Multifunctional information acquisition car networking platform and method

Abstract

The invention discloses an Internet of Vehicles platform and method for multifunctional information acquisition, belongs to the technical field of Internet of Vehicles platforms, and relates to disturbance of scene elements of a vehicle to motion information acquired by a vehicle-mounted sensor, so that the motion information is acquired by analyzing a change rule of previous motion information according to a similar amplitude between the current motion information and the previous motion information. Calibration is carried out on current motion information, the effect of scene elements where a vehicle is located on the motion information collected by a vehicle-mounted sensor can be weakened, the situation that the motion information belonging to driving information is wrong or lost is efficiently reduced, and the accuracy and credibility of displaying and storing the registered motion information are improved; according to the method, the current motion information is calibrated and improved, so that the motion information of the vehicle-mounted sensor is more stable and credible, a platform can more stably respond to the operation of the vehicle, and the situation that the vehicle network information performance is poor due to inaccurate or unstable motion information is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle networking platforms, and specifically relates to a vehicle networking platform and method for multifunctional information collection. Background Art

[0002] The connotation of the Internet of Vehicles mainly refers to: the on-board equipment on the vehicle effectively utilizes the dynamic information of all vehicles in the information network platform through wireless communication technology, and provides different functional services during vehicle operation. It can be found that the Internet of Vehicles has the following characteristics: the Internet of Vehicles can provide protection for the distance between vehicles and reduce the probability of vehicle collision accidents; the Internet of Vehicles can help car owners navigate in real time, and improve the efficiency of traffic operation through communication with other vehicles and network systems.

[0003] To collect information from the Internet of Vehicles, the existing technical solution with patent publication number "CN114845267B" is often used to collect information from the Internet of Vehicles, that is, the first terminal (the first terminal can be a computer or an industrial computer) is communicated with the on-board sensor installed on the vehicle, and the on-board sensor is used to collect the current vehicle's motion information and transmit it to the first terminal. The motion information includes the current vehicle's speed, acceleration or turning angular velocity.

[0004] The vehicle's motion information is sampled in real time through on-board sensors. However, the on-board sensors are greatly affected by the scene conditions in which the vehicle is located, and their sensitivity and accuracy are often disturbed by the scene elements in which the vehicle is located, which makes them often have defects such as high error rate and lack of accuracy, which is not suitable for the accuracy and reliability of motion information sampling. Summary of the invention

[0005] In order to solve the defects existing in the prior art, the present invention proposes a multifunctional information collection vehicle networking platform and method, which involves the disturbance of the scene elements in which the vehicle is located on the motion information collected by the on-board sensors. Therefore, by analyzing the change law of the past motion information, the current motion information is calibrated according to the similar amplitude between the current motion information and the past motion information, which is suitable for weakening the effect of the scene elements in which the vehicle is located on the motion information collected by the on-board sensors, effectively reducing the error or loss of the motion information attributed to the driving information, and improving the accuracy and reliability of the displayed and stored registered motion information; this method improves the current motion information by calibrating it, so that the motion information of the on-board sensors is more stable and reliable, so that the platform can respond to the operation of the vehicle more smoothly, and reduce the poor performance of the vehicle network information due to inaccurate or unstable motion information.

[0006] The present invention utilizes the following technical solutions.

[0007] A control method for a multifunctional information collection vehicle networking platform, comprising:

[0008] The vehicle-mounted sensor collects the current vehicle motion information and transmits it to the first terminal for processing and storage, and the motion information includes the current vehicle speed, acceleration or turning angular velocity;

[0009] The method for transmitting motion information to a first terminal for processing includes:

[0010] Step 1, obtain the motion information transmitted by the vehicle-mounted sensor during the operation of the vehicle, and form a time point queue, which is defined as a past information queue;

[0011] Step 2, according to the similarity between the information of the motion information in the past information queue, obtain the similar information cluster; by analyzing the arrangement rules of the similar information clusters in terms of time points and information values, select the reference information cluster from the similar information cluster; according to the information value of the motion information in the reference information cluster, group the motion information to obtain the driving information and static information;

[0012] Step 3, calculating the probability of information authenticity of the current motion information according to the similar amplitude of the current motion information and the past motion information, obtaining the calibration key of the current motion information through the information authenticity probability of the current motion information; constructing the correction coefficient of the current motion information according to the time interval between the similar past motion information and the current motion information; using the correction key and the correction coefficient to calibrate the current motion information to obtain the latest information;

[0013] Step 4, display and store the latest information on the first terminal.

