a longer time, especially meat. When frozen seafood and meat arrive at the market, the meat quality has changed, but at that time, it is better to have meat to eat than not to have meat. In real life, pre-cooling technology plays a vital role in food preservation, especially in the storage and transportation of fruits, vegetables and meat products.

[0003] Cold chain transportation refers to the transportation process in which the transported goods are kept at a certain temperature. Cold chain transportation can be by road, water, rail, air, or a combination of multiple modes. If freshly picked fruits and vegetables, or poultry such as newly slaughtered chickens and ducks, are not quickly precooled to around 0°C by physical or chemical means within 1 hour to allow them to enter a dormant period, their internal enzyme activity and microorganisms will continue to multiply during transportation and storage, resulting in a rapid decline in quality (case: rotten fruit in the refrigerator).

[0004] Therefore, in the field of cold chain logistics, temperature control is the key to ensuring the quality of sensitive goods such as food and medicine. Traditional cold chain transportation and refrigeration processes often have problems such as information asymmetry, many monitoring blind spots, and slow response speed. It is difficult to meet the needs of modern logistics industry for efficiency, transparency, and traceability, and provide real-time positioning, temperature monitoring, and data management functions.

[0005] Therefore, it is urgent to design a cold chain transportation system using the Beidou system to solve the above problems. Summary of the invention

[0006] In view of the defects of the above-mentioned prior art, the purpose of the present invention is to provide a cold chain transportation system using the Beidou system. The system architecture can be divided into a perception layer, a network and an application layer. The perception layer collects the real-time environmental parameters of the cold chain mobile cabin transport vehicle through environmental sensors such as cameras, BDS, temperature and humidity. The network layer uses wireless and wired networks to encode, authenticate and transmit the collected data and the control instructions of the console to the perception layer and the application layer. The application layer performs persistent visualization processing on the acquired collected data and presents it to the PC interface, so that managers can control the status of remote cold chain mobile cabin transport vehicles in real time, and remotely handle abnormal situations of the collected data in each temperature zone in the cold chain mobile cabin in real time.

[0007] In order to achieve the above purpose, the technical solution adopted by the present invention is shown in the following figure:

[0008] A cold chain transportation system using the Beidou system, characterized by:

[0009] The perception layer is used to collect the real-time environmental parameters of the cold chain mobile cabin transport vehicles and upload them to the network layer;

[0010] The network layer encodes, authenticates and transmits the uploaded collected data and control instructions of the application layer to the perception layer and application layer;

[0011] At the application layer, the acquired data is processed in a persistent and visualized manner and presented on the PC interface, so that management personnel can monitor the status of remote cold chain mobile cabin transport vehicles in real time and remotely handle abnormal situations of the collected data in each temperature zone in the cold chain mobile cabin in a timely manner.

[0012] Preferably, the perception layer includes a plurality of mobile cold chain cabin vehicle-mounted monitoring nodes, and the mobile cold chain cabin vehicle-mounted monitoring nodes are arranged in the cold chain cabin transport vehicle, and the mobile cold chain cabin vehicle-mounted monitoring nodes include a control unit and a collection unit;

[0013] The control unit controls the mixed temperature PCM passive phase change refrigeration system and various execution devices in the cold chain container transport vehicle in real time according to the control instructions received from the application layer, and handles abnormal conditions in a timely manner to reduce unnecessary losses;

[0014] The collection unit is used to collect the environment, cargo images, and vehicle geographic location inside the cold chain container transport vehicle, and upload the collected data to the network layer through the MQTT protocol.

[0015] Preferably, the collection unit collects the environment inside the cold chain container transport vehicle, cargo images, and vehicle geographic location through an infrared camera, BDS, and temperature and humidity environmental sensors.

[0016] Preferably, the network layer is a big data cloud platform, which is responsible for networking the distributed perception layer and application layer and realizing data interaction through the MQTT protocol.

[0017] Preferably, the application layer is a real-time monitoring center node, which is deployed on a PC or a mobile terminal. The real-time monitoring center node includes: a data processing module, a historical data playback module, and a remote control module;

[0018] The data processing module is used to parse the MQTT protocol data at the network layer and process the data, and then persist and visualize the processed data to grasp the operation status of the cold chain container transport vehicle in real time;

[0019] Historical data playback module, which realizes data storage and playback;

[0020] The remote control module controls the PCM passive phase change refrigeration system and various execution devices of the remote mobile cold chain cabin in real time through a graphical interface, executes the received control instructions, and adjusts the relevant parameters in each temperature zone in the cold chain mobile cabin.

[0021] Preferably, the data processing module uses the preprocessing technology of remote sensing image BNs classification to preprocess the cold chain cargo image data uploaded by the perception layer, and then uses the Odds feature algorithm to extract and calculate the features of the preprocessed image data, thereby realizing real-time monitoring and early warning of the status of cold chain cargo.

