Intelligent network connection data dynamic scheduling method, system and device and storage medium

Abstract

The invention relates to an intelligent network connection data dynamic scheduling method, system and device and a storage medium. The intelligent network connection data dynamic scheduling method comprises the following steps: dynamically adjusting the priority of data based on an identified vehicle operation scene and a preset priority adjustment rule; wherein the vehicle operation scene comprises an environment scene, a network scene and a vehicle state; and executing a data transmission strategy according to the adjusted priority. According to the data transmission mode, fixed grading is broken through, the vehicle scene features serve as the core basis of priority adjustment, specific transmission rules are designed for different operation scenes, the same rule is not commonly used, the priority of the data can be dynamically adjusted for different operation scenes, and the data transmission efficiency is improved. Priority transmission of key data in each operation scene is effectively guaranteed, key data transmission delay is remarkably reduced, the timeliness of emergency response is improved, and the technical problems that in the related technology, key data are blocked by low-priority data and delayed, and safety risks are increased are solved.

Description

Technical Field

[0001] This application relates to the field of intelligent connected vehicle technology, specifically to an intelligent connected data dynamic scheduling method, system, device, and storage medium. Background Technology

[0002] Currently, in intelligent connected vehicle systems, the real-time performance and reliability of data transmission are crucial to driving safety and operational efficiency.

[0003] In related technologies, the mainstream technical solutions for current intelligent connected systems generally adopt a static priority strategy to manage data transmission, that is, to pre-set fixed data priority rules (such as "fault codes > status data") and use them for a long time. However, commercial vehicles generally face a variety of complex operating scenarios, and this static priority strategy can cause critical data to be blocked and delayed by low-priority data in some operating scenarios, increasing safety risks.

[0004] Therefore, it is necessary to design a dynamic scheduling method for intelligent connected data to overcome the above problems. Summary of the Invention

[0005] This application provides a method, system, device, and storage medium for dynamic scheduling of intelligent connected data, which can solve the technical problem in related technologies where critical data is blocked and delayed by low-priority data, increasing security risks.

[0006] In a first aspect, embodiments of this application provide a method for dynamic scheduling of intelligent connected data, the method comprising: Based on the identified vehicle operation scenarios and pre-set priority adjustment rules, the priority of the data is dynamically adjusted; among which, the vehicle operation scenarios include environmental scenarios, network scenarios, and vehicle status. The data transmission strategy is executed according to the adjusted priority.

[0007] In conjunction with the first aspect, in one embodiment, the intelligent connected data dynamic scheduling method further includes: The vehicle's environmental scene is identified based on its coordinates; the network scene is identified based on the monitored communication signal strength; and the vehicle's status is identified based on the detected vehicle load data. The vehicle's operating scenario is determined by comprehensively considering the environmental context, network conditions, and vehicle status.

[0008] In conjunction with the first aspect, in one implementation, the environmental scenario includes mining areas, mountainous areas, urban areas, and highways; the network scenario includes signal strength and bandwidth; and the vehicle status includes vehicle speed and load.

[0009] In conjunction with the first aspect, in one implementation, the step of dynamically adjusting the data priority based on the identified vehicle operation scenario and pre-set priority adjustment rules includes: When the vehicle operation scenario is identified as a heavy-duty mining scenario, the brake pedal signal and obstacle distance data are marked as Level 1 data, the engine speed and transmission oil temperature are marked as Level 2 data, and the air conditioning status and entertainment system log are marked as Level 3 data.

[0010] In conjunction with the first aspect, in one implementation, the pre-set priority adjustment rule is as follows: Set scenario-based emergency data as Level 1 data, scenario-based important data as Level 2 data, and scenario-based routine data as Level 3 data.

