System and method for optimizing and switching global short message suppliers of Internet of Vehicles

Abstract

The invention belongs to the technical field of Internet of Vehicles short message switching, and discloses an Internet of Vehicles global short message supplier optimization switching system and method. The method comprises the following steps: acquiring navigation route information of a running vehicle; based on the navigation route information, acquiring position data of the vehicle running per unit time; feature processing is carried out on the position data, feature processing data are obtained, and the feature processing data comprise regional names, position features and time interval data; setting a supplier serial number corresponding to the global short message supplier; acquiring short message data of global short message suppliers, wherein the short message data comprises a short message sending success rate, average delay time and short message cost; establishing a digital model based on the feature processing data, the supplier serial number and the short message data; the user experience can be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking short message switching, and more specifically, to a vehicle networking global short message supplier optimization switching system and method. Background Art

[0002] The existing Chinese patent with authorization announcement number CN104394188B discloses a method and system for data transmission in an Internet of Vehicles. By configuring a wireless AP on a vehicle host, the wireless AP receives a first data message sent from a background server of the Internet of Vehicles, so that the vehicle host obtains the first data message sent by the background server of the Internet of Vehicles, and performs corresponding operations according to the first data message. The first data message can be in multiple forms, which overcomes the defect that data transmission in the Internet of Vehicles must be multimedia data, and does not rely on cloud backup technology or remote control technology, which provides convenience for data transmission in the Internet of Vehicles.

[0003] The existing technology still has the following problems: When a vehicle is traveling long distances across regions, the signal strength in different geographical locations varies significantly. At the same time, different SMS providers have different response times and SMS costs when sending SMS to the Internet of Vehicles system. However, existing technologies usually do not implement the switching of SMS providers in advance according to actual conditions to achieve the optimal supply of Internet of Vehicles SMS, resulting in the need to improve user experience.

[0004] In view of this, the present invention proposes a vehicle networking global SMS provider optimization switching system and method to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for optimizing switching of global SMS providers in an Internet of Vehicles, comprising: Obtaining navigation route information of a moving vehicle; Based on the navigation route information, the position data of the vehicle traveling per unit time is obtained; Performing feature processing on the location data to obtain feature processing data, wherein the feature processing data includes a region name, location features, and time interval data; Set the supplier serial number corresponding to the global SMS supplier; Obtain SMS data from global SMS providers, including SMS delivery success rate, average delay time, and SMS cost; Build a digital model based on feature processing data, supplier serial number and SMS data; Setting target conditions based on the digital model, the target conditions including shortest total delay time, lowest total SMS cost and highest reliability; Random Generation Pre-provisioning solutions; According to the target conditions, the pre-provisioning plan is optimized using an adaptive allocation algorithm, and the optimized pre-provisioning plan is output.

[0006] Furthermore, the method for optimizing the pre-provisioning scheme by the adaptive allocation algorithm includes: Step N1, initialize the particle swarm, each particle swarm represents a pre-supply plan, and the pre-supply plan includes the supplier serial number selected when the vehicle is traveling in different regional names, location characteristics and corresponding time interval data; Step N2, obtaining the total delay time, total SMS cost and reliability of each pre-provisioning solution; Step N3: Calculate based on total delay time, total SMS cost and reliability Comprehensive fitness of the pre-supply schemes ; Step N4, updating the speed and position of the particle, that is, updating the supplier serial number selected by the vehicle when driving in different region names, location characteristics and corresponding time interval data, and generating a new pre-supply plan; Step N5: If the first The reliability of a particle is greater than the reliability corresponding to the best historical position, then let ; If the first The reliability of each particle is greater than the reliability corresponding to the global optimal position, then let ; For the The particle in The position at the iteration; For the The best historical position found by each particle in the corresponding pre-supply scheme, the best position in the corresponding pre-supply scheme has the highest reliability; is the global best position found among all particles; Step N6, repeating steps N2 to N5 until the preset number of repetitions is reached, selecting the pre-supply scheme with the highest comprehensive fitness, the pre-supply scheme with the highest comprehensive fitness is the output optimized pre-supply scheme that meets the set target conditions.

[0007] Furthermore, the method for obtaining the reliability includes: ; In the formula, For reliability; is the total delay time; For the The SMS sending success rate corresponding to the group feature processing data; and are the corresponding preset weights respectively; The total number of feature processing data groups corresponding to the current navigation route information; .

