Intelligent information processing system of automobile internet of things

Abstract

The invention relates to the technical field of information communication, in particular to an automobile Internet of Things intelligent information processing system which comprises a vehicle scheduling module, a route planning module, a traffic information module, a comparative analysis module, a delay control module and a remote control module. According to the invention, dynamic management of real-time position and load state information is realized by analyzing the information of the vehicle Internet of Things, the vehicle scheduling efficiency is optimized, path selection is carried out by combining a geographic position and a current traffic condition, time and distance optimization of path planning is realized, traffic flow and accident information of each road section are collected and analyzed, and the traffic flow and accident information of each road section are analyzed. By combining a traffic pressure value, effectively predicting and adapting to changing traffic conditions, improving the real-time adjustment capability of a route, and applying to the aspects of network delay information processing and control signal sending opportunity prediction, the response time and accuracy of a vehicle control system are ensured, the effectiveness and safety of remote control are remarkably improved, and the method is suitable for popularization and application in the aspects of network delay information processing and control signal sending opportunity prediction. And the flexibility of vehicle scheduling is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of information communication technology, and in particular to an intelligent information processing system for an automobile Internet of Things. Background Art

[0002] The field of information and communication technology includes a wide range of technologies, involving the transmission, reception and processing of data, and mainly revolves around how to effectively transmit and process information between various systems, including network communication protocols, mobile communication technologies, data encryption and compression technologies, etc. The core content covers the design and optimization of network architecture, the improvement of communication efficiency, data security and privacy protection measures, and the standardization of cross-platform information interaction. With the advancement of technology, this field is also constantly introducing new technologies such as 5G, cloud computing and the Internet of Things to support more complex and efficient information interaction systems.

[0003] Among them, the automotive Internet of Things intelligent information processing system refers to an information system specially designed to process and manage various types of IoT devices and services related to automobiles. The patent subject is used to integrate information sources inside and outside the vehicle and optimize the practicality of data through specific information processing technologies. The patent content includes technical matters such as information collection, information synchronization processing, and information output management. The system processes data from the vehicle itself, on-board equipment, and external networks through a combination of hardware and software, aiming to improve data processing efficiency and accuracy and ensure seamless connection of information between different devices and platforms.

[0004] Existing information and communication technologies have obvious limitations in vehicle information processing and management. Existing systems cannot update traffic conditions in real time, resulting in inaccurate or slow route planning. For example, traditional vehicle dispatch systems rely on preset routes and dispatch plans, lack the ability to respond quickly to real-time traffic data, and cannot adjust itineraries in a timely manner when emergencies such as traffic accidents or road closures occur, causing delays and increased mileage. Traditional technologies encounter network latency problems when processing and transmitting batches of real-time data, affecting the real-time and accuracy of vehicle control systems. Technical limitations reduce the efficiency and safety of vehicle dispatch. The shortcomings reflect the obvious shortcomings of existing technologies in terms of adaptability and processing capabilities in dynamic environments, leading to low resource utilization and high operating costs. Summary of the invention

[0005] In order to solve the obvious limitations of vehicle information processing and management in the prior art, the existing system cannot update traffic conditions in real time, resulting in inaccurate path planning or slow response. For example, the traditional vehicle dispatch system relies on preset routes and dispatch plans, lacks the ability to respond quickly to real-time traffic data, and cannot adjust the itinerary in time when emergencies such as traffic accidents or road closures occur, causing delays and increasing mileage. Traditional technology encounters network delay problems in processing and transmitting batch real-time data, affecting the real-time and accuracy of vehicle control systems, and technical limitations reduce the efficiency and safety of vehicle dispatch. The shortcomings reflect the obvious shortcomings of the prior art in terms of adaptability and processing capabilities in dynamic environments, resulting in technical problems such as low resource utilization and high operating costs. The embodiment of the present invention provides an intelligent information processing system for the Internet of Things for automobiles. The technical solution is as follows:

[0006] On the one hand, an automotive Internet of Things intelligent information processing system is provided, the system comprising:

[0007] The vehicle dispatch module collects the real-time location, load status and idle status information of the vehicle, sorts the load status by using the automotive Internet of Things, analyzes the tasks and itineraries of the vehicle, and obtains the initialization vehicle dispatch list;

[0008] The route selection module extracts the vehicle task information in the initialization scheduling list, selects a path according to the geographical location and traffic conditions of the task, and uses the automotive Internet of Things to analyze the time, distance and traffic conditions of the path to generate a path planning result;

[0009] The traffic information module analyzes the path information in the path planning result, collects traffic flow, accident information and road closure conditions of multiple road sections, and intelligently analyzes the traffic capacity of the road section in combination with the traffic pressure value of the road section to obtain real-time traffic status data;

[0010] The comparison and analysis module uses the real-time traffic status data to compare the road section closure information with the path information, analyzes and processes the time fluctuations and estimated traffic bottlenecks of the differentiated paths, and obtains a dynamic scheduling optimization plan;

[0011] The delay control module uses the dynamic scheduling optimization scheme to process network delay information, intelligently predict the timing of sending the vehicle control signal, and intelligently delay the duration according to the network status to obtain the control signal sending record;

[0012] The remote control module uses the control signal to send records, makes adjustments according to the real-time status of the vehicle, analyzes the deviation between the real-time response of the vehicle and the expected response, and generates the vehicle control instruction execution result.

[0013] As a further solution of the present invention, the initialized car dispatch list includes vehicle priority sorting, estimated arrival time, and recommended routes; the path planning results include optimized driving routes, estimated driving time, and estimated fuel consumption; the real-time traffic condition data include differentiated road section traffic flow, real-time road section accident conditions, and road section closure update information; the dynamic scheduling optimization plan includes adjusted departure time, changed route plan, and estimated delay impact; the control signal sending record includes signal sending time, signal delay length, and delay cause; the vehicle control instruction execution result includes the response time of the execution instruction, execution status, and potential deviation adjustment.

[0014] As a further solution of the present invention, the vehicle dispatching module includes:

[0015] The vehicle status monitoring submodule collects the real-time location, load status and idle status information of the vehicle, extracts the vehicle positioning data and performs geographic location calibration, sorts and classifies the vehicle load values, detects the idle status of the vehicle and records the idle time, and obtains the vehicle status information set;

[0016] The task demand analysis submodule extracts task-related information and analyzes task load requirements based on the vehicle status information set, matches task conditions with vehicle loads, selects vehicles with idle time conditions for task matching, performs scheduling detection in combination with itinerary arrangements, and obtains a task vehicle matching list;

[0017] The vehicle dispatch optimization submodule utilizes the task vehicle matching list, performs dispatch planning with reference to matching vehicles and task information, sorts vehicles by priority, matches the driving paths of vehicles, and generates an initialized vehicle dispatch list.