[0011] In conjunction with the first aspect, in one implementation, executing the data transmission strategy according to the adjusted priority includes: When the vehicle is in a mining area with weak signal, it transmits first-level and second-level data, and third-level data is buffered locally and retransmitted after the signal is restored. When vehicles are in a high-speed platooning scenario, the first-level data is transmitted through a dedicated network slice, the second-level data is packaged and transmitted, and the third-level data is transmitted once every preset time interval. When vehicles are in congested urban areas, Level 1 data is transmitted directly via V2X, Level 2 data is adaptively selected using the appropriate communication network, and Level 3 data is uploaded in a WiFi environment.

[0012] In conjunction with the first aspect, in one embodiment, the intelligent connected data dynamic scheduling method further includes: When bandwidth is insufficient, secondary data transmission is interrupted, and all bandwidth is released to primary data. Missing data during the secondary data pause is marked as pending transmission and stored in the local cache.

[0013] Secondly, embodiments of this application provide an intelligent connected data dynamic scheduling system, the intelligent connected data dynamic scheduling system comprising: The data processing unit is used to dynamically adjust the priority of data based on the identified vehicle operation scenario and the pre-set priority adjustment rules; wherein the vehicle operation scenario includes environmental scenario, network scenario and vehicle status. The communication module is used to execute the data transmission strategy according to the adjusted priority.

[0014] Thirdly, embodiments of this application provide an intelligent connected data dynamic scheduling device, which includes a processor, a memory, and an intelligent connected data dynamic scheduling program stored in the memory and executable by the processor. When the intelligent connected data dynamic scheduling program is executed by the processor, it implements the steps of the above-described intelligent connected data dynamic scheduling method.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing an intelligent connected data dynamic scheduling program, wherein when the intelligent connected data dynamic scheduling program is executed by a processor, it implements the steps of the above-described intelligent connected data dynamic scheduling method.

[0016] The beneficial effects of the technical solutions provided in this application include: This embodiment dynamically adjusts the data priority based on the identified vehicle operation scenario and pre-set priority adjustment rules, and executes the data transmission strategy according to the adjusted priority. This data transmission method breaks through the fixed hierarchy, takes vehicle scenario characteristics as the core basis for priority adjustment, and designs unique transmission rules for different operation scenarios, rather than using the same universal rule. It can dynamically adjust the data priority for different operation scenarios, effectively ensuring that key data is transmitted first in each operation scenario, significantly reducing the latency of key data transmission, improving the timeliness of emergency response, and solving the technical problem in related technologies where key data is blocked and delayed by low-priority data, increasing safety risks. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the intelligent connected vehicle data dynamic scheduling method of this application; Figure 2 This is a flowchart illustrating another embodiment of the intelligent connected vehicle data dynamic scheduling method of this application; Figure 3 This is a schematic diagram of the hardware structure of the intelligent connected data dynamic scheduling device involved in the embodiments of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] In related technologies, current vehicle data transmission systems have the following problems: Static prioritization cannot adapt to different scenarios: Data transmission systems have a fixed priority for data (e.g., "fault codes > status data"), but the "urgency" of data varies significantly in scenarios such as mining areas (weak signals), highway platooning (requiring low latency), and urban congestion (signal fluctuations). For example, braking signals in mining areas require absolute priority, while braking signals in urban areas can tolerate slightly higher latency; current technology cannot dynamically adjust this.

[0020] The current solution is disconnected from the actual vehicle scenario and transmission strategy: It transmits data based solely on data type or network status (such as signal strength), without considering the specific vehicle scenario. For example, when a vehicle enters a tunnel (where the signal suddenly drops), the full data is still transmitted according to the conventional strategy, causing critical steering commands to be delayed due to bandwidth congestion.

[0021] Critical data is easily overwhelmed during bandwidth conflicts: When bandwidth is insufficient, current technologies often use "first-come, first-served" or "random discarding," which may cause high-priority data (such as collision warnings) to be delayed by low-priority data (such as entertainment logs), posing a security risk.

[0022] This application provides a method, system, device, and storage medium for dynamic scheduling of intelligent connected data, which can solve the technical problem in related technologies where critical data is blocked and delayed by low-priority data, increasing security risks.