[0014] Preferably, Step 1 specifically includes:

[0015] The motion information collected by the vehicle-mounted sensor is obtained, and the collected motion information is arranged in the order of the collection time points to form a time point queue, which is defined as a past information queue.

[0016] Preferably, the acquisition rate of the vehicle-mounted sensor is 1 time / S, and continuous motion information is collected within a working day. The collected data is arranged according to the detection time and defined as a past information queue.

[0017] Preferably, Step 2 specifically includes:

[0018] In the past information queue, the similarity between information is calculated according to the information value difference of the motion information, and the motion information whose similarity between information is higher than the pre-defined critical value Z1 is regarded as similar information, and a queue consisting of continuously adjacent similar information is obtained, which is defined as a similar information cluster;

[0019] Then, the timing of the past information queue is calculated, and the benchmark information cluster within the similar information cluster is selected according to the arrangement rules of the similar information clusters in terms of time points and information values;

[0020] Then, according to the information value of the motion information in the reference information cluster, the motion information is grouped to obtain driving information and static information.

[0021] Preferably, the method for calculating the similarity between information is: based on the modulus of the amount obtained by subtracting the information value of the bth motion information from the information value of the jth motion information in the previous information queue, the similarity between the bth motion information and the jth motion information is obtained, and the modulus of the amount obtained by subtraction is negatively correlated with the similarity between information.

[0022] Preferably, the operational equation for the similarity between information is:

[0023]

[0024] In the equation, T b，j represents the similarity between the b-th motion information and the j-th motion information, T b Represents the information value of the b-th motion information, T j represents the information value of the jth motion information, and e represents the Euler number.

[0025] Preferably, the timing of the past information queue is calculated based on the interval between the corresponding time points of the motion information with the same sequence number in a pair of adjacent similar information clusters; the equation for obtaining the timing of the past information queue is:

[0026]

[0027] In the equation, U represents the timing of the previous information queue, u h，l represents the time point corresponding to the lth motion information in the hth similar information cluster, u h-1，l represents the time point corresponding to the lth motion information in the h-1th similar information cluster, n h represents the number of motion information in the hth similar information cluster, and p represents the number of similar information clusters in the previous information queue.

[0028] Preferably, the timing estimation between the adjacent information clusters is calculated based on the variance of the time span of each adjacent information cluster and the variance of the time interval of the motion information with the same sequence number in the adjacent adjacent information clusters;

[0029] The timing estimate of the close information cluster is obtained by:

[0030] Select a random pair of adjacent similar information clusters, obtain the modulus of the amount obtained by subtracting the corresponding time points of the motion information under the same sequence number in the adjacent pair of similar information clusters, define it as the time deviation coefficient, calculate the average of the corresponding time deviation coefficients of all the motion information under the same sequence number in the adjacent pair of similar information clusters, define it as the average time deviation index of the adjacent pair of similar information clusters, divide the average time deviation index by the timing of the previous information queue to obtain the corresponding quotient value, thereby obtaining the timing estimation of the adjacent pair of similar information clusters, thereby obtaining the timing estimation of all adjacent pairs of similar information clusters;

[0031] A critical value Z2 is defined, and the similar information clusters whose timing estimated values ​​are not less than the critical value Z2 are selected, and the selected similar information clusters are defined as reference information clusters.

[0032] Preferably, Step 3 specifically includes: obtaining the probability of information authenticity of the current motion information according to the similar amplitudes of the current motion information and the past motion information;

[0033] The calculation equation of the probability of the current motion information being true is:

[0034]

[0035] In the equation, I represents the probability of the authenticity of the current motion information, y represents the information value of the current motion information, and y l represents the information value of the lth driving information, u represents the time point of the current motion information, and u l represents the last time point of the cluster of similar information where the lth driving information is located, U represents the timing of the previous information queue, P represents the number of driving information, and e represents the Euler number;

[0036] The calibration criticality r of the current motion information is obtained through the probability of the information authenticity of the current motion information:

[0037]

[0038] In the equation, y represents the information value of the current motion information, I represents the probability that the current motion information is attributed to driving information, yh represents the information value of driving information, yg represents the information value of static information, e represents the Euler number, and Z3 represents the predefined indicator three, that is, the critical value for selecting the probability that the current motion information is attributed to driving information;

[0039] The equation for constructing the correction coefficients of the current motion information is:

[0040]

[0041] In the equation, f represents the correction coefficient of the current motion information, y d represents the dth past motion information that is similar to the current motion information; Δud represents the time interval between the dth past motion information and the current motion information; N represents the total amount of similar information of the current motion information, and e represents the Euler number.