[0018] As a further solution of the present invention, the route planning module includes:

[0019] The task extraction submodule extracts the vehicle task information in the initialization scheduling list, extracts the task corresponding to the vehicle and the geographical coordinate information of the target location, analyzes the task geographical area and marks the task distribution range, identifies the mapping relationship between the task and the geographical location, and obtains the task geographical location mapping table;

[0020] The road information analysis submodule uses the task geographic location mapping table to collect real-time traffic conditions and road information corresponding to the task location, analyze the traffic information of differentiated road sections, calculate the travel time of differentiated paths, and generate path time and traffic analysis results;

[0021] The path sorting submodule selects multiple feasible paths based on the path time and traffic analysis results, adjusts the path sorting according to the time, distance and traffic congestion, and outputs the path planning results.

[0022] As a further solution of the present invention, the formula for calculating the travel time of the differentiated path is as follows:

[0023]

[0024] Among them, T path Represents the optimized travel time value of the target path, t i represents the real-time travel time of the i-th road section, d i represents the length of the i-th road segment, s i represents the real-time traffic speed of the i-th road segment, s avg represents the average traffic speed of the target path, c i represents the congestion factor of the i-th road section, v i represents the traffic flow of the i-th road section, r i Represents the road grade adjustment coefficient of the i-th road section, w1, w2, w3 are weight coefficients, α1, α2, α3 are adjustment parameters, and n is the number of road sections.

[0025] As a further solution of the present invention, the traffic information module includes:

[0026] The path information analysis submodule extracts the path information in the path planning result, analyzes the distribution and connection relationship of the road sections in the path through the automotive Internet of Things, marks the starting and ending positions of the road sections and establishes a road section index, calibrates the geographical locations and road section numbers of multiple road sections in the path, and generates a path section distribution table;

[0027] The accident type identification submodule uses the path section distribution table to collect real-time traffic flow data of the corresponding section, identify the accident and record the accident type and location, monitor the closure status of the section and mark the closure time, and obtain the accident status record;

[0028] The traffic capacity analysis submodule calculates the traffic pressure values ​​of multiple road sections and evaluates the pressure levels based on the accident status records, analyzes the traffic capacity with reference to the traffic pressure values ​​and road section closure information, and obtains real-time traffic status data by combining flow, accidents and traffic capacity.

[0029] As a further solution of the present invention, the comparative analysis module includes:

[0030] The path feasibility comparison submodule extracts the road section closure information and matches the path section distribution based on the real-time traffic status data, analyzes the distribution range of the closed road sections in the path and compares the closure status with the path feasibility, calibrates the abnormal conditions of the road sections in the differentiated path, and generates a path and closure difference comparison result;

[0031] The deviation value analysis submodule uses the comparison result of the path and the closed difference to calculate the deviation value of the real-time travel time and the estimated time of the road section in the path, extracts the time abnormal section according to the time fluctuation amplitude, analyzes the overall time distribution of the path and classifies the time deviation characteristics, and generates the path time fluctuation analysis result;

[0032] The bottleneck impact analysis submodule uses the automotive Internet of Things to locate the traffic bottleneck section in the path and evaluate the bottleneck impact range based on the path time fluctuation analysis results, adjusts the path planning to avoid time deviation, optimizes the relationship between path allocation and vehicle scheduling, and outputs a dynamic scheduling optimization plan.

[0033] As a further solution of the present invention, the delay control module includes:

[0034] The delay data extraction submodule adopts the dynamic scheduling optimization scheme to extract the vehicle scheduling path and the network connection data of the automotive Internet of Things, analyze the network delay information and mark the delay characteristics, collect the signal response time of the vehicle terminal, analyze the correlation between the delay characteristics and the signal transmission, and obtain the network delay characteristic record;

[0035] The sending timing prediction submodule analyzes the response requirements of the vehicle control signal through the network delay feature record, calculates the time window for the signal to reach the target vehicle, intelligently predicts the optimal timing for signal sending, records the predicted sending time of the control signal, and obtains the control signal sending timing table;

[0036] The signal delay adjustment submodule adjusts the signal sending time to optimize the signal response according to the control signal sending timing table and the delay duration data in the network status, records the adjusted control signal real-time sending time and the corresponding network delay, and obtains the control signal sending record.

[0037] As a further solution of the present invention, the remote control module includes:

[0038] The real-time status monitoring submodule utilizes the control signal to send records, extracts the real-time position, speed and load status information of the vehicle, identifies the real-time running status of the vehicle, monitors the synchronization between the vehicle status and the control signal in real time, and generates a real-time status data set of the vehicle;

[0039] The impact range assessment submodule uses the vehicle real-time status data set to extract the vehicle's real-time response data and the expected response data of the control signal, analyzes the deviation between the real-time response and the expected response, calculates the impact range value of the deviation, and obtains the vehicle response deviation analysis result;

[0040] The execution status recording submodule extracts the vehicle control instructions within the deviation range according to the vehicle response deviation analysis result, adjusts the instruction execution logic, sends the optimized control instructions and records the execution status, and outputs the vehicle control instruction execution result.

[0041] As a further solution of the present invention, the formula for calculating the influence range value of the deviation is as follows:

[0042]

[0043] Among them, DF represents the deviation impact range value, R t represents the response data of the vehicle real-time status data at time t, P t represents the value of the expected response data of the vehicle control signal at time t, α′ is the weight coefficient of the absolute value of the deviation, β is the weight coefficient of the square root of the deviation, γ is the denominator adjustment coefficient, and δ is the reference adjustment parameter.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] By analyzing the information of the automotive Internet of Things, dynamic management of real-time location and load status information is achieved, vehicle dispatch efficiency is optimized, vehicle mission information is extracted, and path selection is performed in combination with geographic location and current traffic conditions to achieve time and distance optimization of path planning. Traffic flow and accident information of each section is collected and analyzed, and combined with traffic pressure values, the changing traffic conditions are effectively predicted and adapted to improve the real-time adjustment capability of the route. The application of network delay information processing and control signal sending timing prediction ensures the response time and accuracy of the vehicle control system, significantly improves the effectiveness and safety of remote control, and greatly improves the flexibility and overall efficiency of vehicle dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the drawings without creative work.

