Intelligent logistics monitoring method and system based on Internet of Things

Abstract

The invention is suitable for the technical field of logistics monitoring, and provides an intelligent logistics monitoring method and system based on the Internet of Things, sensor information is acquired, logistics information is acquired through the sensor information, and the logistics information comprises cargo information, transportation information, warehousing information and flow information; the logistics information is uploaded through the Internet of Things, edge processing is carried out on the logistics information, and the edge processing is used for carrying out data acquisition on the logistics information and carrying out preprocessing on the data; analyzing the processed logistics information through the visual monitoring platform, comparing the logistics information with a threshold value, and if the logistics information exceeds the threshold value, judging that the logistics information is abnormal, and sending an abnormal value to the user terminal; according to the intelligent logistics monitoring method and system, the transmission data is encrypted, the transmission mode is obtained, and the transmission mode is sent to the user terminal, so that the problems that the latest progress of the whole logistics process cannot be mastered in real time and the intelligent logistics monitoring effect is poor due to the fact that the intelligent logistics process based on the Internet of Things cannot be monitored in real time are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics monitoring, and in particular relates to an intelligent logistics monitoring method and system based on the Internet of Things. Background Art

[0002] The Internet of Things is an important part of the new generation of information technology. As the name suggests, the Internet of Things is the Internet that connects everything. This has two meanings: first, the core and foundation of the Internet of Things is still the Internet, which is an extension and expansion of the Internet; second, its user end extends and expands to any object to exchange information and communicate. Applying the Internet of Things technology to the logistics system will make the management of logistics more accurate, intelligent and complete.

[0003] However, the existing logistics monitoring cannot monitor the smart logistics process based on the Internet of Things in real time, resulting in the inability to grasp the latest progress of the entire logistics process in real time and poor smart logistics monitoring effect. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide a smart logistics monitoring method based on the Internet of Things, aiming to solve the problems raised in the third part of the background technology.

[0005] The embodiment of the present invention is implemented as follows: a smart logistics monitoring method based on the Internet of Things, the method comprising:

[0006] Acquire sensor information, and acquire logistics information through the sensor information, wherein the logistics information includes cargo information, transportation information, warehousing information and process information;

[0007] Upload logistics information through the Internet of Things and perform edge processing on the logistics information. The edge processing is used to collect data on the logistics information and pre-process the data;

[0008] The processed logistics information is analyzed through the visual monitoring platform, and compared with the threshold. If the logistics information exceeds the threshold, it is judged as abnormal and the abnormal value is sent to the user terminal;

[0009] Encrypt the transmission data, obtain the transmission method, and send the transmission method to the user terminal.

[0010] Preferably, the step of uploading logistics information through the Internet of Things and performing edge processing on the logistics information, wherein the edge processing is used to collect data on the logistics information and pre-process the data, specifically includes:

[0011] Upload logistics information through the Internet of Things, obtain logistics information in real time, and send the logistics information to the edge processing component, which includes edge devices and edge servers;

[0012] Preprocessing the data by edge processing components, wherein the preprocessing includes real-time data processing and analysis;

[0013] The pre-processed data is analyzed and predicted through intelligent algorithms, including image recognition, anomaly detection and predictive maintenance.

[0014] Preferably, the step of analyzing the processed logistics information through the visual monitoring platform, comparing the logistics information with the threshold, and determining it as abnormal if the logistics information exceeds the threshold, and sending the abnormal value to the user terminal specifically includes:

[0015] Analyze and process logistics information through the visual monitoring platform, display logistics status through charts, and generate reports through the visual monitoring platform;

[0016] Compare the logistics information with the threshold. If the logistics information exceeds the threshold, it is judged as abnormal. If the logistics information does not exceed the threshold, it is judged as normal.

[0017] Determine the abnormality level, take different countermeasures according to the abnormality level, and send the countermeasures to the user terminal.

[0018] Preferably, the steps of encrypting the transmission data, obtaining the transmission mode, and sending the transmission mode to the user terminal specifically include:

[0019] Obtain the data type, select the transmission method according to the data type, and encrypt the transmitted data;

[0020] Obtaining an encryption method and generating an encryption protocol, wherein the encryption protocol is used to ensure the security and privacy of data transmission;

[0021] Get the request from the user terminal and send the transmission mode to the user terminal.

[0022] Preferably, the reporting network includes temperature change reports, transportation route reports and inventory status reports.

[0023] Another object of an embodiment of the present invention is to provide a smart logistics monitoring system based on the Internet of Things, the system comprising:

[0024] An information acquisition module acquires sensor information, and acquires logistics information through the sensor information, wherein the logistics information includes cargo information, transportation information, warehousing information and process information;

[0025] The edge processing module uploads the logistics information through the Internet of Things and performs edge processing on the logistics information. The edge processing is used to collect data on the logistics information and pre-process the data;

[0026] The abnormality judgment module analyzes the processed logistics information through the visual monitoring platform, compares the logistics information with the threshold, and if the logistics information exceeds the threshold, it is judged as abnormal and sends the abnormal value to the user terminal;

[0027] The encryption module encrypts the transmission data, obtains the transmission mode, and sends the transmission mode to the user terminal.