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0024] In a first aspect, embodiments of this application provide a method for dynamic scheduling of intelligent connected data.

[0025] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the intelligent connected vehicle data dynamic scheduling method of this application. The intelligent connected vehicle data dynamic scheduling method provided in this embodiment is mainly applied to the data transmission and scheduling direction of commercial vehicles, targeting complex scenarios such as mining areas, highway platooning, and urban congestion. Of course, other vehicle types can also use this dynamic scheduling method.

[0026] like Figure 1 As shown, the intelligent connected vehicle data dynamic scheduling method includes: S100: Based on the identified vehicle operation scenario and the pre-set priority adjustment rules, dynamically adjust the priority of the data; whereby the vehicle operation scenario includes environmental scenario, network scenario and vehicle status.

[0027] In this embodiment, the environmental scenarios can include mining areas, mountainous areas, urban areas, and highways; for example, weak or heavily loaded signals in mining areas; fluctuating signals in mountainous areas; traffic congestion in urban areas; and platooning on highways. The network scenarios include signal strength and bandwidth; for example, strong, medium, and weak signal strength; and bandwidth includes high and low bandwidth. The vehicle status includes vehicle speed and load; for example, scenarios such as driving at high speed and driving at low speed.

[0028] In this system, environmental scenarios can be located using a GPS module; communication signal strength can be monitored in real time using a signal strength sensor (RSSI); and vehicle load data can be read using a load sensor. This is achieved by selecting multi-point pressure sensors specifically designed for commercial vehicles (such as those installed at four points connecting the cargo box and the chassis). The pressure data collected by the sensors is then calculated and converted into real-time load data. In the commercial vehicle sector, 5G / NB-IoT dual-mode modules often integrate V2X functionality (such as supporting direct PC5 connection). 5G modules integrating V2X can directly connect to surrounding vehicles via the PC5 interface, exchanging GPS location, vehicle speed, and heading angle data every 100ms. When the vehicle and at least two surrounding vehicles meet the following conditions: a distance of 50-100 meters and linear arrangement, a speed difference of <5 km / h, and a heading deviation of <5°, and this state lasts for ≥30 seconds, it is considered to be traveling in platoons.

[0029] Bandwidth level identification is achieved through a three-layer mechanism of "hardware awareness + protocol parsing + dynamic threshold judgment," as detailed below: 1. Real-time sampling at the hardware layer: Relying on the vehicle-mounted 5G / NB-IoT dual-mode communication module, the downlink / uplink rate (unit: Mbps) is collected every 50ms, and the channel quality indication (CQI) fed back by the base station is recorded to form raw bandwidth data.

[0030] 2. Protocol layer data parsing: The real-time throughput of the TCP / UDP transport layer is extracted through the protocol parsing unit built into the communication module, eliminating instantaneous fluctuation interference.

[0031] 3. Dynamic threshold determination: Preset bandwidth thresholds based on the needs of commercial vehicle scenarios, for example: In high-speed formation scenarios: when the average speed is ≥20Mbps and CQI is ≥12, it is judged as "high bandwidth"; when the average speed is <10Mbps or CQI is <8, it is judged as "low bandwidth".

[0032] In mining areas: due to generally weak signals, an average speed ≥ 5Mbps and CQI ≥ 6 are considered "high bandwidth", while an average speed < 2Mbps or CQI < 4 are considered "low bandwidth".

[0033] Furthermore, in one embodiment, step S010 may be included before step S100: S011: Identify the vehicle's environment based on vehicle coordinates, identify the network environment based on monitored communication signal strength, and identify the vehicle's status based on detected vehicle load data.

[0034] S012: The vehicle's operating scenario is determined by comprehensively considering the environmental scenario, network scenario, and vehicle status.

