Vehicle-mounted data transmission and exception handling system and method for digital logistics platform

Abstract

The invention discloses a vehicle-mounted data transmission and exception handling system and method for a digital logistics platform, and the system comprises a data collection module which is used for collecting vehicle-mounted data and dividing the vehicle-mounted data into core data and non-core data; the self-adaptive transmission module is used for dynamically adjusting the vehicle-mounted data based on the current network condition and a preset transmission strategy; the multi-path transmission module is used for transmitting the vehicle-mounted data based on the constructed main / standby channel, and caching the core data through a deployed local cache unit when the network is disconnected; the path early warning module is used for comparing the real-time position of the vehicle uploaded in real time with a preset optimal path, and generating early warning information when any preset deviation condition is met; and in response to the early warning information, generating an error correction path in combination with a real-time road condition, and pushing an early warning and error correction path to a driver / dispatching center. According to the method, the network condition can be dynamically adapted, and dynamic error correction suggestions are provided in combination with real-time road conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital logistics, and in particular to a vehicle-mounted data transmission and abnormality processing system and method of a digital logistics platform. BACKGROUND

[0002] With the rapid development of digital logistics, the dependence of platforms on the real-time and completeness of vehicle-mounted data has significantly increased. Vehicle-mounted data is not only the core basis for platforms to achieve comprehensive supervision, but also the key basis for developing route optimization.

[0003] However, the existing vehicle-mounted data transmission and abnormality processing scheme of a digital logistics platform has the following core defects:

[0004] 1. The transmission strategy lacks adaptability. Existing systems mostly use a fixed protocol and a fixed frequency transmission mode, but do not dynamically adjust in combination with network conditions.

[0005] 2. The path early warning and error correction synergy is poor. The path analysis of existing platforms mostly relies on a timing comparison mode, and the early warning response is lagging. Moreover, dynamic error correction suggestions are not provided in combination with real-time traffic, and only simple early warnings are sent to the driver and the dispatch center, which cannot assist the driver in quickly returning to the optimal route and affects transportation efficiency.

[0006] Therefore, in view of the above problems, there is an urgent need for a vehicle-mounted data transmission and abnormality processing scheme with adaptive transmission capability and real-time early warning and error correction function. SUMMARY

[0007] In view of the technical defects mentioned in the background, the purpose of the embodiments of the present application is to provide a vehicle-mounted data transmission and abnormality processing system and method of a digital logistics platform, to dynamically adapt to network conditions and provide dynamic error correction suggestions in combination with real-time traffic.

[0008] To achieve the above-mentioned purpose, in a first aspect, the embodiments of the present application provide a vehicle-mounted data transmission and abnormality processing system of a digital logistics platform, which comprises:

[0009] a data acquisition module for acquiring vehicle-mounted data and dividing the vehicle-mounted data into core data and non-core data; wherein the core data includes real-time vehicle position, driving speed, vehicle fault code, overspeed alarm and path deviation alarm;

[0010] an adaptive transmission module for dynamically adjusting the vehicle-mounted data based on current network conditions and a preset transmission strategy; wherein the transmission strategy includes reducing the upload frequency of non-core data when the network is congested, and the frequency of core data remains unchanged; when the signal is weak, the transmission of non-core data is suspended, the core data is switched to the MQTT-SN protocol, and the upload frequency of the core data is then reduced to ensure that the core data is not interrupted;

[0011] a multi-path transmission module, configured to transmit the vehicle data based on the constructed primary / backup channel, and store the core data in the deployed local cache unit when the network is disconnected;

[0012] a path warning module, configured to:

[0013] compare the real-time position of the vehicle uploaded in real time with a preset optimal path, and generate a warning information when any preset deviation condition is met;

[0014] in response to the warning information, generate a correction path in combination with real-time road conditions and push the warning and the correction path to the driver / scheduling center.

[0015] As a preferred implementation of the present application, the multi-path transmission module is further configured to trigger the following supplement transmission process when network recovery is detected:

[0016] verify through the local cache unit to ensure the integrity of the cached data;

[0017] adopt an incremental compression manner, and preferentially supplement the core data during the network disconnection;

[0018] analyze the supplemented data, and if there is a non-warned illegal behavior, trigger a warning and record a log.

