A method and system for monitoring and managing goods in transit based on digital twinning

Abstract

The application provides a cargo monitoring management method and system in transportation based on digital twinning. The cargo monitoring management method in transportation based on digital twinning comprises the following steps: arranging a sensor group in a warehouse, and collecting environmental data information in the warehouse by using the sensor; creating a virtual 3D model corresponding to the warehouse by using a data twinning mode, and performing virtual 3D model display; and monitoring whether the environmental information of the warehouse meets the environmental requirements in real time, and performing alarm when the environmental information of the warehouse does not meet the environmental requirements. The system comprises modules corresponding to the method steps.

Description

Technical Field

[0001] This invention proposes a method and system for monitoring and managing goods in transportation based on digital twins, belonging to the field of cargo monitoring and management technology. Background Technology

[0002] Light sensors are used to monitor in real time whether goods have been accidentally opened; vibration sensors are used to detect whether goods have been impacted or vibrated during transportation, and whether goods have been damaged; tilt angle sensors are used to monitor the stability of the goods; and positioning technology is used to monitor the position of the goods in real time. However, simply providing monitoring functions and displaying data is not intuitive enough, such as vibration sensor and temperature monitoring. It also lacks comprehensiveness; the data is relatively fragmented and cannot reflect the overall situation. Summary of the Invention

[0003] This invention provides a method and system for monitoring and managing goods in transportation based on digital twins, addressing the shortcomings of existing technologies that only provide monitoring functions and display data, which are not intuitive enough (e.g., vibration sensors and temperature monitoring). These technologies also lack comprehensiveness, with data presented in a fragmented manner and failing to reflect the overall situation.

[0004] A method for monitoring and managing cargo in transportation based on digital twins, the method comprising:

[0005] Sensor arrays were deployed inside the warehouse, and environmental data was collected using these sensors.

[0006] A virtual 3D model of the warehouse is created using data twin technology, and then displayed as a virtual 3D model.

[0007] The system monitors the warehouse environment in real time to ensure it meets environmental requirements. If the warehouse environment does not meet the requirements, an alarm is triggered.

[0008] Furthermore, sensor arrays are deployed within the warehouse, and these sensors are used to collect data within the warehouse, including:

[0009] A sensor array is deployed inside the warehouse; wherein the sensor array includes a temperature sensor, a humidity sensor, and a wind speed sensor for detecting warehouse ventilation.

[0010] The data transmission time interval of the sensor is set according to the data acquisition time interval of the sensor;

[0011] The sensor is controlled to transmit the collected environmental data to the monitoring and management platform according to the data transmission time interval.

[0012] Furthermore, setting the data transmission time interval of the sensor according to the data acquisition time interval of the sensor includes:

[0013] Extract the data acquisition time interval for each of the sensors;

[0014] Extract the data acquisition delay duration of each of the sensors;

[0015] The data transmission time interval of the sensor is set according to the data acquisition time interval and data acquisition delay duration of the sensor;

[0016] The data transmission time interval of the sensor is obtained by the following formula:

[0017] T = [1 + exp(1 - T)] s / T0)]×T g

[0018] Where T represents the data transmission time interval of the sensor; T g Ts represents the preset data transmission time interval reference value; Ts represents the data acquisition delay duration of the sensor; T0 represents the data acquisition time interval of the sensor.

[0019] Furthermore, a virtual 3D model of the warehouse is created using data twin technology, and this virtual 3D model is then displayed, including:

[0020] Extract warehouse data; wherein the data includes the location information of the goods, the layout information of the goods, and the quantity information of the goods;

[0021] Using data twin technology, a virtual 3D model of the warehouse is created based on the location, layout, and quantity information of the goods.

[0022] The environmental data collected by the sensors is associated with the virtual 3D model, so that the virtual 3D model can display the environmental information data of the warehouse in real time, forming a virtual 3D model with real-time changes in environmental information data.

[0023] Visualize virtual 3D models that contain real-time changes in environmental information data.

[0024] Furthermore, the digital twin-based cargo monitoring and management method in transportation also includes:

[0025] Step 1: Suppose a certain type of sensor collects n normal environmental information data points, arrange them in chronological order, and let F... i Let be the value of the i-th normal environmental information data point, where i is the data point number and is an integer greater than or equal to 1 and less than or equal to n. Then the mean and standard deviation of these n normal environmental information data points are:

[0026]

[0027] Where F avg For the mean of n normal information data, F std For this, the standard deviation of n normal information numbers;

[0028] Step 2: Let V i For the i-th environmental information data increment, its calculation formula is:

[0029] V i =F i -F i-1

[0030] The average data increment of normal environmental information collected by this type of sensor is:

[0031]

[0032] Where V avg This represents the average data increment of normal environmental information collected by this type of sensor;

[0033] Step 3: Based on the calculation results of Step 1 and Step 2, calculate the safe variation range of the data collected by this type of sensor. The calculation formula is as follows:

[0034]

[0035] Where V U This is the upper limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is greater than V. U This indicates a possible sudden change in the local environment, and an alarm should be issued; V D This is the lower limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is less than V. D This indicates that there may be a sudden change in the local environment, and an alarm should be issued.