[0035] For example, the GPS module continuously acquires vehicle coordinates and matches them with a high-precision map to identify whether the vehicle has entered a mining area geofence; the signal strength sensor monitors the communication signal strength in real time, marking it as a "weak signal" when the detected signal value is less than a set value (e.g., -100dBm); the load sensor reads the vehicle's load data, and if the detected cargo weight exceeds a set load percentage of the rated load (e.g., 70%), it is determined to be in a "heavy load state". The overall scenario determination is: when the GPS location is a mining area, the signal strength is <-100dBm, and the load is >70%, the vehicle is determined to be in a "heavy load scenario in a mining area".

[0036] Furthermore, in some optional embodiments, the pre-set priority adjustment rule is as follows: setting scenario-based emergency data as level 1 data, scenario-based important data as level 2 data, and scenario-based regular data as level 3 data.

[0037] In this embodiment, the dynamic priority division rule is as follows: Applying "dynamic labels" to data based on scene characteristics (breaking through fixed hierarchical structures): Level 1 (Red): Scenario-specific emergency data, with a latency requirement of less than 100ms. Examples of scenario-specific emergency data include braking signals in mining areas and distance data in high-speed platooning.

[0038] Level 2 (Yellow): Context-sensitive data with a latency requirement of less than 500ms. Examples of context-sensitive data include traffic light signals in urban areas and tire pressure in mountainous regions.

[0039] Level 3 (Green): Contextualized routine data, with a latency greater than 1 second. Contextualized routine data includes, for example, the air conditioning status when parked and fuel consumption in non-mining areas.

[0040] Furthermore, in one embodiment, the dynamic adjustment of data priority based on the identified vehicle operation scenario and pre-set priority adjustment rules includes: when the vehicle operation scenario is identified as a heavy-load mining scenario, the brake pedal signal and obstacle distance data are marked as primary data, the engine speed and transmission oil temperature are marked as secondary data, and the air conditioning status and entertainment system logs are marked as tertiary data. For example, the real-time collected "brake pedal signal" and "obstacle distance data" are automatically bound to "red tags," with a defined transmission delay requirement of less than 100ms; "engine speed" and "transmission oil temperature" are bound to "yellow tags," with a delay requirement of less than 500ms; and "air conditioning status" and "entertainment system logs" are bound to "green tags," allowing a delay greater than 1s or temporary storage locally.

[0041] S200: Execute the data transmission strategy according to the adjusted priority.

[0042] In one embodiment, the data transmission strategy executed according to the adjusted priority includes: when the vehicle is in a weak signal scenario in a mining area, transmitting primary and secondary data, buffering tertiary data locally, and retransmitting it after the signal is restored; when the vehicle is in a high-speed platoon scenario, transmitting primary data through a dedicated network slice, such as a dedicated 5G slice, packaging and transmitting secondary data, and transmitting tertiary data once every preset time interval; when the vehicle is in a congested urban area scenario, transmitting primary data directly using V2X, adaptively selecting a communication network for secondary data, and uploading tertiary data in a WiFi environment. In this embodiment, the adaptively selected communication network can be, for example, a 5G network or a 4G network.

[0043] This embodiment automatically adjusts transmission rules for different scenarios, as shown in the table below: Table 1

[0044] Furthermore, in one embodiment, the intelligent connected data dynamic scheduling method may also include an intelligent conflict arbitration mechanism, that is, when bandwidth is insufficient, sorting is performed according to "scenario priority > data level > timestamp": Among data of the same priority level, safety data in mining scenarios (such as collision warnings) takes precedence over other scenarios; tertiary data transmission is paused, and if it is still insufficient, secondary data is compressed (keeping core fields) to ensure that primary data is not interrupted.

[0045] Among data of the same priority level, such as "collision warning data" in mining areas, "pedestrian warning data" in urban areas, and "curve passing warning data" in mountainous areas, all are first-level data with red labels. If data with the same priority is encountered and bandwidth is insufficient, it is necessary to further determine the priority according to the scenario.

[0046] The reasons why there may be simultaneous transmission needs for the same level of data in different scenarios may be: 1. Concurrent transmission needs caused by the sharing of network resources among multiple vehicles and multiple scenarios, that is, multiple vehicles working in different scenarios within the same communication coverage area; 2. A single vehicle traversing multiple scenarios in a short period of time (such as entering the edge of a mining area from a mountainous area or entering an urban area from a highway). Within 1-3 seconds of scenario switching, the first-level data of two scenarios may be triggered simultaneously.