[0042] Using the calibration key and the calibration coefficient, the current motion information is calibrated to obtain the latest information;

[0043] The specific equation for obtaining the most recent information is:

[0044] Y=f*(1-r)+y*r

[0045] In the equation, Y represents the most recent information, f represents the calibration coefficient of the current motion information, r represents the calibration criticality of the current motion information, and y represents the information value of the current motion information.

[0046] Preferably, the method for obtaining the driving information and the static information specifically includes: obtaining the first segmentation point and the third segmentation point in the four-equal division points of the entire motion information in the entire reference information cluster, defining the motion information corresponding to the first segmentation point and the third segmentation point as the starting centroid, and using the K-medoids method to perform grouping on the entire motion information in the entire reference information cluster to obtain multiple groups;

[0047] Obtain the final group centroid of a pair of multiple groups, and define the motion information corresponding to the final group centroid with the highest information value as driving information; define the motion information corresponding to the final group centroid with the lowest information value as static information, and obtain multiple driving information and static information. The information values ​​of all motion information in the driving information are the same, and the information values ​​of all motion information in the static information are the same.

[0048] A multifunctional information collection vehicle networking platform, comprising:

[0049] The first terminal is connected to a vehicle-mounted sensor on the vehicle, and the vehicle-mounted sensor is used to collect the current vehicle motion information and transmit it to the first terminal for processing and storage, and the motion information includes the current vehicle speed, acceleration or turning angular velocity;

[0050] The modules running on the first terminal include:

[0051] A composition module is used to obtain motion information transmitted by the vehicle-mounted sensor during the operation of the vehicle, and to form a time point queue, which is defined as a past information queue;

[0052] A grouping module is used to obtain similar information clusters according to the similarity between the information of the motion information in the past information queue; select a reference information cluster from the similar information clusters by analyzing the arrangement rules of the similar information clusters in terms of time points and information values; group the motion information according to the information values ​​of the motion information in the reference information clusters to obtain driving information and static information;

[0053] A calibration module is used to calculate the probability of information authenticity of the current motion information based on the similar amplitudes of the current motion information and the past motion information, and obtain the calibration key of the current motion information through the information authenticity probability of the current motion information; construct the correction coefficient of the current motion information based on the time interval between the similar past motion information and the current motion information; and perform calibration on the current motion information using the calibration key and the correction coefficient to obtain the latest information;

[0054] The display module is used to display and store the latest information on the first terminal.

[0055] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0056] The disturbance of the scene elements in which the vehicle is located on the motion information collected by the on-board sensors is involved. Therefore, by analyzing the change law of past motion information and based on the similar amplitude between the current motion information and the past motion information, the current motion information is calibrated, which is suitable for weakening the influence of the scene elements in which the vehicle is located on the motion information collected by the on-board sensors, effectively reducing the error or loss of motion information belonging to driving information, and improving the accuracy and reliability of the displayed and stored registered motion information; this method improves the current motion information by calibrating it, so that the motion information of the on-board sensors is more stable and reliable, so that the platform can respond to the operation of the vehicle more stably, and reduce the poor performance of vehicle network information due to inaccurate or unstable motion information. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of a control method of a multifunctional information collection vehicle networking platform according to the present invention;

[0058] Figure 2 It is a partial structural diagram of the vehicle networking platform for multifunctional information collection described in the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention clearer, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely express the technical solution of the present invention. The embodiments expressed in the present invention are only some embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0060] like Figure 1 As shown, the control method of a multifunctional information collection vehicle networking platform of the present invention includes:

[0061] The vehicle-mounted sensor collects the current vehicle motion information and transmits it to the first terminal for processing and storage, and the motion information includes the current vehicle speed, acceleration or turning angular velocity;

[0062] The method for transmitting motion information to a first terminal for processing includes:

[0063] Step 1, obtain the motion information transmitted by the vehicle-mounted sensor during the operation of the vehicle, and form a time point queue, which is defined as a past information queue;

[0064] In a preferred but non-limiting embodiment of the present invention, Step 1 specifically includes:

[0065] The motion information collected by the vehicle-mounted sensor is obtained, and the collected motion information is arranged in the order of the collection time points to form a time point queue, which is defined as a past information queue.

