Intelligent car networking system based on Internet of Things

Abstract

The invention discloses an intelligent Internet of Vehicles system based on Internet of Things, which comprises a capture event generation module, a fixed event generation module, an emergency prediction module, an energy complementation cost evaluation module and an energy complementation decision making module, and is characterized in that the capture time generation module is used for capturing a planned travel event of a vehicle owner through an Internet of Vehicles technology; the fixed event generation module is used for identifying and obtaining a fixed travel event of a vehicle owner through the Internet of Vehicles technology, the emergency prediction module is used for collecting historical emergencies of the vehicle owner and analyzing and predicting an emergency influence index of the current vehicle owner, and the energy complement cost evaluation module is used for analyzing the energy complement cost of the vehicle owner. And the energy complementation decision making module is used for providing a decision plan of an energy complementation opportunity for the vehicle owner and reducing the anxiety of the vehicle owner in the energy complementation problem, and the system has the characteristics of intelligently providing a charging decision and reducing the endurance anxiety of the vehicle owner.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking, and in particular to an intelligent vehicle networking system based on the Internet of Things. Background Art

[0002] The concept of Internet of Vehicles originated from the Internet of Things, namely the Vehicle Internet of Things. It takes moving vehicles as information perception objects and uses the new generation of information and communication technology to achieve network connections between vehicles, people, roads, and service platforms, thereby improving the overall intelligent driving level of vehicles, providing users with safe, comfortable, intelligent, and efficient driving experience and transportation services, while improving traffic operation efficiency and enhancing the intelligence level of social transportation services.

[0003] With the rapid development of new energy vehicles, the application of intelligent Internet of Vehicles is becoming more and more extensive. In terms of the current market situation and recent development, new energy vehicles mainly refer to electric vehicles. However, pure electric vehicles need to plan their own charging plans every time they go out, and charging during the trip delays driving, which brings endurance anxiety to car owners, leading to lack of confidence in travel and always planning travel. It seriously affects the mood of travel and violates the purpose of cars serving people. This is also one of the factors restricting the development of pure electric vehicles. Although the existing technology can automatically plan the charging location during navigation, the owner's planned itinerary is prone to sudden changes, and they are worried that the temporary plan will be affected by the time consumed by charging. The impact of charging price, distance, convenience, etc. is not considered, and the degree of personalization is low, resulting in low satisfaction of car owners with the existing automatic planning charging plan, focusing on different factors, which is tantamount to ineffectiveness. In the end, they still plan by themselves and cannot solve the endurance anxiety problem well. Therefore, it is necessary to design an intelligent Internet of Vehicles system and method based on the Internet of Things that can intelligently provide charging decisions and reduce the owner's endurance anxiety. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent vehicle networking system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent Internet of Vehicles system and method based on the Internet of Things, comprising a capture event generation module, a fixed event generation module, an emergency event prediction module, an energy replenishment cost evaluation module and an energy replenishment decision-making module, wherein the capture time generation module is used to capture the owner's planned travel events through the Internet of Vehicles technology, the fixed event generation module is used to identify and obtain the owner's fixed travel events through the Internet of Vehicles technology, the emergency event prediction module is used to collect the owner's historical emergency events and analyze and predict the emergency event impact index of the current owner, the energy replenishment cost evaluation module is used to analyze the owner's energy replenishment cost and make an evaluation data report, and the energy replenishment decision-making module is used to provide the owner with a decision plan for the timing of energy replenishment to reduce the owner's anxiety about the energy replenishment issue.

[0006] According to the above technical solution, the capture event generation module includes an authority granting module, a keyword recognition module, a semantic recognition module and a plan input module. The authority granting module is used to obtain the owner's authorization to grant the system the authority to collect the owner's information. The keyword recognition module is used to identify and extract itinerary keywords in the owner's information data. The semantic recognition module is used to use natural language processing technology to understand the semantics before and after the keywords. The plan input module is used to input the plan in the Internet of Vehicles system based on the keywords and semantic extraction to complete the capture of travel events.

[0007] According to the above technical solution, the emergency prediction module includes a travel distance recording module, a planned completion time recording module, an actual completion time recording module and a historical event log library. The travel distance recording module is used to record the single travel distance of an emergency, the planned completion time recording module is used to record the planned completion time of each emergency, the actual completion time recording module is used to record the actual completion time of each emergency, and the historical event log library is used to store all historical emergencies.