[0047] Intelligent conflict arbitration mechanisms could also include interrupting secondary data transmission when bandwidth is insufficient, releasing all bandwidth for primary data, and marking missing data during the secondary data pause as data to be retransmitted and stored in the local cache.

[0048] This embodiment dynamically adjusts data priority based on identified vehicle operation scenarios and pre-set priority adjustment rules, and executes data transmission strategies according to the adjusted priorities. This data transmission method breaks through fixed hierarchies, using vehicle scenario characteristics as the core basis for priority adjustment, and designs unique transmission rules for different operation scenarios, rather than a universal rule. It can dynamically adjust data priority for different operation scenarios, effectively ensuring the priority transmission of critical data in each operation scenario, significantly reducing the latency of critical data transmission, improving the timeliness of emergency response, and solving the technical problem in related technologies where critical data is blocked and delayed by low-priority data, increasing safety risks. This application achieves scenario-based adaptation of commercial vehicle data transmission through a three-layer mechanism of "scenario recognition - dynamic hierarchical classification - intelligent scheduling," solving the defects of related technologies.

[0049] Compared with solutions in related technologies, this embodiment effectively ensures the priority transmission of critical information such as braking signals and vehicle distance data in scenarios with complex signals or high safety requirements, such as mining areas and highways, significantly reducing the latency of critical data transmission and improving the timeliness of emergency response. In complex scenarios such as weak signals and bandwidth congestion, dynamic priority scheduling and intelligent conflict arbitration greatly enhance the reliability of critical data transmission and reduce the risk of decision-making errors due to data loss. Based on scenario characteristics, bandwidth resources are dynamically allocated to reduce the invalid transmission of non-critical data, improve bandwidth utilization efficiency, and reduce traffic resource consumption. It has multi-scenario adaptive capabilities and can automatically adapt to various typical commercial vehicle operating scenarios such as mining areas, highway platooning, and urban congestion, and can realize intelligent adjustment of transmission strategies without manual intervention.

[0050] The technical solution of this application will now be explained and illustrated with a specific embodiment: Hardware components: Scene recognition unit: Includes a GPS module (positioning accuracy 1m), an RSSI signal sensor (detection range -120dBm to -30dBm), and a load sensor. Data processing unit: Embedded chip (e.g., STM32H743), supporting multi-threaded processing of scene recognition and data classification. Communication module: 5G / NB-IoT dual-mode module (supports dynamic bandwidth allocation). Storage module: 16GB local cache (supports breakpoint resume).

[0051] See Figure 2 As shown, the steps of the dynamic scheduling method in the mining area scenario are as follows: S1: System initialization and parameter configuration.

[0052] Hardware startup: The vehicle terminal is powered on, initializes the GPS module (positioning accuracy 1m), RSSI signal sensor (detection range -120dBm~-30dBm), and load sensor, establishes a connection with the 5G / NB-IoT dual-mode communication module, and loads 16GB of local cache space.

[0053] Software loading: The embedded chip (such as STM32H743) starts the scene recognition algorithm, dynamic priority division rules and transmission strategy library, and presets the initial parameters of the mining scene (such as the first-level data bandwidth accounting for 80%).

[0054] S2: Real-time scene recognition and status determination.

[0055] Multidimensional data collection: (1) The GPS module continuously acquires vehicle coordinates, matches them with high-precision maps, and identifies whether the vehicle has entered the mining area geofence.

[0056] (2) The RSSI sensor monitors the strength of the communication signal in real time. When the detected signal value is <-100dBm, it is marked as "weak signal".

[0057] (3) The load sensor reads the vehicle load data. If the weight of the cargo exceeds 70% of the rated load, it is determined to be "overloaded".