[0022] Preferably, the steps of using BDS to locate the vehicle's geographic location include:

[0023] S1. Calculate the horizontal and vertical positioning accuracy of multiple positioning points of the vehicle respectively;

[0024] S2. Calculate the three-dimensional positioning error ΔS of multiple positioning points of the vehicle based on the horizontal and vertical positioning accuracy j ;

[0025] S3. According to ΔS j The valid positioning points are sorted from small to large according to the value of , and the horizontal positioning component and vertical positioning component of the n×66.7% point are taken as the horizontal positioning accuracy and vertical positioning accuracy, and then the current vehicle position is positioned.

[0026] Preferably, the horizontal positioning accuracy Δh j The calculation formula is:

[0027] Where, ΔE j =E j ′-E j ; ΔN j =N j ′-N j

[0028] Wherein, j is the sample number of the positioning result participating in the statistics, j=1, 2, ..., n, and n is the total number of the positioning result samples participating in the statistics; E j ′ is the east component of the jth positioning result calculated by the receiver, E j is the east component of the jth positioning moment of the actual coordinate point, N j ′ is the north component of the jth positioning result calculated by the receiver, N j is the north component of the actual coordinate point at the jth positioning moment;

[0029] Vertical positioning accuracy Δu j The calculation formula is:

[0030] Δuj =|U j ′-U j |

[0031] Among them, U j ′ is the vertical component of the jth positioning result calculated by the terminal, U j It is the vertical component of the actual coordinate point at the jth positioning moment.

[0032] Preferably, ΔS j The calculation formula is:

[0033]

[0034] Among them, when ΔS j When it is greater than 50 meters, the positioning point of the corresponding epoch is judged to be invalid.

[0035] Preferably, the process of calculating the extracted eigenvalue X using the Odds feature algorithm includes:

[0036] S1. Construct Odds expression, Among them, P refers to the probability of a particular event occurring;

[0037] S2. Based on the Odds expression, a linear formula of Odds and eigenvalue X is constructed. The linear formula is:

[0038] logit(odds)=wx+b (1)

[0039] Among them, x represents the eigenvalue, W represents the weight coefficient, and b represents the bias term;

[0040] Further deducing formula (1), we can get:

[0041]

[0042] From formula (2), we can further obtain:

[0043]

[0044] Assuming z = wx + b, equation (3) can be expressed as:

[0045]

[0046] Where, exp represents the natural exponential function;

[0047] From formula (4), we can further obtain:

[0048]

[0049] Among them, P1 refers to the predicted probability of a specific event occurring.

[0050] The beneficial effects of the present invention are as follows: the present invention discloses a cold chain transportation system using the Beidou system. Compared with the prior art, the improvements of the present invention are:

[0051] (1) The present invention integrates the Beidou positioning system and the Internet of Things technology to build an intelligent cold chain logistics management system, which realizes real-time monitoring and intelligent regulation of key parameters such as temperature and humidity during cold chain transportation. The system can provide precise temperature control to ensure the safety and quality of goods during transportation. At the same time, it provides scientific decision-making basis through data analysis, helping enterprises optimize resource allocation and improve market competitiveness.

[0052] (2) The Beidou system of the present invention can provide accurate geographic location information for cold chain transport vehicles, so that logistics managers can track the transportation status of goods in real time, optimize transportation routes, and improve transportation efficiency; the Beidou system can also record key data during the transportation process, such as temperature, humidity, location, etc., and transmit these data back to the monitoring center for analysis, which helps logistics companies to conduct quality control and prevent potential supply chain risks; the Beidou system can also assist in monitoring the fuel consumption of vehicles, detect anomalies in a timely manner, prevent goods from being stolen or illegally operated, and thus protect the safety of corporate assets; further, by integrating temperature sensors, the Beidou system can monitor the temperature changes inside the cold chain vehicles in real time, ensure that the goods are transported under appropriate temperature conditions, and prevent damage or deterioration of goods due to temperature fluctuations; through the application of the Beidou system, cold chain logistics companies can provide more transparent and reliable services, enhance customer trust, and improve corporate competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The overall architecture of the system of the present invention Figure 1 ;

[0054] Figure 2 Schematic diagram of the temperature-mixable PCM passive phase change refrigeration system of the present invention;

[0055] Figure 3 The cold chain mobile cabin transport vehicle of the present invention Figure 1 ;

[0056] Figure 4 This is a diagram of the internal cargo compartment of the cold chain mobile cabin transport vehicle of the present invention;

[0057] Figure 5 The cold chain mobile cabin transport vehicle of the present invention Figure 2 .