[0008] According to the above technical solution, the energy replenishment cost assessment module includes a rapid energy replenishment cost assessment module and a household energy replenishment cost output module. The rapid energy replenishment cost assessment module is used to analyze the current car owner's car usage environment and evaluate the cost index of rapid energy replenishment. The rapid energy replenishment cost assessment module further includes a charging pile density retrieval submodule and a charging pile electricity price retrieval submodule. The charging pile density retrieval submodule is used to retrieve the charging pile configuration density near the car owner's permanent residence area, and the charging pile electricity price retrieval submodule is used to retrieve the average charging price of the charging piles near the car owner's permanent residence area. The household energy replenishment cost output module is used to output a fixed index of household energy replenishment cost.

[0009] According to the above technical solution, the energy replenishment decision-making module includes an energy replenishment timing calculation module and an energy replenishment range output module. The energy replenishment timing calculation module is used to analyze and calculate the vehicle energy replenishment timing of the current owner, and the energy replenishment range output module is used to output vehicle energy replenishment decision suggestions and automatically plan the vehicle navigation to a nearby charging station.

[0010] According to the above technical solution, the operation method of the intelligent vehicle networking system includes the following steps:

[0011] Step S1: deploying the Internet of Vehicles system to connect to the vehicle system of the electric vehicle, and requesting the owner's information collection authority through the authority granting module. When the owner grants the information collection authority, the capture event generation module starts to run, captures the owner's travel plan during the owner's use of the car and in his life within the authority, and generates a capture event;

[0012] Step S2: Obtain the navigation setting information of the vehicle owner, and generate the daily travel to and from the destination into a fixed event through a fixed event generation module, wherein the fixed event takes "day" as a cycle, including all the travel information of the daily fixed travel, as well as the travel distance and arrival time of each travel;

[0013] Step S3: When the vehicle owner is traveling, if the current travel itinerary is neither captured and generated in advance by the capture event generation module nor a fixed event generated by the fixed event generation module, the system determines the current travel itinerary as an emergency event, and at the same time, the system obtains the planned completion time t1 of the current travel from the vehicle owner through the vehicle computer, and after the current travel is completed, records the actual completion time t2 of the current travel, and marks this emergency event when t1 < t2;

[0014] Step S4: Establish a historical event log library, store the owner's historical emergency time, emergency travel distance, and emergency marking status in the historical event log library, and the emergency prediction module analyzes and predicts the emergency impact index based on the owner's log library data;

[0015] Step S5: Further retrieve the owner's energy replenishment facility construction status through the Internet of Vehicles technology, analyze and evaluate the owner's rapid energy replenishment cost index when traveling, and output the family energy replenishment cost fixed index when recharging at home;

[0016] Step S6: When the vehicle owner is using the vehicle, the system analyzes and calculates the energy replenishment opportunity. When it is determined that the energy replenishment opportunity has been reached, the energy replenishment trip is automatically planned on the vehicle computer through the energy replenishment trip output module.

[0017] According to the above technical solution, step S1 further includes the following steps:

[0018] Step S11: The system obtains microphone information, driving plan setting information and chat information within the information collection authority of the car owner;

[0019] Step S12: first convert the microphone recognized voice into text through voice recognition technology, then start the keyword recognition module to recognize the preset keyword chat information and the keywords in the voice-converted text information;

[0020] Step S13: After obtaining the keyword, extract the sentence corresponding to the text information where the keyword is located for semantic recognition, and after extracting the semantic understanding results of the keyword and its sentence, automatically generate them into driving plan setting information, wherein the driving plan information includes the planned travel distance and the estimated time to arrive at the destination;

[0021] Step S14: The existing driving plan setting information and the currently automatically generated driving plan setting information are transmitted to the capture event generation module and listed as the vehicle owner's planned travel event captured by the system.

