Intelligent logistics path assessment method based on Internet of Things

Abstract

The invention relates to an intelligent logistics path assessment method based on the Internet of Things, and the method comprises the steps: collecting the dynamic data of a logistics path in real time through a Beidou or GPS dual-mode positioning terminal, an RFID goods state sensor and a road side unit RSU traffic flow monitor which are disposed on a transportation vehicle, a data acquisition process is carried out according to dynamic data of an acquired logistics path, bidirectional authentication is carried out through an equipment digital certificate, encrypted transmission is carried out by adopting a national cryptographic SM4 algorithm, encrypted dynamic data is output, a multi-target evaluation function under space-time constraint is constructed according to the encrypted dynamic data, a digital signature is added in an optimal path, and a multi-target evaluation result is obtained. According to the deviation between the actual transportation result and the prediction index, the evaluation function weight coefficient is updated through a gradient descent method, and a path evaluation value is obtained through correction.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a smart logistics route evaluation method based on the Internet of Things. Background Technology

[0002] In the field of smart logistics, route assessment methods directly affect transportation efficiency and cargo safety. Current mainstream technologies face three major bottlenecks:

[0003] 1. Limited Data Collection Dimensions: Existing solutions rely heavily on GPS positioning and basic road condition data, lacking coordinated monitoring of cargo status and real-time traffic flow. This leads to route decisions ignoring cargo damage risks and sudden congestion.

[0004] 2. Insufficient security and dynamism: Data transmission generally uses plaintext or weak encryption, making it susceptible to tampering; the path optimization algorithm does not incorporate robust verification mechanisms.

[0005] 3. Rigid model and lack of trust: The evaluation function weights rely on manual setting and cannot be adaptively optimized according to the transportation results. The decision-making process lacks a credible traceability mechanism, and the operation logs are easily denied in the event of disputes over rights and responsibilities.

[0006] Therefore, a path evaluation method integrating multi-source IoT data security collaboration and dynamic trusted decision-making is proposed to address the shortcomings of the existing technologies. By fusing multiple sensors such as Beidou, RFID, and RSU, the method incorporates the status of goods, vehicle pose, and road environment into a unified evaluation system for the first time. It constructs a data transmission chain with national cryptographic SM4 encryption and device certificate authentication to solve the risk of dynamic data tampering. The improved A* algorithm and Monte Carlo risk simulation are run in a Trusted Execution Environment (TEE) to ensure path robustness. Based on the gradient descent method, a closed-loop weight optimization mechanism and digital signature notarization are used to achieve self-evolution of the evaluation model and non-repudiation of operations. Summary of the Invention

[0007] To address the problems existing in the prior art, this application aims to provide a smart logistics route evaluation method based on the Internet of Things.

[0008] The smart logistics route evaluation method based on the Internet of Things described in this application includes the following steps:

[0009] S101. Real-time dynamic data of logistics routes are collected by deploying Beidou or GPS dual-mode positioning terminals, RFID cargo status sensors and roadside unit (RSU) traffic flow monitors on transport vehicles.

[0010] S102. The data acquisition process is carried out by two-way authentication of the device digital certificate based on the dynamic data of the collected logistics path, and the transmission is encrypted using the national cryptographic SM4 algorithm. The encrypted dynamic data is output, and a multi-objective evaluation function under spatiotemporal constraints is constructed based on the encrypted dynamic data.

[0011] S103. Based on the multi-objective evaluation function, the real-time collected data is input into the evaluation function. An improved A* algorithm is used to generate an initial path set. Monte Carlo risk simulation is used to verify the robustness of the path set. The optimal path that meets the constraints is output. The algorithm execution process is completed in a Trusted Execution Environment (TEE).

[0012] S104. Add a digital signature to the optimal path. Based on the deviation between the actual transportation results and the predicted indicators, update the weight coefficients of the evaluation function using the gradient descent method and correct the path evaluation value.

