Intelligent logistics transportation process monitoring method based on Internet of Things

Abstract

The invention relates to the technical field of intelligent logistics monitoring, and discloses an intelligent logistics transportation process monitoring method based on the Internet of Things, which comprises the following steps: step 1, installing an environment monitoring sensor, a positioning module and a wireless communication module on a logistics transportation carrier, obtaining environment data, position information and communication channel parameters in a transportation process, and sending the environment data, the position information and the communication channel parameters to a server; the collected data are transmitted to the edge computing unit through the wireless communication module; and 2, carrying out channel state analysis on the received data in the edge calculation unit. The channel state is analyzed in real time in the edge computing unit, the environment data, the position information and the communication channel parameters are obtained, the channel coding mode and the transmitting power of the wireless communication module are dynamically adjusted according to the current channel condition, the optimal allocation of channel resources is achieved, and the communication efficiency is improved. The problem of unbalanced bandwidth occupation caused by a traditional fixed coding scheme in different channel environments is avoided, and the stability of data transmission and the communication efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent logistics monitoring, and particularly to a method for monitoring the intelligent logistics transportation process based on the Internet of Things. Background Art

[0002] In the modern logistics transportation process, the Internet of Things technology is widely used to improve transportation efficiency, optimize logistics management, and enhance the ability to monitor goods. However, there are still many technical bottlenecks in the existing intelligent logistics monitoring systems, which affect the reliability of data transmission and the intelligent level of logistics transportation. The specific problems include the following aspects:

[0003] Firstly, the data transmission stability of traditional logistics monitoring systems is poor in complex environments. Most of the existing logistics data transmissions adopt fixed coding methods and transmission powers, and cannot be dynamically adjusted according to the changes in the channel state. As a result, in the complex logistics transportation environment, data transmission is easily affected by signal attenuation, interference, and network congestion factors, resulting in a high data loss rate and low channel utilization rate, affecting the real-time and accuracy of logistics monitoring data.

[0004] Secondly, the existing logistics data transmission path optimization strategies are insufficient. The data transmission in the traditional logistics transportation process usually adopts a preset fixed path. Once a certain path is interfered with and the channel quality deteriorates, the data transmission cannot adjust the path in time, easily leading to data transmission delay or failure. Most of the current advanced path optimization methods rely on static historical data and lack the analysis of real-time channel state, resulting in low flexibility in optimizing the data transmission path during the logistics transportation process.

[0005] In addition, the transmission priority management mechanism for emergency data is imperfect. During the logistics transportation process, in case of emergencies such as the over-limit of the temperature and humidity of goods or abnormal vibration, there is no obvious difference in the processing methods of ordinary data and abnormal data in the existing system, resulting in the inability of emergency data to be preferentially processed due to network congestion or bandwidth limitation, affecting the rapid response ability of the logistics monitoring system to emergencies.

[0006] In view of the above problems, the present invention proposes a method for monitoring the intelligent logistics transportation process based on the Internet of Things to solve the above-mentioned problems. Summary of the Invention

[0007] Aiming at the deficiencies of the prior art, the present invention provides a method for monitoring the intelligent logistics transportation process based on the Internet of Things to solve the problems raised in the above background art.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for monitoring the intelligent logistics transportation process based on the Internet of Things, comprising:

[0009] Step 1: Install environmental monitoring sensors, positioning modules, and wireless communication modules on the logistics transportation carrier to obtain environmental data, location information, and communication channel parameters during transportation, and transmit the collected data to the edge computing unit through the wireless communication module;

[0010] Step 2: Perform channel state analysis on the received data in the edge computing unit, including calculating the signal-to-noise ratio, data loss rate, and available bandwidth of the channel, and updating the channel state parameters according to the changes in the communication environment. Store the calculated channel state information in the edge computing unit and provide it to the subsequent data transmission optimization module;

[0011] Step 3: Adjust the channel coding method and transmission power of the wireless communication module according to the channel state information provided by the edge computing unit. Determine the coding scheme according to the current channel conditions, apply the adjusted coding parameters to the data transmission process, and feedback the adjusted coding scheme parameters to the edge computing unit;