[0036] A digital twin-based cargo monitoring and management system for transportation, comprising:

[0037] The sensor deployment module is used to deploy sensor groups in the warehouse and use the sensors to collect environmental data information in the warehouse.

[0038] The virtual 3D model display module is used to create a virtual 3D model of the warehouse using data twin technology and to display the virtual 3D model.

[0039] The alarm module is used to monitor the warehouse environment in real time to see if it meets the environmental requirements. When the warehouse environment does not meet the environmental requirements, an alarm will be triggered.

[0040] Furthermore, the sensor deployment module includes:

[0041] A deployment execution module is used to deploy a sensor group in the warehouse; wherein the sensor group includes a temperature sensor, a humidity sensor, and a wind speed sensor for detecting warehouse ventilation;

[0042] The time interval setting module is used to set the data transmission time interval of the sensor according to the data acquisition time interval of the sensor;

[0043] An environmental data transmission module is used to control the sensor to transmit the collected environmental data to the monitoring and management platform according to the data transmission time interval.

[0044] Furthermore, the time interval setting module includes:

[0045] A data acquisition time interval extraction module is used to extract the data acquisition time interval for each of the sensors.

[0046] The data acquisition delay duration extraction module is used to extract the data acquisition delay duration of each of the sensors;

[0047] The execution module is configured to set the data transmission time interval of the sensor according to the data acquisition time interval and the data acquisition delay duration of the sensor.

[0048] The data transmission time interval of the sensor is obtained by the following formula:

[0049] T = [1 + exp(1 - T)] s / T0)]×T g

[0050] Where T represents the data transmission time interval of the sensor; T g Ts represents the preset data transmission time interval reference value; Ts represents the data acquisition delay duration of the sensor; T0 represents the data acquisition time interval of the sensor.

[0051] Furthermore, the virtual 3D model display module includes:

[0052] The warehouse information extraction module is used to extract data information from the warehouse; wherein, the data information includes the location information of the goods, the layout information of the goods, and the quantity information of the goods;

[0053] The 3D model creation module is used to create a virtual 3D model of the warehouse based on the location information, layout information, and quantity information of the goods using data twin technology.

[0054] The model and information association module is used to associate the environmental data information collected by the sensor with the virtual 3D model, so that the virtual 3D model can display the environmental information data of the warehouse in real time, forming a virtual 3D model with real-time changes in environmental information data;

[0055] The visualization module is used to visualize virtual 3D models that display real-time changes in environmental information data.

[0056] Furthermore, the digital twin-based cargo monitoring and management system for transportation also includes:

[0057] Step 1: Suppose a certain type of sensor collects n normal environmental information data points, arrange them in chronological order, and let F... i Let be the value of the i-th normal environmental information data point, where i is the data point number and is an integer greater than or equal to 1 and less than or equal to n. Then the mean and standard deviation of these n normal environmental information data points are:

[0058]

[0059] Where F avg For the mean of n normal information data, F std For this, the standard deviation of n normal information numbers;

[0060] Step 2: Let V i For the i-th environmental information data increment, its calculation formula is:

[0061] V i =F i -F i-1

[0062] The average data increment of normal environmental information collected by this type of sensor is:

[0063]

[0064] Where V avg This represents the average data increment of normal environmental information collected by this type of sensor;

[0065] Step 3: Based on the calculation results of Step 1 and Step 2, calculate the safe variation range of the data collected by this type of sensor. The calculation formula is as follows:

[0066]

[0067] Where V UThis is the upper limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is greater than V. U This indicates a possible sudden change in the local environment, and an alarm should be issued; V D This is the lower limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is less than V. D This indicates that there may be a sudden change in the local environment, and an alarm should be issued.

[0068] Beneficial effects of this invention:

[0069] This invention proposes a digital twin-based method and system for monitoring and managing goods during transportation. It constructs a virtual model of the loading and unloading process within a warehouse using digital twin technology, reflecting in real time whether the warehouse has been opened, the movement, damage, and stability of the goods, as well as changes in the storage environment such as temperature. Simultaneously, it can construct a virtual model that intuitively and comprehensively displays the condition of the goods within the warehouse. Attached Figure Description

[0070] Figure 1 This is a flowchart of the method described in this invention;

[0071] Figure 2 This is a system block diagram of the system described in this invention. Detailed Implementation

[0072] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0073] This invention proposes a method for monitoring and managing goods in transportation based on digital twins, such as... Figure 1 As shown, the cargo monitoring and management method in transportation based on digital twins includes:

[0074] S1. Deploy sensor arrays inside the warehouse and use the sensors to collect environmental data information inside the warehouse;

[0075] S2. Create a virtual 3D model of the warehouse using data twin technology, and display the virtual 3D model.