[0013] Furthermore, the real-time collection of dynamic data on the logistics path through BeiDou or GPS dual-mode positioning terminals, RFID cargo status sensors, and roadside unit (RSU) traffic flow monitors deployed on transport vehicles includes:

[0014] The dual-mode terminal integrates BeiDou and GPS data to obtain the vehicle's real-time location and combines road network traffic flow to determine route congestion.

[0015] If the traffic exceeds the threshold, a dynamic path adjustment mechanism is triggered to generate an optimized path.

[0016] Based on the optimized path, the RFID sensor continuously monitors the status of the goods. If the data deviates from the preset range, an alarm is immediately generated and a processing command is triggered. The status of the goods is stabilized by adjusting the vehicle's operating parameters.

[0017] By combining the adjusted operating status with real-time data, the support vector machine algorithm is used to evaluate vehicle efficiency and identify optimization space. Based on this, a dynamic adjustment strategy is generated. After verifying its matching with real-time traffic data, the final operating plan is output and the transportation plan is updated.

[0018] Furthermore, the data acquisition process based on the dynamic data of the collected logistics path is performed through two-way authentication using the device's digital certificate and encrypted transmission using the national cryptographic algorithm SM4. The encrypted dynamic data is then output, and a multi-objective evaluation function under spatiotemporal constraints is constructed based on the encrypted dynamic data, including:

[0019] After the encrypted dynamic data stream is decrypted by the parsing module, its integrity is verified to obtain a preliminary dataset;

[0020] Based on this dataset, the spatiotemporal constraint characteristics of the extraction flow path are extracted, key nodes are identified, and their distribution characteristics are analyzed.

[0021] By combining the distribution characteristics of key nodes with a multi-objective evaluation function, bottleneck areas in the path can be detected.

[0022] If the bottleneck flow exceeds the threshold, the data flow adjustment mechanism is triggered to generate optimized path distribution information. The support vector machine algorithm is then used to optimize the vehicle scheduling scheme based on the optimized path.

[0023] The system monitors the operating status in real time based on scheduling parameters. If the deviation from the preset standard is detected, an adjustment instruction is generated and the updated operating data is output.

[0024] Multi-objective integrated analysis is performed on the updated data to verify that it meets the evaluation function and then the final result is output. Combined with spatiotemporal constraints, the dynamic logistics data is continuously updated, and the actual operation record is generated through matching verification.

[0025] Furthermore, the process involves inputting real-time collected data into a multi-objective evaluation function, generating an initial path set using an improved A* algorithm, and performing robustness verification of the path set using Monte Carlo risk simulation. The optimal path that satisfies the constraints is then output. The algorithm execution process is completed within a Trusted Execution Environment (TEE), including:

[0026] After real-time data acquisition is preprocessed and its integrity verified, a preliminary data stream is generated.

[0027] Based on the data stream, configure the evaluation function parameters and build an evaluation tool suitable for the current scenario;

[0028] By combining evaluation tools with path generation mechanisms, an initial set of paths that meet the constraints is constructed. After verifying the robustness of the paths through risk simulation and multi-scenario testing, a reliable path combination is output.

[0029] Perform path optimization calculations in a trusted environment to select the optimal path that meets the preset objectives;

[0030] By continuously updating path information in conjunction with real-time new data streams, verifying its consistency with current constraints, generating dynamic adjustment schemes, implementing long-term monitoring of dynamic paths, capturing abnormal data and assessing its impact on overall stability, and finally outputting path adjustment records.

[0031] Furthermore, the step of attaching a digital signature to the optimal path, updating the weight coefficients of the evaluation function using gradient descent based on the deviation between the actual transportation results and the predicted indicators, and correcting the obtained path evaluation value includes:

[0032] Digital signatures are added to the optimal route to verify data integrity, generate signed route data, extract key indicators based on actual transportation results, and analyze their matching degree with predicted indicators.