[0028] Preferably, the edge processing module includes:

[0029] A real-time acquisition unit uploads logistics information through the Internet of Things, acquires logistics information in real time, and sends the logistics information to an edge processing component, wherein the edge processing component includes an edge device and an edge server;

[0030] A preprocessing unit, which preprocesses data through an edge processing component, wherein the preprocessing includes real-time data processing and analysis;

[0031] The prediction unit analyzes and predicts the pre-processed data through intelligent algorithms, and the analysis and prediction include image recognition, anomaly detection and predictive maintenance.

[0032] Preferably, the abnormality determination module includes:

[0033] Visualization unit, which analyzes and processes logistics information through the visual monitoring platform, displays logistics status through charts, and generates reports through the visual monitoring platform;

[0034] The abnormality determination unit compares the logistics information with the threshold value. If the logistics information exceeds the threshold value, it is determined to be abnormal; if the logistics information does not exceed the threshold value, it is determined to be normal;

[0035] The abnormality level unit determines the abnormality level, takes different response measures according to the abnormality level, and sends the response measures to the user terminal.

[0036] Preferably, the encryption module comprises:

[0037] The transmission unit obtains the data type, selects the transmission method according to the data type, and encrypts the transmission data;

[0038] An encryption unit, which obtains an encryption method and generates an encryption protocol, wherein the encryption protocol is used to ensure the security and privacy of data transmission;

[0039] The sending unit obtains a request from the user terminal and sends a transmission mode to the user terminal.

[0040] Preferably, the reporting network includes temperature change reports, transportation route reports and inventory status reports.

[0041] An embodiment of the present invention provides an intelligent logistics monitoring method based on the Internet of Things, which obtains sensor information, obtains logistics information through sensor information, and can achieve the function of real-time monitoring of environmental parameters through temperature sensors, humidity sensors, vibration sensors, light sensors, etc., uploads logistics information through the Internet of Things, realizes warehouse and short-distance data transmission through Wi-Fi, has high bandwidth and speed, realizes data transmission in a wide area through 5G, provides high-speed, low-latency network connection, is particularly suitable for vehicles and mobile scenarios, performs edge processing on logistics information, performs local processing and caching on sensor data, reduces network transmission delay and central server load, analyzes and processes logistics information through a visual monitoring platform, and intuitively displays real-time data and analysis results in the form of charts, maps, etc., to help managers quickly understand the logistics status, compare logistics information with a threshold, and if the logistics information exceeds the threshold, it is determined to be abnormal, and the abnormal value is sent to a user terminal, the transmission data is encrypted, the transmission method is obtained, and the transmission method is sent to the user terminal, which solves the problem that the intelligent logistics process based on the Internet of Things cannot be monitored in real time, resulting in the inability to grasp the latest progress of the entire logistics process in real time, and the intelligent logistics monitoring effect is poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of a smart logistics monitoring method based on the Internet of Things provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of the steps of uploading logistics information through the Internet of Things and performing edge processing on the logistics information provided by an embodiment of the present invention;

[0044] Figure 3 A flowchart of the steps of analyzing and processing logistics information through a visual monitoring platform and comparing the logistics information with a threshold value provided in an embodiment of the present invention;

[0045] Figure 4 A flowchart of the steps of encrypting transmission data, obtaining a transmission method, and sending the transmission method to a user terminal provided in an embodiment of the present invention;

[0046] Figure 5 An architecture diagram of an IoT-based smart logistics monitoring system provided by an embodiment of the present invention;

[0047] Figure 6 An architecture diagram of an edge processing module provided by an embodiment of the present invention;

[0048] Figure 7 An architecture diagram of an abnormality determination module provided in an embodiment of the present invention;

[0049] Figure 8 The present invention provides an encryption module architecture diagram. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] like Figure 1 As shown, a smart logistics monitoring method based on the Internet of Things is provided in an embodiment of the present invention, and the method includes:

[0052] S100, acquiring sensor information, and acquiring logistics information through the sensor information, wherein the logistics information includes cargo information, transportation information, warehousing information and process information.

[0053] In this step, sensor information is obtained, and logistics information is collected in real time by deploying various sensors and intelligent devices in the logistics process. Temperature sensors are used to monitor the temperature of the environment during transportation and storage, especially for food and medicine that require cold chain transportation. Humidity sensors are used to detect humidity in warehouses and transport vehicles to ensure that goods are stored under suitable humidity conditions. Vibration sensors are used to monitor the vibration of goods during transportation to prevent damage to goods due to severe vibration. Light sensors are used to monitor whether goods are exposed to inappropriate light conditions, especially for goods that need to be stored away from light. GPS devices are used to track the location of transport vehicles and goods in real time to ensure that goods are transported according to the predetermined route. RFID tags and readers are used for automatic identification and tracking of goods and inventory, which facilitates rapid inventory and in-and-out management.