[0047] Figure 1 is a schematic diagram of an automotive Internet of Things intelligent information processing system provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0049] Figure 3 It is a flow chart of the vehicle dispatching module in the present invention;

[0050] Figure 4It is a flow chart of the route selection module in the present invention;

[0051] Figure 5 is a flow chart of the traffic information module in the present invention;

[0052] Figure 6 It is a flow chart of the comparative analysis module in the present invention;

[0053] Figure 7 is a flow chart of the delay control module in the present invention;

[0054] Figure 8 It is a flow chart of the remote control module in the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The embodiment of the present invention provides an intelligent information processing system for automobile Internet of Things, such as Figure 1-2 The schematic diagram of the automotive Internet of Things intelligent information processing system shown in the figure includes:

[0061] The vehicle dispatch module collects the real-time location, load status and idle status information of the vehicle, sorts the load status by using the automotive IoT, compares the dispatch requirements of the vehicle, analyzes the tasks and itineraries of the vehicle, and obtains the initialized vehicle dispatch list;

[0062] The route selection module extracts the vehicle task information in the initialization scheduling list, and selects the path according to the task's geographical location, road network data and traffic conditions. It uses the automotive Internet of Things to analyze the time, distance and traffic conditions of the path, and selects based on road section restrictions and traffic flow to generate path planning results.

[0063] The traffic information module analyzes the path information in the path planning results, collects traffic flow, accident information and road closures of multiple sections, analyzes traffic data and weather data, combines the traffic pressure values ​​of differentiated sections, intelligently analyzes the traffic capacity of each section, and obtains real-time traffic status data;

[0064] The comparative analysis module uses real-time traffic status data to compare traffic flow, accidents and road closure information with the path information in the path planning results, analyzes and processes the time fluctuations and estimated traffic bottlenecks of differentiated paths, and screens through path adjustments to obtain dynamic scheduling optimization solutions;

[0065] The delay control module extracts the real-time position and speed information of the vehicle in the dynamic scheduling optimization scheme, processes the network delay information, intelligently predicts the sending time of the vehicle control signal, calculates the time difference between the real-time position of the vehicle and the target position, adjusts the sending time, and intelligently corrects and delays the duration according to the network status to obtain the control signal sending record;

[0066] The remote control module uses control signal transmission records, makes adjustments based on the vehicle's real-time status and feedback information, adjusts the control signal to match the network delay, analyzes the deviation between the vehicle's real-time response and the expected response, and generates the vehicle control command execution result.

[0067] The initialized car dispatch list includes vehicle priority sorting, estimated arrival time, and recommended routes. The path planning results include the optimized driving route, estimated travel time, and estimated fuel consumption. Real-time traffic condition data includes differentiated road section traffic flow, real-time road section accident conditions, and road section closure update information. The dynamic scheduling optimization plan includes the adjusted departure time, the changed route plan, and the expected delay impact. The control signal sending record includes the signal sending time, the signal delay length, and the delay reason. The vehicle control command execution results include the response time of the execution command, the execution status, and the potential deviation adjustment.

[0068] See also Figure 2 , 3 , the vehicle dispatch module includes:

[0069] The vehicle status monitoring submodule collects the real-time location, load status and idle status information of the vehicle, extracts the vehicle positioning data and performs geographic location calibration, sorts and classifies the vehicle load values, detects the idle status of the vehicle and records the idle time, and obtains the execution process of the vehicle status information set as follows;

[0070] The real-time location information of the vehicle is obtained through the vehicle positioning device, and the latitude and longitude data of the vehicle is recorded in real time. The latitude and longitude data are solved using the geographic information system, and the geographic location is calibrated in combination with the specific road network. During the geographic calibration process, the latitude and longitude points are matched with the road network nodes, and abnormal positioning data that deviates from the main road is eliminated to ensure the accuracy of the location data. The current load information of the vehicle is obtained through the on-board sensor. The on-board weight sensor monitors the load of the vehicle and compares it with the rated maximum load. The load information of all vehicles is arranged in order from small to large. The vehicles are divided into three categories: light load, medium load and full load according to the load value range. The category boundaries are set by analyzing historical load data. The detection of the idle state of the vehicle is realized through the engine state and the vehicle running trajectory. The time record of engine start and shutdown is monitored by the on-board equipment, and the length of time the vehicle is not dispatched is recorded. Based on this, the idle time data is generated to obtain the vehicle status information set.

[0071] The task demand analysis submodule extracts task-related information and analyzes task load requirements based on the vehicle status information set, matches task conditions with vehicle loads, selects vehicles with idle time conditions for task matching, and performs scheduling inspection in combination with the itinerary to obtain the task vehicle matching list. The execution process is as follows;

[0072] Detailed explanation of the formula and the process of formula calculation and derivation:

[0073] The task load Q is obtained through the load requirement field in the task file of the dispatch center;

[0074] The completion time P is calculated from the start and end time fields in the task plan;

[0075] The idle time U is directly extracted from the idle state data recorded by the vehicle-mounted device;

[0076] The load availability V is determined by the ratio of the vehicle’s current load to its maximum load;

[0077] The initial distance R and return distance S are calculated by the vehicle's current location and the longitude and latitude of the mission's starting and ending points;

[0078] Assume that the load Q of a task is 200 units, the completion time P is 4 hours, the idle time U of a vehicle is 6 hours, the load availability V is 0.75, the initial distance R is 10 kilometers, and the return distance S is 15 kilometers. Substitute the formula into the calculation:

[0079]

[0080] The product of task load and completion time: 200·4=800;

[0081] The product of vehicle idle time and load availability: 6 0.75 = 4.5;

[0082] Add the above results: 800+4.5=804.5;

[0083] Calculate the denominator: 10+15=25;

[0084] The matching results are:

[0085]

[0086] The results show that the load matching degree between the task and the vehicle is 32.18. The higher the value, the higher the priority of the vehicle in the current task scheduling. The result is directly used to screen the task vehicle matching list and provide a basic basis for subsequent scheduling planning.