[0033] When the difference between the actual and predicted indicators exceeds the threshold, the deviation data is recorded, the gradient descent method is used to iteratively adjust the weight coefficients of the evaluation function, a new weight configuration is generated, and the path evaluation value is recalculated. After verification that it meets the preset standard, the value is output.

[0034] The validated evaluation values ​​are filtered for outliers to obtain corrected data. The updated results are compared and verified through a multi-dimensional result validation mechanism, and finally, the path optimization scheme is output.

[0035] The IoT-based smart logistics route evaluation method described in this application has the advantage of real-time collection of encrypted dynamic logistics route data by deploying BeiDou or GPS dual-mode positioning terminals, RFID sensors, and roadside units. Based on this data, a multi-objective evaluation function is constructed, an improved A* algorithm is used to generate an initial route set, and Monte Carlo risk simulation verification is performed. The optimal route is output in a trusted execution environment, and the evaluation function weights are updated based on actual transportation results using gradient descent, continuously optimizing the route evaluation. This method can achieve real-time optimization of logistics routes while ensuring data security, improving transportation efficiency and reliability, and providing intelligent route planning solutions for logistics companies. Attached Figure Description

[0036] Figure 1 This application describes a process for an IoT-based smart logistics route evaluation method. Figure 1 ;

[0037] Figure 2 This application describes a process for an IoT-based smart logistics route evaluation method. Figure 2 . Detailed Implementation

[0038] like Figures 1-2 As shown, the smart logistics route evaluation method based on the Internet of Things described in this application includes:

[0039] like Figures 1-2 As shown, S101 collects dynamic data of the logistics path in real time through Beidou or GPS dual-mode positioning terminals, RFID cargo status sensors and roadside unit (RSU) traffic flow monitors deployed on the transport vehicle.

[0040] Furthermore, in step S101, Beidou positioning and GPS positioning data are collected by the dual-mode terminal on the transport vehicle, and the two positioning signals are fused to determine the real-time location information of the transport vehicle on the logistics path;

[0041] Based on real-time location information and traffic flow data monitored by roadside units, the congestion situation on the logistics route is analyzed to determine whether the route is smooth. If the traffic flow data exceeds the preset threshold, the route adjustment mechanism is triggered to obtain an optimized route plan.

[0042] By adopting an optimized route plan and linking RFID sensors on the transport vehicle, the system continuously collects cargo status data, monitors abnormal situations of the cargo during transportation, and determines whether the cargo status is stable.

[0043] By analyzing the cargo status data, we can identify possible vibration or temperature anomalies during transportation. If the cargo status data deviates from the preset range, we can generate an anomaly alarm and obtain alarm processing instructions.

[0044] According to the alarm processing instructions, adjust the operating parameters of the transport vehicle, update the dynamic data on the logistics path synchronously, determine whether the adjusted operating parameters meet the preset standards, and obtain the adjusted operating status.

[0045] The adjusted operating status is obtained, and combined with the real-time collected dynamic data, the operating efficiency of the transport vehicle is evaluated using the support vector machine algorithm to determine the optimization space of the operating efficiency.

[0046] To optimize operational efficiency, a dynamic adjustment strategy is generated, the operation plan of the transport vehicles is updated, and it is determined whether the adjustment strategy matches the real-time traffic flow data to obtain the final operation plan.

[0047] Specifically, in step S101, the location data of the vehicle can be obtained in real time by deploying a Beidou or GPS dual-mode positioning terminal on the transport vehicle. For example, latitude and longitude information is collected every 5 seconds, with the accuracy controlled within ±2.5 meters. The data is transmitted to the cloud server through 4G and 5G networks. The positioning data is smoothed by using a Kalman filter algorithm to reduce signal drift error and reduce the error rate to below 0.5%.

[0048] The processed data is then matched with the electronic map to calculate the vehicle's real-time speed and estimated arrival time. For example, if the current speed is 60 km / h, the estimated time to reach the next node is 30 minutes. Based on historical data analysis, the average error is ±3 minutes, which provides a basis for subsequent route optimization.