[0022] According to the above technical solution, step S4 further includes the following steps:

[0023] Step S41: Obtain all the times judged as emergencies in the historical event log library, and then use half a year as a statistical calculation period to calculate the number of times n judged as emergencies in the calculation period;

[0024] Step S42: Also taking half a year as a cycle, the travel distances of all emergencies within half a year are counted in the historical event log library, and the average travel distance l of the emergencies within the period is calculated by the summation and average formula;

[0025] Step S43: then count and calculate the ratio b of the marked emergency events to all emergency events in the historical event log library;

[0026] Step S44: According to the statistical analysis and collation of the above steps, the emergency prediction module calculates the emergency impact index U through a formula; wherein:

[0027]

[0028] According to the above technical solution, step S5 further includes the following steps:

[0029] Step S51: The system retrieves the density of charging piles near the car owner's permanent residence through the Internet, including the density of charging piles p1 within 5 kilometers of the car owner's permanent residence and the density of charging piles p2 within 200 kilometers of the car owner's permanent residence;

[0030] Step S52: the system further retrieves the electricity price of charging piles near the car owner's permanent residence area, and calculates the average electricity price j1 of charging piles within a radius of 5 kilometers and the average electricity price j2 of charging piles within a radius of 200 kilometers;

[0031] Step S53: By formula Calculate the owner's short-distance rapid energy replenishment cost index R1; through the formula Calculate the cost index R2 of long-distance rapid energy replenishment for vehicle owners;

[0032] Step S53: outputting the household energy replenishment cost fixed index R3;

[0033] Step S54: When the owner's family has a fixed charging parking space, the charging cost evaluation module outputs a comprehensive charging cost index When the owner's family does not have a fixed charging parking space, the charging cost assessment module outputs a comprehensive charging cost index

[0034] According to the above technical solution, step S6 further includes the following steps:

[0035] Step S61: The system estimates the remaining cruising range S of the current vehicle based on the actual power consumption per 100 kilometers and the current remaining power of the current vehicle;

[0036] Step S62: Taking "day" as a cycle, the system retrieves all the planned travel events of the car owner before the next cycle and all the trip information of the daily fixed travel corresponding to the required total mileage c;

[0037] Step S63: Calculate the remaining cruising range S0 until charging is ready by using a formula; where S0 satisfies:

[0038]

[0039] Where δ is the control parameter of the remaining cruising range S0 at the time of charging, which is a constant greater than 0, and S max The maximum remaining range given by the system when the charging time is reached, when S0 is greater than S max When S0=S max Analyze and calculate the results for energy replenishment opportunities;

[0040] Step S64: When Sc≤S0, the energy replenishment timing calculation module triggers an electrical signal, and the system controls the vehicle computer to automatically plan the vehicle computer navigation to a nearby charging station. The owner receives the system planning information to perform rapid energy replenishment of the electric vehicle or home charging.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by providing a capture event generation module, a fixed event generation module, an emergency event prediction module, an energy replenishment cost assessment module and an energy replenishment decision-making module, can capture the owner's planned itinerary, daily fixed itinerary and historical emergency itinerary during the daily use of the electric vehicle, and deeply analyze the comprehensive impact index of the emergency itinerary applicable to the current owner on his travel and endurance, and then synchronously analyze and evaluate the owner's comprehensive energy replenishment cost before making an energy replenishment decision, so that the energy replenishment decision suggestion is more in line with the current owner's car use habits and car use environment, avoiding the owner's excessive range anxiety and confusion about when to recharge during the use of the car, and achieving a worry-free and practical effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 The present invention provides a technical solution: an intelligent Internet of Vehicles system and method based on the Internet of Things, including a capture event generation module, a fixed event generation module, an emergency event prediction module, an energy replenishment cost evaluation module and an energy replenishment decision-making module. The capture time generation module is used to capture the owner's planned travel events through the Internet of Vehicles technology. The fixed event generation module is used to identify and obtain the owner's fixed travel events through the Internet of Vehicles technology. The emergency event prediction module is used to collect the owner's historical emergency events and analyze and predict the emergency event impact index of the current owner. The energy replenishment cost evaluation module is used to analyze the owner's energy replenishment cost and make an evaluation data report. The energy replenishment decision-making module is used to provide the owner with a decision plan for the energy replenishment opportunity, reducing Reduce the anxiety of car owners on the issue of recharging; by setting up a capture event generation module, a fixed event generation module, an emergency prediction module, a recharging cost assessment module and a recharging decision-making module, the car owner's planned itinerary, daily fixed itinerary and historical emergency itinerary can be captured during the daily use of the electric car, and the comprehensive impact index of the emergency itinerary applicable to the current car owner on his travel and endurance can be deeply analyzed. Then, the car owner's comprehensive recharging cost is simultaneously analyzed and evaluated before recharging decision-making, so that the recharging decision suggestion is more in line with the current car owner's car use habits and car use environment, avoiding the car owner's excessive range anxiety and confusion about when to recharge during the use of the car, achieving a worry-free and practical effect.