[0058] Figure 6 The overall architecture of the system of the present invention Figure 2 ;

[0059] Figure 7The overall architecture of the system of the present invention Figure 3 . DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0061] Example: Refer to the attached Figure 1-7 A cold chain transportation system using the Beidou system is shown, according to the cold chain mobile cabin transportation vehicle (attached Figure 3-5 The operation control scenario shown in the figure can divide the entire data collection and monitoring system architecture into the perception layer, network layer and application layer. The perception layer collects the real-time environmental parameters and location information of the cold chain mobile cabin transport vehicle through infrared cameras, BDS locators, temperature and humidity and other environmental sensors deployed on the cold chain transport vehicle, and uploads it to the application layer through the network layer for processing and analysis; the network layer uses wireless and wired networks to encode, authenticate and transmit the uploaded collected data and control instructions of the application layer to the perception layer and application layer. As the core part of the system, the application layer processes and analyzes big data based on the intelligent multi-grid temperature zone mobile cabin cold chain cargo information uploaded by the perception layer; specifically, the pre-processing technology of remote sensing image BNs (Bayesian network) classification is used, combined with the Odds feature algorithm, to dynamically extract and classify the cold chain cargo big data, and the acquired collected data is persistently visualized and presented to the PC or mobile terminal, so that managers can control the status of remote cold chain mobile cabin transport vehicles in real time, and remotely handle abnormal situations of collected data in each temperature zone in the cold chain mobile cabin in a timely manner. The overall architecture is as follows: Figure 1 As shown;

[0062] Cold chain mobile cabin transport vehicles such as Figure 3-5 As shown, the period includes multiple cold chain cubicles, including large cargo compartment cold chain storage compartments, medium cargo compartment cold chain storage compartments, small cargo compartment cold chain storage compartments, and on-board generator set battery packs, which are used to provide power for the cargo compartment cold chain storage compartments to perform functions and various sensing layers;

[0063] The perception layer includes multiple on-board monitoring nodes for mobile cold chain cabins, among which the on-board monitoring nodes for mobile cold chain cabins are composed of a control unit and a collection unit, and the terminal is set in the cold chain cabin transport vehicle. The collection unit collects data such as the environment in the compartment, cargo images, and vehicle geographic location through infrared cameras, BDS (Beidou Satellite Navigation System), temperature and humidity and other environmental sensors, and uploads the data to the big data cloud platform through the MQTT protocol (message queue telemetry transmission protocol). Among them, a public mobile communication high-speed data transmission router based on the 5G transmission standard is adopted, which has a dual network card and dual antenna interface. The advantage of this design is that it ensures stable operation and high fault tolerance. The 5G base station connects the router and the mobile phone terminal through the cloud server to complete the remote communication function. The high-speed and stable communication link ensures the timeliness and accuracy of data transmission between various devices, and provides reliable information interaction guarantee for the entire remote monitoring and control system; the control unit monitors the mobile cold chain cabin's mixed temperature PCM passive phase change refrigeration system (such as Figure 2 As shown in the figure, various execution equipment are controlled in real time, abnormal conditions are handled promptly to reduce unnecessary losses;

[0064] Specifically, the temperature and humidity sensors are arranged in the cold storage room and connected to the cab through wires, so that the driver can observe the temperature changes in the cold storage room in real time. Once it exceeds the safe range, it will automatically alarm. The mobile cold chain container on-board monitoring node also has a Beidou positioning function, which can process, display, statistically analyze and store the vehicle's real-time location information together with the temperature data of the cold storage room through the GPRS network. This not only allows managers and cargo owners to track the specific location of the goods in real time, but also understand the temperature changes of the cold storage room environment.

[0065] (1) The temperature and humidity sensors collect temperature and humidity data in the cabin in real time and transmit the data to the core control box, which is then transmitted to the mobile client and PC through the network layer.

[0066] (2) Infrared cameras can be used to monitor the cargo status and equipment operation status in the cabin, and video information can also be transmitted to the mobile client and PC in real time.

[0067] (3) Various execution devices include oil-filled radiator heaters, humidifiers, oxygen generators, etc. These devices execute instructions from the mobile phone or PC to complete heating, humidification and oxygen generation functions. For example, when the temperature and humidity sensor detects that the temperature is too low or the humidity is insufficient, the mobile client or PC can send instructions to start the oil-filled radiator heater or humidifier to adjust the environment, and the oxygen generator can work according to the oxygen content demand in the cabin to ensure that the cargo is in a suitable oxygen environment.

[0068] The network layer is a big data cloud platform, which is responsible for networking multiple distributed mobile cold chain cabin vehicle-mounted monitoring nodes with real-time monitoring center nodes and realizing data interaction through the MQTT protocol.

[0069] The application layer is a real-time monitoring center node, which consists of a data processing module, a historical data playback module, and a remote control module. It is deployed on a PC or mobile terminal. The data processing module parses the MQTT protocol data of the cloud platform and processes the data, and then persists and visualizes the processed data to grasp the operation of the mobile cold chain cabin transport vehicle in real time; for example, it can remotely view the real-time cabin temperature and humidity, which is very important for low-temperature environment monitoring in cold chain transportation. The temperature and humidity data can intuitively reflect whether the cold chain transportation is in suitable environmental conditions, and the environment can be remotely monitored, such as to check whether there are abnormal conditions or potential safety hazards;

[0070] The historical data playback module realizes data storage and playback. Abnormal conditions discovered after the transportation is completed can be traced back through the historical data playback module to analyze the causes and avoid similar conditions in advance.