[0054] The cargo information can provide the cargo name, type, quantity, weight, volume and packaging method, current status (such as in transit, in storage, received, etc.), cargo temperature, humidity, vibration, etc. The transportation information can provide the vehicle number, vehicle model, load capacity, driver information, fleet information, etc. The storage information can provide the warehouse location, warehouse type (ordinary warehouse, cold storage, etc.), warehouse area, shelf location, etc. The process information can provide the order number, customer information, order content, order time, delivery time, receipt time, etc., to get a more comprehensive understanding of the logistics status.

[0055] S200, uploading logistics information through the Internet of Things, and performing edge processing on the logistics information, wherein the edge processing is used to collect data on the logistics information and pre-process the data.

[0056] In this step, the logistics information is uploaded through the Internet of Things. Edge computing is a distributed computing architecture that deploys computing resources and data storage at the edge of the network close to the data source, rather than in a central data center or cloud platform. Its main goal is to reduce the latency of data transmission, reduce bandwidth consumption, and improve the response speed and reliability of the system;

[0057] Perform edge processing on logistics information, collect data on edge devices, and pre-process the data, such as filtering, denoising, and aggregation, to reduce data transmission volume and processing burden, perform real-time data processing and analysis on edge nodes, respond to local events in a timely manner, improve the system's response speed and decision-making ability, and use intelligent algorithms to perform intelligent analysis and prediction on edge nodes, such as image recognition, anomaly detection, and predictive maintenance.

[0058] S300, the processed logistics information is analyzed through the visual monitoring platform, and the logistics information is compared with the threshold. If the logistics information exceeds the threshold, it is determined to be abnormal and the abnormal value is sent to the user terminal.

[0059] In this step, the visual monitoring platform analyzes the processed logistics information to provide a comprehensive dashboard that displays the key indicators (KPIs) of logistics operations, such as the real-time location of transport vehicles, cargo status, temperature and humidity conditions, etc. The dashboard is usually presented intuitively in the form of charts, graphs and maps, which allows users to quickly understand the overall situation, provide access to historical data and analysis functions, help users understand past logistics trends, and identify common problems and bottlenecks. Data change trends can be displayed in the form of line charts, bar charts, etc.

[0060] When an abnormal situation occurs during the logistics process (such as excessive temperature, transportation delays, route deviations, etc.), the system will automatically generate early warning information by comparing the logistics information with the threshold, and notify relevant personnel via SMS, email or APP push notifications. Users can customize notification rules and select indicators and abnormal situations of concern. The system will send notifications in real time according to the set rules to ensure that relevant personnel can respond in a timely manner.

[0061] S400, encrypt the transmission data, obtain the transmission method, and send the transmission method to the user terminal.

[0062] In this step, the transmitted data is encrypted through a data transmission protocol. The data transmission protocol is the key to ensure that data can be transmitted safely, reliably and efficiently. The HTTP protocol can be used. HTTP is an application layer protocol used in distributed, collaborative hypermedia information systems. HTTPS is its secure version, using SSL / TLS encryption;

[0063] Get the transmission method and send it to the user terminal. HTTPS is encrypted by SSL / TLS to ensure the security and privacy of data transmission. It is suitable for data reporting and query, such as uploading logistics data to the cloud platform or querying logistics status.

[0064] like Figure 2 As shown, as a preferred embodiment of the present invention, the logistics information is uploaded through the Internet of Things, and the logistics information is edge processed. The edge processing is used to collect data on the logistics information and pre-process the data, specifically including:

[0065] S201, upload logistics information through the Internet of Things, obtain logistics information in real time, and send the logistics information to the edge processing component, which includes edge devices and edge servers.

[0066] In this step, the logistics information is uploaded through the Internet of Things, the logistics information is obtained in real time, the accuracy of the logistics status is ensured, and the logistics information is sent to the edge processing component, and the logistics data is processed by the edge processing component. The edge processing component includes edge devices, edge servers and edge gateways;

[0067] Edge devices include sensors, smart cameras, industrial gateways, routers, and drones, which are responsible for data collection and preliminary processing. Edge servers are deployed on edge nodes, have strong computing and storage capabilities, and can perform complex data processing tasks. Edge gateways connect edge devices with cloud platforms and are responsible for data transmission, protocol conversion, security encryption and other functions.

[0068] S202, preprocessing the data through an edge processing component, wherein the preprocessing includes real-time data processing and analysis.

[0069] In this step, the data is preprocessed by the edge processing component, data is collected on the edge device, and the data is preprocessed, such as filtering, denoising, aggregation, etc., to reduce the amount of data transmission and processing burden, and real-time data processing and analysis are performed on the edge node to respond to local events in a timely manner. The response speed and decision-making ability of the system are improved, and the computing and storage tasks are distributed on multiple edge nodes to achieve load balancing and fault tolerance mechanisms, thereby improving the scalability and reliability of the system.

[0070] S203, analyzing and predicting the pre-processed data through intelligent algorithms, wherein the analysis and prediction include image recognition, anomaly detection and predictive maintenance.