[0046] The capture event generation module includes an authority granting module, a keyword recognition module, a semantic recognition module and a plan input module. The authority granting module is used to obtain the owner's authorization to grant the system the authority to collect the owner's information. The keyword recognition module is used to identify and extract itinerary keywords in the owner's information data. The semantic recognition module is used to use natural language processing technology to understand the semantics before and after the keywords. The plan input module is used to input the plan in the Internet of Vehicles system based on keywords and semantic extraction to complete the capture of travel events.

[0047] The emergency prediction module includes a travel distance recording module, a planned completion time recording module, an actual completion time recording module and a historical event log library. The travel distance recording module is used to record the single travel distance of an emergency, the planned completion time recording module is used to record the planned completion time of each emergency, the actual completion time recording module is used to record the actual completion time of each emergency, and the historical event log library is used to store all historical emergencies.

[0048] The energy replenishment cost assessment module includes a rapid energy replenishment cost assessment module and a household energy replenishment cost output module. The rapid energy replenishment cost assessment module is used to analyze the current vehicle owner's vehicle usage environment and evaluate the cost index of rapid energy replenishment. The rapid energy replenishment cost assessment module further includes a charging pile density retrieval submodule and a charging pile electricity price retrieval submodule. The charging pile density retrieval submodule is used to retrieve the charging pile configuration density near the vehicle owner's permanent residence area, and the charging pile electricity price retrieval submodule is used to retrieve the average charging price of the charging piles near the vehicle owner's permanent residence area. The household energy replenishment cost output module is used to output a fixed index of household energy replenishment cost.

[0049] The energy replenishment decision-making module includes an energy replenishment timing calculation module and an energy replenishment trip output module. The energy replenishment timing calculation module is used to analyze and calculate the vehicle energy replenishment timing of the current owner. The energy replenishment trip output module is used to output vehicle energy replenishment decision suggestions and automatically plan the vehicle navigation to a nearby charging station.

[0050] The operation method of the intelligent vehicle networking system includes the following steps:

[0051] Step S1: deploying the Internet of Vehicles system to connect to the vehicle system of the electric vehicle, and requesting the owner's information collection authority through the authority granting module. When the owner grants the information collection authority, the capture event generation module starts to run, captures the owner's travel plan during the owner's use of the car and in his life within the authority, and generates a capture event;

[0052] Step S2: Obtain the navigation setting information of the vehicle owner, and generate the daily travel to and from the destination into a fixed event through a fixed event generation module, wherein the fixed event takes "day" as a cycle, including all the travel information of the daily fixed travel, as well as the travel distance and arrival time of each travel;

[0053] Step S3: When the car owner is traveling, if the current travel itinerary is neither captured and generated in advance by the capture event generation module nor a fixed event generated by the fixed event generation module, the system determines the current travel itinerary as an emergency event. At the same time, the system obtains the planned completion time t1 of the current travel from the car owner through the vehicle computer. After the current travel is completed, the actual completion time t2 of the current travel is recorded. When t1<t2, the emergency event is marked. The marked emergency event indicates that the event is not only an emergency event, but also a high possibility that the emergency event cannot be completed due to the urgency of the emergency and the vehicle cannot replenish energy in time. Therefore, the marked emergency event is an important indicator of the system's statistical index of the impact of emergencies on car owners;

[0054] Step S4: Establish a historical event log library, store the owner's historical emergency time, emergency travel distance, and emergency marking status in the historical event log library, and the emergency prediction module analyzes and predicts the emergency impact index based on the owner's log library data;

[0055] Step S5: Further retrieve the owner's energy replenishment facility construction status through the Internet of Vehicles technology, analyze and evaluate the owner's rapid energy replenishment cost index when traveling, and output the family energy replenishment cost fixed index when recharging at home;

[0056] Step S6: When the vehicle owner is using the vehicle, the system analyzes and calculates the energy replenishment opportunity. When it is determined that the energy replenishment opportunity has been reached, the energy replenishment trip is automatically planned on the vehicle computer through the energy replenishment trip output module.