[0008] Furthermore, the method for obtaining the success rate of SMS sending includes: Step S1, obtaining historical transmission status data, the historical transmission status data including the sending status, sending time and delivery time of the SMS; dividing the historical transmission status data into different area names, corresponding different location features and different time interval data Group transfer data; statistics The sending status of all SMS messages corresponding to the group transmission data, wherein the sending status includes success and failure; is a positive integer greater than 1; Step S2: For each group of transmission data, divide the SMS messages with a successful sending status by the total number of SMS messages to calculate the corresponding SMS sending success rate.

[0009] Furthermore, the method for obtaining the average delay time includes: Step M1: Statistics The sending and delivery time of all SMS messages corresponding to the group transmission data; Step M2: Subtract the sending time from the delivery time of each SMS message to calculate the delay time corresponding to each SMS message; Step M3: average the delay times of all the short messages in each group of transmission data, and calculate the corresponding average delay time.

[0010] Furthermore, the method for obtaining the total delay time includes: Step Q1, for all feature processing data on the current navigation route of the traveling vehicle, obtain the average delay time corresponding to each set of feature processing data; Step Q2, sum up all the average delay times to calculate the total delay time; The total SMS cost is obtained in the same way as the total delay time.

[0011] Furthermore, the comprehensive fitness The calculation methods include: ; In the formula, , and is the preset weight coefficient; is the total SMS cost, For reliability, is the total delay time.

[0012] Furthermore, the particle velocity update method includes: ; In the formula, For the The particle in The speed at the iteration; For the The particle in The speed at the iteration; For the The particle in The position at the iteration; is the inertia weight, which controls the continuity of particle velocity; is the individual learning factor; is the group learning factor; and All are random numbers in [0,1]; Particle position update methods include: .

[0013] Furthermore, the location data includes the vehicle location and corresponding timestamp data; the location features include urban areas, highway areas, mountainous areas and township areas; the geographical name is the name of the urban area to which the vehicle location belongs; and the time interval data is the hourly time period corresponding to the current specific time.

[0014] A vehicle networking global SMS provider optimization switching system, which implements a vehicle networking global SMS provider optimization switching method, includes: A first acquisition module, used to acquire navigation route information of a moving vehicle; A second acquisition module acquires the position data of the vehicle traveling per unit time based on the navigation route information; A feature processing module, used to perform feature processing on the location data to obtain feature processing data, wherein the feature processing data includes a region name, location features and time interval data; The serial number setting module is used to set the supplier serial number corresponding to the global SMS supplier; A third acquisition module is used to acquire SMS data of global SMS providers, wherein the SMS data includes SMS sending success rate, average delay time and SMS cost; Digital modeling module, which builds digital models based on feature processing data, supplier serial numbers and SMS data; A target setting module, which sets target conditions based on the digital model, wherein the target conditions include shortest total delay time, lowest total SMS cost and highest reliability; Scheme generation module, used for random generation Pre-provisioning solutions; The solution analysis module optimizes the pre-provisioning solution using an adaptive allocation algorithm according to target conditions and outputs the optimized pre-provisioning solution.

[0015] The technical effects and advantages of the vehicle networking global SMS provider optimization switching system and method of the present invention are as follows: When a vehicle is traveling long distances across regions, the present invention obtains the navigation route of the traveling vehicle, obtains the region name, location characteristics and time interval data corresponding to the vehicle at different locations, takes the shortest total delay time, the lowest total SMS cost and the highest reliability as target conditions, and selects the optimal pre-provisioning plan corresponding to the current navigation route in advance through an adaptive allocation algorithm, thereby reducing the SMS sending cost and system response time, while also improving the SMS sending efficiency of the SMS provider, thereby further improving the user experience.

[0016] By obtaining the SMS sending success rate, average delay time, and SMS cost, the system can evaluate the reliability of each SMS provider in different geographical locations and at different times; selecting providers with high success rate, short delay time and good stability can significantly improve the reliability of information transmission and ensure that key notifications, traffic warnings and emergency communications can be delivered accurately and in a timely manner.