[0076] S3. Monitor the warehouse environment information in real time to see if it meets the environmental requirements. If the warehouse environment information does not meet the environmental requirements, an alarm will be triggered.

[0077] Typically, in the same warehouse environment, the data collected by various types of sensors are basically consistent. For example, in the same environment, the temperature data collected by various temperature sensors is basically the same, and the humidity data collected by various humidity sensors is almost identical. Normally, changes in the overall environmental information of the warehouse can be promptly collected by the sensors and trigger alarms. However, when environmental changes occur in a certain location within the warehouse—that is, localized changes—the changes may not have yet spread to the entire warehouse environment. For example, a short circuit in an electrical wire. If such environmental changes are not detected and alarmed in time, they may cause significant safety hazards. To solve the above problem, the following algorithm is adopted:

[0078] Step 1: Suppose a certain type of sensor collects n normal environmental information data points, arrange them in chronological order, and let F... i Let be the value of the i-th normal environmental information data point, where i is the data point number and is an integer greater than or equal to 1 and less than or equal to n. Then the mean and standard deviation of these n normal environmental information data points are:

[0079]

[0080] Where F avg For the mean of n normal information data, F std This is the standard deviation of n normal information values.

[0081] Step 2: Let V i For the i-th environmental information data increment, its calculation formula is:

[0082] V i =F i -F i-1

[0083] The average data increment of normal environmental information collected by this type of sensor is:

[0084]

[0085] Where V avg This represents the average data increment of normal environmental information collected by this type of sensor.

[0086] Step 3: Based on the calculation results of Step 1 and Step 2, calculate the safe variation range of the data collected by this type of sensor. The calculation formula is as follows:

[0087]

[0088] Where V U This is the upper limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is greater than V. UThis indicates a possible sudden change in the local environment, and an alarm should be issued; V D This is the lower limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is less than V. D This indicates that there may be a sudden change in the local environment, and an alarm should be issued.

[0089] This algorithm obtains the safe variation range of such environmental data by collecting normal data and the changes in each data collection, ensuring that it can promptly and accurately identify and alarm when there is a sudden change in the local environment, thus preventing safety accidents from occurring.

[0090] The working principle of the above technical solution is as follows: Sensor data acquisition (S1): Sensor arrays are deployed within the warehouse. These sensors are used to collect environmental data within the warehouse. This environmental data may include various parameters such as temperature, humidity, air pressure, and light intensity. The sensor arrays collect this data in real time.

[0091] Data Twin Model Creation (S2): Using data twin technology, environmental data from the actual warehouse is mapped onto a virtual 3D model. This model reflects the warehouse's layout and environmental characteristics. This model can be a digital twin, essentially a digital representation of the actual environment. Through this virtual 3D model, users can intuitively understand the situation within the warehouse.

[0092] Real-time Environmental Monitoring and Alarms (S3): Through a virtual 3D model, the system monitors warehouse environmental information in real time to ensure it meets predetermined environmental requirements. If the monitored environmental data does not meet these requirements, the system will trigger an alarm, notifying relevant personnel to take necessary measures to correct or address the environmental problem. For example, if the temperature or humidity exceeds safe ranges, the system can automatically sound an alarm so that timely measures can be taken to prevent damage to goods or other harm.

[0093] The effects of the above technical solution are as follows: Real-time monitoring: The technical solution of this embodiment makes the environmental data in the warehouse visible in real time. Through the display of the virtual 3D model, users can understand the environmental conditions at any time.

[0094] Environmental compliance: Through real-time monitoring and alerts, ensure that the warehouse environment meets specific environmental requirements, thereby protecting goods within the warehouse from damage.

[0095] Rapid Response: Once an environmental problem is detected, the system can quickly issue an alert to prompt action to resolve the issue, thereby reducing cargo loss or other potential problems.

[0096] The technical solution in this embodiment combines digital twins, sensor technology, and real-time monitoring to provide a powerful method for managing and monitoring the environmental conditions of goods in transit. This is extremely useful for ensuring cargo quality and reducing potential losses.

[0097] One embodiment of the present invention involves deploying a sensor array within a warehouse and using the sensors to collect data information within the warehouse, including:

[0098] S101. Install a sensor group in the warehouse; wherein the sensor group includes a temperature sensor, a humidity sensor, and a wind speed sensor for detecting warehouse ventilation;

[0099] S102. Set the data transmission time interval of the sensor according to the data acquisition time interval of the sensor;

[0100] S103. Control the sensor to transmit the collected environmental data to the monitoring and management platform according to the data transmission time interval.

[0101] The working principle of the above technical solution is as follows: Sensor group deployment (S101): A sensor group is deployed in the warehouse, including different types of sensors, such as temperature sensors, humidity sensors, and wind speed sensors. These sensors are typically deployed in key areas within the warehouse to ensure comprehensive monitoring of environmental conditions.