[0071] In this step, the preprocessed data is analyzed and predicted through intelligent algorithms, and machine learning and artificial intelligence algorithms are used to perform intelligent analysis and prediction on edge nodes, such as image recognition, anomaly detection, and predictive maintenance. Security measures such as data encryption, access control, and identity authentication are implemented on edge devices and gateways to protect the security and privacy of data. Edge computing adopts a distributed architecture. Even if an edge node fails, other nodes can continue to work, which improves the reliability and fault tolerance of the system.

[0072] like Figure 3 As shown, as a preferred embodiment of the present invention, the logistics information processed by the visual monitoring platform is analyzed, the logistics information is compared with the threshold, and if the logistics information exceeds the threshold, it is determined to be abnormal, and the step of sending the abnormal value to the user terminal specifically includes:

[0073] S301, analyze and process the logistics information through the visual monitoring platform, display the logistics status through charts, and generate reports through the visual monitoring platform.

[0074] In this step, the processed logistics information is analyzed through the visual monitoring platform, and tools such as Tableau, PowerBI, and ECharts are used to create intuitive dashboards and charts to help managers monitor logistics in real time. By integrating the geographic information system, a map view of goods and vehicles is provided to intuitively display logistics routes and locations.

[0075] The system can automatically generate various types of reports (such as daily, weekly, and monthly reports) based on preset templates and data to help managers understand the performance of logistics operations.

[0076] S302, comparing the logistics information with the threshold. If the logistics information exceeds the threshold, it is determined to be abnormal; if the logistics information does not exceed the threshold, it is determined to be normal.

[0077] In this step, we compare the logistics information with the thresholds and determine which indicators need to be monitored. Different types of goods and logistics links will have different key indicators. For example, food and medicine in cold chain logistics require temperature control. For fragile goods or precision instruments, vibration monitoring is required. Some goods need to be stored away from light. Monitoring light intensity can prevent damage to the goods.

[0078] Reasonable thresholds for various indicators are set based on the characteristics of the goods and the requirements of the logistics process. For example, the temperature of refrigerated food is controlled at 2-8°C, and that of frozen food is controlled below -18°C. For light-proof goods, the light intensity is set not to exceed a certain value, such as 500lx. The safe speed range for transport vehicles is set, such as 30-100km / h. During the logistics process, sensors installed on vehicles, warehouses, and goods are used to collect data on various indicators in real time and transmit them to the data processing platform via wireless networks (such as Wi-Fi, 4G / 5G).

[0079] The real-time collected data is compared with the set threshold in real time. If the data of a certain indicator exceeds the preset threshold, it is judged as abnormal. For example, if the temperature of refrigerated goods exceeds 8°C or is lower than 2°C, it is judged as temperature abnormality; if the location of the transport vehicle deviates from the scheduled route and exceeds the set geographical range, it is judged as location abnormality.

[0080] When transporting cold chain food and medicines, temperature sensors monitor temperature changes in real time. Once the temperature exceeds the safe range, the system immediately issues an alarm and recommends emergency measures, such as replacing refrigeration equipment or adjusting the transportation route.

[0081] S303, determining the abnormality level, taking different coping measures according to the abnormality level, and sending the coping measures to the user terminal.

[0082] In this step, the abnormality level is determined. According to the severity of the abnormality, a multi-level alarm mechanism can be set so that different response measures can be taken;

[0083] Level 1 alarm: Minor abnormalities can be automatically adjusted by the system or the operator can be alerted. For example, when the temperature fluctuates slightly, the system automatically adjusts the refrigeration equipment;

[0084] Level 2 alarm: moderate abnormality, requiring operator intervention. For example, if the temperature is obviously above the standard, the driver needs to be notified to check the refrigeration equipment;

[0085] Level 3 alarm: Severe abnormality, emergency measures need to be taken immediately. For example, if the temperature of refrigerated goods exceeds the standard, the relevant departments will be notified for emergency handling, which may require replacing the transport vehicle or rearranging the storage of goods.

[0086] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of encrypting the transmission data, obtaining the transmission mode, and sending the transmission mode to the user terminal specifically include:

[0087] S401, obtaining a data type, selecting a transmission method according to the data type, and encrypting the transmission data.

[0088] In this step, the data type is obtained and the transmission method is selected according to the data type. HTTP is an application layer protocol used in distributed, collaborative hypermedia information systems. HTTPS is its secure version, which uses SSL / TLS encryption. HTTPS uses SSL / TLS encryption to ensure the security and privacy of data transmission.

[0089] For example, the GPS device installed on the transport vehicle regularly uploads the vehicle's location information through the HTTP / HTTPS protocol to ensure real-time tracking of vehicle dynamics. The central server or cloud platform uses the HTTP / HTTPS protocol to interact with the back-end database to store, query and update data.

[0090] S402, obtaining an encryption method and generating an encryption protocol, wherein the encryption protocol is used to ensure the security and privacy of data transmission.