[0017] The Internet of Vehicles system will determine the vehicle's passing locations (such as urban areas, highways, mountainous areas, etc.) and corresponding time periods in advance based on vehicle navigation, and dynamically select the appropriate SMS provider. For example, when a vehicle enters a mountainous area, it may choose a provider with strong signal penetration and low latency; while in urban areas or highways, it will choose a provider with a high success rate and good coverage, improving the overall flexibility and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a vehicle networking global SMS provider optimization switching system according to Embodiment 1 of the present invention; Figure 2 A flow chart of a method for optimizing and switching a global SMS provider of an Internet of Vehicles according to Embodiment 2 of the present invention; Figure 3 A simplified schematic diagram of a navigation route of a traveling vehicle according to Embodiment 1 of the present invention; Figure 4 This is a flow chart of the adaptive allocation algorithm of embodiment 1 of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] Example 1 See also Figure 1 As shown, the vehicle networking global SMS supplier optimization switching system described in this embodiment includes: a first acquisition module, a second acquisition module, a feature processing module, a sequence number setting module, a third acquisition module, a digital modeling module, a target setting module, a solution generation module and a solution analysis module, and each module is connected by wire and / or wirelessly.

[0021] The first acquisition module is used to acquire navigation route information of the traveling vehicle, wherein the navigation route information includes the current location, the destination location, the passing area and the total time spent; the passing area includes mountainous areas, urban areas and high-speed areas, etc.; Figure 3 As shown, a simplified schematic diagram of the navigation route of a moving vehicle is shown.

[0022] By obtaining the navigation route information of the moving vehicle, the geographical location and driving route of the vehicle during the driving process are determined, and the total time spent is obtained, which helps to obtain the vehicle's location data based on the navigation route later, so as to select the best SMS supplier's pre-supply plan.

[0023] The navigation route information is obtained through a third-party navigation platform (such as Baidu Maps and Amap).

[0024] The second acquisition module acquires the position data of the vehicle traveling per unit time based on the navigation route information, wherein the position data includes the vehicle position and corresponding timestamp data; and the length of the unit time is less than the length of the total time spent.

[0025] The feature processing module is used to perform feature processing on the location data to obtain feature processing data, wherein the feature processing data includes a region name, location features and time interval data; the location features include urban areas, highway areas, mountainous areas and township areas; the region name is the name of the urban area to which the vehicle location belongs; and the time interval data is the hourly time period corresponding to the current specific time.

[0026] For example, in the position data, the current vehicle position is longitude 117.0000, latitude 39.0000, expressed as [117.0000, 39.0000]; the timestamp data corresponding to the vehicle position is December 1, 2024 10:30:00, expressed as [2024, 12, 01, 103000]; in the corresponding feature processing data, the region name is Tianjin; the position feature is the highway area; the time interval data is between 10 and 11 o'clock, expressed as [10, 11].

[0027] The serial number setting module is used to set the supplier serial number corresponding to the global SMS supplier.

[0028] Exemplarily, the global SMS providers include Tencent Cloud, MTC, MTN and AWS; the provider number corresponding to Tencent Cloud is a1, the provider number corresponding to MTC is a2, the provider number corresponding to MTN is a3, and the provider number corresponding to AWS is a4.

[0029] The supplier serial number is automatically set through the API interface corresponding to the supplier.

[0030] The third acquisition module is used to acquire SMS data of global SMS providers, wherein the SMS data includes SMS sending success rate, average delay time and SMS cost.

[0031] The SMS sending success rate refers to the proportion of SMS messages sent through a global SMS provider that are successfully delivered within a period of time, which directly reflects the reliability of the provider. A high success rate means that the SMS can effectively reach the destination, while a low success rate means that some SMS messages are lost or not delivered, which is very dangerous for important notifications and information transmission in Internet of Vehicles applications. By obtaining the SMS sending success rate of global SMS providers in different geographical areas and different time periods, it is helpful to improve the calculation rationality of the highest reliability in the subsequent adaptive allocation algorithm, thereby facilitating the selection of the optimal solution for the subsequent pre-supply plan.

[0032] The average delay time indicates the average time from the sending of multiple text messages through a global SMS provider over a period of time to the delivery of the text messages. In Internet of Vehicles applications, the delay in sending text messages will affect the efficiency of certain real-time decisions. For example, vehicles need to receive timely road condition updates, speed limit warnings, and emergency communications. If the delay time is long, it may cause information lag, which in turn affects the decision-making effect of the driver or intelligent driving. By obtaining the average delay time of global SMS providers in different geographical areas and different time periods, it helps the subsequent adaptive allocation algorithm to calculate the total delay time in the target condition, thereby facilitating the selection of the optimal solution for the subsequent pre-supply plan.