[0102] Data acquisition time interval setting (S102): Determine the data acquisition time interval for each sensor. This means that the sensor will measure environmental data, such as temperature, humidity, and wind speed, within the specified time interval.

[0103] Data transmission to the monitoring and management platform (S103): Based on the data acquisition time interval of each sensor, control the sensors to transmit the collected environmental data to the monitoring and management platform. Sensors may transmit data to the cloud-based monitoring and management platform via wired or wireless communication protocols for real-time monitoring and data recording.

[0104] The benefits of the above technical solution are: comprehensive monitoring: by deploying sensor arrays within the warehouse, the system can comprehensively monitor the environmental conditions inside the warehouse, including temperature, humidity, and ventilation. This helps ensure that goods are stored under suitable environmental conditions.

[0105] Real-time data: Sensors collect data at set time intervals and transmit it to the monitoring and management platform, enabling users to obtain real-time environmental data information so that timely measures can be taken.

[0106] Data logging: The collected data can be recorded and stored on the monitoring and management platform, which helps with future data analysis, reporting, and trend analysis to improve warehouse environmental management.

[0107] The technical solution of this embodiment provides an efficient method for warehouse environmental monitoring through sensors and real-time data transmission, ensuring that environmental conditions within the warehouse meet requirements and reducing the risk of damage to goods quality. Furthermore, it can improve the traceability and management efficiency of environmental data.

[0108] In one embodiment of the present invention, setting the data transmission time interval of the sensor according to the data acquisition time interval of the sensor includes:

[0109] S1021. Extract the data acquisition time interval for each of the sensors;

[0110] S1022. Extract the data acquisition delay duration of each sensor;

[0111] S1023. Set the data transmission time interval of the sensor according to the data acquisition time interval and data acquisition delay duration of the sensor;

[0112] The data transmission time interval of the sensor is obtained by the following formula:

[0113] T = [1 + exp(1 - T)] s / T0)]×T g

[0114] Where T represents the data transmission time interval of the sensor; T g This represents the preset data transmission time interval reference value; T s T0 represents the data acquisition delay duration of the sensor; T0 represents the data acquisition time interval of the sensor.

[0115] The working principle of the above technical solution is as follows: Extracting the data acquisition time interval (S1021): First, for each sensor, the system obtains its data acquisition time interval. This time interval indicates how often the sensor performs data acquisition.

[0116] Extracting data acquisition delay duration (S1022): For each sensor, the system also extracts its data acquisition delay duration. This delay duration indicates how long the sensor needs to wait after triggering data acquisition before completing the data acquisition.

[0117] Set data transmission time interval (S1023): The data transmission time interval is set based on the extracted data acquisition time interval, the data acquisition delay duration, and a preset data transmission time interval reference value.

[0118] The effect of the above technical solution is as follows: Dynamic data transmission time interval: The technical solution of this embodiment allows the data transmission time interval of the sensor to be dynamically set according to the data acquisition time interval and data acquisition delay duration of the sensor. This helps to optimize data transmission to adapt to the data acquisition characteristics of different sensors.

[0119] Energy and bandwidth savings: Adjusting the data transmission interval of sensors can reduce energy consumption and network bandwidth usage. When sensors do not collect data frequently, the frequency of data transmission can be reduced, thereby lowering energy and bandwidth costs.

[0120] More precise data transmission control: By taking into account the data acquisition time interval and delay duration, the system can more precisely control the data transmission of the sensors, ensuring that data is transmitted on demand and avoiding unnecessary data redundancy.

[0121] The technical solution in this embodiment helps optimize data transmission, improve the efficiency of the sensor system, reduce costs, and ensure timely acquisition of the required environmental data.

[0122] One embodiment of the present invention utilizes data twin technology to create a virtual 3D model corresponding to a warehouse and displays the virtual 3D model, including:

[0123] S201. Extract data information from the warehouse; wherein the data information includes the location information of the goods, the layout information of the goods, and the quantity information of the goods;

[0124] S202. Using data twin technology, create a virtual 3D model of the warehouse based on the location information, layout information, and quantity information of the goods.

[0125] S203. Associate the environmental data information collected by the sensor with the virtual 3D model, so that the virtual 3D model can display the environmental information data of the warehouse in real time, forming a virtual 3D model with real-time changes in environmental information data.

[0126] S204. Visualize and display the virtual 3D model with real-time changes in environmental information data.

[0127] The working principle of the above technical solution is as follows: Extracting warehouse data information (S201): First, the system obtains data information from the warehouse, including the location information of the goods, the layout information of the goods, and the quantity information of the goods. This information may be obtained through sensors, scanning devices, or other means.

[0128] Creating a virtual 3D model of the warehouse (S202): Using data twin technology, the system creates a virtual 3D model of the warehouse based on extracted data, particularly the location, layout, and quantity of goods. This virtual model will reflect the layout and distribution of goods in the actual warehouse.