[0012] Step 4: Establish a Markov decision model in the edge computing unit based on historical channel optimization information. Calculate the state transition probabilities of different transmission paths according to the current channel state, data transmission path, and historical transmission results. Store the calculated optimal transmission path parameters in the edge computing unit and dynamically adjust the data transmission path during the data transmission process;

[0013] Step 5: During the data transmission process, adjust the data transmission path of the wireless communication module according to the optimal transmission path parameters, and monitor the transmission state during the transmission process. Real-time feedback the transmission state information to the edge computing unit to update the Markov decision model;

[0014] Step 6: Perform abnormal screening and compression processing on the transmitted data in the edge computing unit, mark the abnormal data, reduce the data volume through data compression methods, store the processed data in the edge computing unit, and give priority to transmitting key data. At the same time, send data summary information to the cloud;

[0015] Step 7: Analyze the data summary information received in the cloud, combine the transmission state information provided by the edge computing unit, calculate the stability parameters of the overall logistics data transmission, feedback the calculation results to the edge computing unit to optimize the subsequent data transmission strategy, and store the historical data transmission records in the cloud to support the monitoring and management of the logistics transportation process.

[0016] Preferably, the signal-to-noise ratio calculation in the channel state analysis is calculated using the following formula:

[0017]

[0018] where SNR 2is the signal-to-noise ratio, is the received signal power, is the noise power in the communication channel.

[0019] Preferably, the data loss rate is calculated using the following formula:

[0020] where L 3 is the data loss rate, is the number of successfully received data packets, is the total number of data packets sent.

[0021] Preferably, the coding schemes on which the channel coding method is adjusted include low-density parity-check codes, turbo codes, and convolutional codes. The optimal coding scheme is determined according to the current channel state. Among them, if the signal-to-noise ratio is higher than the set threshold, a high-rate coding scheme is determined; if the signal-to-noise ratio is lower than the set threshold, a low-rate coding scheme is determined.

[0022] Preferably, the optimization objective of the adjustment of the channel coding method is to maximize the channel capacity. The formula for calculating the channel capacity is as follows: C 5 = B 5 ·log 2 (1 + SNR 5 ),

[0023] where C 5 is the channel capacity, B 5 is the channel bandwidth, SNR 5 is the currently calculated signal-to-noise ratio, and log 2 represents the growth rate of the channel capacity calculated according to the Shannon formula.

[0024] Preferably, the Markov decision model calculates the optimal transmission path based on historical channel optimization information. The formula for calculating the state transition probability is as follows:

[0025]

[0026] where P 6 (s′|s,a) is the probability of transitioning from state s to state s′ through action a,

[0027] R 6 (s,a) is the reward value for executing action a in the current state s,

[0028] exp(R 6 (s,a)) is the exponential function, and Σ s″ exp(R 6 (s″,a)) is the normalization factor.

[0029] Preferably, after determining the optimal transmission path, dynamic adjustment is performed on data transmission. When it is detected that the current channel quality deteriorates, it is preferentially switched to the path with the highest transmission success rate in historical data, and a path optimization scheme for subsequent data transmission is recalculated.

[0030] Preferably, the data compression process uses Fourier transform for data dimensionality reduction to reduce the amount of transmitted data and improve transmission efficiency. The Fourier transform calculation formula is as follows:

[0031]

[0032] where, X 8 (k) is the transformed data, x 8 (n) is the original data, N 8 is the data length, k is the frequency index, j is the imaginary unit, n is the time index,

[0033] is the complex exponential function.

[0034] Preferably, the priority scheduling strategy after the data compression process is based on the priority transmission of key data. The key data includes the situations of temperature and humidity exceeding the limit and abnormal vibration of the goods. The priority data adopts a weight scheduling method to ensure the priority transmission of abnormal data.

[0035] Preferably, the cloud stores the historical data transmission records, and calculates the overall transmission performance based on the data transmission stability parameters. If the historical data analysis result shows that there is a continuous high data loss rate on a specific logistics line, the wireless communication mode of the line is automatically adjusted, and optimization suggestions are provided to the logistics management platform.