[0019] As a specific implementation of the present application, the deviation condition includes:

[0020] deviation from the path for a preset time;

[0021] the vehicle enters a preset value range of a no-entry zone radius;

[0022] the driving speed is greater than a preset percentage of the current road speed limit, and the duration reaches a set value.

[0023] As a preferred implementation of the present application, the path warning module is further configured to perform real-time anomaly diagnosis, specifically including:

[0024] monitor the online state of the device through the heartbeat packet, so as to find that the vehicle-mounted device is offline in time, and judge the network delay and platform processing capability through the response time;

[0025] if the vehicle-mounted device is diagnosed as abnormal, generate a maintenance task to optimize the subsequent scheduling arrangement of the vehicle;

[0026] if the performance data of the vehicle is diagnosed as abnormal, analyze the risk of the road section in combination with the real-time position and road conditions, so as to provide a safe dimension decision basis for subsequent path optimization, or issue a safe driving reminder to the current driver.

[0027] As a specific implementation manner of the present application, the selection of the backup channel is based on the following steps:

[0028] The historical abnormal transmission data is normalized and taken as a training sample to establish a deep belief network model, wherein the abnormal transmission data includes corresponding channels and data types.

[0029] The deep belief network model is used for prediction to obtain a corresponding alternative channel.

[0030] According to the selected alternative backup channel, the network configuration is modified or the routing table is modified to ensure that the data can be correctly forwarded and transmitted.

[0031] As a specific implementation manner of the present application, the push is accompanied by a pop-up warning sent to the dispatch center and a notification sent to the driver.

[0032] In a second aspect, the present application also provides a vehicle-mounted data transmission and abnormal processing method of a digital logistics platform, which is applied to the vehicle-mounted data transmission and abnormal processing system of the digital logistics platform in the first aspect, and the method comprises the following steps:

[0033] Vehicle-mounted data is collected, and the vehicle-mounted data is divided into core data and non-core data, wherein the core data includes real-time vehicle position, driving speed, vehicle fault code, overspeed alarm and path deviation alarm.

[0034] The vehicle-mounted data is dynamically adjusted based on the current network condition and a preset transmission strategy, wherein the transmission strategy includes reducing the uploading frequency of the non-core data when the network is congested, and the frequency of the core data remains unchanged; when the signal is weak, the transmission of the non-core data is suspended, the core data is switched to the MQTT-SN protocol, and the uploading frequency of the core data is reduced to ensure that the core data is not interrupted.

[0035] The vehicle-mounted data is transmitted based on the constructed primary / backup channel, and the core data is cached by the deployed local cache unit when the network is disconnected.

[0036] The real-time vehicle position uploaded in real time is compared with a preset optimal path, and when any preset deviation condition is met, a warning information is generated.

[0037] In response to the warning information, an error correction path is generated in combination with real-time traffic, and the warning and the error correction path are pushed to the driver / dispatch center.

[0038] The technical scheme provided by the present application has the following beneficial effects:

[0039] 1, greatly improve the reliability of data transmission: by adjusting the vehicle data based on the current network conditions and the preset transmission strategy, the core data transmission is prioritized in weak signal / congestion environment;

[0040] 2, improve the integrity of data: by constructing the main / backup channel to transmit the vehicle data, and in the case of network interruption, the local cache unit is deployed to cache the core data, so as to reduce data loss;

[0041] 3, real-time warning and error correction function is obvious: the preset optimal path is combined with the real-time road condition to generate a dynamic error correction path suggestion, so as to assist the driver to quickly return to the optimal route and improve the transportation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced below.

[0043] Figure 1 is a principle block diagram of a vehicle data transmission and exception handling system of a digital logistics platform provided by an embodiment of the present application;

[0044] Figure 2 is a flowchart of a vehicle data transmission and exception handling method of a digital logistics platform provided by an embodiment of the present application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0046] It should be understood that when used in the present specification and the appended claims, the terms "comprise" and "include" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or sets thereof.

[0047] MQTT-SN: Message Queuing Telemetry Transport for Sensor Networks is a message protocol specially designed for low-power and bandwidth-limited Internet of Things devices; compared with the standard MQTT protocol, MQTT-SN aims to reduce packet size and optimize network bandwidth usage.