[0049] Next, RFID cargo status sensors are used to monitor the cargo status. The sensors scan the cargo's tag information every 10 minutes, reading temperature, humidity, and vibration data. For example, the temperature is controlled between 5-10 degrees Celsius, the humidity is maintained between 40%-60%, and the vibration frequency is below 0.3 Hz. If the threshold is exceeded, an alarm is triggered. The data is uploaded to the platform through an IoT gateway. The system predicts the trend of cargo status changes based on time series analysis algorithms. For example, if it predicts that the temperature may rise by 2 degrees Celsius in the next 2 hours, it automatically generates adjustment suggestions and pushes them to the vehicle's air conditioning control system to ensure cargo safety.

[0050] Furthermore, road dynamic information is collected through roadside unit (RSU) traffic flow monitors, which count the number of vehicles passing through and the average vehicle speed every minute. For example, if the current traffic flow on a certain road segment is 2,000 vehicles per hour and the average vehicle speed is 45 km / h, the system uses a density clustering-based algorithm to analyze the traffic congestion index and obtains a current index of 0.75 (range 0-1, close to 1 indicates severe congestion). Combined with historical traffic data, the system predicts the congestion trend for the next hour. If the index is expected to rise to 0.85, the system automatically adjusts the logistics route to bypass the congested road segment, which is expected to save 15 minutes of transportation time after optimization.

[0051] The aforementioned data is ultimately integrated into the logistics management platform, and a comprehensive transportation efficiency report is generated through big data analysis. For example, the efficiency of the current route has improved by 8.3%, which provides support for subsequent scheduling decisions and forms a complete closed loop from data collection to route optimization.

[0052] like Figures 1-2 As shown in S102, the data acquisition process based on the dynamic data of the collected logistics path is performed through two-way authentication with the device digital certificate and encrypted transmission using the national cryptographic SM4 algorithm. The encrypted dynamic data is output, and a multi-objective evaluation function under spatiotemporal constraints is constructed based on the encrypted dynamic data.

[0053] Further, in step S102, the encrypted dynamic data stream is decrypted using a pre-established data parsing module to obtain the decrypted logistics path information, and it is determined whether the decrypted information is complete to obtain the pre-processed data set.

[0054] Based on the pre-processed dataset, spatiotemporal constrained feature extraction is performed on the logistics route information to obtain key node data in the route and determine the distribution characteristics of the key node data.

[0055] By analyzing the distribution characteristics of key node data and combining it with a multi-objective evaluation function, path analysis is performed to identify bottleneck areas in the path. If the traffic data in the bottleneck area exceeds a preset threshold, a data flow adjustment mechanism is triggered to obtain the adjusted path distribution information.

[0056] Based on the adjusted path distribution information, the support vector machine algorithm is used to optimize and analyze the scheduling scheme of transportation vehicles, obtain the optimized scheduling parameters, and determine the applicable scope of the scheduling parameters.

[0057] By combining optimized scheduling parameters with real-time collected dynamic data, the operating status of the transport vehicle is continuously monitored to determine whether the operating status meets the preset standards. If it does not meet the preset standards, a status adjustment command is generated to obtain updated operating data.

[0058] The updated runtime data is obtained, and the data flow is integrated for the data analysis module. The integrated data is then used to determine whether it meets the requirements of the multi-objective evaluation function, and the final analysis results are obtained.

[0059] Based on the final analysis results and combined with spatiotemporal constraints, the dynamic data of the logistics path is continuously updated to determine whether the updated data matches the actual operating status, and the matched data records are output.

[0060] Specifically, in step S102, during the collection of dynamic data on the logistics path, the system first achieves two-way authentication through device digital certificates to ensure the security of the data source and transmission channel. For example, a certificate mechanism based on the PKI system is adopted, and each device needs to verify the legality of the certificate validity period within 365 days when connecting. The authentication process generates a unique identifier through the SHA-256 hash algorithm, and the verification success rate reaches more than 99.9%. Subsequently, the data transmission link is encrypted using the national cryptographic SM4 algorithm, with a key length of 128 bits, and the size of each data packet is controlled within 512 bytes. The data transmission delay after encryption is controlled within 0.2 seconds to ensure real-time performance. The system automatically records encryption logs for subsequent auditing, and the log storage period is 30 days, forming a secure traceability chain.