[0087] The vehicle scheduling optimization submodule uses the task vehicle matching list to refer to the matching vehicle and task information for scheduling planning, and performs vehicle priority sorting, matches the vehicle's driving path, and generates the execution process of the initialization vehicle scheduling list as follows;

[0088] Carry out detailed scheduling planning for the vehicles included in the list, prioritize the sorting based on the load matching degree between the vehicles and the tasks, and calculate the optimal driving path based on the geographical distance between the starting position of the vehicle and the starting position of the task. Dynamically optimize the path through real-time traffic data to ensure the rationality of path planning. The scheduling plan for all vehicles is adjusted according to the task time window. The definition of the time window is based on the start time and end time of the task. By eliminating the path occupancy conflicts existing during the task period, the optimized path planning includes the driving path, driving time and arrival time, etc. The task start and end points, scheduling path and expected completion time of each vehicle are recorded, which will serve as specific operational guidance for the execution of subsequent tasks. All scheduling plans are uploaded to the scheduling center in real time for storage and backup to support subsequent scheduling management and dynamic adjustment, and generate an initialization car scheduling list.

[0089] See also Figure 2 , 4 , the route planning module includes:

[0090] The task extraction submodule extracts the vehicle task information in the initialization scheduling list, extracts the task corresponding to the vehicle and the geographical coordinate information of the target location, analyzes the task geographical area and marks the task distribution range, identifies the mapping relationship between the task and the geographical location, and obtains the execution process of the task geographical location mapping table as follows;

[0091] Extract the task information corresponding to each vehicle, including the task number, target location and geographic coordinates, and further analyze the geographical distribution of the tasks. By extracting the latitude and longitude coordinates of the task target location, use the geographic information system to visualize the location of each task, and analyze the distribution range of the task in combination with the task coverage radius parameter to form a task geographical distribution map. According to the associated data of vehicle tasks and geographic coordinates, identify the mapping relationship between tasks and geographic locations, associate the task number with the geographic area, including the task number, the geographic coordinates of the target location and the distribution range data, to provide support for the subsequent optimization of path planning and vehicle scheduling, and obtain the task geographic location mapping table.

[0092] The road information analysis submodule uses the task geographic location mapping table to collect the real-time traffic conditions and road information corresponding to the task location, analyze the traffic information of differentiated road sections, calculate the travel time of differentiated paths, and generate the path time and traffic analysis results. The execution process is as follows;

[0093] The formula for calculating the travel time of differentiated paths is as follows:

[0094]

[0095] Among them, T path Represents the optimized travel time value of the target path, t i represents the real-time travel time of the i-th road section, d i represents the length of the i-th road segment, s i represents the real-time traffic speed of the i-th road segment, s avg represents the average traffic speed of the target path, c i represents the congestion factor of the i-th road section, v i represents the traffic flow of the i-th road section, r i represents the road grade adjustment coefficient of the i-th road segment, w1, w2, w3 are weight coefficients, α1, α2, α3 are adjustment parameters, and n is the number of road segments;

[0096] Parameter meaning and setting value:

[0097] t i is the real-time travel time of the i-th road section, collected through traffic flow monitors and cameras, and the specific setting value is 12 minutes;

[0098] d iis the length of the i-th road section, obtained through satellite positioning and map ranging, and the specific setting value is 5.6 kilometers;

[0099] s i is the real-time traffic speed of the i-th road section, monitored by a speedometer, and the specific setting value is 40 kilometers per hour;

[0100] s avg is the average traffic speed of the target route, which is obtained by collecting the speed monitoring values ​​of all sections on the route and calculating the average value. The specific setting value is 45 kilometers per hour;

[0101] c i is the congestion factor of the ith road section, which is calculated by the ratio of the road section flow to the actual traffic capacity, and the specific setting value is 0.7;

[0102] v i is the traffic flow of the i-th road section, which means the number of vehicles passing through per unit time, obtained through traffic monitoring points, and the specific setting value is 600 vehicles per hour;

[0103] r i is the road grade adjustment coefficient of the i-th road section, which is set according to the national road grade classification standard. The value for expressways is 1.0, and the value for urban trunk roads is 1.5. This road section is an urban trunk road, and the set value is 1.5;

[0104] w1, w2, and w3 are weight coefficients, which respectively reflect the influence of travel time, distance length, and speed fluctuation on travel time. The weights are adjusted dynamically with real-time traffic characteristics. w1=0.5 has a high weight, indicating that travel time has a greater impact on the path optimization result. w2=0.3 has a medium weight, indicating that the importance of path length is lower than time but higher than speed fluctuation. w3=0.2 has a low weight, indicating that speed fluctuation has a smaller impact on path optimization.

[0105] α1, α2, and α3 are adjustment parameters used to balance the influence weights of congestion factor, traffic flow, and road grade. The specific setting values ​​are: α1 = 1.2, used to strengthen the influence of congestion factor, α2 = 0.8, used to reduce the interference of traffic flow on the optimization result, and α3 = 1.0, used to maintain the original weight of road grade adjustment;

[0106] Substitute the parameters into the formula for calculation:

[0107] Calculate the numerator part:

[0108]

[0109] Substitute the parameters:

[0110] w1=0.5,t i =12, w2=0.3, di =5.6, w3=0.2, s i =40,s avg =45;

[0112] calculate:

[0113] w1·t i =0.5·12=6;

[0114] w2·d i =0.3·5.6=1.68;

[0115] w3·|s i -s avg |=0.2·|40-45|=0.2·5=1;

[0116] Sum:

[0117]

[0118] Calculate the denominator:

[0119]

[0120] Substitute the parameters:

[0121] α1=1.2,c i =0.7, α2=0.8, v i =600,α3=1.0,r i =1.5;

[0122] calculate:

[0123]

[0124] α2·v i =0.8·600=480;

[0125] α3·r i =1.0·1.5=1.5;

[0126] Sum:

[0127]

[0128] Square root:

[0129]

[0130] Calculated value:

[0131]

[0132] The results show that the optimized travel time is 0.395 hours, which is about 23.7 minutes. After comprehensive optimization of real-time traffic characteristics, the travel time of the target path is greatly reduced, providing a basis for subsequent path optimization decisions. The weight coefficient and adjustment parameters have a greater impact on the optimization results. By adjusting the weight parameters, the adaptability of the analysis results under different traffic scenarios can be further improved.