[0048] Please refer toFigure 1 The embodiment of the present application provides a vehicle-mounted data transmission and exception processing system of a digital logistics platform, and the system comprises:

[0049] a data acquisition module, which is used for acquiring vehicle-mounted data and dividing the vehicle-mounted data into core data and non-core data; wherein the core data comprises a vehicle real-time position, a driving speed, a vehicle fault code, an overspeed alarm and a path deviation alarm;

[0050] an adaptive transmission module, which is used for dynamically adjusting the vehicle-mounted data based on current network conditions and a preset transmission strategy; wherein the transmission strategy comprises reducing the uploading frequency of the non-core data when the network is congested, and keeping the frequency of the core data unchanged; when the signal is weak, the transmission of the non-core data is suspended, the core data is switched to an MQTT-SN protocol, and then the uploading frequency of the core data is reduced, so that the core data is not interrupted;

[0051] a multi-path transmission module, which is used for transmitting the vehicle-mounted data based on a constructed main / backup channel, and buffering the core data through a deployed local cache unit when the network is disconnected;

[0052] a path early warning module, which is used for:

[0053] comparing the vehicle real-time position uploaded in real time with a preset optimal path, and generating early warning information when any preset deviation condition is met;

[0054] in response to the early warning information, generating a correction path in combination with real-time road conditions and pushing the early warning and correction path to a driver / scheduling center.

[0055] wherein a plurality of alternative correction paths are generated, and the "expected arrival time, estimated fuel consumption difference, congested road section prompt" of each path is labeled for the driver to select;

[0056] The system is applied to a transport vehicle, and the vehicle is further provided with vehicle-mounted devices such as sensors, GPS or Beidou, a lane deviation early warning system (loaded with a lane deviation early warning algorithm) and the like of a conventional application, which will not be described herein again.

[0057] The non-core data comprises indoor temperature and humidity, a conventional video clip, a cumulative driving mileage, a vehicle cumulative driving mileage and a conventional state log; the network conditions are divided into four state levels of good, congestion, weak signal and disconnection, and each level corresponds to different transmission protocols, compression algorithms and uploading frequencies.

[0058] For example, when the network is good: a TCP protocol (ensuring data integrity) + an LZ4 compression algorithm (compression rate ≥ 60%, reducing bandwidth occupation) is adopted, and the uploading frequency is set to be: core data 1 time / second, and non-core data 1 time / 30 seconds.

[0059] Network congestion: automatically switch to UDP-Lite lightweight protocol (reduce transmission overhead), reduce non-core data upload frequency to 1 / 5 minutes, core data frequency remains 1 / second;

[0060] Weak signal: suspend non-core data transmission, core data switch to MQTT-SN protocol (suitable for low bandwidth scenarios), upload frequency adjusted to 1 / 5 seconds to ensure core data uninterrupted.

[0061] When the application, the multi-channel transmission module and the adaptive transmission module are linked to realize the transmission mechanism of main channel priority and backup channel backup:

[0062] Main channel: default 4G / 5G mobile network, covering most driving scenarios;

[0063] Backup channel: including Beidou short message (remote areas without mobile signal, supporting 100-1000 bytes / core alarm data transmission per time), LoRa wireless communication (short-distance data forwarding between vehicles in the fleet, such as vehicle-to-vehicle transmission in the fleet), Wi-Fi hotspots (service areas, parking lots, and other fixed scenarios for batch retransmission of non-core data);

[0064] Network interruption processing: when the main / backup channel is interrupted, the local cache unit (using SSD+SD card dual backup to prevent data loss due to hardware failure) automatically caches core data, including timestamp, VIN code, data type, specific value, etc.

[0065] Further, on the basis of the above technical solutions, the multi-channel transmission module is also used to trigger the following retransmission process when network recovery is detected:

[0066] Verify through the local cache unit to ensure the integrity of the cached data;

[0067] Using incremental compression (only transmitting newly added core data during network interruption, not full retransmission), retransmitting core data during network interruption in ascending order of timestamp, and retransmitting core data during network interruption first;

[0068] Analyze the retransmitted data, and if there is a non-warning violation, trigger a warning and record a log.