[0071] The remote control module uses a graphical interface to control the PCM passive phase change refrigeration system (such as Figure 2 As shown in the figure, the system can remotely control the operation status of the cold chain mobile cabin and various execution equipment in real time, deal with abnormal conditions in a timely manner to reduce unnecessary losses, and execute the received control instructions to adjust the relevant parameters in each temperature zone in the cold chain mobile cabin; for example, it can remotely control the working status of infrared cameras and heating and humidification equipment, start the infrared camera when it is necessary to check the status of the goods in the cabin, or adjust the heating and humidification equipment according to the temperature and humidity conditions.

[0072] Furthermore, the core control box of the remote control module uses DAM0455 as the core controller. The core control box is the core hardware of the system. DAM0455 is selected as the core controller, which can complete the communication with the mobile client or PC and the control execution equipment. It has input optocoupler isolation and relay output contact isolation functions; its communication interface supports Beidou satellite communication and network port, and communicates with 5G routers to realize data interaction with external devices (such as mobile client through 5G base station-cloud server-4G router link), supports standard Modbus protocol, and supports RTU and TCP formats, and performs remote communication through cloud server configuration.

[0073] The control box has two modes of manual and automatic control, and has a timing function. For example, it can remotely control the switches of heating and humidifying equipment, oxygen making equipment and other executive equipment. Manual control is convenient for direct operation in special circumstances (such as equipment debugging, emergency fault handling, etc.), and automatic control can automatically adjust the working state of the equipment according to preset conditions (such as temperature and humidity range, etc.); among them, the manual switch or switch trigger device is linked to the control output function: it has two-stage circuit breaker cascade and centralized wiring of live and neutral wires. This wiring method helps to improve the safety and stability of the circuit. The multi-way switch control and easy expansion characteristics make the system more convenient and flexible when it needs to add equipment or functions in the future. It has flash-on and flash-off functions. It can carry parameters in the instructions and operate the relay to automatically close after a period of time. It has a strobe function and can control the periodic switching of the relay. It has a timing control function and can read the time setting parameters externally to the timer to open or close the contact switch at a fixed time.

[0074] Specifically, the application layer uses the preprocessing technology of remote sensing image BNs (Bayesian network) classification, combined with the Odds feature algorithm, to dynamically extract and classify the cold chain cargo big data. The specific principles are as follows:

[0075] In the application layer, the data processing module uses the preprocessing technology of remote sensing image BNs classification to preprocess the cold chain cargo image data uploaded by the perception layer. This step aims to improve the quality and accuracy of the image data and lay the foundation for subsequent feature extraction and classification. Subsequently, the Odds feature algorithm is used to extract and calculate the features of the preprocessed image data. The extracted features are marked as feature values ​​X. Odds (odds ratio / odds ratio) is a way to measure the degree of association between classifications in features. It refers to the ratio of the probability of an event occurring to the probability of the event not occurring. In the big data of cold chain cargo, the Odds feature algorithm can help the system identify features related to changes in key parameters such as cargo temperature and humidity, thereby realizing real-time monitoring and early warning of the status of cold chain cargo.

[0076] The odds ratio (odds ratio) Odds actually compares the chances of the event occurring in two groups (Odds), which can be expressed as: Where P refers to the probability of a particular event occurring; for the sake of clarity, let’s use an example of cold chain transportation to elaborate:

[0077] Suppose we want to evaluate the impact of temperature fluctuations on the likelihood of food spoilage (or any other sensitive goods being damaged), and use the odds ratio as a measure. Here, P(+) represents the probability of food spoiling under certain conditions (such as high temperature exposure), and P(-) represents the probability of food remaining intact under control conditions (normal low temperature refrigeration). However, the odds ratio focuses on the comparison between two odds, that is, odds of event, rather than pure probability. Therefore,

[0078] Odds of + (O+) are the odds that food will spoil under adverse conditions, calculated as P(+) / [1-P(+)]; Odds of - (O-) are the odds that food will remain intact under good conditions, calculated as P(-) / [1-P(-)]. The ratio of the odds ratio OR (O+) to O- (O-) gives how many times the relative probability of food spoiling under adverse conditions compared to normal conditions. In cold chain logistics, this may help operators identify the risk level of food spoilage under specific conditions, so that preventive measures can be taken, such as improved packaging, enhanced monitoring or optimized route planning, to reduce losses. By monitoring data on factors such as temperature and humidity, combined with the precise location tracking and other sensor information of the Beidou system, transportation strategies can be adjusted in real time to ensure that food arrives safely at its destination.

[0079] There is a linear relationship between logit(odds) and eigenvalue X, which can be obtained:

[0080] logit(odds)=wx+b (1)

[0081] Among them, x represents the feature value, W represents the weight coefficient, which describes the influence of each feature on the prediction result. Specifically, corresponding to the i-th feature x i , indicating the contribution of this feature to the change in the logarithmic probability of predicting the target variable. If w i >0, it means that when x i When w increases, the probability of the event increasing; on the contrary, if w i <0, it means that when x i When increases, the probability of the event decreases. B represents the bias term, which does not depend on any eigenvalue. The role of the bias term is to give an initial base probability without any feature information. In other words, when all eigenvalues ​​are zero, B represents the default logarithmic probability, which can be regarded as a starting point for the model, and then adjust this baseline prediction by adding eigenvalues ​​and their corresponding weights.