[0133] The path sorting submodule selects multiple feasible paths based on the path time and traffic analysis results, adjusts the path sorting according to the time, distance and traffic congestion, and outputs the path planning results. The execution process is as follows;

[0134] Detailed optimization and adjustment of multiple feasible paths are carried out to obtain the estimated travel time, total length of each path, and traffic congestion data of each path. The comprehensive priority of the path is determined by weighted calculation of the travel time and congestion degree of each section of the path. During the optimization process, the path information is updated in real time according to the traffic conditions, and the paths with excessive delays due to congestion are eliminated. At the same time, the remaining paths are sorted according to the comprehensive priority, including the optimized path number, estimated travel time, path length, congestion index and other data, to provide detailed path reference for the specific scheduling of vehicles and task execution, and output the path planning results.

[0135] See also Figure 2 , 5 , the traffic information module includes:

[0136] The path information analysis submodule extracts the path information from the path planning results, analyzes the distribution and connection relationship of the road sections in the path through the automotive Internet of Things, marks the starting and ending positions of the road sections and establishes the road section index, calibrates the geographical locations and road section numbers of multiple road sections in the path, and generates the path section distribution table. The execution process is as follows;

[0137] Obtain detailed data of the route, including the sequence of sections, geographic coordinates of the starting point and the end point, obtain information about the sections that the vehicle passes along the way and the corresponding time points through the automotive IoT device, analyze the connection relationship between sections and build a section topology model, and mark the specific longitude and latitude coordinates of the starting point and the end point of each section to establish a section index table. The section index table completes the geographic location calibration of multiple sections by matching the relationship between the geographic location and the section number, and integrates the information of each section in the path, including the section number, starting point coordinates, end point coordinates, and the connection relationship table of the section, so as to facilitate the subsequent accurate analysis and tracking of the traffic conditions and accident information of the section, and generate a path section distribution table.

[0138] The accident type identification submodule uses the path section distribution table to collect real-time traffic flow data of the corresponding section, identify the accident situation and record the accident type and location, monitor the closure status of the section and mark the closure time, and obtain the execution process of the accident status record as follows;

[0139] Dynamically monitor the real-time traffic data of each section on the route, obtain the traffic flow data of the corresponding section through the traffic flow sensor, analyze the fluctuation trend of traffic flow in real time, extract features of abnormal fluctuations and identify the accident type in combination with the historical traffic accident data model, and associate the identified accident type with the location data for storage. For the section where the accident occurred, further monitor the closure status of the section, record the closure time by obtaining the section closure sign signal or traffic control notice, including parameters such as accident type, location, closure status and closure time, provide comprehensive accident data support, and obtain accident status records.

[0140] The traffic capacity analysis submodule calculates the traffic pressure values ​​of multiple road sections and evaluates the pressure level based on the accident status records, analyzes the traffic capacity with reference to the traffic pressure values ​​and road section closure information, and obtains the real-time traffic status data by combining the flow, accidents and traffic capacity. The execution process is as follows;

[0141] Calculate the traffic pressure values ​​of multiple road sections and evaluate the pressure level according to the formula:

[0142]

[0143] In the formula, P represents the traffic pressure value, A represents the peak flow rate, D represents the accident closure time, F represents the road traffic flow, C represents the accident severity coefficient, and B represents the rated traffic capacity of the road section;

[0144] Detailed explanation of the formula and the process of formula calculation and derivation:

[0145] The flow peak value A is obtained by the maximum flow detected by the traffic flow sensor in a unit time;

[0146] The accident closure time D is extracted through the closure time field in the accident status record table;

[0147] Traffic flow F is measured by sensors to count the number of vehicles passing through the road section during normal operation;

[0148] The accident severity coefficient C is obtained by matching the historical accident type severity level data table;

[0149] The rated traffic capacity B is determined by the planning and design parameters of the road section;

[0150] Assume that the peak flow rate A of a certain road section is 1200 vehicles / hour, the closure time D is 3 hours, the traffic flow F is 800 vehicles / hour, the accident severity coefficient C is 0.6, and the rated traffic capacity B of the road section is 1500 vehicles / hour. Substitute the formula into the calculation:

[0151]

[0152] The product of peak flow rate and closure time:

[0153] 1200·3=3600;

[0154] The product of traffic volume and accident severity coefficient:

[0155] 800·0.6=480;

[0156] Add the above results together:

[0157] 3600+480=4080;

[0158] Calculate the denominator:

[0159] B = 1500;

[0160] The pressure value calculation result is:

[0161]

[0162] The results show that the traffic pressure value is 2.72. According to the pressure value classification standard, it can be determined that the current road section pressure level is high and an optimized scheduling or diversion plan is needed. This value is directly used to evaluate the road section capacity and update the real-time traffic status data table.

[0163] See also Figure 2 , 6 , the comparative analysis module includes:

[0164] The path feasibility comparison submodule extracts the road section closure information and matches the path section distribution based on the real-time traffic status data, analyzes the distribution range of the closed sections in the path and compares the closure status with the path feasibility, calibrates the abnormal conditions of the sections in the differentiated path, and generates the path and closure difference comparison results. The execution process is as follows;

[0165] The extracted road closure information needs to be obtained and parsed from traffic management equipment, traffic monitoring data and intelligent traffic equipment. The parsing process includes converting the data into a binary encoding format for rapid processing. When matching the path section distribution, the unique identification information of each section in the path needs to be cross-checked with the identification of the traffic closure section, and a dynamic data matching matrix is ​​established to record the matching results. Coordinate matching technology needs to be introduced in the analysis to spatially locate the road coordinate points, and a path distribution model is constructed on the road topology structure. When analyzing the distribution range of closed sections in the path, a path distribution map needs to be constructed. The impact range of the closed state of the section on the overall path passability is calculated based on the closed state of the section. The closed state will be reflected in the path distribution map through the change of the section pass weight. Combined with the path characteristics, the abnormal conditions of the sections in the differentiated paths are calibrated, including the closure ratio, location and continuity. For the abnormal conditions of the sections, the associated path algorithm is used to extract other path features associated with the closed sections, and the path and closure difference comparison results are generated.