[0036] The present invention provides an Internet of Things-based intelligent logistics transportation process monitoring method. It has the following beneficial effects:

[0037] 1. By performing real-time analysis of the channel state in the edge computing unit, the present invention obtains the environmental data, location information, and communication channel parameters during the transportation process, and dynamically adjusts the channel coding method and transmission power of the wireless communication module according to the current channel conditions, realizing the optimal allocation of channel resources, avoiding the problem of unbalanced bandwidth occupation caused by traditional fixed coding schemes in different channel environments, and improving the stability and communication efficiency of data transmission.

[0038] 2. The present invention establishes a Markov decision model based on historical channel optimization information, calculates the state transition probabilities of different transmission paths, combines the current channel state and historical transmission results, dynamically determines the optimal path for data transmission, and realizes the intelligent adjustment of the data transmission path. Compared with the traditional fixed-path transmission method, it can adapt to the complex and changeable logistics transportation environment, reduce the transmission delay caused by the deterioration of the channel conditions, and improve the stability and success rate of data transmission.

[0039] 3. The present invention introduces a priority scheduling strategy during the data transmission process, preferentially transmits the data of temperature and humidity exceeding the limit and abnormal vibration of goods, and adopts a weighted queue scheduling method to ensure that emergency data is preferentially processed in a limited bandwidth environment. Compared with the traditional data transmission method, it can ensure a quick response to sudden abnormal situations during the logistics transportation process and improve the timeliness and reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] The present invention will be described in detail below with reference to the accompanying drawings:

[0043] Embodiment:

[0044] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for monitoring the intelligent logistics transportation process based on the Internet of Things, including:

[0045] Step 1: Install an environmental monitoring sensor, a positioning module, and a wireless communication module on the logistics transportation carrier to obtain environmental data, location information, and communication channel parameters during the transportation process, and transmit the collected data to the edge computing unit through the wireless communication module;

[0046] Step 2: Analyze the channel state of the received data in the edge computing unit, including calculating the signal-to-noise ratio, data loss rate, and available bandwidth of the channel, updating the channel state parameters according to the change of the communication environment, storing the calculated channel state information in the edge computing unit, and providing it to the subsequent data transmission optimization module;

[0047] Step 3: According to the channel state information provided by the edge computing unit, the channel coding mode and transmit power of the wireless communication module are adjusted, the coding scheme is determined according to the current channel conditions, the adjusted coding parameters are applied to the data transmission process, and the adjusted coding scheme parameters are fed back to the edge computing unit;

[0048] Step 4: Establish a Markov decision model based on historical channel optimization information in the edge computing unit, calculate the state transition probability of different transmission paths according to the current channel state, data transmission path and historical transmission results, store the calculated optimal transmission path parameters in the edge computing unit, and dynamically adjust the data transmission path during the data transmission process;

[0049] Step 5: During the data transmission process, the data transmission path of the wireless communication module is adjusted according to the optimal transmission path parameters, and the transmission status is monitored during the transmission process, and the transmission status information is fed back to the edge computing unit in real time to update the Markov decision model;

[0050] Step 6: In the edge computing unit, the transmitted data is screened and compressed for abnormalities, abnormal data is marked, the amount of data is reduced by data compression methods, the processed data is stored in the edge computing unit, key data is transmitted first, and data summary information is sent to the cloud at the same time;

[0051] Step 7: Analyze the received data summary information in the cloud, combine it with the transmission status information provided by the edge computing unit, calculate the stability parameters of the overall logistics data transmission, and feed the calculation results back to the edge computing unit to optimize the subsequent data transmission strategy, and store historical data transmission records in the cloud to support the monitoring and management of the logistics transportation process.

[0052] Benefits of Step 1 The application of edge computing units reduces dependence on the cloud, improves the timeliness of data processing, and avoids monitoring lags caused by network delays during data transmission.

[0053] Benefits of step 2 Dynamically updating channel state parameters according to changes in the communication environment helps optimize data transmission strategies, improve the stability and reliability of data transmission, and reduce the impact of packet loss rate and channel interference.

[0054] The benefit of step 3 is that by feeding back the adjusted encoding parameters to the edge computing unit, historical optimization data is formed, making future communication strategies intelligent and improving the system's adaptability to complex environments.