[0082] Further deducing formula (1), we can get:

[0083]

[0084] From formula (2), we can further obtain:

[0085]

[0086] Wherein, exp represents the natural exponential function, which is an exponential function with the Euler number e (approximately equal to 2.71828) as the base; the expression exp(x) is equivalent to e^x, where "e" is the base of the natural logarithm, and "x" is the independent variable of the function; in the present invention, it plays a bridge role between logit(odds) and the original odds, and is also one of the key steps in converting logit(odds) into probability; the exp function is often used to convert logit(odds) back to odds form, because the logit function is a logarithmic probability transformation, which is the logarithmic form of odds. If there is a logit(odds) = wx + b, then odds can be calculated using the following formula:

[0087] Odds = exp(wx + b) = e (wx+b)

[0088] Assuming z = wx + b, equation (3) can be expressed as:

[0089]

[0090] From formula (4), we can further obtain:

[0091]

[0092] Among them, P1 refers to the probability of a specific event occurring. Through P1, the probability of spoilage of goods during the re-transportation process of the cold chain transportation system can be predicted; p is associated with the temperature and humidity of goods in the cold chain transportation system, which means that we can quantify the extent to which different environmental factors affect the state of goods; specifically, in the correlation analysis involving changes in key parameters such as temperature and humidity of goods in the cold chain transportation system and a certain result (such as whether the product is damaged or not), p may represent the probability of damage to goods under a given set of specific conditions (such as a specific temperature T and humidity H). Furthermore, through model training and parameter optimization, it is possible to identify which features (such as temperature or humidity) are the most critical factors and how they change the probability of events occurring. This helps to take corresponding preventive measures in practical applications, such as setting warning thresholds, improving storage conditions, etc., to reduce the risk of loss.

[0093] By combining remote sensing image BNs classification technology and Odds feature algorithm, the application layer system can accurately extract dynamic features from cold chain cargo big data, providing a strong basis for subsequent decision support and optimized management.

[0094] Specifically, in the perception layer, the steps of BDS (Beidou Satellite Navigation System) locating the vehicle's geographic location include:

[0095] S1. Calculate the horizontal and vertical positioning accuracy of multiple positioning points of the vehicle respectively;

[0096] Among them, the horizontal positioning accuracy Δh i The calculation formula is:

[0097] Where, ΔE j =E j ′-E j ; ΔN j =N j ′-N j

[0098] Wherein, j is the sample number of the positioning result participating in the statistics (j=1, 2, ..., n), and n is the total number of the positioning result samples participating in the statistics; E j ′ is the east component of the jth positioning result calculated by the receiver, E j N is the east component of the jth positioning moment of the actual coordinate point; j ′ is the north component of the jth positioning result calculated by the receiver, N j is the north component of the actual coordinate point at the jth positioning moment;

[0099] Vertical positioning accuracy Δu j Calculation formula:

[0100] Δu j =|U j ′-U j |, (j=1, 2, ..., n);

[0101] Among them, U j ′ is the vertical component of the jth positioning result calculated by the terminal; U j is the vertical component of the actual coordinate point at the jth positioning moment;

[0102] s2. Calculate the three-dimensional positioning error ΔS of multiple positioning points of the vehicle based on the horizontal and vertical positioning accuracy j , ΔS j The calculation formula is:

[0103]

[0104] Among them, when ΔS j When it is greater than 50 meters, the positioning point of the corresponding epoch is judged to be invalid.

[0105] S3. According to ΔS jThe valid positioning points are sorted from small to large according to the value of , and the horizontal positioning component and vertical positioning component of the (n×66.7%, where n is the number of values, generally the median is taken to reduce the error)th point are taken as the horizontal positioning accuracy and vertical positioning accuracy in this application mode, and then the current vehicle position is determined.

[0106] Based on the Beidou satellite navigation system, it has multiple functions, including real-time vehicle monitoring, historical track playback, alarm information prompts, remote vehicle locking, remote diagnosis, etc. The Beidou positioning and tracking real-time monitoring system is a mode based on BDS technology applied to the cold chain logistics transportation process control machine, which provides the architecture of the entire logistics information system. This information system organically integrates advanced RFID and BDS technologies, and implements all-round monitoring and management of transportation operations, which not only improves the accuracy of information in transportation, but also realizes real-time automation of transportation management.

[0107] The system of the present invention mainly solves the following technical problems: (1) improving the pre-cooling efficiency and temperature control accuracy in cold chain logistics; (2) providing real-time positioning, temperature monitoring, data management and other service functions. It ensures the quality and safety of temperature-sensitive commodities such as freshly picked fruits and vegetables, or freshly slaughtered chickens and ducks, poultry foods, and medicines before and during transportation. Especially in a mobile cold chain environment, it can effectively respond to temperature changes and monitor in real time to ensure that the goods maintain suitable temperature conditions throughout the cold chain logistics process.

[0108] The system of the present invention optimizes the temperature management in the cold chain logistics process by utilizing the high-precision positioning and time synchronization functions of the Beidou navigation system and combining the control data of the multi-temperature zone cold chain mobile cabin. Specifically,

[0109] (1) Accuracy of temperature control: Through high-precision positioning and time synchronization, the temperature stability and consistency of each temperature zone during cold chain transportation are ensured, reducing cargo losses caused by temperature fluctuations.