[0166] The deviation value analysis submodule uses the path and closure difference comparison results to calculate the deviation value of the real-time travel time and the estimated time of the road section in the path. According to the time fluctuation amplitude, it extracts the time abnormal section, analyzes the overall time distribution of the path and classifies the time deviation characteristics. The execution process of generating the path time fluctuation analysis result is as follows;

[0167] The travel time deviation formula is used to calculate the overall time fluctuation range and abnormal sections of the route, according to the formula:

[0168]

[0169] Where ΔT represents the average path time deviation, T obs,k represents the real-time travel time of the kth road section, T est,k represents the estimated time of the kth segment, and N is the total number of segments in the path;

[0170] Detailed explanation of the formula and the process of formula calculation and derivation:

[0171] T obs,k Real-time data collection through traffic sensors or IoT devices, in seconds;

[0172] T est,k Calculated through historical traffic data and current traffic prediction model, in seconds;

[0173] Collect the real-time travel time T of a certain path obs,k is 120 seconds, the estimated time T est,k The time is 100 seconds, and the path is set to include 5 sections in total;

[0174] The calculation is as follows:

[0175]

[0176] The results show that the overall average time deviation of the path is 17 seconds, indicating that there is a certain deviation between the real-time travel time and the estimated time of the path, and the distribution of time-abnormal sections can be further extracted by calculating the fluctuation amplitude.

[0177] The bottleneck impact analysis submodule uses the automotive Internet of Things to locate the traffic bottleneck section in the path and evaluate the bottleneck impact range based on the path time fluctuation analysis results, adjusts the path planning to avoid time deviation, optimizes the relationship between path allocation and vehicle scheduling, and outputs the execution process of the dynamic scheduling optimization plan as follows;

[0178] The actual position of the traffic bottleneck section in the route is calibrated by the positioning device of the automotive Internet of Things. When evaluating the impact range of the bottleneck, it is necessary to obtain parameters such as vehicle speed, vehicle density, and travel delay of each section in the route, and calculate its bottleneck capacity based on traffic flow theory. The calculation of bottleneck capacity requires the transformation of the traffic status of the path section, and the establishment of a dynamic relationship between the time consumption of the bottleneck section and the length of the vehicle queue. When calibrating the bottleneck position, the real-time traffic density distribution is obtained through multi-point positioning equipment. The screening of the bottleneck range needs to be judged in combination with the section traffic capacity threshold. The path distribution map is overlapped with the bottleneck range to screen out the most affected sections, and the traffic data within the bottleneck impact range is included in the dynamic path planning model. Through the model adjustment plan, the vehicle flow within the impact range is allocated to the remaining feasible paths. According to the changes in the vehicle driving route, the implementation effect is further verified and feedback is given to output the dynamic scheduling optimization plan.

[0179] See also Figure 2 , 7 , the delay control module includes:

[0180] The delay data extraction submodule adopts a dynamic scheduling optimization scheme to extract the vehicle scheduling path and the network connection data of the automotive Internet of Things, analyze the network delay information and mark the delay characteristics, collect the signal response time of the vehicle terminal, analyze the correlation between the delay characteristics and the signal transmission, and obtain the execution process of the network delay feature record as follows;

[0181] When extracting vehicle dispatch paths and network connection data of the automotive Internet of Things, it is necessary to synchronize data from the on-board terminal device and the background dispatch system. The data extraction process includes quickly capturing dispatch path data through a multi-threaded interface and using the Internet of Things protocol stack to extract real-time status parameters of the network connection. When analyzing network delay information, the delay parameters need to be divided into three types: transmission delay, queuing delay, and processing delay, and marked separately. During the marking process, a delay feature matrix needs to be constructed to distinguish different types of delay data. When collecting the signal response time of the vehicle terminal, a high-frequency sampling device needs to be used to accurately calibrate the signal sending and return time, and the response time needs to be averaged. When analyzing the correlation between delay characteristics and signal sending, it is necessary to combine the delay feature matrix to perform regression analysis on the response time distribution of signal sending to obtain a network delay feature record.

[0182] The sending timing prediction submodule records the network delay characteristics, analyzes the response requirements of the vehicle control signal, calculates the time window for the signal to reach the target vehicle, intelligently predicts the optimal timing of signal sending, records the predicted sending time of the control signal, and obtains the execution process of the control signal sending timing table as follows;

[0183] When analyzing the response requirements of vehicle control signals, it is necessary to establish a multidimensional analytical model based on the priority of the control signal, the current state of the vehicle and the signal delay characteristics, and classify different signal requirements. When calculating the time window for the signal to reach the target vehicle, it is necessary to combine the actual operating speed of the vehicle, the network transmission rate and the response time of the target vehicle. The optimal transmission path of the signal is accurately calculated through a dynamic time prediction model. When intelligently predicting the optimal time for signal sending, it is necessary to dynamically adjust the historical delay characteristic data and the current network status. When recording the predicted sending time of the control signal, the calculation result needs to be calibrated with millisecond-level accuracy and stored in the sending timing table to obtain the control signal sending timing table.

[0184] The signal delay adjustment submodule adjusts the signal sending time to optimize the signal response according to the control signal sending timing table and the delay duration data in the network status, and records the adjusted control signal real-time sending time and the corresponding network delay to obtain the execution process of the control signal sending record as follows;

[0185] When adjusting the signal sending time in combination with the delay duration data in the network conditions, the sending time needs to be optimized in segments. The specific optimization process includes constructing a signal sending time adjustment model, adjusting the dynamic distribution of the sending time through the minimum delay optimization principle, and recording the adjusted control signal real-time sending time. It is necessary to sample and store each time node in the adjustment process, and record the delay data of each network node in the signal sending path at the same time. The adjusted signal sending effect needs to be verified twice through the overall response time of the signal path, and the recorded results are stored for reference in subsequent path optimization or delay adjustment to obtain the control signal sending record.

[0186] See also Figure 2 , 8 , the remote control module includes:

[0187] The real-time status monitoring submodule uses the control signal to send records, extract the real-time position, speed and load status information of the vehicle, identify the real-time operation status of the vehicle, monitor the synchronization of the vehicle status and the control signal in real time, and generate the vehicle real-time status data set. The execution process is as follows;

[0188] When extracting the real-time position, speed and load status information of the vehicle, it is necessary to obtain the position data through the GPS module of the automotive IoT device, and combine the acceleration sensor and load monitoring sensor to collect the vehicle's speed and load status in real time. When identifying the real-time operating status of the vehicle, it is necessary to build a motion state model based on the position and speed data, and calculate the vehicle's operating status characteristics in combination with the dynamic changes of the load data. When monitoring the synchronization of the vehicle status and control signal in real time, it is necessary to compare the difference between the vehicle status data and the control signal execution time and analyze the synchronization accuracy. At the same time, mark the abnormal situation of the unsynchronized part and store the abnormal situation to generate a real-time vehicle status data set.