[0057] Step S1 further comprises the following steps:

[0058] Step S11: The system obtains microphone information, driving plan setting information and chat information within the information collection authority of the car owner;

[0059] Step S12: first convert the microphone recognized voice into text through voice recognition technology, then start the keyword recognition module to recognize the preset keyword chat information and the keywords in the voice-converted text information;

[0060] Step S13: After obtaining the keyword, extract the sentence corresponding to the text information where the keyword is located for semantic recognition, and after extracting the semantic understanding results of the keyword and its sentence, automatically generate them into driving plan setting information, wherein the driving plan information includes the planned travel distance and the estimated time to arrive at the destination;

[0061] Step S14: The existing driving plan setting information and the currently automatically generated driving plan setting information are transmitted to the capture event generation module and listed as the vehicle owner's planned travel event captured by the system.

[0062] Step S4 further comprises the following steps:

[0063] Step S41: Obtain all the times judged as emergencies in the historical event log library, and then use half a year as a statistical calculation period to calculate the number of times n judged as emergencies in the calculation period;

[0064] Step S42: Also taking half a year as a cycle, the travel distances of all emergencies within half a year are counted in the historical event log library, and the average travel distance l of the emergencies within the period is calculated by the summation and average formula;

[0065] Step S43: then count and calculate the ratio b of the marked emergency events to all emergency events in the historical event log library;

[0066] Step S44: According to the statistical analysis and collation of the above steps, the emergency prediction module calculates the emergency impact index U through a formula; wherein:

[0067]

[0068] Where k is the coefficient value, which is a constant greater than 0. It can be seen from the formula that the emergency impact index is proportional to the number of times judged as emergencies within the period, the average travel distance of emergencies within the period, and the ratio of marked emergencies to all emergencies. The larger the above parameters are, the higher the proportion of emergencies relative to the total vehicle travel, and thus the greater the emergency impact index.

[0069] Step S5 further comprises the following steps:

[0070] Step S51: The system retrieves the density of charging piles near the car owner's permanent residence through the Internet, including the density of charging piles p1 within 5 kilometers of the car owner's permanent residence and the density of charging piles p2 within 200 kilometers of the car owner's permanent residence;

[0071] Step S52: the system further retrieves the electricity price of charging piles near the car owner's permanent residence area, and calculates the average electricity price j1 of charging piles within a radius of 5 kilometers and the average electricity price j2 of charging piles within a radius of 200 kilometers;

[0072] Step S53: By formula Calculate the owner's short-distance rapid energy replenishment cost index R1; through the formula Calculate the cost index R2 of long-distance rapid energy replenishment for vehicle owners;

[0073] In the formula, α and β are control coefficients, both of which are constants greater than 0. Among them, j1 and j2 are proportional to the short-distance rapid energy replenishment cost index R1 and the long-distance rapid energy replenishment cost index R2 respectively, and p1 and p2 are inversely proportional to the short-distance rapid energy replenishment cost index R1 and the long-distance rapid energy replenishment cost index R2 respectively. When the average electricity price of the charging piles near the owner's permanent residence area is higher and the density of the charging piles is lower, the charging cost index is higher; otherwise, the lower it is; and by calculating the configuration and electricity price of the rapid energy replenishment charging piles within a radius of 5 kilometers and a radius of 200 kilometers of the owner's permanent residence area, the clear and definite energy replenishment cost index under various working conditions such as daily travel and long-distance driving of the owner can be obtained respectively, and then the owner's energy replenishment cost can be fully considered;

[0074] Step S53: outputting the household energy replenishment cost fixed index R3;

[0075] Step S54: When the owner's family has a fixed charging parking space, the charging cost evaluation module outputs a comprehensive charging cost index When the owner's family does not have a fixed charging parking space, the charging cost assessment module outputs a comprehensive charging cost index

[0076] Step S6 further comprises the following steps:

[0077] Step S61: The system estimates the remaining cruising range S of the current vehicle based on the actual power consumption per 100 kilometers and the current remaining power of the current vehicle;