[0033] The SMS cost refers to the fee for each SMS message, which directly affects the economic benefits of the system, especially in the related systems of the Internet of Vehicles application, which involves a large number of SMS messages. Optimizing costs helps reduce unnecessary expenses and ensures that the system continues to operate within the budget. By obtaining the SMS costs of global SMS suppliers in different geographical areas and different time periods, it helps to select the optimal solution for subsequent pre-supply solutions.

[0034] The method for obtaining the success rate of SMS sending includes: Step S1, obtaining historical transmission status data, the historical transmission status data including the sending status, sending time and delivery time of the SMS; dividing the historical transmission status data into different area names, corresponding different location features and different time interval data Group transfer data; statistics The sending status of all SMS messages corresponding to the group transmission data, wherein the sending status includes success and failure; is a positive integer greater than 1; the historical transmission status data is obtained through the supplier's API interface.

[0035] Step S2: For each group of transmission data, divide the SMS messages with a successful sending status by the total number of SMS messages to calculate the corresponding SMS sending success rate.

[0036] The method for obtaining the average delay time includes: Step M1: Statistics The sending and delivery time of all SMS messages corresponding to the group transmission data.

[0037] Step M2: Subtract the sending time from the delivery time of each SMS message to calculate the delay time corresponding to each SMS message.

[0038] Step M3: average the delay times of all the short messages in each group of transmission data, and calculate the corresponding average delay time.

[0039] The SMS costs are obtained through pricing information on the supplier’s official website.

[0040] The digital modeling module builds a digital model based on feature processing data, supplier serial number and SMS data.

[0041] The target setting module sets target conditions based on the digital model, wherein the target conditions include the shortest total delay time, the lowest total SMS cost and the highest reliability.

[0042] The reliability refers to the applicability of the pre-provisioning solution corresponding to the current navigation route information.

[0043] Scheme generation module, used for random generation A pre-provisioning solution.

[0044] The solution analysis module optimizes the pre-supply solution using an adaptive allocation algorithm according to the target conditions and outputs the optimized pre-supply solution to meet the set target conditions.

[0045] See also Figure 4 , the method for optimizing the pre-provisioning scheme by the adaptive allocation algorithm includes: Step N1, initialize the particle swarm, each particle swarm represents a pre-supply plan, and the pre-supply plan includes the supplier serial number selected when the vehicle is traveling in different regional names, location characteristics and corresponding time interval data.

[0046] For example, the domain name of the current location of the vehicle is Tianjin; the location feature is a highway area; when the time interval data is between 10 o'clock and 11 o'clock, the supplier serial number a1 corresponding to Tencent Cloud is selected.

[0047] Step N2: Obtain the total delay time, total SMS cost and reliability of each pre-provisioning solution.

[0048] The method for obtaining the total delay time includes: Step Q1: for all feature processing data on the current navigation route of the traveling vehicle, obtain the average delay time corresponding to each set of feature processing data.

[0049] Step Q2: Sum all the average delay times to calculate the total delay time.

[0050] The total SMS cost is obtained by using the method for obtaining the total delay time; the method for obtaining the total SMS cost includes: Step V1: for all feature processing data on the current navigation route of the traveling vehicle, obtain the SMS cost corresponding to each set of feature processing data.

[0051] Step V2: Sum up all SMS costs to calculate the total SMS cost.

[0052] The method for obtaining the reliability includes: .

[0053] In the formula, For reliability; is the total delay time; For the The SMS sending success rate corresponding to the group feature processing data; and are the corresponding preset weights respectively; The total number of feature processing data groups corresponding to the current navigation route information; .

[0054] Step N3: Calculate based on total delay time, total SMS cost and reliability Comprehensive fitness of the pre-supply schemes The comprehensive fitness The calculation methods include: .

[0055] In the formula, , and is the preset weight coefficient; is the total SMS cost.

[0056] Step N4, updating the speed and position of the particle, that is, updating the supplier serial number selected by the vehicle when driving in different region names, location characteristics and corresponding time interval data, and generating a new pre-supply plan; the method of updating the particle speed includes: .