[0069] In this embodiment, after receiving the vehicle data, the following process is used to realize path warning and error correction:

[0070] Default optimal path generation: the path warning module generates a default optimal path based on "real-time traffic (third-party traffic API access, such as Gaode, Baidu Map, etc.), transportation distance, estimated fuel consumption, prohibited areas (such as restricted road sections, dangerous goods prohibited areas)".

[0071] Real-time comparison and determination: At a frequency of 1 time / 2 seconds, the real-time position of the vehicle is compared with the preset optimal path. When any of the following "deviation conditions" is met, it is determined that "warning is needed", and the corresponding warning information is generated; the deviation conditions include:

[0072] Deviate from the path for a preset time;

[0073] The vehicle enters a preset value range of the radius of the no-entry zone;

[0074] The driving speed is greater than a preset percentage of the current road speed limit, and the duration reaches a set value.

[0075] At the time of pushing, a pop-up warning is sent to the dispatch center, and a notification is sent to the driver; specifically including:

[0076] The vehicle side issues an audible and visual alarm (red indicator light flashes + voice prompt "deviated from the path, please confirm"), and displays a preview of the error correction path;

[0077] The platform side pushes a pop-up warning to the dispatch center, and sends an SMS notification to the driver / dispatcher (including "deviation position, violation type, and recommended error correction path").

[0078] Further, the path warning module is also used for real-time anomaly diagnosis, specifically including:

[0079] The heartbeat packet is used to monitor the online state of the device, so as to timely find that the vehicle-mounted device is offline, and the response time is used to judge network delay and platform processing capacity;

[0080] If the vehicle-mounted device is diagnosed as abnormal, a maintenance task is generated to optimize the subsequent scheduling arrangement of the vehicle;

[0081] If the performance data of the vehicle is diagnosed as abnormal, the real-time position and road conditions are combined to analyze the risk of the section, so as to provide a safe dimension decision basis for subsequent path optimization, or to issue a safe driving reminder to the current driver.

[0082] In the embodiment, the selection of the backup channel is based on the following steps:

[0083] The historical abnormal transmission data is normalized and used as a training sample to establish a deep belief network model; wherein the abnormal transmission data includes corresponding channels and data types;

[0084] Based on the deep belief network model, prediction is performed to obtain a corresponding alternative channel;

[0085] According to the selected alternative backup channel, the network configuration is modified or the routing table is modified to ensure that data can be correctly forwarded and transmitted.

[0086] In application, the deviated path is determined by a lane deviation early warning algorithm, and the lane deviation early warning algorithm is optimized by an adversarial imitation learning method to reduce lane deviation false positives and improve accuracy.

[0087] By analyzing the lane deviation behavior characteristics of vehicle drivers in different scenarios and combining these characteristics, the lane deviation early warning algorithm is optimized using the generative adversarial imitation learning method to improve the accuracy of lane deviation judgment.

[0088] The above scheme greatly improves the reliability of data transmission: by dynamically adjusting the vehicle data based on the current network conditions and the preset transmission strategy, the core data transmission is prioritized in weak signal / congestion environments;

[0089] Improve data integrity: transmit the vehicle data through the primary / backup channel constructed, and cache the core data through the local cache unit deployed when the network is disconnected to reduce data loss;

[0090] Real-time error correction function is obvious: the preset optimal path generates a dynamic error correction path suggestion in combination with real-time traffic to assist the driver in quickly returning to the optimal route and improve transportation efficiency.

[0091] Reference Figure 2 Based on the same inventive concept, the embodiments of the present application also provide a vehicle data transmission and exception handling method for a digital logistics platform, applied to the vehicle data transmission and exception handling system of the first aspect, the method comprising the following steps:

[0092] S101, collect vehicle data and divide the vehicle data into core data and non-core data; wherein the core data includes vehicle real-time position, driving speed, vehicle fault code, overspeed alarm and path deviation alarm;