[0061] Next, based on the encrypted dynamic data, the system constructs a multi-objective evaluation function under spatiotemporal constraints. For example, the time window limit is set to no more than 2 hours for each path segment, and the spatial constraint is that the total path length does not exceed 100 kilometers. Weight parameters are introduced into the evaluation function, with the time weight set to 0.6 and the distance weight set to 0.4. Optimization calculations are performed through a genetic algorithm, with the number of iterations set to 500. The convergence rate of the optimal solution is 95%. Combined with historical transportation data analysis, such as the average transportation time of the past 7 days being 8.5 hours, the system automatically adjusts the evaluation function parameters to adapt to dynamic changes. Finally, the multi-objective evaluation results are output, providing data support for subsequent logistics scheduling. The evaluation results are stored in a cloud database and updated hourly to ensure the timeliness and relevance of the data.

[0062] like Figures 1-2 As shown in step S103, the real-time collected data is input into the evaluation function according to the multi-objective evaluation function. An improved A* algorithm is used to generate an initial path set, and Monte Carlo risk simulation is used to verify the robustness of the path set. The optimal path that meets the constraints is output. The algorithm execution process is completed in a Trusted Execution Environment (TEE).

[0063] Furthermore, in step S103, the raw information is preprocessed by real-time collected data and combined with the data input process to determine the integrity of the data. If the integrity meets the preset threshold, the data stream after preliminary screening is obtained.

[0064] Based on the data stream after initial screening, the parameters of the evaluation function are configured, the configured function model is obtained, it is determined whether the model is suitable for the current scenario, and the adapted evaluation tool is output.

[0065] Using the adapted evaluation tool and the path generation mechanism, an initial path is constructed, and it is determined whether the initial path meets the constraints. If the constraints are met, a set of usable paths is obtained.

[0066] Using the available set of paths, we conduct multi-scenario tests using risk simulation methods, obtain risk distribution data after the tests, determine whether the distribution data meets the robustness verification standard, and output the verified path combination.

[0067] Based on the verified path combination, the calculation is performed in a trusted environment to obtain the calculated path optimization results. It is then determined whether the optimization results meet the preset goals to obtain the selected optimal path.

[0068] For the selected optimal path, the path information is continuously updated by combining the new data stream collected in real time. It is then determined whether the updated information is consistent with the current constraints to obtain a dynamically adjusted path scheme.

[0069] By dynamically adjusting the path scheme, the path set is monitored over a long period of time, abnormal data is obtained during the monitoring process, it is determined whether the abnormal data affects the overall path stability, and the final path adjustment record is output.

[0070] Specifically, in step S103, during the logistics route optimization process, the system first inputs the real-time collected transportation data into a preset multi-objective evaluation function. The data includes vehicle location, road condition information, and estimated arrival time. For example, the average real-time speed of each vehicle is 40 km / h, and the road congestion index is 3.2 (out of 5). The system calculates the comprehensive cost of each route based on these data, of which time cost accounts for 0.7 and road condition impact accounts for 0.3.

[0071] Next, the system uses an improved A* algorithm to generate an initial path set. The algorithm introduces a heuristic function, sets the step size of the path search to 0.5 kilometers, prioritizes road segments with a congestion index of less than 2.5, and generates a path set containing 10 candidate paths. The estimated travel time for each path is controlled within 1.5 hours.

[0072] Subsequently, the system robustly verifies the path set through Monte Carlo risk simulation. During the simulation, 1000 random scenarios are set, considering the probability of sudden road condition changes such as temporary road closures as 0.15. The stability of each path under different scenarios is analyzed, and finally, paths with travel time fluctuations of less than 10 minutes are selected as candidates. The entire algorithm execution process is completed in a Trusted Execution Environment (TEE). The system automatically allocates computing resources and limits the computing time of each path to no more than 0.1 seconds to ensure the security and independence of data processing.