[0189] The impact range assessment submodule uses the vehicle real-time status data set to extract the vehicle's real-time response data and the expected response data of the control signal, analyzes the deviation between the real-time response and the expected response, calculates the impact range value of the deviation, and obtains the vehicle response deviation analysis result. The execution process is as follows;

[0190] The formula for calculating the impact range value of the deviation is as follows:

[0191]

[0192] Among them, DF represents the deviation impact range value, R t represents the response data of the vehicle real-time status data at time t, P t represents the value of the expected response data of the vehicle control signal at time t, α′ is the weight coefficient of the absolute value of the deviation, β is the weight coefficient of the square root of the deviation, γ is the denominator adjustment coefficient, and δ is the reference adjustment parameter;

[0193] Parameter meaning and setting value:

[0194] D is the deviation impact range value, and the result is used to evaluate the deviation range of the vehicle system and provide a basis for quantitative analysis;

[0195] R t is the real-time response data of the vehicle at time point t, and its value is acquired through vehicle sensor detection and collection. In the actual example, the value is the current speed of the vehicle, and the collected value is 65km / h;

[0196] P t is the expected response data of the vehicle control signal, and its value is obtained by setting the reference value of the control system. In the actual calculation example, the value is the expected speed of the vehicle, and the setting value is 70km / h;

[0197] α is the weight coefficient of the absolute value of the deviation, which is used to balance the importance of the response deviation at the absolute value level. It is set to 0.8 and is determined by the weight of the absolute deviation in the debugging system, and fluctuates with the robustness requirements of different vehicle systems.

[0198] β is the weight coefficient of the square root of the deviation, which is used to enhance the sensitivity of small deviations. The value is 0.5. Experimental data show that the system response optimization effect is best when small deviations are amplified to this weight.

[0199] γ is the adjustment coefficient of the square root of time, which is used to smooth the influence of deviation and reduce the influence of short-time anomalies. Its value is 1.2 and is determined by analyzing the experimental response of the control system to short-time deviations.

[0200] δ is the deviation range benchmark adjustment parameter, which is used to ensure a reasonable baseline value of the deviation impact. Its value is 2 and is determined by the minimum deviation range statistics of the vehicle's historical operating data.

[0201] T is the response time interval at the current moment, in seconds, obtained through the time sampling accuracy inside the vehicle. In the actual example, the value is 3 seconds;

[0202] Substitute the parameters into the formula for calculation:

[0203] Real-time response data t =65, expected response data P t =70, calculate the absolute value and square root of the deviation,

[0204] Calculate the absolute value of the deviation:

[0205] |R t -P t |=|65-70|=5;

[0206] Calculate the square root of the deviation:

[0207]

[0208] Substituting into the numerator part of the formula:

[0209]

[0210] Calculate the denominator:

[0211]

[0212] Calculate the deviation impact range value:

[0213]

[0214] The results show that the deviation between the real-time response of the vehicle and the expected response has a medium range of influence relative to the system-set baseline value, and the need for adjustment of vehicle behavior is relatively moderate. The control system can be optimized and adjusted in a short time to ensure stable operation.

[0215] The execution status recording submodule extracts the vehicle control instructions within the deviation range according to the vehicle response deviation analysis results, adjusts the instruction execution logic, sends the optimized control instructions and records the execution status, and outputs the vehicle control instruction execution results;

[0216] When extracting vehicle control instructions within the deviation range, it is necessary to extract the time deviation data during the execution of the control instructions through the deviation analysis model. When adjusting the instruction execution logic, it is necessary to adjust the execution priority and trigger conditions of the instructions in combination with the logic optimization algorithm within the deviation range. During the optimization process, the logic adjustment results need to be dynamically verified. When sending the optimized control instructions, the execution time of the optimized instructions needs to be calibrated one by one and sent to the vehicle terminal. When recording the execution status, the execution process of the optimized instructions needs to be tracked in real time through the execution status data fed back by the vehicle. It is necessary to compare the actual vehicle status with the execution expectations of the optimized instructions and store them, and output the vehicle control instruction execution results.

[0217] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. The automotive Internet of Things intelligent information processing system is characterized by: The system comprises: The vehicle dispatch module collects the real-time location, load status and idle status information of the vehicle, sorts the load status by using the automotive Internet of Things, analyzes the tasks and itineraries of the vehicle, and obtains the initialization vehicle dispatch list; The route selection module extracts the vehicle task information in the initialization scheduling list, selects a path according to the geographical location and traffic conditions of the task, and uses the automotive Internet of Things to analyze the time, distance and traffic conditions of the path to generate a path planning result; The traffic information module analyzes the path information in the path planning result, collects traffic flow, accident information and road closure conditions of multiple road sections, and intelligently analyzes the traffic capacity of the road section in combination with the traffic pressure value of the road section to obtain real-time traffic status data; The comparison and analysis module uses the real-time traffic status data to compare the road section closure information with the path information, analyzes and processes the time fluctuations and estimated traffic bottlenecks of the differentiated paths, and obtains a dynamic scheduling optimization plan; The delay control module uses the dynamic scheduling optimization scheme to process network delay information, intelligently predict the timing of sending the vehicle control signal, and intelligently delay the duration according to the network status to obtain the control signal sending record; The remote control module uses the control signal to send records, makes adjustments according to the real-time status of the vehicle, analyzes the deviation between the real-time response of the vehicle and the expected response, and generates the vehicle control instruction execution result.

2. The automotive Internet of Things intelligent information processing system according to claim 1 is characterized in that: The initialized car dispatch list includes vehicle priority sorting, estimated arrival time, and recommended routes. The path planning results include optimized driving routes, estimated travel time, and estimated fuel consumption. The real-time traffic condition data includes differentiated road section traffic flow, real-time road section accident conditions, and road section closure update information. The dynamic scheduling optimization plan includes adjusted departure time, changed route plan, and estimated delay impact. The control signal sending record includes signal sending time, signal delay length, and delay cause. The vehicle control instruction execution result includes the response time of the execution instruction, execution status, and potential deviation adjustment.