[0093] S102, dynamically adjust the vehicle data based on the current network conditions and the preset transmission strategy; wherein the transmission strategy includes reducing the upload frequency of non-core data when the network is congested, and the frequency of core data remains unchanged; when the signal is weak, suspend the transmission of non-core data, switch the core data to MQTT-SN protocol, and then lower the upload frequency of the core data to ensure that the core data is not interrupted;

[0094] S103, transmit the vehicle data based on the primary / backup channel constructed, and cache the core data through the local cache unit deployed when the network is disconnected;

[0095] S104, compare the real-time uploaded vehicle real-time position with the preset optimal path, and generate an early warning information when any of the preset deviation conditions is met;

[0096] S105, in response to the early warning information, generate a correction path in combination with real-time road conditions and push the early warning and correction path to the driver / scheduling center.

[0097] The deviation condition includes:

[0098] Deviation from the path for a preset time;

[0099] The vehicle enters a no-entry zone with a radius within a preset value range;

[0100] The driving speed is greater than the preset percentage of the current road speed limit, and the duration reaches a set value.

[0101] Further, the method further comprises triggering the following data transmission process when network recovery is detected:

[0102] Check by the local cache unit to ensure the integrity of the cached data;

[0103] Adopting incremental compression, and preferentially transmitting core data during network interruption;

[0104] Analyzing the data for transmission, if there is a non-warning violation behavior, supplementing the triggering of the early warning and recording the log.

[0105] In this embodiment, the selection of the backup channel is based on the following steps:

[0106] Normalizing historical abnormal transmission data and using it as a training sample to establish a deep belief network model; wherein the abnormal transmission data includes corresponding channels and data types;

[0107] Based on the deep belief network model, the corresponding alternative channel is obtained;

[0108] According to the selected alternative backup channel, modify the network configuration or modify the routing table to ensure that the data can be correctly forwarded.

[0109] It should be noted that the more specific workflow of the method embodiment is described in the foregoing system embodiment section, which will not be repeated here.

[0110] Data transmission reliability is greatly improved: through adaptive transmission strategy, priority is given to core data transmission in weak signal / congestion environment, and through cache to ensure data not lost during network interruption;

[0111] The path warning timeliness is significantly enhanced: real-time comparison frequency is adopted, combined with multiple transmission channels, key alarm information (such as deviation from the path) is warned, which can effectively reduce the transportation risk caused by delayed warning;

[0112] Platform supervision and transportation efficiency optimization: preset optimal path combined with dynamic error correction suggestion, which can reduce the transportation detour rate and fuel consumption, and realize the life cycle supervision of the logistics transportation process.

[0113] In the embodiments provided by the present application, it should be understood that the disclosed method and system can also be implemented in other ways. The embodiments described above are only illustrative. It should also be noted that in some alternative implementations, the functions labeled in the blocks can occur in different orders from those described in the flowcharts. For example, two consecutive blocks can actually be executed substantially in parallel, and they can also be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0114] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0115] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The above description is only preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0116] The above description is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A vehicle-mounted data transmission and exception handling system of a digital logistics platform, characterized in that, The system comprises: a data acquisition module for acquiring vehicle data and dividing the vehicle data into core data and non-core data; wherein the core data includes real-time vehicle position, driving speed, vehicle fault code, overspeed alarm and path deviation alarm; an adaptive transmission module for dynamically adjusting the vehicle data based on current network conditions and a preset transmission strategy; wherein the transmission strategy includes reducing the upload frequency of non-core data when the network is congested, and the frequency of core data remains unchanged; when the signal is weak, the transmission of non-core data is suspended, the core data is switched to the MQTT-SN protocol, and the upload frequency of the core data is reduced to ensure that the core data is not interrupted; a multi-path transmission module for transmitting the vehicle data based on the constructed primary / backup channel, and storing the core data through the deployed local cache unit when the network is disconnected; a path warning module for: comparing the real-time uploaded vehicle real-time position with the preset optimal path, and generating a warning information when any of the preset deviation conditions is met; in response to the warning information, generating a correction path based on real-time traffic and pushing the warning and correction path to the driver / scheduling center.

2. The vehicle-mounted data transmission and exception handling system of a digital logistics platform according to claim 1, characterized in that, The multi-path transmission module is also used to trigger the following supplement transmission process when network recovery is detected: verify through the local cache unit to ensure the integrity of the cached data; use incremental compression and prioritize the supplement transmission of core data during network disconnection; analyze the supplemented data, and if there is a non-warning violation, trigger a warning and record a log.