[0073] Finally, the system outputs the optimal route based on constraints, such as a total route length limit of 80 kilometers, an estimated travel time of no more than 1.2 hours, and an average traffic index of 3.0 over the past 5 days based on historical data analysis. The system automatically adjusts its route selection preferences, prioritizing the avoidance of highly congested areas, to form the final optimal route plan, providing a reliable basis for logistics scheduling.

[0074] like Figures 1-2 As shown in S104, a digital signature is added to the optimal path. Based on the deviation between the actual transportation results and the predicted indicators, the weight coefficients of the evaluation function are updated using the gradient descent method, and the path evaluation value is corrected.

[0075] Furthermore, in step S104, a digital signature is attached to the optimal path to verify the integrity of the path data. The path information is processed through a pre-established signature mechanism to obtain the signed path data.

[0076] Based on the signed path data, obtain the data stream of the actual transportation results, extract key indicator information from the transportation results, and determine the degree of matching with the predicted indicators;

[0077] To determine the degree of matching, a deviation comparison method is used to handle the difference between the actual transportation results and the predicted indicators. If the difference exceeds a preset threshold, the deviation data is recorded.

[0078] By using the deviation data, the weight coefficients of the evaluation function are adjusted, and the function parameters are iteratively optimized using the gradient descent method to obtain the adjusted weight configuration.

[0079] Based on the adjusted weight configuration, the path evaluation values ​​are recalculated, and the calculation results are verified to determine whether they meet the preset evaluation criteria.

[0080] For the verified path evaluation values, a value correction operation is performed. Outliers are filtered out through information processing to obtain the corrected evaluation data.

[0081] Based on the revised evaluation data, updated results are generated. The updated results are then compared in multiple dimensions using a result verification mechanism to determine the final path optimization scheme.

[0082] Specifically, in step S104, in the field of logistics transportation optimization, the system first generates a digital signature for the selected optimal route using an encryption algorithm to ensure the integrity and immutability of the route data. Specifically, the SHA-256 hash algorithm is used to process the route data to generate a unique 256-bit signature value, and the signature is bound to the route information and stored in a distributed database. The latency of each database access is controlled within 0.05 seconds to ensure the efficiency of data verification.

[0083] Subsequently, the system collects actual transportation result data. For example, the actual travel time for a certain route is 1.8 hours, while the predicted time is 1.6 hours, with a deviation of 0.2 hours. At the same time, the actual fuel consumption is 15 liters, while the predicted fuel consumption is 13 liters, with a deviation rate of 15.38%. The system inputs these deviation data into a preset error analysis model and evaluates the accuracy of the prediction model by calculating the root mean square error (RMSE) of the deviation, which is 0.18.

[0084] Next, the system uses gradient descent to dynamically update the weight coefficients of the evaluation function. The initial weights are set to 0.6 for time cost and 0.4 for fuel cost, with a learning rate of 0.01. After 10 iterations, the weight of time cost is adjusted to 0.58 and the weight of fuel cost is adjusted to 0.42. The system records that the loss function value of each iteration decreases from the initial 0.25 to 0.19, indicating that the model gradually converges.

[0085] Based on this, the system recalculates the path evaluation value according to the updated weight coefficients. For example, if the original evaluation score of a path is 75.5 points, the corrected score is 78.2 points. The system compares this value with historical data and finds that the average evaluation score of the past 7 days is 76.8 points, thus determining that the current path evaluation result has high reliability.

[0086] To further optimize the system, the load data of transport vehicles is introduced as an auxiliary variable. For example, when the load exceeds 5 tons, the weight of fuel consumption cost is automatically increased to 0.45, forming a dynamic adjustment mechanism related to business, ensuring that the evaluation results are more in line with the actual transportation scenario. The entire process is completed automatically by the system, and the data processing time is controlled within 0.08 seconds.