[0091] In this step, the encryption method is obtained, and the logistics management personnel access the front-end interface of the logistics monitoring system through a web browser (through the HTTPS protocol) to view key indicators and status (such as cargo temperature, vehicle location, environmental conditions, etc.) in real time. When the system detects an abnormal situation, an alarm message is sent through the HTTPS protocol.

[0092] S403, obtaining a request from the user terminal, and sending a transmission method to the user terminal.

[0093] In this step, a request from a user terminal is obtained and a transmission mode is sent to the user terminal;

[0094] For example, the temperature sensor in the refrigerated truck collects temperature data in real time and transmits it to the edge computing device through the MQTT protocol. The edge device compares the temperature data with the threshold of 2-8℃. Once the threshold is exceeded, an alarm is immediately generated and sent to the cloud platform through the MQTT protocol. Finally, it is displayed on the real-time monitoring platform through the HTTPS protocol.

[0095] The vibration sensor collects vibration data in real time and transmits it to the edge computing device through the MQTT protocol. The edge device compares the vibration data with the set threshold. Once the data exceeds the standard, an alarm is immediately generated and sent to the cloud platform through the MQTT protocol. Finally, it is displayed on the real-time monitoring platform through the HTTPS protocol.

[0096] like Figure 5 As shown, an intelligent logistics monitoring system based on the Internet of Things provided by an embodiment of the present invention includes:

[0097] The information acquisition module 100 is used to acquire sensor information, and acquire logistics information through the sensor information. The logistics information includes cargo information, transportation information, warehousing information and process information.

[0098] In this system, the information acquisition module 100 acquires sensor information and collects logistics information in real time by deploying various sensors and intelligent devices in the logistics process. The temperature sensor is used to monitor the temperature of the environment during transportation and storage, especially for food and medicine that need cold chain transportation. The humidity sensor is used to detect the humidity in the warehouse and transportation vehicle to ensure that the goods are stored under suitable humidity conditions. The vibration sensor is used to monitor the vibration of the goods during transportation to prevent the goods from being damaged by severe vibration. The light sensor is used to monitor whether the goods are exposed to inappropriate light conditions, especially for goods that need to be stored away from light. The GPS device is used to track the location of the transport vehicle and goods in real time to ensure that the goods are transported according to the predetermined route. The RFID tag and reader are used for automatic identification and tracking of goods and inventory, which is convenient for rapid inventory and in-and-out management.

[0099] The cargo information can provide the cargo name, type, quantity, weight, volume and packaging method, current status (such as in transit, in storage, received, etc.), cargo temperature, humidity, vibration, etc. The transportation information can provide the vehicle number, vehicle model, load capacity, driver information, fleet information, etc. The storage information can provide the warehouse location, warehouse type (ordinary warehouse, cold storage, etc.), warehouse area, shelf location, etc. The process information can provide the order number, customer information, order content, order time, delivery time, receipt time, etc., to get a more comprehensive understanding of the logistics status.

[0100] The edge processing module 200 is used to upload logistics information through the Internet of Things and perform edge processing on the logistics information. The edge processing is used to collect data on the logistics information and pre-process the data.

[0101] In this system, the edge processing module 200 uploads logistics information through the Internet of Things. Edge computing is a distributed computing architecture that deploys computing resources and data storage at the edge of the network close to the data source, rather than concentrating them in a central data center or cloud platform. Its main goal is to reduce data transmission latency, reduce bandwidth consumption, and improve system response speed and reliability;

[0102] Perform edge processing on logistics information, collect data on edge devices, and pre-process the data, such as filtering, denoising, and aggregation, to reduce data transmission volume and processing burden, perform real-time data processing and analysis on edge nodes, respond to local events in a timely manner, improve the system's response speed and decision-making ability, and use intelligent algorithms to perform intelligent analysis and prediction on edge nodes, such as image recognition, anomaly detection, and predictive maintenance.

[0103] The abnormality determination module 300 is used to analyze the processed logistics information through the visual monitoring platform, compare the logistics information with the threshold, and if the logistics information exceeds the threshold, it is determined to be abnormal and the abnormal value user terminal is sent.

[0104] In this system, the abnormality determination module 300 analyzes the processed logistics information through the visual monitoring platform and provides a comprehensive dashboard to display the key indicators (KPI) of logistics operations, such as the real-time location of transport vehicles, cargo status, temperature and humidity conditions, etc. The dashboard is usually presented intuitively in the form of charts, graphs and maps, which is convenient for users to quickly understand the overall situation, provide access to historical data and analysis functions, help users understand past logistics trends, and identify common problems and bottlenecks. Data change trends can be displayed in the form of line charts, bar charts, etc.

[0105] When an abnormal situation occurs during the logistics process (such as excessive temperature, transportation delays, route deviations, etc.), the system will automatically generate early warning information by comparing the logistics information with the threshold, and notify relevant personnel via SMS, email or APP push notifications. Users can customize notification rules and select indicators and abnormal situations of concern. The system will send notifications in real time according to the set rules to ensure that relevant personnel can respond in a timely manner.