3. The automotive Internet of Things intelligent information processing system according to claim 1 is characterized in that: The vehicle dispatching module comprises: The vehicle status monitoring submodule collects the real-time location, load status and idle status information of the vehicle, extracts the vehicle positioning data and performs geographic location calibration, sorts and classifies the vehicle load values, detects the idle status of the vehicle and records the idle time, and obtains the vehicle status information set; The task demand analysis submodule extracts task-related information and analyzes task load requirements based on the vehicle status information set, matches task conditions with vehicle loads, selects vehicles with idle time conditions for task matching, performs scheduling detection in combination with itinerary arrangements, and obtains a task vehicle matching list; The vehicle dispatch optimization submodule utilizes the task vehicle matching list, performs dispatch planning with reference to matching vehicles and task information, sorts vehicles by priority, matches the driving paths of vehicles, and generates an initialized vehicle dispatch list.

4. The automotive Internet of Things intelligent information processing system according to claim 1, characterized in that: The route planning module includes: The task extraction submodule extracts the vehicle task information in the initialization scheduling list, extracts the task corresponding to the vehicle and the geographical coordinate information of the target location, analyzes the task geographical area and marks the task distribution range, identifies the mapping relationship between the task and the geographical location, and obtains the task geographical location mapping table; The road information analysis submodule uses the task geographic location mapping table to collect real-time traffic conditions and road information corresponding to the task location, analyze the traffic information of differentiated road sections, calculate the travel time of differentiated paths, and generate path time and traffic analysis results; The path sorting submodule selects multiple feasible paths based on the path time and traffic analysis results, adjusts the path sorting according to the time, distance and traffic congestion, and outputs the path planning results.

5. The automotive Internet of Things intelligent information processing system according to claim 4 is characterized in that: The formula for calculating the travel time of the differentiated path is as follows: Among them, T path Represents the optimized travel time value of the target path, t i represents the real-time travel time of the i-th road section, d i represents the length of the i-th road segment, s i represents the real-time traffic speed of the i-th road segment, s avg represents the average traffic speed of the target path, c i represents the congestion factor of the i-th road section, v i represents the traffic flow of the i-th road section, r i Represents the road grade adjustment coefficient of the i-th road section, w1, w2, w3 are weight coefficients, α1, α2, α3 are adjustment parameters, and n is the number of road sections.

6. The automotive Internet of Things intelligent information processing system according to claim 1, characterized in that: The traffic information module includes: The path information analysis submodule extracts the path information in the path planning result, analyzes the distribution and connection relationship of the road sections in the path through the automotive Internet of Things, marks the starting and ending positions of the road sections and establishes a road section index, calibrates the geographical locations and road section numbers of multiple road sections in the path, and generates a path section distribution table; The accident type identification submodule uses the path section distribution table to collect real-time traffic flow data of the corresponding section, identify the accident and record the accident type and location, monitor the closure status of the section and mark the closure time, and obtain the accident status record; The traffic capacity analysis submodule calculates the traffic pressure values ​​of multiple road sections and evaluates the pressure levels based on the accident status records, analyzes the traffic capacity with reference to the traffic pressure values ​​and road section closure information, and obtains real-time traffic status data by combining flow, accidents and traffic capacity.

7. The automotive Internet of Things intelligent information processing system according to claim 1, characterized in that: The comparative analysis module includes: The path feasibility comparison submodule extracts the road section closure information and matches the path section distribution based on the real-time traffic status data, analyzes the distribution range of the closed road sections in the path and compares the closure status with the path feasibility, calibrates the abnormal conditions of the road sections in the differentiated path, and generates a path and closure difference comparison result; The deviation value analysis submodule uses the comparison result of the path and the closed difference to calculate the deviation value of the real-time travel time and the estimated time of the road section in the path, extracts the time abnormal section according to the time fluctuation amplitude, analyzes the overall time distribution of the path and classifies the time deviation characteristics, and generates the path time fluctuation analysis result; The bottleneck impact analysis submodule uses the automotive Internet of Things to locate the traffic bottleneck section in the path and evaluate the bottleneck impact range based on the path time fluctuation analysis results, adjusts the path planning to avoid time deviation, optimizes the relationship between path allocation and vehicle scheduling, and outputs a dynamic scheduling optimization plan.

8. The automotive Internet of Things intelligent information processing system according to claim 1, characterized in that: The delay control module comprises: The delay data extraction submodule adopts the dynamic scheduling optimization scheme to extract the vehicle scheduling path and the network connection data of the automotive Internet of Things, analyze the network delay information and mark the delay characteristics, collect the signal response time of the vehicle terminal, analyze the correlation between the delay characteristics and the signal transmission, and obtain the network delay characteristic record; The sending timing prediction submodule analyzes the response requirements of the vehicle control signal through the network delay feature record, calculates the time window for the signal to reach the target vehicle, intelligently predicts the optimal timing for signal sending, records the predicted sending time of the control signal, and obtains the control signal sending timing table; The signal delay adjustment submodule adjusts the signal sending time to optimize the signal response according to the control signal sending timing table and the delay duration data in the network status, records the adjusted control signal real-time sending time and the corresponding network delay, and obtains the control signal sending record.

9. The automotive Internet of Things intelligent information processing system according to claim 1, characterized in that: The remote control module comprises: The real-time status monitoring submodule utilizes the control signal to send records, extracts the real-time position, speed and load status information of the vehicle, identifies the real-time running status of the vehicle, monitors the synchronization between the vehicle status and the control signal in real time, and generates a real-time status data set of the vehicle; The impact range assessment submodule uses the vehicle real-time status data set to extract the vehicle's real-time response data and the expected response data of the control signal, analyzes the deviation between the real-time response and the expected response, calculates the impact range value of the deviation, and obtains the vehicle response deviation analysis result; The execution status recording submodule extracts the vehicle control instructions within the deviation range according to the vehicle response deviation analysis result, adjusts the instruction execution logic, sends the optimized control instructions and records the execution status, and outputs the vehicle control instruction execution result.

10. The automotive Internet of Things intelligent information processing system according to claim 9, characterized in that: The formula for calculating the impact range value of the deviation is as follows: Among them, DF represents the deviation impact range value, R t represents the response data of the vehicle real-time status data at time t, P t represents the value of the expected response data of the vehicle control signal at time t, α′ is the weight coefficient of the absolute value of the deviation, β is the weight coefficient of the square root of the deviation, γ is the denominator adjustment coefficient, and δ is the reference adjustment parameter.

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