[0055] The benefit of step 4 is that compared with the traditional fixed-path transmission method, it can dynamically determine the optimal path, automatically adjust the transmission path when the network environment is poor, improve the data transmission success rate, reduce latency, and ensure the stability of logistics data transmission.

[0056] The benefits of Step 5 can dynamically adapt to changes in channel quality, optimize the transmission path, avoid data transmission failures caused by environmental interference, and improve the response ability of the logistics monitoring system.

[0057] The benefits of Step 6 are to use data compression technology to reduce the amount of transmitted data, relieve the pressure on data storage and transmission, and improve data transmission efficiency. The mechanism of prioritizing the transmission of key data ensures that the logistics monitoring system can quickly respond to abnormal situations.

[0058] The benefits of Step 7 are to store historical data transmission records in the cloud, make the data analysis of the logistics transportation process intelligent, provide a basis for management decisions, and improve the visualization ability and management efficiency of the logistics transportation process.

[0059] The signal-to-noise ratio in the channel state analysis is calculated using the following formula:

[0060]

[0061] where SNR 2 is the signal-to-noise ratio, is the received signal power, is the noise power in the communication channel.

[0062] The data loss rate is calculated using the following formula:

[0063] where L 3 is the data loss rate, is the number of successfully received data packets, is the total number of data packets sent.

[0064] By calculating the signal-to-noise ratio, the communication quality of the current channel is evaluated, and the ratio of signal strength to noise is quantified. In a wireless communication environment, the signal-to-noise ratio is a key indicator affecting data transmission quality. By calculating the signal-to-noise ratio in real time, the channel coding method and transmission power are dynamically adjusted to improve communication reliability. Using the signal-to-noise ratio as the optimization basis, high-rate coding can be used when the channel condition is good to improve the transmission rate; low-rate coding can be used when the channel condition is poor to enhance the stability of data transmission.

[0065] By calculating the data loss rate, the proportion of data lost during transmission is evaluated, and whether the channel condition is stable is identified. A low data loss rate means high data transmission reliability, which helps to improve the integrity and accuracy of logistics transportation data. When an increase in the data loss rate is detected, the channel coding method is adjusted, the data transmission path is optimized, and redundant coding is increased to reduce data loss and improve the stability of the logistics monitoring system.

[0066] The coding schemes based on which the channel coding method is adjusted include low - density parity - check codes, turbo codes, and convolutional codes. The optimal coding scheme is determined according to the current channel state. Among them, if the signal - to - noise ratio is higher than the set threshold, a high - rate coding scheme is determined; if the signal - to - noise ratio is lower than the set threshold, a low - rate coding scheme is determined.

[0067] The optimization objective of the channel coding method adjustment is to maximize the channel capacity. The formula for calculating the channel capacity is as follows: C 5 = B 5 ·log 2 (1 + SNR 5 )

[0068] where C 5 is the channel capacity, B 5 is the channel bandwidth, SNR 5 is the currently calculated signal - to - noise ratio, and log 2 represents the growth rate of the channel capacity calculated according to the Shannon formula.

[0069] At low signal - to - noise ratios, by enhancing the error - correction ability, the bit - error rate is reduced and data loss is decreased. At high signal - to - noise ratios, an efficient coding method is adopted to reduce data redundancy and improve the transmission speed. The coding scheme can be dynamically adjusted to optimize the performance of the wireless communication module and ensure the stable transmission of logistics transportation data.

[0070] The calculation of the channel capacity can quantify the data - transmission ability and ensure the full utilization of communication resources. Adjusting the coding method according to the calculation result of the channel capacity can make the data - transmission rate close to the maximum bearable value of the channel and improve the logistics data - transmission efficiency. Adjusting the transmission power and coding method in real - time according to the signal - to - noise ratio can keep the data transmission efficient and stable in complex environments.

[0071] The Markov decision model calculates the optimal transmission path based on historical channel optimization information. The formula for the state - transition probability is as follows:

[0072]

[0073] where P 6 (s′|s,a) is the probability of transitioning from state s to state s′ through action a.

[0074] R 6 (s,a) is the reward value for executing action a in the current state s.