[0106] The encryption module 400 is used to encrypt the transmission data, obtain the transmission mode, and send the transmission mode to the user terminal.

[0107] In this system, the encryption module 400 encrypts the transmission data and implements encryption through the data transmission protocol. The data transmission protocol is the key to ensure that data can be transmitted safely, reliably and efficiently. The HTTP protocol can be used. HTTP is an application layer protocol used in distributed and collaborative hypermedia information systems. HTTPS is its secure version, using SSL / TLS encryption;

[0108] Get the transmission method and send it to the user terminal. HTTPS is encrypted by SSL / TLS to ensure the security and privacy of data transmission. It is suitable for data reporting and query, such as uploading logistics data to the cloud platform or querying logistics status.

[0109] like Figure 6 As shown, as a preferred embodiment of the present invention, the edge processing module 200 includes:

[0110] The real-time acquisition unit 201 is used to upload logistics information through the Internet of Things, obtain logistics information in real time, and send the logistics information to the edge processing component, which includes an edge device and an edge server.

[0111] In this module, the real-time acquisition unit 201 uploads logistics information through the Internet of Things, obtains logistics information in real time, ensures the accuracy of logistics status, sends logistics information to the edge processing component, and processes logistics data through the edge processing component. The edge processing component includes edge devices, edge servers and edge gateways;

[0112] Edge devices include sensors, smart cameras, industrial gateways, routers, and drones, which are responsible for data collection and preliminary processing. Edge servers are deployed on edge nodes, have strong computing and storage capabilities, and can perform complex data processing tasks. Edge gateways connect edge devices with cloud platforms and are responsible for data transmission, protocol conversion, security encryption and other functions.

[0113] The preprocessing unit 202 is used to preprocess the data through the edge processing component, and the preprocessing includes real-time data processing and analysis.

[0114] In this module, the preprocessing unit 202 preprocesses the data through the edge processing component, collects data on the edge device, and preprocesses the data, such as filtering, denoising, aggregation, etc., to reduce the amount of data transmission and processing burden, perform real-time data processing and analysis on the edge node, respond to local events in a timely manner, improve the system's response speed and decision-making ability, distribute computing and storage tasks on multiple edge nodes, implement load balancing and fault tolerance mechanisms, and improve the scalability and reliability of the system.

[0115] The prediction unit 203 is used to analyze and predict the pre-processed data through intelligent algorithms, and the analysis and prediction include image recognition, anomaly detection and predictive maintenance.

[0116] In this module, the prediction unit 203 analyzes and predicts the pre-processed data through intelligent algorithms, and uses machine learning and artificial intelligence algorithms to perform intelligent analysis and prediction on edge nodes, such as image recognition, anomaly detection, and predictive maintenance. Security measures such as data encryption, access control, and identity authentication are implemented on edge devices and gateways to protect the security and privacy of data. Edge computing adopts a distributed architecture. Even if an edge node fails, other nodes can continue to work, which improves the reliability and fault tolerance of the system.

[0117] like Figure 7 As shown, as a preferred embodiment of the present invention, the abnormality determination module 300 includes:

[0118] The visualization unit 301 is used to analyze and process the logistics information through the visualization monitoring platform, display the logistics status through charts, and generate reports through the visualization monitoring platform.

[0119] In this module, the visualization unit 301 analyzes the processed logistics information through the visualization monitoring platform, and uses tools such as Tableau, PowerBI, and ECharts to create intuitive dashboards and charts to help managers monitor the logistics situation in real time. By integrating the geographic information system, it provides a map view of goods and vehicles to intuitively display the logistics routes and locations;

[0120] The system can automatically generate various types of reports (such as daily, weekly, and monthly reports) based on preset templates and data to help managers understand the performance of logistics operations.

[0121] The abnormality determination unit 302 is used to compare the logistics information with the threshold. If the logistics information exceeds the threshold, it is determined to be abnormal; if the logistics information does not exceed the threshold, it is determined to be normal.

[0122] In this module, the abnormality determination unit 302 compares the logistics information with the threshold value and needs to determine which indicators need to be monitored. Different types of goods and logistics links will have different key indicators. For example, food and medicine in cold chain logistics require temperature control. For fragile goods or precision instruments, vibration monitoring is required. Some goods need to be stored away from light. Monitoring light intensity can prevent damage to the goods.

[0123] Reasonable thresholds for various indicators are set based on the characteristics of the goods and the requirements of the logistics process. For example, the temperature of refrigerated food is controlled at 2-8°C, and that of frozen food is controlled below -18°C. For light-proof goods, the light intensity is set not to exceed a certain value, such as 500lx. The safe speed range for transport vehicles is set, such as 30-100km / h. During the logistics process, sensors installed on vehicles, warehouses, and goods are used to collect data on various indicators in real time and transmit them to the data processing platform via wireless networks (such as Wi-Fi, 4G / 5G).