[0110] (2) Real-time monitoring and data recording: The patented method enables real-time monitoring of the cold chain process and accurately records temperature data for easy tracking and quality control.

[0111] (3) Improve energy efficiency: By optimizing the pre-cooling process and temperature management, energy consumption can be reduced and the energy efficiency of the entire cold chain system can be improved.

[0112] (4) Enhance supply chain transparency: Real-time monitoring and data recording help improve supply chain transparency, making it easier for regulators and consumers to trace product origins and quality.

[0113] (5) Adapt to complex and changing transportation environments: The multi-temperature zone design allows for the simultaneous transportation of a variety of temperature-sensitive commodities, improving transportation flexibility and cargo loading rates.

[0114] (6) Reduce food safety risks: Accurate temperature control helps maintain the freshness and safety of food during transportation and reduces the risk of foodborne diseases.

[0115] The system of the present invention integrates multiple functions such as real-time monitoring of cold chain vehicles, Beidou BDS satellite positioning, command and dispatch, vehicle temperature monitoring, etc. to achieve the following purposes:

[0116] 1. Improve rapid precooling technology: Use rapid precooling technology, such as cold water precooling, ice water precooling or liquid nitrogen precooling, which can reduce the temperature of items to the required low temperature in a short time. Vacuum cooling: Use vacuum cooling technology to accelerate the evaporation of water by reducing pressure, thereby quickly taking away heat and achieving rapid cooling. Cold storage precooling: Precooling in a cold storage, where the temperature and humidity can be precisely controlled, is conducive to quickly and evenly reducing the temperature of items.

[0117] 2. Measures to improve energy efficiency and reduce energy consumption: Adopt efficient refrigeration technology and equipment: Update the refrigeration system, use energy-saving compressors and heat exchangers, optimize working parameters, and reduce energy consumption. Optimize the layout of cold storage and cold storage rooms: Rationally design the cold storage structure, improve space utilization, reduce the thermal bridge effect inside the cold storage, and reduce energy consumption. Improve the insulation performance of the air supply duct: Use high-performance insulation materials to reduce energy loss during the heat exchange process. Implement intelligent temperature control: Install an intelligent temperature control system to automatically adjust the temperature according to the type and quantity of goods and reduce unnecessary energy consumption.

[0118] 3. Solve the energy consumption problem of mixed cargo loading. (1) Multi-temperature zone design: The interior of the cold chain mobile cabin is divided into multiple independent temperature zones, each of which can be set and controlled according to the specific temperature requirements of the stored cargo. (2) Temperature sensor: Temperature sensors are deployed in each temperature zone to collect temperature data in real time. These sensors can provide continuous and accurate temperature readings to ensure the real-time and reliability of temperature monitoring. This can adapt to the temperature requirements of different cargoes and reduce energy waste.

[0119] 4. The problem of "broken chain" in cold chain transportation. Install temperature sensors and monitoring equipment, and control data can be transmitted back to the monitoring center in real time through the Beidou satellite navigation system or other wireless communication technologies. Beidou BDS uses real-time temperature control and electronic temperature measurement throughout the entire process to achieve real-time monitoring of the temperature in the carriage. Once abnormal temperature is found, measures are taken immediately. Ensure the freshness of the goods; unloading alarms can prevent the goods from being replaced or lost during transportation. Cold chain vehicles can control the vehicle fuel consumption in real time. If there is abnormal fuel consumption, managers can remotely monitor the temperature and intervene when necessary.

[0120] System Application and Advantages

[0121] The system tracks the target data of the intelligent multi-temperature zone mobile cabin cold chain transportation big data in real time, and combines with Beidou positioning technology to realize real-time monitoring of cold chain data collection and positioning tracking. It not only has powerful real-time monitoring and early warning functions, but also has high scalability and flexibility, bringing significant advantages to the cold chain logistics industry.

[0122] 1) High scalability and flexibility;

[0123] Through modular design, more sensors and data sources, such as vibration sensors, gas sensors, etc., can be integrated to further enhance the comprehensive monitoring and management capabilities of cold chain transport vehicles. The scalability enables the system to adapt to the cold chain logistics needs of different industries and scenarios, providing users with more customized solutions.

[0124] The system is also highly flexible and can be adjusted and configured according to the actual needs of users. Whether it is the positioning accuracy requirements for transport vehicles or the real-time monitoring needs for cargo temperature and humidity, the system can provide flexible configuration options to meet the diverse needs of users.

[0125] 2) Real-time monitoring and early warning functions;

[0126] Through real-time monitoring and early warning functions, potential transportation risks can be discovered and handled in a timely manner to ensure the quality and safety of goods. Using advanced algorithms and data processing technologies, various data in the cold chain transportation process are analyzed and evaluated in real time. Once an abnormal situation is found, the early warning mechanism is immediately triggered to notify relevant personnel to handle it.