[0087] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A smart logistics route evaluation method based on the Internet of Things, characterized in that, include: Dynamic data of the logistics path is collected in real time by deploying Beidou or GPS dual-mode positioning terminals, RFID cargo status sensors and roadside unit (RSU) traffic flow monitors on transport vehicles. The data acquisition process is carried out by two-way authentication of the device digital certificate based on the dynamic data of the collected logistics route, and the transmission is encrypted using the national cryptographic SM4 algorithm. The encrypted dynamic data is output, and a multi-objective evaluation function under spatiotemporal constraints is constructed based on the encrypted dynamic data. Real-time collected data is input into the multi-objective evaluation function. An improved A* algorithm is used to generate an initial path set. Monte Carlo risk simulation is used to verify the robustness of the path set and output the optimal path that meets the constraints. The algorithm execution process is completed in a Trusted Execution Environment (TEE). A digital signature is attached to the optimal path. Based on the deviation between the actual transportation results and the predicted indicators, the weight coefficients of the evaluation function are updated using the gradient descent method, and the path evaluation value is corrected.

2. The method for evaluating smart logistics routes based on the Internet of Things according to claim 1, characterized in that, The real-time collection of dynamic data on logistics routes specifically includes: The vehicle's latitude and longitude information is collected at a preset period using a dual-mode BeiDou and GPS positioning terminal. The positioning data is smoothed using a filtering algorithm based on the vehicle's latitude and longitude information to reduce signal drift error. Based on signal drift, the RFID sensor periodically monitors the temperature, humidity, and vibration frequency data of the goods to control the range of fluctuations in the goods' condition. To control the fluctuation range of cargo status, roadside unit (RSU) data is used to predict future congestion trends: When the congestion index exceeds a critical value, an alternative route is generated. When the traffic flow monitor detects that the traffic flow on a road segment exceeds the congestion threshold, it triggers a dynamic data path adjustment mechanism.

3. The method for evaluating smart logistics routes based on the Internet of Things according to claim 1, characterized in that, The multi-objective evaluation function includes: Use digital certificates based on public key infrastructure for two-way device authentication, and set an initial weight ratio between time cost and distance cost; The initial weight ratio is used to encrypt dynamic data using the national cryptographic SM4 algorithm and control the data packet size. The evaluation function parameters are dynamically adjusted according to the historical transportation time, and the evaluation function parameter database is periodically updated to adapt to real-time traffic conditions. When constructing a multi-objective evaluation function using updated evaluation function parameter data, set path time window constraints and total length constraints.

4. The method for evaluating smart logistics routes based on the Internet of Things according to claim 1, characterized in that, The optimal path includes: In the improved A* algorithm, the path search step size is set and low-congestion road segments are selected first. Multi-scenario tests are performed through Monte Carlo risk simulation on low-congestion road segments to verify the robustness of the path and to select paths whose travel time fluctuations meet the preset tolerance range. The path robustness is adopted to limit the computation time of a single path in the Trusted Execution Environment (TEE), and the road condition risk level information is marked synchronously according to the path. When outputting the optimal path, the path selection is adjusted by combining historical road condition data.

5. The method for evaluating smart logistics routes based on the Internet of Things according to claim 1, characterized in that, The evaluation function weight coefficients include: A hash algorithm is used to generate a digital signature, which is then appended to the optimal path to establish a correlation model between load and fuel consumption weights. If the deviation between the actual traffic indicator and the predicted indicator exceeds the threshold, the weight update and the evaluation model correction are triggered. The corrected evaluation value is compared with the historical average value for verification, and the prediction accuracy is evaluated using an error analysis model. The weight coefficients of the evaluation function are iteratively adjusted using the gradient descent method through an error analysis model. The fuel consumption cost weights are dynamically corrected based on the data. The path evaluation score is recalculated after each weight update, and the weight coefficients are locked when the set range is reached.

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