3. The vehicle-mounted data transmission and exception handling system of a digital logistics platform of claim 1, wherein, The deviation conditions include: deviating from the path for a preset time; the vehicle enters the radius of the preset value range of the no-entry area; the driving speed is greater than the preset percentage of the current road speed limit, and the duration reaches the set value.

4. The vehicle-mounted data transmission and exception handling system of a digital logistics platform of claim 1, wherein, The path warning module is also used for real-time anomaly diagnosis, specifically including: monitoring the online state of the device through the heartbeat packet to detect the disconnection of the vehicle-mounted device in time, and judging the network delay and platform processing capacity through the response time; if the vehicle-mounted device is diagnosed as abnormal, a maintenance task is generated to optimize the subsequent scheduling arrangement of the vehicle; if the performance data of the vehicle is diagnosed as abnormal, analyze the risk of the section based on the real-time position and traffic to provide a safe dimension decision basis for subsequent path optimization, or send a safe driving reminder to the current driver.

5. The vehicle-mounted data transmission and exception handling system of a digital logistics platform according to any one of claims 1 to 4, characterized in that, The selection of the backup channel is based on the following steps: normalize the historical abnormal transmission data and use it as a training sample to establish a deep confidence network model; wherein the abnormal transmission data includes the corresponding channel and data type; predict based on the deep confidence network model to get the corresponding alternative channel; according to the selected alternative backup channel, modify the network configuration or modify the routing table to ensure that the data can be correctly forwarded and transmitted.

6. The vehicle-mounted data transmission and exception handling system of a digital logistics platform according to claim 5, characterized in that, When pushing, send a pop-up warning to the scheduling center and send a notification to the driver.

7. A vehicle-mounted data transmission and exception handling method of a digital logistics platform, characterized in that, A vehicle data transmission and anomaly processing system for a digital logistics platform as claimed in claim 1, the method comprising the following steps: Collecting vehicle data and dividing the vehicle data into core data and non-core data; wherein the core data includes real-time vehicle location, driving speed, vehicle fault code, overspeed alarm and path deviation alarm; Adjusting the vehicle data based on current network conditions and a preset transmission strategy; wherein the transmission strategy includes reducing the upload frequency of non-core data when the network is congested, and the frequency of core data remains unchanged; when the signal is weak, suspend the transmission of non-core data, switch the core data to MQTT-SN protocol, and then reduce the upload frequency of the core data to ensure that the core data is not interrupted; Transmitting the vehicle data based on the constructed primary / backup channel, and caching the core data through the deployed local cache unit when the network is disconnected; Comparing the real-time uploaded vehicle real-time location with the preset optimal path, and generating a warning information when any of the preset deviation conditions is met; In response to the warning information, generating a correction path based on real-time traffic and pushing the warning and correction path to the driver / scheduling center.

8. The vehicle-mounted data transmission and exception handling method of a digital logistics platform according to claim 7, characterized in that, The method further includes triggering the following supplement transmission process when network recovery is detected: Verify through the local cache unit to ensure the integrity of the cached data; Use incremental compression and prioritize the supplement transmission of core data during network disconnection; Analyze the supplemented data, and if there is a non-alerted violation, trigger an alert and record a log.

9. The vehicle-mounted data transmission and exception handling method of a digital logistics platform according to claim 8, characterized in that, The deviation conditions include: Deviation from the path for a preset time; The vehicle enters the radius of the preset value of the no-entry area; The driving speed is greater than the preset percentage of the current road speed limit, and the duration reaches the set value.

10. The vehicle-mounted data transmission and exception handling method of a digital logistics platform of claim 8, wherein, The selection of the backup channel is based on the following steps: Normalize the historical abnormal transmission data and use it as a training sample to establish a deep confidence network model; wherein the abnormal transmission data includes the corresponding channel and data type; Based on the deep confidence network model, the corresponding alternative channel is obtained by prediction; According to the selected alternative backup channel, modify the network configuration or modify the routing table to ensure that the data can be correctly forwarded.

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