[0058] Scenario-based comprehensive judgment: When the GPS location is a mining area, the signal strength is <-100dBm and the load is >70%, the system triggers the "Mining Area Heavy Load Scenario" flag and activates the corresponding transmission strategy.

[0059] S3: Data hierarchy and dynamic tag binding.

[0060] Level 1 data tagging: Automatically bind the real-time collected "brake pedal signal" and "obstacle distance data" to "red tags", and define the transmission delay requirement as <100ms.

[0061] Secondary data labeling: "Engine speed" and "Transmission oil temperature" are bound to "yellow labels" with a delay requirement of <500ms.

[0062] Level 3 data tagging: Bind "Air conditioning status" and "Entertainment system log" to "green label", allowing a delay of >1 second or temporary storage locally.

[0063] S4: Execution of scenario-based transmission strategies.

[0064] Level 1 data transmission: The braking signal and the distance to the obstacle are collected at a period of 10ms and sent first through the NB-IoT module, occupying 80% of the communication bandwidth; a scenario-based priority identifier (such as "mining area - emergency") is added before the data is sent to ensure that the base station processes it first.

[0065] Secondary data transmission: Data such as engine speed is collected at 100ms intervals, packaged and compressed, and then transmitted via NB-IoT, occupying the remaining 20% ​​of the bandwidth; the compression algorithm retains core parameters (such as speed values ​​and temperature thresholds) and discards redundant check bits to reduce the amount of data.

[0066] Three-level data transmission: Real-time transmission is paused, and data is written to a 16GB local cache, recording timestamps and scene tags; the cache adopts a FIFO (first-in, first-out) mechanism, and when the storage space is occupied by more than 90%, the oldest non-critical data is automatically overwritten.

[0067] S5: Real-time conflict arbitration and strategy adjustment.

[0068] Bandwidth insufficiency handling: If there is a sudden increase in primary data (such as continuous braking causing the data volume to double), the system will: immediately interrupt the secondary data transmission and release all bandwidth to the primary data; missing data during the secondary data pause is marked as "to be retransmitted" and stored in the local cache.

[0069] Temporary signal recovery processing: If the RSSI signal briefly rises above -90dBm: temporarily allocate 10% bandwidth to secondary data, prioritize the transmission of backlogged engine speed data; do not restore tertiary data transmission, maintain local buffer status, and avoid transmission interruption caused by weak signal fluctuations.

[0070] S6: Scene Exit and Strategy Reset.

[0071] Scene switching detection: When a vehicle leaves the mining area's geographical fence, the signal strength is ≥-90dBm, and the load is <30%, the system determines that it is exiting the "heavy load mining area scene".

[0072] Policy Reset: Clear all contextual tags for data and restore default priority rules; reset bandwidth allocation to the normal mode of "40% for Level 1 data, 50% for Level 2 data, and 10% for Level 3 data"; locally cached Level 3 data will be re-uploaded to the cloud server in timestamp order when the signal is good.

[0073] Secondly, embodiments of this application also provide an intelligent connected data dynamic scheduling system.

[0074] In one embodiment, the intelligent connected data dynamic scheduling system includes: a data processing unit, which is used to dynamically adjust the priority of data based on the identified vehicle operation scenario and the pre-set priority adjustment rules; wherein the vehicle operation scenario includes environmental scenario, network scenario and vehicle status; and a communication module, which is used to execute the data transmission strategy according to the adjusted priority.

[0075] Furthermore, in one embodiment, the intelligent connected data dynamic scheduling system further includes a scene recognition unit, which is used to identify the environmental scene where the vehicle is located based on the vehicle coordinates, identify the network scene where the vehicle is located based on the monitored communication signal strength, and identify the vehicle status based on the detected vehicle load data; and comprehensively determine the vehicle operation scene based on the environmental scene, network scene and vehicle status.

[0076] In one alternative approach, the scene recognition unit includes a GPS module, an RSSI signal sensor, and a load sensor. New modules or devices can also be added to the scene recognition unit as needed.