[0129] Linking Environmental Data with Virtual 3D Model (S203): The system will link environmental data collected by sensors with the virtual 3D model. This means that environmental data, such as temperature and humidity, will be mapped onto the virtual 3D model in real time to display the environmental information inside the actual warehouse.

[0130] Visualization (S204): A virtual 3D model with real-time changes in environmental information data will be displayed to operators or stakeholders in a visual manner. This could be a real-time 3D graphical interface displaying the layout of goods and environmental data within the warehouse to help users understand the warehouse's status.

[0131] The above technical solution achieves the following effects: Real-time environmental monitoring: By associating environmental data collected by sensors with a virtual 3D model, the system enables real-time monitoring of the warehouse environment. This helps to detect potential problems or anomalies in a timely manner.

[0132] Visualized Information Presentation: By visually displaying virtual 3D models, users can gain a more intuitive understanding of the actual situation in the warehouse. This enhances their understanding of the warehouse's internal environment and cargo layout.

[0133] Decision support: Real-time display of environmental information and cargo distribution helps operators or decision-makers better manage and optimize warehouse operations. This technology facilitates timely action to address changing environmental conditions.

[0134] The technical solution in this embodiment combines data twin and visualization technologies to provide more comprehensive environmental monitoring and information presentation for warehouse management, which helps to improve efficiency and reduce potential problems.

[0135] This invention proposes a cargo monitoring and management system based on digital twins during transportation, such as... Figure 2 As shown, the cargo monitoring and management system based on digital twins in transportation includes:

[0136] The sensor deployment module is used to deploy sensor groups in the warehouse and use the sensors to collect environmental data information in the warehouse.

[0137] The virtual 3D model display module is used to create a virtual 3D model of the warehouse using data twin technology and to display the virtual 3D model.

[0138] The alarm module is used to monitor the warehouse environment in real time to see if it meets the environmental requirements. When the warehouse environment does not meet the environmental requirements, an alarm will be triggered.

[0139] Typically, in the same warehouse environment, the data collected by various types of sensors are basically consistent. For example, in the same environment, the temperature data collected by various temperature sensors is basically the same, and the humidity data collected by various humidity sensors is almost identical. Normally, changes in the overall environmental information of the warehouse can be promptly collected by the sensors and trigger alarms. However, when environmental changes occur in a certain location within the warehouse—that is, localized changes—the changes may not have yet spread to the entire warehouse environment. For example, a short circuit in an electrical wire. If such environmental changes are not detected and alarmed in time, they may cause significant safety hazards. To solve the above problem, the following algorithm is adopted:

[0140] Step 1: Suppose a certain type of sensor collects n normal environmental information data points, arrange them in chronological order, and let F... i Let be the value of the i-th normal environmental information data point, where i is the data point number and is an integer greater than or equal to 1 and less than or equal to n. Then the mean and standard deviation of these n normal environmental information data points are:

[0141]

[0142] Where F avg For the mean of n normal information data, F std This is the standard deviation of n normal information values.

[0143] Step 2: Let V i For the i-th environmental information data increment, its calculation formula is:

[0144] V i =F i -F i-1

[0145] The average data increment of normal environmental information collected by this type of sensor is:

[0146]

[0147] Where V avg This represents the average data increment of normal environmental information collected by this type of sensor.

[0148] Step 3: Based on the calculation results of Step 1 and Step 2, calculate the safe variation range of the data collected by this type of sensor. The calculation formula is as follows:

[0149]

[0150] Where V UThis is the upper limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is greater than V. U This indicates a possible sudden change in the local environment, and an alarm should be issued; V D This is the lower limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is less than V. D This indicates that there may be a sudden change in the local environment, and an alarm should be issued.

[0151] This algorithm obtains the safe variation range of such environmental data by collecting normal data and the changes in each data collection, ensuring that it can promptly and accurately identify and alarm when there is a sudden change in the local environment, thus preventing safety accidents from occurring.

[0152] The working principle of the above technical solution is as follows: a sensor array is deployed inside the warehouse. These sensors are used to collect environmental data within the warehouse. This environmental data can include various parameters such as temperature, humidity, air pressure, and light intensity. The sensor array collects this data in real time.

[0153] By using data twins, environmental data from an actual warehouse is mapped onto a virtual 3D model. This model reflects the warehouse's layout and environmental characteristics. It can be a digital twin model, essentially a digital representation of the actual environment. Through this virtual 3D model, users can intuitively understand the situation inside the warehouse.

[0154] Using a virtual 3D model, the system monitors warehouse environmental information in real time to ensure it meets predetermined environmental requirements. If the monitored environmental data fails to meet these requirements, the system will trigger an alarm, notifying relevant personnel to take necessary corrective or remedial measures. For example, if the temperature or humidity exceeds safe limits, the system can automatically sound an alarm to allow for timely intervention to prevent damage to goods or other harm.