[0078] Step S62: Taking "day" as a cycle, the system retrieves all the planned travel events of the car owner before the next cycle and all the trip information of the daily fixed travel corresponding to the required total mileage c;

[0079] Step S63: Calculate the remaining cruising range S0 until charging is ready by using a formula; where S0 satisfies:

[0080]

[0081] Where δ is the control parameter of the remaining cruising range S0 at the time of charging, which is a constant greater than 0, and S max The maximum remaining range given by the system when the charging time is reached, when S0 is greater than S max When S0=S max is the result of the analysis and calculation of the timing of energy replenishment; the impact index U of the emergency event is proportional to the timing of energy replenishment. When the frequency and impact of the emergency events in the current car owner's car use process are relatively large, the timing of energy replenishment will be advanced to reserve as much reserve power as possible for use in emergencies; the comprehensive energy replenishment cost index R of the car owner is inversely proportional to the timing of energy replenishment. When the energy replenishment cost is higher, the time cost of the car owner's energy replenishment will be greatly increased, and the control in the formula will affect the delayed energy replenishment timing; ultimately, it can achieve the role of fully, comprehensively and accurately making decisions for the car owner, achieving a worry-free effect;

[0082] Step S64: When Sc≤S0, the energy replenishment timing calculation module triggers an electrical signal, and the system controls the vehicle computer to automatically plan the vehicle computer navigation to a nearby charging station. The owner receives the system planning information to perform rapid energy replenishment of the electric vehicle or home charging.