[0124] The real-time collected data is compared with the set threshold in real time. If the data of a certain indicator exceeds the preset threshold, it is judged as abnormal. For example, if the temperature of refrigerated goods exceeds 8°C or is lower than 2°C, it is judged as temperature abnormality; if the location of the transport vehicle deviates from the scheduled route and exceeds the set geographical range, it is judged as location abnormality.

[0125] When transporting cold chain food and medicines, temperature sensors monitor temperature changes in real time. Once the temperature exceeds the safe range, the system immediately issues an alarm and recommends emergency measures, such as replacing refrigeration equipment or adjusting the transportation route.

[0126] The abnormality level unit 303 is used to determine the abnormality level, take different coping measures according to the abnormality level, and send the coping measures to the user terminal.

[0127] In this module, the abnormality level unit 303 determines the abnormality level. According to the severity of the abnormality, a multi-level alarm mechanism can be set so as to take different response measures;

[0128] Level 1 alarm: Minor abnormalities can be automatically adjusted by the system or the operator can be alerted. For example, when the temperature fluctuates slightly, the system automatically adjusts the refrigeration equipment;

[0129] Level 2 alarm: moderate abnormality, requiring operator intervention. For example, if the temperature is obviously above the standard, the driver needs to be notified to check the refrigeration equipment;

[0130] Level 3 alarm: Severe abnormality, emergency measures need to be taken immediately. For example, if the temperature of refrigerated goods exceeds the standard, the relevant departments will be notified for emergency handling, which may require replacing the transport vehicle or rearranging the storage of goods.

[0131] like Figure 8 As shown, as a preferred embodiment of the present invention, the encryption module 400 includes:

[0132] The transmission unit 401 is used to obtain a data type, select a transmission method according to the data type, and encrypt the transmission data.

[0133] In this module, the transmission unit 401 obtains the data type and selects the transmission mode according to the data type. HTTP is an application layer protocol used in distributed and collaborative hypermedia information systems. HTTPS is its secure version, using SSL / TLS encryption. HTTPS uses SSL / TLS encryption to ensure the security and privacy of data transmission;

[0134] For example, the GPS device installed on the transport vehicle regularly uploads the vehicle's location information through the HTTP / HTTPS protocol to ensure real-time tracking of vehicle dynamics. The central server or cloud platform uses the HTTP / HTTPS protocol to interact with the back-end database to store, query and update data.

[0135] The encryption unit 402 is used to obtain an encryption method and generate an encryption protocol, wherein the encryption protocol is used to ensure the security and privacy of data transmission.

[0136] In this module, the encryption unit 402 obtains the encryption method, and the logistics management personnel access the front-end interface of the logistics monitoring system through a Web browser (through the HTTPS protocol) to view key indicators and status (such as cargo temperature, vehicle location, environmental conditions, etc.) in real time. When the system detects an abnormal situation, it sends an alarm message through the HTTPS protocol.

[0137] The sending unit 403 is used to obtain a request from the user terminal and send a transmission mode to the user terminal.

[0138] In this module, the sending unit 403 obtains the request of the user terminal and sends the transmission mode to the user terminal;

[0139] For example, the temperature sensor in the refrigerated truck collects temperature data in real time and transmits it to the edge computing device through the MQTT protocol. The edge device compares the temperature data with the threshold of 2-8℃. Once the threshold is exceeded, an alarm is immediately generated and sent to the cloud platform through the MQTT protocol. Finally, it is displayed on the real-time monitoring platform through the HTTPS protocol.

[0140] The vibration sensor collects vibration data in real time and transmits it to the edge computing device through the MQTT protocol. The edge device compares the vibration data with the set threshold. Once the data exceeds the standard, an alarm is immediately generated and sent to the cloud platform through the MQTT protocol. Finally, it is displayed on the real-time monitoring platform through the HTTPS protocol.

[0141] In one embodiment, a computer device is provided, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0142] Acquire sensor information, and acquire logistics information through the sensor information, wherein the logistics information includes cargo information, transportation information, warehousing information and process information;

[0143] Upload logistics information through the Internet of Things and perform edge processing on the logistics information. The edge processing is used to collect data on the logistics information and pre-process the data;

[0144] The processed logistics information is analyzed through the visual monitoring platform, and compared with the threshold. If the logistics information exceeds the threshold, it is judged as abnormal and the abnormal value is sent to the user terminal;

[0145] Encrypt the transmission data, obtain the transmission method, and send the transmission method to the user terminal.

[0146] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor performs the following steps:

[0147] Acquire sensor information, and acquire logistics information through the sensor information, wherein the logistics information includes cargo information, transportation information, warehousing information and process information;

[0148] Upload logistics information through the Internet of Things and perform edge processing on the logistics information. The edge processing is used to collect data on the logistics information and pre-process the data;

[0149] The processed logistics information is analyzed through the visual monitoring platform, and compared with the threshold. If the logistics information exceeds the threshold, it is judged as abnormal and the abnormal value is sent to the user terminal;

[0150] Encrypt the transmission data, obtain the transmission method, and send the transmission method to the user terminal.