[0155] The effects of the above technical solution are as follows: Real-time monitoring: The technical solution of this embodiment makes the environmental data in the warehouse visible in real time. Through the display of the virtual 3D model, users can understand the environmental conditions at any time.

[0156] Environmental compliance: Through real-time monitoring and alerts, ensure that the warehouse environment meets specific environmental requirements, thereby protecting goods within the warehouse from damage.

[0157] Rapid Response: Once an environmental problem is detected, the system can quickly issue an alert to prompt action to resolve the issue, thereby reducing cargo loss or other potential problems.

[0158] The technical solution in this embodiment combines digital twins, sensor technology, and real-time monitoring to provide a powerful method for managing and monitoring the environmental conditions of goods in transit. This is extremely useful for ensuring cargo quality and reducing potential losses.

[0159] In one embodiment of the present invention, the sensor group deployment module includes:

[0160] A deployment execution module is used to deploy a sensor group in the warehouse; wherein the sensor group includes a temperature sensor, a humidity sensor, and a wind speed sensor for detecting warehouse ventilation;

[0161] The time interval setting module is used to set the data transmission time interval of the sensor according to the data acquisition time interval of the sensor;

[0162] An environmental data transmission module is used to control the sensor to transmit the collected environmental data to the monitoring and management platform according to the data transmission time interval.

[0163] The working principle of the above technical solution is as follows: A sensor array is deployed within the warehouse, including different types of sensors such as temperature sensors, humidity sensors, and wind speed sensors. These sensors are typically deployed in key areas within the warehouse to ensure comprehensive monitoring of environmental conditions. For each sensor, a data acquisition time interval is determined. This means that the sensor will measure environmental data, such as temperature, humidity, and wind speed, within the specified time intervals.

[0164] Based on the data acquisition time interval of each sensor, the system controls the sensors to transmit the collected environmental data to the monitoring and management platform. The sensors may transmit data to the cloud-based monitoring and management platform via wired or wireless communication protocols for real-time monitoring and data recording.

[0165] The benefits of the above technical solution are: comprehensive monitoring: by deploying sensor arrays within the warehouse, the system can comprehensively monitor the environmental conditions inside the warehouse, including temperature, humidity, and ventilation. This helps ensure that goods are stored under suitable environmental conditions.

[0166] Real-time data: Sensors collect data at set time intervals and transmit it to the monitoring and management platform, enabling users to obtain real-time environmental data information so that timely measures can be taken.

[0167] Data logging: The collected data can be recorded and stored on the monitoring and management platform, which helps with future data analysis, reporting, and trend analysis to improve warehouse environmental management.

[0168] The technical solution of this embodiment provides an efficient method for warehouse environmental monitoring through sensors and real-time data transmission, ensuring that environmental conditions within the warehouse meet requirements and reducing the risk of damage to goods quality. Furthermore, it can improve the traceability and management efficiency of environmental data.

[0169] In one embodiment of the present invention, the time interval setting module includes:

[0170] A data acquisition time interval extraction module is used to extract the data acquisition time interval for each of the sensors.

[0171] The data acquisition delay duration extraction module is used to extract the data acquisition delay duration of each of the sensors;

[0172] The execution module is configured to set the data transmission time interval of the sensor according to the data acquisition time interval and the data acquisition delay duration of the sensor.

[0173] The data transmission time interval of the sensor is obtained by the following formula:

[0174] T = [1 + exp(1 - T)] s / T0)]×T g

[0175] Where T represents the data transmission time interval of the sensor; T g This represents the preset data transmission time interval reference value; T s T0 represents the data acquisition delay duration of the sensor; T0 represents the data acquisition time interval of the sensor.

[0176] The working principle of the above technical solution is as follows: First, for each sensor, the system obtains its data acquisition time interval. This time interval indicates how often the sensor acquires data.

[0177] For each sensor, the system also extracts its data acquisition delay duration. This delay duration indicates how long the sensor needs to wait after triggering data acquisition before completing the acquisition.

[0178] The data transmission interval is set based on the extracted data acquisition interval, the data acquisition delay duration, and a preset data transmission interval reference value.

[0179] The effect of the above technical solution is as follows: Dynamic data transmission time interval: The technical solution of this embodiment allows the data transmission time interval of the sensor to be dynamically set according to the data acquisition time interval and data acquisition delay duration of the sensor. This helps to optimize data transmission to adapt to the data acquisition characteristics of different sensors.

[0180] Energy and bandwidth savings: Adjusting the data transmission interval of sensors can reduce energy consumption and network bandwidth usage. When sensors do not collect data frequently, the frequency of data transmission can be reduced, thereby lowering energy and bandwidth costs.

[0181] More precise data transmission control: By taking into account the data acquisition time interval and delay duration, the system can more precisely control the data transmission of the sensors, ensuring that data is transmitted on demand and avoiding unnecessary data redundancy.