[0083] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0084] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent vehicle networking system based on the Internet of Things, characterized by: The intelligent Internet of Vehicles system includes a capture event generation module, a fixed event generation module, an emergency event prediction module, an energy replenishment cost evaluation module, and an energy replenishment decision-making module. The capture time generation module is used to capture the owner's planned travel events through the Internet of Vehicles technology. The fixed event generation module is used to identify and obtain the owner's fixed travel events through the Internet of Vehicles technology. The emergency event prediction module is used to collect the owner's historical emergency events and analyze and predict the emergency event impact index of the current owner. The energy replenishment cost evaluation module is used to analyze the owner's energy replenishment cost and make an evaluation data report. The energy replenishment decision-making module is used to provide the owner with a decision plan for the energy replenishment opportunity to reduce the owner's anxiety about the energy replenishment issue; The capture event generation module includes an authority granting module, a keyword recognition module, a semantic recognition module and a plan input module. The authority granting module is used to obtain the vehicle owner's authorization to grant the system the vehicle owner's information collection authority. The keyword recognition module is used to identify and extract the itinerary keywords in the vehicle owner's information data. The semantic recognition module is used to understand the semantics before and after the keywords using natural language processing technology. The plan input module is used to input the plan in the Internet of Vehicles system according to the keywords and semantic extraction to complete the capture of travel events. The energy replenishment cost evaluation module includes a rapid energy replenishment cost evaluation module and a household energy replenishment cost output module. The rapid energy replenishment cost evaluation module is used to analyze the current vehicle owner's vehicle usage environment and evaluate the rapid energy replenishment cost index. The rapid energy replenishment cost evaluation module further includes a charging pile density retrieval submodule and a charging pile electricity price retrieval submodule. The charging pile density retrieval submodule is used to retrieve the configuration density of charging piles near the vehicle owner's permanent residence area. The charging pile electricity price retrieval submodule is used to retrieve the average charging price of charging piles near the vehicle owner's permanent residence area. The household energy replenishment cost output module is used to output a household energy replenishment cost fixed index. The energy replenishment decision-making module includes an energy replenishment timing calculation module and an energy replenishment range output module. The energy replenishment timing calculation module is used to analyze and calculate the vehicle energy replenishment timing of the current vehicle owner, and the energy replenishment range output module is used to output vehicle energy replenishment decision suggestions and automatically plan the vehicle navigation to a nearby charging station; The operation method of the intelligent vehicle networking system comprises the following steps: Step S1: deploying the Internet of Vehicles system to connect to the vehicle system of the electric vehicle, and requesting the owner's information collection authority through the authority granting module. When the owner grants the information collection authority, the capture event generation module starts to run, captures the owner's travel plan during the owner's use of the car and in his life within the authority, and generates a capture event; Step S2: Obtain the navigation setting information of the vehicle owner, and generate the daily travel to and from the destination into a fixed event through the fixed event generation module, where the fixed event takes "day" as the cycle, including all the travel information of the daily fixed travel, as well as the travel distance and arrival time of each travel; Step S3: When the vehicle owner is traveling, if the current travel itinerary is neither captured and generated in advance by the capture event generation module nor a fixed event generated by the fixed event generation module, the system determines the current travel itinerary as an emergency event, and at the same time, the system obtains the planned completion time t1 of the current travel from the vehicle owner through the vehicle computer, and after the current travel is completed, records the actual completion time t2 of the current travel, and marks this emergency event when t1 < t2; Step S4: Establish a historical event log library, store the owner's historical emergency time, emergency travel distance, and emergency marking status in the historical event log library, and the emergency prediction module analyzes and predicts the emergency impact index based on the owner's log library data; Step S5: Further retrieve the owner's energy replenishment facility construction status through the Internet of Vehicles technology, analyze and evaluate the owner's rapid energy replenishment cost index when traveling, and output the family energy replenishment cost fixed index when recharging at home; Step S6: When the vehicle owner is using the vehicle, the system analyzes and calculates the energy replenishment timing, and when it is determined that the energy replenishment timing has been reached, the energy replenishment trip is automatically planned on the vehicle computer through the energy replenishment trip output module; The step S1 further comprises the following steps: Step S11: The system obtains microphone information, driving plan setting information and chat information within the information collection authority of the car owner; Step S12: first convert the microphone recognized voice into text through voice recognition technology, then start the keyword recognition module to recognize the preset keyword chat information and the keywords in the voice-converted text information; Step S13: After obtaining the keyword, extract the sentence corresponding to the text information where the keyword is located for semantic recognition, and after extracting the semantic understanding results of the keyword and its sentence, automatically generate them into driving plan setting information, wherein the driving plan information includes the planned travel distance and the estimated time to arrive at the destination; Step S14: transmitting the existing driving plan setting information and the currently automatically generated driving plan setting information to the capture event generation module, and listing them as the vehicle owner's planned travel event captured by the system; The step S4 further comprises the following steps: Step S41: Obtain all the times judged as emergencies in the historical event log library, and then use half a year as a statistical calculation period to calculate the number of times n judged as emergencies in the calculation period; Step S42: Also taking half a year as a cycle, the travel distances of all emergencies within half a year are counted in the historical event log library, and the average travel distance l of the emergencies within the period is calculated by the summation and average formula; Step S43: then count and calculate the ratio b of the marked emergency events to all emergency events in the historical event log library; Step S44: According to the statistical analysis and collation of the above steps, the emergency prediction module calculates the emergency impact index U through a formula; wherein: The step S6 further comprises the following steps: Step S61: The system estimates the remaining cruising range S of the current vehicle based on the actual power consumption per 100 kilometers and the current remaining power of the current vehicle; Step S62: Taking "day" as a cycle, the system retrieves all the planned travel events of the car owner before the next cycle and all the itinerary information of the daily fixed travel corresponding to the required total mileage c; Step S63: Calculate the remaining cruising range S0 until charging is ready by using a formula; where S0 satisfies: Where δ is the control parameter of the remaining cruising range S0 at the time of charging, which is a constant greater than 0, and S max The maximum remaining range given by the system when the charging time is reached, when S0 is greater than S max When S0=S max Analyze and calculate the results for energy replenishment opportunities; Step S64: When Sc≤S0, the energy replenishment timing calculation module triggers an electrical signal, and the system controls the vehicle computer to automatically plan the vehicle computer navigation to a nearby charging station. The owner receives the system planning information to perform rapid energy replenishment of the electric vehicle or home charging.

2. The intelligent vehicle networking system based on the Internet of Things according to claim 1 is characterized in that: The emergency prediction module includes a travel distance recording module, a planned completion time recording module, an actual completion time recording module and a historical event log library. The travel distance recording module is used to record the single travel distance of an emergency, the planned completion time recording module is used to record the planned completion time of each emergency, the actual completion time recording module is used to record the actual completion time of each emergency, and the historical event log library is used to store all historical emergencies.

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