[0127] 3) Intelligent target data positioning and tracking;

[0128] The system also has the function of intelligent target data positioning and tracking. Through dynamic feature analysis, the system can obtain the maximum position of the similarity function, and then use the Bhattacharyya distance coefficient to determine the adaptive weighting and feature selection to complete the precise positioning of target data during the transportation of cold chain goods in the intelligent multi-temperature zone mobile cabin.

[0129] 1. Beidou Satellite Navigation System

[0130] (1) Positioning and tracking. The global coverage of the BeiDou system enables cold chain transport vehicles to be accurately tracked at any location. Its high-precision positioning capability improves the accuracy of transport routes, helps reduce transport time and costs, and enables real-time tracking and route optimization of transport vehicles.

[0131] (2) Navigation planning function: Providing accurate navigation for cold chain transport drivers and planning the optimal driving route based on real-time traffic information is also an important part of improving transportation efficiency.

[0132] (3) Comprehensive and real-time monitoring. Integrated Beidou terminal and sensor network: Each cold chain transport vehicle is equipped with an intelligent terminal integrated with Beidou navigation module and equipped with various sensors such as temperature, humidity, and geographic location. These devices can collect and transmit information such as vehicle location and cargo status (including temperature, humidity, etc.) to the cloud server in real time.

[0133] 2. IoT technology

[0134] (1) Sensor collection function. Installing a variety of sensors on cold chain transport vehicles, such as temperature sensors, humidity sensors, oxygen content sensors, etc., can collect key parameters in the cold chain transportation process in real time, including temperature, humidity, oxygen content, etc., and transmit the data to ensure that the goods are in a suitable transportation environment.

[0135] (2) Smart labels and storage functions: Use smart labels or RFID chips on cold chain packaging to record key information such as batch number, production date, and expiration date of the goods; sensors provide detailed records accurate to the individual product level for cold chain item traceability.

[0136] (3) Video surveillance and AI assistance. Install high-definition cameras inside and outside the car, and use artificial intelligence image recognition technology to automatically identify the door switch status, cargo stacking status, etc., to further reduce monitoring blind spots and ensure transportation safety.

[0137] 3. Monitoring and analysis platform

[0138] (1) Provide real-time data information. Establish a monitoring platform to provide managers and customers with data such as vehicle real-time location, transportation trajectory, temperature and humidity change curves, etc.

[0139] (2) Monitoring and scheduling. Managers can use the platform to conduct real-time monitoring and scheduling, while customers can track the status of cargo transportation and enhance trust.

[0140] (3) Data processing and analysis on the cloud platform. A data processing center based on cloud computing is established to receive real-time data from each vehicle, and the data is quickly processed and analyzed using big data analysis algorithms to identify abnormal conditions (such as temperature deviation from the preset range), immediately trigger the early warning mechanism, and notify management personnel and drivers through SMS, APP push, etc., to achieve rapid response.

[0141] 4. Data transmission, processing and tracing

[0142] (1) Data transmission cloud service function. It supports connecting the database to the cloud service through a database gateway or public network IP, supports cross-border transmission through 5G communication technology or other wireless communication means, and transmits the collected data to the cloud server or data processing center.

[0143] (2) Data verification and real-time processing. In the cloud, real-time business data is synchronized to the analysis system, and the data is analyzed and processed in real time, achieving instant data analysis and decision support, so as to promptly detect abnormal conditions in the transportation process, such as temperature fluctuations, equipment failures, etc.

[0144] (3) Data recording and blockchain encryption: All key data during transportation (including but not limited to location information, temperature and humidity records, door opening records, etc.) will be encrypted and stored, and blockchain technology will be used to ensure that the data cannot be tampered with and is traceable. Consumers or regulators can scan the QR code on the product to access the cloud server and obtain complete supply chain information, thus achieving full-chain traceability of food from source to table.

[0145] 5. Improvement of safe transportation efficiency

[0146] (1) Beidou and dynamic route planning. By utilizing the high-precision positioning and navigation functions of the Beidou system and combining it with real-time traffic information (such as road conditions and weather), intelligent algorithms are used to dynamically plan the optimal driving route for drivers, avoiding congested roads and reducing transportation time and costs.

[0147] (2) Intelligent dispatching system: Build an intelligent dispatching platform based on big data to automatically assign transportation tasks based on factors such as order requirements, vehicle location, and cargo characteristics, optimize vehicle resource allocation, and improve overall transportation efficiency.

[0148] (3) Abnormal alarm system. Abnormal detection: When the system detects abnormal conditions during transportation, such as temperature fluctuations, equipment failures, etc., it can promptly discover and respond to potential risks. Automatic alarm: Automatically trigger the alarm mechanism, such as sending text messages or push notifications to management personnel so that timely measures can be taken. For example, when the temperature exceeds the preset range, an automatic alarm is sounded to remind relevant personnel to adjust the refrigeration equipment to ensure the safety of the goods.