[0075] exp(R 6 (s,a)) is an exponential function, and Σ s″ exp(R 6 (s″,a)) is a normalization factor.

[0076] After determining the optimal transmission path, the data transmission is dynamically adjusted. When it is detected that the current channel quality deteriorates, it preferentially switches to the path with the highest transmission success rate in the historical data, and recalculates the path optimization scheme for subsequent data transmission.

[0077] By calculating the state transition probabilities of different paths through historical channel optimization information, the optimal path can be determined, improving the data transmission success rate. Compared with the fixed transmission path, this method can adjust the path according to the real-time channel state, improving the flexibility of transmission. In the case of poor channel quality, it can intelligently avoid the paths with high packet loss rates and determine the paths with high stability for data transmission.

[0078] Once the channel deterioration is detected, the system can quickly adjust the path to avoid data loss and delay. When the data volume is large and the channel interference is strong, dynamically adjusting the path can reduce communication congestion and improve the overall network utilization rate. The system automatically optimizes the data transmission path based on the Markov model, reducing the need for manual monitoring and intervention and improving the intelligent level of logistics monitoring.

[0079] Data compression processing uses Fourier transform for data dimensionality reduction to reduce the amount of data transmitted and improve the transmission efficiency. The Fourier transform calculation formula is as follows:

[0080]

[0081] where, X 8 (k) is the transformed data, x 8 (n) is the original data, N 8 is the data length, k is the frequency index, j is the imaginary unit, n is the time index,

[0082] is the complex exponential function.

[0083] The priority scheduling strategy after data compression processing is based on the priority transmission of key data. Key data includes the situations of temperature and humidity exceeding the limit and abnormal vibration of goods. The priority data adopts the weight scheduling method to ensure the priority transmission of abnormal data.

[0084] The cloud stores the historical data transmission records, and calculates the overall transmission performance based on the data transmission stability parameters. If the historical data analysis results show that there is a continuous high data loss rate on a specific logistics line, it automatically adjusts the wireless communication mode of the line and provides optimization suggestions to the logistics management platform.

[0085] The signal-to-noise ratio calculation can quantify the channel quality and provide a basis for dynamically adjusting the channel coding and transmission power, ensuring that the data transmission rate and stability can be optimized under various channel conditions.

[0086] The calculation of the data loss rate can evaluate the packet loss situation in the data transmission process in real time, help to detect network problems in a timely manner, optimize the data transmission strategy, and ensure the stable and reliable data transmission of the logistics monitoring system.

[0087] By monitoring and calculating the signal-to-noise ratio and the data loss rate, the logistics monitoring method of the present invention can intelligently adjust the communication strategy, avoid transmission errors caused by poor channel quality, improve the stability and efficiency of data transmission, and ultimately provide technical support for the efficient management of logistics transportation.

[0088] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the intelligent logistics transportation process based on the Internet of Things, characterized in that: include: Step 1: Install environmental monitoring sensors, positioning modules and wireless communication modules on the logistics transport carrier to obtain environmental data, location information and communication channel parameters during transportation, and transmit the collected data to the edge computing unit through the wireless communication module; Step 2: Perform channel status analysis on the received data in the edge computing unit, including calculating the signal-to-noise ratio, data loss rate and available bandwidth of the channel, and updating the channel status parameters according to changes in the communication environment. The calculated channel status information is stored in the edge computing unit and provided to the subsequent data transmission optimization module. Step 3: According to the channel state information provided by the edge computing unit, the channel coding mode and transmit power of the wireless communication module are adjusted, the coding scheme is determined according to the current channel conditions, the adjusted coding parameters are applied to the data transmission process, and the adjusted coding scheme parameters are fed back to the edge computing unit; Step 4: Establish a Markov decision model based on historical channel optimization information in the edge computing unit, calculate the state transition probability of different transmission paths according to the current channel state, data transmission path and historical transmission results, store the calculated optimal transmission path parameters in the edge computing unit, and dynamically adjust the data transmission path during the data transmission process; Step 5: During the data transmission process, the data transmission path of the wireless communication module is adjusted according to the optimal transmission path parameters, and the transmission status is monitored during the transmission process, and the transmission status information is fed back to the edge computing unit in real time to update the Markov decision model; Step 6: In the edge computing unit, the transmitted data is screened and compressed for abnormalities, abnormal data is marked, the amount of data is reduced by data compression methods, the processed data is stored in the edge computing unit, key data is transmitted first, and data summary information is sent to the cloud at the same time; Step 7: Analyze the received data summary information in the cloud, combine it with the transmission status information provided by the edge computing unit, calculate the stability parameters of the overall logistics data transmission, and feed the calculation results back to the edge computing unit to optimize the subsequent data transmission strategy, and store historical data transmission records in the cloud to support the monitoring and management of the logistics transportation process.