[0077] In one embodiment, the environmental scenario includes mining areas, mountainous areas, urban areas, and highways; the network scenario includes signal strength and bandwidth; and the vehicle status includes vehicle speed and load.

[0078] Furthermore, in one embodiment, the data processing unit can be an embedded chip such as STM32H743, supporting multi-threaded processing of scene recognition and data classification. When the vehicle operation scene is identified as a heavy-load mining scene, the data processing unit can mark the brake pedal signal and obstacle distance data as primary data, the engine speed and transmission oil temperature as secondary data, and the air conditioning status and entertainment system log as tertiary data.

[0079] Furthermore, in one embodiment, the pre-set priority adjustment rule is as follows: setting scenario-based emergency data as level 1 data, scenario-based important data as level 2 data, and scenario-based routine data as level 3 data.

[0080] Furthermore, in one embodiment, the communication module can be, for example, a 5G / NB-IoT dual-mode module that supports dynamic bandwidth allocation. When the vehicle is in a mining area with weak signal, the communication module can transmit primary and secondary data, cache tertiary data locally, and retransmit it after the signal is restored. When the vehicle is in a high-speed platooning scenario, primary data is transmitted through a dedicated network slice, secondary data is packaged and transmitted, and tertiary data is transmitted every preset time interval. When the vehicle is in a congested urban area, primary data is transmitted directly using V2X, secondary data adaptively selects the communication network, and tertiary data is uploaded in a WiFi environment.

[0081] The aforementioned intelligent connected data dynamic scheduling system may also include a storage module for storing locally cached data.

[0082] Furthermore, in one embodiment, the communication module is also used to interrupt secondary data transmission when bandwidth is insufficient, release all bandwidth to primary data, and mark the missing data during the secondary data suspension period as to be retransmitted and store it in the local cache.

[0083] The functions of each module in the above-mentioned intelligent connected data dynamic scheduling system correspond to the steps in the above-mentioned intelligent connected data dynamic scheduling method embodiment, and their functions and implementation processes will not be described in detail here.

[0084] This application provides a multi-dimensional scene fusion recognition technology: by fusing multi-dimensional data such as geographical location, network status, and vehicle operating conditions, it achieves accurate determination of special scenarios for commercial vehicles; a scene weight-driven dynamic priority algorithm: adjusts data priority in real time based on scene features to construct a scene-based hierarchical rule system; a bandwidth dynamic allocation strategy for typical commercial vehicle scenarios: formulates dedicated bandwidth quotas and transmission control logic for scenarios such as mining areas and highway platooning; and a multi-dimensional conflict arbitration mechanism of scene-level-timestamp: establishes a three-level priority decision rule to ensure the timeliness of critical data transmission.

[0085] Thirdly, embodiments of this application provide an intelligent connected data dynamic scheduling device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0086] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of the intelligent connected data dynamic scheduling device involved in the embodiments of this application. In the embodiments of this application, the intelligent connected data dynamic scheduling device may include a processor, a memory, a communication interface, and a communication bus.

[0087] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0088] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the intelligent connected data dynamic scheduling device, as well as interfaces used for interconnecting the intelligent connected data dynamic scheduling device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0089] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0090] The processor can be a general-purpose processor, which can call the intelligent connected data dynamic scheduling program stored in the memory and execute the intelligent connected data dynamic scheduling method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the intelligent connected data dynamic scheduling program is called can be referred to the various embodiments of the intelligent connected data dynamic scheduling method of this application, and will not be repeated here.

[0091] Those skilled in the art will understand that Figure 3 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0092] Fourthly, embodiments of this application also provide a readable storage medium.

[0093] The present application stores a dynamic scheduling program for intelligent connected data on a readable storage medium, wherein when the dynamic scheduling program for intelligent connected data is executed by a processor, it implements the steps of the dynamic scheduling method for intelligent connected data as described above.

[0094] The method implemented when the intelligent connected data dynamic scheduling program is executed can be referred to in the various embodiments of the intelligent connected data dynamic scheduling method of this application, and will not be repeated here.