[0182] The technical solution in this embodiment helps optimize data transmission, improve the efficiency of the sensor system, reduce costs, and ensure timely acquisition of the required environmental data.

[0183] In one embodiment of the present invention, the virtual 3D model display module includes:

[0184] The warehouse information extraction module is used to extract data information from the warehouse; wherein, the data information includes the location information of the goods, the layout information of the goods, and the quantity information of the goods;

[0185] The 3D model creation module is used to create a virtual 3D model of the warehouse based on the location information, layout information, and quantity information of the goods using data twin technology.

[0186] The model and information association module is used to associate the environmental data information collected by the sensor with the virtual 3D model, so that the virtual 3D model can display the environmental information data of the warehouse in real time, forming a virtual 3D model with real-time changes in environmental information data;

[0187] The visualization module is used to visualize virtual 3D models that display real-time changes in environmental information data.

[0188] The working principle of the above technical solution is as follows: First, the system acquires data information from within the warehouse, including the location information, layout information, and quantity information of the goods. This information may be acquired through sensors, scanning devices, or other means.

[0189] Using data twin technology, the system creates a virtual 3D model of the warehouse based on extracted data, particularly the location, layout, and quantity of goods. This virtual model reflects the actual warehouse layout and goods distribution.

[0190] The system will associate environmental data collected by sensors with the virtual 3D model. This means that environmental data, such as temperature and humidity, will be mapped onto the virtual 3D model in real time to display the environmental information inside the actual warehouse.

[0191] A virtual 3D model with real-time updated environmental information data will be displayed to operators or stakeholders in a visual manner. This could be a real-time 3D graphical interface showing the layout of goods and environmental data within the warehouse to help users understand the warehouse's status.

[0192] The above technical solution achieves the following effects: Real-time environmental monitoring: By associating environmental data collected by sensors with a virtual 3D model, the system enables real-time monitoring of the warehouse environment. This helps to detect potential problems or anomalies in a timely manner.

[0193] Visualized Information Presentation: By visually displaying virtual 3D models, users can gain a more intuitive understanding of the actual situation in the warehouse. This enhances their understanding of the warehouse's internal environment and cargo layout.

[0194] Decision support: Real-time display of environmental information and cargo distribution helps operators or decision-makers better manage and optimize warehouse operations. This technology facilitates timely action to address changing environmental conditions.

[0195] The technical solution in this embodiment combines data twin and visualization technologies to provide more comprehensive environmental monitoring and information presentation for warehouse management, which helps to improve efficiency and reduce potential problems.

[0196] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring and managing goods in transportation based on digital twins, characterized in that, The digital twin-based cargo monitoring and management method in transportation includes: Sensor arrays were deployed inside the warehouse, and environmental data was collected using these sensors. A virtual 3D model of the warehouse is created using data twin technology, and then displayed as a virtual 3D model. The system monitors the warehouse environment in real time to ensure it meets environmental requirements. If the warehouse environment does not meet the requirements, an alarm is triggered. The digital twin-based cargo monitoring and management method in transportation also includes: Step 1: Suppose a certain type of sensor collects n normal environmental information data points, and arranges them in chronological order. Let be the value of the i-th normal environmental information data point, where i is the data point number and is an integer greater than or equal to 1 and less than or equal to n; then the mean and standard deviation of these n normal environmental information data points are: ； in The mean of n normal information data points. For this, the standard deviation of n normal information numbers; Step Two: Set For the i-th environmental information data increment, its calculation formula is: ； The average data increment of normal environmental information collected by this type of sensor is: ； in This represents the average data increment of normal environmental information collected by this type of sensor; Step 3: Based on the calculation results of Step 1 and Step 2, calculate the safe variation range of the data collected by this type of sensor. The calculation formula is as follows: ； in This is the upper limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is greater than... This indicates that there may be a sudden change in the local environment, and an alarm should be issued. This is the lower limit of the safe variation range for data collected by this type of sensor, meaning that when the change in a certain collected environmental information data compared to the previous collected environmental information data is less than... This indicates that there may be a sudden change in the local environment, and an alarm should be issued.

2. The method for monitoring and managing goods in transportation based on digital twins according to claim 1, characterized in that, Sensor arrays are deployed within the warehouse, and data is collected from the warehouse using these sensors, including: A sensor array is deployed inside the warehouse; wherein the sensor array includes a temperature sensor, a humidity sensor, and a wind speed sensor for detecting warehouse ventilation. The data transmission time interval of the sensor is set according to the data acquisition time interval of the sensor; The sensor is controlled to transmit the collected environmental data to the monitoring and management platform according to the data transmission time interval.