[0149] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A cold chain transportation system using the Beidou system, characterized in that: include The perception layer is used to collect the real-time environmental parameters of the cold chain mobile cabin transport vehicles and upload them to the network layer; The network layer encodes, authenticates and transmits the uploaded collected data and control instructions of the application layer to the perception layer and application layer; At the application layer, the acquired data is processed in a persistent and visualized manner and presented to the PC or mobile interface, so that managers can monitor the status of remote cold chain mobile cabin transport vehicles in real time and remotely handle abnormal situations of the collected data in each temperature zone in the cold chain mobile cabin in a timely manner.

2. According to claim 1, a cold chain transportation system using the Beidou system is characterized in that: The perception layer includes a plurality of mobile cold chain cabin vehicle-mounted monitoring nodes, and the mobile cold chain cabin vehicle-mounted monitoring nodes are arranged in the cold chain cabin transport vehicle, and the mobile cold chain cabin vehicle-mounted monitoring nodes include a control unit and a collection unit; The control unit controls the mixed temperature PCM passive phase change refrigeration system and various execution devices in the cold chain container transport vehicle in real time according to the control instructions received from the application layer, and handles abnormal conditions in a timely manner to reduce unnecessary losses; The collection unit is used to collect the environment, cargo images, and vehicle geographic location inside the cold chain container transport vehicle, and upload the collected data to the network layer through the MQTT protocol.

3. According to claim 2, a cold chain transportation system using the Beidou system is characterized in that: The collection unit collects the environment, cargo images, and vehicle geographic location inside the cold chain container transport vehicle through infrared cameras, BDS, and temperature and humidity environmental sensors.

4. According to claim 1, a cold chain transportation system using the Beidou system is characterized in that: The network layer is a big data cloud platform, which is responsible for networking the distributed perception layer and application layer and realizing data interaction through the MQTT protocol.

5. According to claim 1, a cold chain transportation system using the Beidou system is characterized in that: The application layer is a real-time monitoring center node, which is deployed on a PC or mobile terminal. The real-time monitoring center node includes: a data processing module, a historical data playback module, and a remote control module; The data processing module is used to parse the MQTT protocol data at the network layer and process the data, and then persist and visualize the processed data to grasp the operation status of the cold chain container transport vehicle in real time; Historical data playback module, which realizes data storage and playback; The remote control module controls the PCM passive phase change refrigeration system and various execution devices of the remote mobile cold chain cabin in real time through a graphical interface, executes the received control instructions, and adjusts the relevant parameters in each temperature zone in the cold chain mobile cabin.

6. A cold chain transportation system using Beidou system according to claim 5, characterized in that: The data processing module uses the remote sensing image BNs classification preprocessing technology to preprocess the cold chain cargo image data uploaded by the perception layer. Subsequently, the Odds feature algorithm is used to extract and calculate the features of the preprocessed image data, thereby realizing real-time monitoring and early warning of the status of cold chain cargo.

7. The cold chain transportation system using Beidou system according to claim 3 is characterized by: The steps to use BDS to locate the vehicle's geographic location include: S1. Calculate the horizontal and vertical positioning accuracy of multiple positioning points of the vehicle respectively; S2. Calculate the three-dimensional positioning error ΔS of multiple positioning points of the vehicle based on the horizontal and vertical positioning accuracy j ; S3. According to ΔS j The valid positioning points are sorted from small to large according to the value of , and the horizontal positioning component and vertical positioning component of the n×66.7% point are taken as the horizontal positioning accuracy and vertical positioning accuracy, and then the current vehicle position is positioned.

8. The cold chain transportation system using Beidou system according to claim 7 is characterized in that: Horizontal positioning accuracy Δh j The calculation formula is: Where, ΔE j =E′ j -E j ; ΔN j =N′ j -N j Wherein, j is the sample number of the positioning result participating in the statistics, and n is the total number of the positioning result samples participating in the statistics; E' j is the east component of the jth positioning result solved by the receiver, E j is the east component of the jth positioning moment of the actual coordinate point, N′x is the north component of the jth positioning result solved by the receiver, and N j is the north component of the actual coordinate point at the jth positioning moment; Vertical positioning accuracy Δu j The calculation formula is: Δu j =|U′ j -U j | Among them, U′ j is the vertical component of the jth positioning result calculated by the terminal, U j It is the vertical component of the actual coordinate point at the jth positioning moment.

9. The cold chain transportation system using Beidou system according to claim 7, characterized in that: ΔS j The calculation formula is: Among them, when ΔS j When it is greater than 50 meters, the positioning point of the corresponding epoch is judged to be invalid.

10. The cold chain transportation system using Beidou system according to claim 6, characterized in that: The process of calculating the extracted eigenvalue X using the Odds feature algorithm includes: S1. Construct Odds expression Among them, P refers to the probability of a particular event occurring; S2. Based on the Odds expression, a linear formula of Odds and eigenvalue X is constructed. The linear formula is: logit(odds)=wx+b (1) Among them, x represents the eigenvalue, W represents the weight coefficient, and b represents the bias term; Further deducing formula (1), we can get: From formula (2), we can further obtain: Assuming z = wx + b, equation (3) can be expressed as: Where, exp represents the natural exponential function; From formula (4), we can further obtain: Among them, P1 refers to the predicted probability of a specific event occurring.

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