[0151] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0152] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0153] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0155] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart logistics monitoring method based on the Internet of Things, characterized in that: The method comprises: Acquire sensor information, and acquire logistics information through the sensor information, wherein the logistics information includes cargo information, transportation information, warehousing information and process information; Upload logistics information through the Internet of Things and perform edge processing on the logistics information. The edge processing is used to collect data on the logistics information and pre-process the data; The processed logistics information is analyzed through the visual monitoring platform, and compared with the threshold. If the logistics information exceeds the threshold, it is judged as abnormal and the abnormal value is sent to the user terminal; Encrypt the transmission data, obtain the transmission method, and send the transmission method to the user terminal.

2. According to the method of intelligent logistics monitoring based on the Internet of Things in claim 1, it is characterized in that: The step of uploading logistics information through the Internet of Things and performing edge processing on the logistics information, wherein the edge processing is used to collect data on the logistics information and pre-process the data, specifically includes: Upload logistics information through the Internet of Things, obtain logistics information in real time, and send the logistics information to the edge processing component, which includes edge devices and edge servers; Preprocessing the data by edge processing components, wherein the preprocessing includes real-time data processing and analysis; The pre-processed data is analyzed and predicted through intelligent algorithms, including image recognition, anomaly detection and predictive maintenance.

3. According to the method of intelligent logistics monitoring based on the Internet of Things in claim 1, it is characterized in that: The steps of analyzing the processed logistics information through the visual monitoring platform, comparing the logistics information with the threshold, and determining that the logistics information is abnormal if it exceeds the threshold, and sending the abnormal value to the user terminal specifically include: Analyze and process logistics information through the visual monitoring platform, display logistics status through charts, and generate reports through the visual monitoring platform; Compare the logistics information with the threshold. If the logistics information exceeds the threshold, it is judged as abnormal. If the logistics information does not exceed the threshold, it is judged as normal. Determine the abnormality level, take different countermeasures according to the abnormality level, and send the countermeasures to the user terminal.

4. According to the method of intelligent logistics monitoring based on the Internet of Things in claim 1, it is characterized in that: The steps of encrypting the transmission data, obtaining the transmission mode, and sending the transmission mode to the user terminal specifically include: Obtain the data type, select the transmission method according to the data type, and encrypt the transmitted data; Obtaining an encryption method and generating an encryption protocol, wherein the encryption protocol is used to ensure the security and privacy of data transmission; Get the request from the user terminal and send the transmission mode to the user terminal.

5. The method for intelligent logistics monitoring based on the Internet of Things according to claim 1 is characterized in that: The reporting network includes temperature change reports, transportation route reports and inventory status reports.

6. A smart logistics monitoring system based on the Internet of Things, characterized in that: The system comprises: An information acquisition module acquires sensor information, and acquires logistics information through the sensor information, wherein the logistics information includes cargo information, transportation information, warehousing information and process information; The edge processing module uploads the logistics information through the Internet of Things and performs edge processing on the logistics information. The edge processing is used to collect data on the logistics information and pre-process the data; The abnormality judgment module analyzes the processed logistics information through the visual monitoring platform, compares the logistics information with the threshold, and if the logistics information exceeds the threshold, it is judged as abnormal and sends the abnormal value to the user terminal; The encryption module encrypts the transmission data, obtains the transmission mode, and sends the transmission mode to the user terminal.

7. The intelligent logistics monitoring system based on the Internet of Things according to claim 6 is characterized in that: The edge processing module comprises: A real-time acquisition unit uploads logistics information through the Internet of Things, acquires logistics information in real time, and sends the logistics information to an edge processing component, wherein the edge processing component includes an edge device and an edge server; A preprocessing unit, which preprocesses data through an edge processing component, wherein the preprocessing includes real-time data processing and analysis; The prediction unit analyzes and predicts the pre-processed data through intelligent algorithms, and the analysis and prediction include image recognition, anomaly detection and predictive maintenance.

8. The smart logistics monitoring system based on the Internet of Things according to claim 7 is characterized in that: The abnormality determination module comprises: Visualization unit, which analyzes and processes logistics information through the visual monitoring platform, displays logistics status through charts, and generates reports through the visual monitoring platform; The abnormality determination unit compares the logistics information with the threshold value. If the logistics information exceeds the threshold value, it is determined to be abnormal; if the logistics information does not exceed the threshold value, it is determined to be normal; The abnormality level unit determines the abnormality level, takes different response measures according to the abnormality level, and sends the response measures to the user terminal.

9. The smart logistics monitoring system based on the Internet of Things according to claim 8 is characterized in that: The encryption module comprises: The transmission unit obtains the data type, selects the transmission method according to the data type, and encrypts the transmission data; An encryption unit, which obtains an encryption method and generates an encryption protocol, wherein the encryption protocol is used to ensure the security and privacy of data transmission; The sending unit obtains a request from the user terminal and sends a transmission mode to the user terminal.

10. The smart logistics monitoring system based on the Internet of Things according to claim 9 is characterized in that: The reporting network includes temperature change reports, transportation route reports and inventory status reports.

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