3. The method for monitoring and managing goods in transportation based on digital twins according to claim 1, characterized in that, The data transmission time interval of the sensor is set according to the data acquisition time interval of the sensor, including: Extract the data acquisition time interval for each of the sensors; Extract the data acquisition delay duration of each of the sensors; The data transmission time interval of the sensor is set according to the data acquisition time interval and data acquisition delay duration of the sensor; The data transmission time interval of the sensor is obtained by the following formula: T=[1+exp(1-T s / T0)]×T g Where T represents the data transmission time interval of the sensor; T g This represents the preset data transmission time interval reference value; T s T0 represents the data acquisition delay duration of the sensor; T0 represents the data acquisition time interval of the sensor.

4. The method for monitoring and managing goods in transportation based on digital twins according to claim 1, characterized in that, Create a virtual 3D model of the warehouse using data twin technology, and then display the virtual 3D model, including: Extract warehouse data; wherein the data includes the location information of the goods, the layout information of the goods, and the quantity information of the goods; Using data twin technology, a virtual 3D model of the warehouse is created based on the location, layout, and quantity information of the goods. The environmental data collected by the sensors is associated with the virtual 3D model, so that the virtual 3D model can display the environmental information data of the warehouse in real time, forming a virtual 3D model with real-time changes in environmental information data. Visualize virtual 3D models that contain real-time changes in environmental information data.

5. A cargo monitoring and management system based on digital twins in transportation, characterized in that, The digital twin-based cargo monitoring and management system for transportation includes: The sensor deployment module is used to deploy sensor groups in the warehouse and use the sensors to collect environmental data information in the warehouse. The virtual 3D model display module is used to create a virtual 3D model of the warehouse using data twin technology and to display the virtual 3D model. The alarm module is used to monitor the warehouse environment in real time to see if it meets the environmental requirements. When the warehouse environment does not meet the environmental requirements, an alarm will be triggered. The digital twin-based cargo monitoring and management method in transportation also includes: Step 1: Suppose a certain type of sensor collects n normal environmental information data points, and arranges them in chronological order. Let be the value of the i-th normal environmental information data point, where i is the data point number and is an integer greater than or equal to 1 and less than or equal to n; then the mean and standard deviation of these n normal environmental information data points are: ； in The mean of n normal information data points. For this, the standard deviation of n normal information numbers; Step Two: Set For the i-th environmental information data increment, its calculation formula is: ； The average data increment of normal environmental information collected by this type of sensor is: ； in This represents the average data increment of normal environmental information collected by this type of sensor; Step 3: Based on the calculation results of Step 1 and Step 2, calculate the safe variation range of the data collected by this type of sensor. The calculation formula is as follows: ； in This is the upper limit of the safe variation range for data collected by this type of sensor, that is, when the change in a certain collected environmental information data compared to the previous collected environmental information data is greater than... This indicates that there may be a sudden change in the local environment, and an alarm should be issued. This is the lower limit of the safe variation range for data collected by this type of sensor, meaning that when the change in a certain collected environmental information data compared to the previous collected environmental information data is less than... This indicates that there may be a sudden change in the local environment, and an alarm should be issued.

6. The cargo monitoring and management system based on digital twins in transportation according to claim 5, characterized in that, The sensor deployment module includes: A deployment execution module is used to deploy a sensor group in the warehouse; wherein the sensor group includes a temperature sensor, a humidity sensor, and a wind speed sensor for detecting warehouse ventilation; The time interval setting module is used to set the data transmission time interval of the sensor according to the data acquisition time interval of the sensor; An environmental data transmission module is used to control the sensor to transmit the collected environmental data to the monitoring and management platform according to the data transmission time interval.

7. The cargo monitoring and management system based on digital twins in transportation according to claim 5, characterized in that, The time interval setting module includes: A data acquisition time interval extraction module is used to extract the data acquisition time interval for each of the sensors. The data acquisition delay duration extraction module is used to extract the data acquisition delay duration of each of the sensors; The execution module is configured to set the data transmission time interval of the sensor according to the data acquisition time interval and the data acquisition delay duration of the sensor. The data transmission time interval of the sensor is obtained by the following formula: T=[1+exp(1-T s / T0)]×T g Where T represents the data transmission time interval of the sensor; T g This represents the preset data transmission time interval reference value; T s T0 represents the data acquisition delay duration of the sensor; T0 represents the data acquisition time interval of the sensor.

8. The cargo monitoring and management system based on digital twins in transportation according to claim 5, characterized in that, The virtual 3D model display module includes: The warehouse information extraction module is used to extract data information from the warehouse; wherein, the data information includes the location information of the goods, the layout information of the goods, and the quantity information of the goods; The 3D model creation module is used to create a virtual 3D model of the warehouse based on the location, layout, and quantity information of the goods using data twin technology. The model and information association module is used to associate the environmental data information collected by the sensor with the virtual 3D model, so that the virtual 3D model can display the environmental information data of the warehouse in real time, forming a virtual 3D model with real-time changes in environmental information data; The visualization module is used to visualize virtual 3D models that display real-time changes in environmental information data.

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