[0095] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0096] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0097] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0098] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0099] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0101] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for intelligent network connection data dynamic scheduling, characterized in that, The intelligent network connection data dynamic scheduling method comprises: Based on the identified vehicle operation scene and the pre-set priority adjustment rule, the priority of the data is dynamically adjusted; wherein the vehicle operation scene comprises an environmental scene, a network scene and a vehicle state; According to the adjusted priority, the data transmission strategy is executed. 2.The intelligent network connection data dynamic scheduling method of claim 1, wherein, The intelligent network connection data dynamic scheduling method further comprises: Based on the vehicle coordinate, the environmental scene where the vehicle is located is identified, based on the monitored communication signal strength, the network scene where the vehicle is located is identified, and based on the detected vehicle load data, the vehicle state is identified; The vehicle operation scene is comprehensively determined according to the environmental scene, the network scene and the vehicle state where the vehicle is located.

3. The intelligent network connection data dynamic scheduling method of claim 1, wherein The environmental scene comprises a mining area, a mountainous area, an urban area and a highway; The network scene comprises signal strength and bandwidth; and the vehicle state comprises vehicle speed and load. 4.The intelligent network connection data dynamic scheduling method of claim 1, wherein, The priority of the data is dynamically adjusted based on the identified vehicle operation scene and the pre-set priority adjustment rule, comprising: When it is identified that the vehicle operation scene is a heavy load scene in a mining area, the brake pedal signal and the obstacle distance data are marked as first-level data, the engine speed and the gearbox oil temperature are marked as second-level data, and the air conditioning state and the entertainment system log are marked as third-level data. 5.The intelligent network connection data dynamic scheduling method of claim 1, wherein, The pre-set priority adjustment rule is: The scenario-based emergency data is set as first-level data, the scenario-based important data is set as second-level data, and the scenario-based routine data is set as third-level data. 6.The intelligent network connection data dynamic scheduling method of claim 5, wherein, According to the adjusted priority, the data transmission strategy is executed, comprising: When the vehicle is in a weak signal scene in a mining area, the first-level data and the second-level data are transmitted, and the third-level data is locally cached, and the missing data during the suspension of the second-level data transmission is marked as to-be-supplemented and stored in the local cache when the signal is restored. When the vehicle is in a high-speed platoon scene, the first-level data is transmitted through the established dedicated network slice, the second-level data is packaged and transmitted, and the third-level data is transmitted once every preset time interval; When the vehicle is in an urban congestion scene, the first-level data is directly transmitted through V2X, the second-level data is adaptively selected for communication network, and the third-level data is transmitted in a WiFi environment.

7. The intelligent network connection data dynamic scheduling method of claim 5, wherein, The intelligent network connection data dynamic scheduling method further comprises: When the bandwidth is insufficient, the second-level data transmission is interrupted, all bandwidth is released to the first-level data, and the missing data during the suspension of the second-level data transmission is marked as to-be-supplemented and stored in the local cache.

8. An intelligent network connection data dynamic scheduling system, characterized in that, The intelligent network connection data dynamic scheduling system comprises: A data processing unit is configured to dynamically adjust the priority of the data based on the identified vehicle operation scene and the pre-set priority adjustment rule; wherein the vehicle operation scene comprises an environmental scene, a network scene and a vehicle state; A communication module is configured to execute the data transmission strategy according to the adjusted priority.

9. An intelligent network connection data dynamic scheduling device, characterized in that, The intelligent network connection data dynamic scheduling device comprises a processor, a memory, and an intelligent network connection data dynamic scheduling program stored in the memory and executable by the processor, wherein when the intelligent network connection data dynamic scheduling program is executed by the processor, the steps of the intelligent network connection data dynamic scheduling method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores an intelligent network connection data dynamic scheduling program, wherein the intelligent network connection data dynamic scheduling program, when executed by the processor, implements the steps of the intelligent network connection data dynamic scheduling method in any one of claims 1 to 7.

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