2. According to the method for monitoring the intelligent logistics transportation process based on the Internet of Things in claim 1, it is characterized in that: The signal-to-noise ratio in the channel state analysis is calculated using the following formula: Where SNR2 is the signal-to-noise ratio, is the received signal power, is the noise power in the communication channel.

3. According to the method for monitoring the intelligent logistics transportation process based on the Internet of Things in claim 2, it is characterized in that: The data loss rate is calculated using the following formula: Among them, L3 is the data loss rate, is the number of packets successfully received, The total number of packets sent.

4. According to the method for monitoring the intelligent logistics transportation process based on the Internet of Things in claim 1, it is characterized in that: The coding schemes used when adjusting the channel coding method include low-density parity-check codes, turbo codes and convolutional codes. The optimal coding scheme is determined according to the current channel state. If the signal-to-noise ratio is higher than a set threshold, a high-rate coding scheme is determined, and if the signal-to-noise ratio is lower than the set threshold, a low-rate coding scheme is determined.

5. According to the method for monitoring the intelligent logistics transportation process based on the Internet of Things in claim 4, it is characterized in that: The optimization goal of adjusting the channel coding method is to maximize the channel capacity. The channel capacity calculation formula is as follows: C5=B5·log2(1+SNR5), Among them, C5 is the channel capacity, B5 is the channel bandwidth, SNR5 is the currently calculated signal-to-noise ratio, and log2 represents the channel capacity growth rate calculated according to the Shannon formula.

6. The method for monitoring the intelligent logistics transportation process based on the Internet of Things according to claim 1 is characterized in that: The Markov decision model calculates the optimal transmission path based on historical channel optimization information, and the state transition probability calculation formula is as follows: Among them, P6(s′|s,a) is the probability of transitioning from state s to state s′ through action a, R6(s,a) is the reward value for executing action a in the current state s. exp(R6(s,a)) is the exponential function, ∑ s″ exp(R6(s″,a)) is the normalization factor.

7. The method for monitoring the intelligent logistics transportation process based on the Internet of Things according to claim 6 is characterized in that: After determining the optimal transmission path, the data transmission is dynamically adjusted. If it is detected that the quality of the current channel has decreased, the path with the highest transmission success rate in the historical data is preferentially switched, and the path optimization plan for subsequent data transmission is recalculated.

8. The method for monitoring the intelligent logistics transportation process based on the Internet of Things according to claim 1 is characterized in that: The data compression process uses Fourier transform to reduce the data dimension to reduce the amount of transmitted data and improve the transmission efficiency. The Fourier transform calculation formula is as follows: Among them, X8(k) is the transformed data, x8(n) is the original data, N8 is the data length, k is the frequency index, j is the imaginary unit, and n is the time index. is a complex exponential function.

9. The method for monitoring the intelligent logistics transportation process based on the Internet of Things according to claim 8 is characterized in that: The priority scheduling strategy after data compression processing is based on the priority transmission of key data, which includes temperature and humidity exceeding the limit and abnormal vibration of goods. The priority data adopts a weighted scheduling method to ensure that abnormal data is transmitted first.

10. The method for monitoring the intelligent logistics transportation process based on the Internet of Things according to claim 9 is characterized in that: The cloud stores historical data transmission records and calculates the overall transmission performance based on data transmission stability parameters. If the historical data analysis results show that a specific logistics route has a continuously high data loss rate, the wireless communication mode of the route is automatically adjusted and optimization suggestions are provided to the logistics management platform.

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