[0057] In the formula, For the The particle in The speed at the iteration; For the The particle in The speed at the iteration; For the The particle in The position at the iteration; For the The best historical position found by each particle in the corresponding pre-supply scheme, the best position in the corresponding pre-supply scheme has the highest reliability; is the global (i.e., all pre-supply scenarios) best position found among all particles; is the inertia weight, which controls the continuity of particle velocity; is the individual learning factor, The larger the value of , the more particles will tend to rely on their own historical experience; is the group learning factor, The larger the value of , the more particles will tend to rely on the common experience of the group; and They are all random numbers in [0,1] to increase the randomness of the search.

[0058] For example, The parameters of each particle are as follows: ; ; ; ; ; ; ; ; .

[0059] Inertia term calculation: .

[0060] Individual cognitive item calculation: .

[0061] Group cognition item calculation: .

[0062] Update speed calculation: .

[0063] Particle position update methods include: .

[0064] For example, The updated position of a particle is: .

[0065] Step N5: If the first The reliability of a particle is greater than the reliability corresponding to the best historical position, then let ; If the first The reliability of each particle is greater than the reliability corresponding to the global optimal position, then let .

[0066] Step N6, repeating steps N2 to N5 until the preset number of repetitions is reached, selecting the pre-supply scheme with the highest comprehensive fitness, the pre-supply scheme with the highest comprehensive fitness is the output optimized pre-supply scheme that meets the set target conditions.

[0067] Example 2 See also Figure 2 As shown, this embodiment provides a method for optimizing switching of global SMS providers in an Internet of Vehicles, including: Obtaining navigation route information of a moving vehicle; Based on the navigation route information, the position data of the vehicle traveling per unit time is obtained; Performing feature processing on the location data to obtain feature processing data, wherein the feature processing data includes a region name, location features, and time interval data; Set the supplier serial number corresponding to the global SMS supplier; Obtain SMS data from global SMS providers, including SMS delivery success rate, average delay time, and SMS cost; Build a digital model based on feature processing data, supplier serial number and SMS data; Setting target conditions based on the digital model, the target conditions including shortest total delay time, lowest total SMS cost and highest reliability; Random Generation Pre-provisioning solutions; According to the target conditions, the pre-provisioning plan is optimized using an adaptive allocation algorithm, and the optimized pre-provisioning plan is output.

[0068] Example 3 The electronic device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, steps in an embodiment of a method for optimizing switching of global SMS providers in an Internet of Vehicles are implemented, such as Figure 2 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 1 Functionality of the modules shown.

[0069] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0070] Those skilled in the art will appreciate that these are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0071] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0072] The memory may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), etc. equipped on the terminal device. Furthermore, the memory may include both an internal storage unit and an external storage device of the terminal device. The memory is used to store the computer program and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.

[0073] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0074] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0075] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0077] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for optimizing and switching global SMS providers in Internet of Vehicles, characterized in that: include: Obtaining navigation route information of a moving vehicle; Based on the navigation route information, the position data of the vehicle traveling per unit time is obtained; Performing feature processing on the location data to obtain feature processing data, wherein the feature processing data includes a region name, location features, and time interval data; Set the supplier serial number corresponding to the global SMS supplier; Obtain SMS data from global SMS providers, including SMS delivery success rate, average delay time, and SMS cost; Build a digital model based on feature processing data, supplier serial number and SMS data; Setting target conditions based on the digital model, the target conditions including shortest total delay time, lowest total SMS cost and highest reliability; Random Generation Pre-provisioning solutions; According to the target conditions, the pre-provisioning plan is optimized using an adaptive allocation algorithm, and the optimized pre-provisioning plan is output.

2. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 1, characterized in that: The method for optimizing the pre-provisioning scheme by the adaptive allocation algorithm includes: Step N1, initialize the particle swarm, each particle swarm represents a pre-supply plan, and the pre-supply plan includes the supplier serial number selected when the vehicle is traveling in different regional names, location characteristics and corresponding time interval data; Step N2, obtaining the total delay time, total SMS cost and reliability of each pre-provisioning solution; Step N3: Calculate based on total delay time, total SMS cost and reliability Comprehensive fitness of the pre-supply schemes ; Step N4, updating the speed and position of the particle, that is, updating the supplier serial number selected by the vehicle when driving in different region names, location characteristics and corresponding time interval data, and generating a new pre-supply plan; Step N5: If the first The reliability of a particle is greater than the reliability corresponding to the best historical position, then let ; If the first The reliability of each particle is greater than the reliability corresponding to the global optimal position, then let ; For the The particle in The position at the iteration; For the The best historical position found by each particle in the corresponding pre-supply scheme, the best position in the corresponding pre-supply scheme has the highest reliability; is the global best position found among all particles; Step N6, repeating steps N2 to N5 until the preset number of repetitions is reached, selecting the pre-supply scheme with the highest comprehensive fitness, the pre-supply scheme with the highest comprehensive fitness is the output optimized pre-supply scheme that meets the set target conditions.

3. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 2, characterized in that: The method for obtaining the reliability includes: ； In the formula, For reliability; is the total delay time; For the The SMS sending success rate corresponding to the group feature processing data; and are the corresponding preset weights respectively; The total number of feature processing data groups corresponding to the current navigation route information; .

4. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 1, characterized in that: The method for obtaining the success rate of SMS sending includes: Step S1, obtaining historical transmission status data, the historical transmission status data including the sending status, sending time and delivery time of the SMS; dividing the historical transmission status data into different area names, corresponding different location features and different time interval data Group transfer data; statistics The sending status of all SMS messages corresponding to the group transmission data, wherein the sending status includes success and failure; is a positive integer greater than 1; Step S2: For each group of transmission data, divide the SMS messages with a successful sending status by the total number of SMS messages to calculate the corresponding SMS sending success rate.

5. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 1, characterized in that: The method for obtaining the average delay time includes: Step M1: Statistics The sending and delivery time of all SMS messages corresponding to the group transmission data; Step M2: Subtract the sending time from the delivery time of each SMS message to calculate the delay time corresponding to each SMS message; Step M3: average the delay times of all the short messages in each group of transmission data, and calculate the corresponding average delay time.

6. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 1, characterized in that: The method for obtaining the total delay time includes: Step Q1, for all feature processing data on the current navigation route of the traveling vehicle, obtain the average delay time corresponding to each set of feature processing data; Step Q2, sum up all the average delay times to calculate the total delay time; The total SMS cost is obtained in the same way as the total delay time.

7. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 2, characterized in that: The comprehensive fitness The calculation methods include: ； In the formula, , and is the preset weight coefficient; is the total SMS cost, For reliability, is the total delay time.

8. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 2, characterized in that: The particle velocity update methods include: ； In the formula, For the The particle in The speed at the iteration; For the The particle in The speed at the iteration; For the The particle in The position at the iteration; is the inertia weight, which controls the continuity of particle velocity; is the individual learning factor; is the group learning factor; and All are random numbers in [0,1]; Particle position update methods include: 。 9. The method for optimizing and switching global SMS providers in Internet of Vehicles according to claim 1, characterized in that The location data includes the vehicle location and the corresponding timestamp data; the location features include urban areas, highway areas, mountainous areas and township areas; the geographical name is the name of the urban area to which the vehicle location belongs; and the time interval data is the hourly time period corresponding to the current specific time.

10. A vehicle networking global SMS provider optimization switching system, implementing a vehicle networking global SMS provider optimization switching method as claimed in any one of claims 1 to 9, characterized in that: include: A first acquisition module, used to acquire navigation route information of a moving vehicle; A second acquisition module acquires the position data of the vehicle traveling per unit time based on the navigation route information; A feature processing module, used to perform feature processing on the location data to obtain feature processing data, wherein the feature processing data includes a region name, location features and time interval data; The serial number setting module is used to set the supplier serial number corresponding to the global SMS supplier; A third acquisition module is used to acquire SMS data of global SMS providers, wherein the SMS data includes SMS sending success rate, average delay time and SMS cost; Digital modeling module, which builds digital models based on feature processing data, supplier serial numbers and SMS data; A target setting module, which sets target conditions based on the digital model, wherein the target conditions include shortest total delay time, lowest total SMS cost and highest reliability; Scheme generation module, used for random generation Pre-provisioning solutions; The solution analysis module optimizes the pre-provisioning solution using an adaptive allocation algorithm according to target conditions and outputs the optimized pre-provisioning solution.

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