[0066] In a preferred but non-limiting embodiment of the present invention, the acquisition rate of the vehicle-mounted sensor is 1 time / S, and continuous motion information is collected within a working day. The collected data is arranged according to the detection time and defined as a past information queue.

[0067] Therefore, the past information queue is obtained through the above method.

[0068] Step 2, according to the similarity between the information of the motion information in the past information queue, obtain the similar information cluster; by analyzing the arrangement rules of the similar information clusters in terms of time points and information values, select the reference information cluster from the similar information cluster; according to the information value of the motion information in the reference information cluster, group the motion information to obtain the driving information and static information;

[0069] During the operation of the vehicle, vehicles on the same route operate in the same way, have the same performance, and take the same time to operate. Therefore, the time the vehicle stops is traceable, and therefore the motion information of the on-board sensors is also traceable. When the vehicle is driving, the information value of the motion information collected by the on-board sensors is in a large range, and the information value collected at this time can be regarded as driving information; when the vehicle is stopped, the information value collected by the on-board sensors is in a small range, and the motion information at this time can be regarded as static information. The current motion information is calibrated based on the driving information and the static information to remove the disturbance of the scene conditions in which the vehicle is located on the motion information sampled by the on-board sensors.

[0070] In a preferred but non-limiting embodiment of the present invention, Step 2 specifically includes:

[0071] In the past information queue, the similarity between information is calculated according to the information value difference of the motion information, and the motion information whose similarity between information is higher than the pre-defined critical value Z1 is regarded as similar information, and a queue consisting of continuously adjacent similar information is obtained, which is defined as a similar information cluster;

[0072] The critical value Z1=3 / 4 defined in advance based on experience can be changed according to specific conditions.

[0073] In a preferred but non-restrictive implementation of the present invention, the method for calculating the similarity between information is: based on the modulus of the amount obtained by subtracting the information value of the bth motion information from the information value of the jth motion information in the previous information queue, the similarity between the bth motion information and the jth motion information is obtained, and the modulus of the amount obtained by subtraction is negatively correlated with the similarity between the information.

[0074] In a preferred but non-limiting embodiment of the present invention, the operation equation of the similarity between information is:

[0075]

[0076] In the equation, T b，j represents the similarity between the b-th motion information and the j-th motion information, T b Represents the information value of the b-th motion information, T j represents the information value of the jth motion information, and e represents the Euler number.

[0077] Then, the timing of the past information queue is calculated, and the benchmark information cluster within the similar information cluster is selected according to the arrangement rules of the similar information clusters in terms of time points and information values;

[0078] Since the duration of a vehicle's stop is traceable, the motion information from the vehicle's sensors is also traceable. Therefore, the present application performs timing estimation on similar information clusters through the arrangement rules of similar information clusters in terms of time points and information values ​​to obtain a reference information cluster with better timing properties.

[0079] In a preferred but non-limiting embodiment of the present invention, the timing of the past information queue is calculated based on the interval between the corresponding time points of the motion information with the same sequence number in a pair of adjacent similar information clusters; the equation for obtaining the timing of the past information queue is:

[0080]

[0081] In the equation, U represents the timing of the previous information queue, u h，l represents the time point corresponding to the lth motion information in the hth similar information cluster, u h-1，l represents the time point corresponding to the lth motion information in the h-1th similar information cluster, n hrepresents the number of motion information in the hth similar information cluster, and p represents the number of similar information clusters in the previous information queue.

[0082] In a preferred but non-limiting embodiment of the present invention, the timing estimation between the adjacent information clusters is calculated based on the variance of the time span of each adjacent information cluster and the variance of the time interval of the motion information with the same sequence number in the adjacent adjacent information clusters;

[0083] The timing estimate of the close information cluster is obtained by:

[0084] Select a random pair of adjacent similar information clusters, obtain the modulus of the amount obtained by subtracting the corresponding time points (the time points are the collection time points) of the motion information under the same sequence number in the adjacent pair of similar information clusters, define it as the time deviation coefficient, calculate the average of the corresponding time deviation coefficients of all the motion information under the same sequence number in the adjacent pair of similar information clusters, define it as the average time deviation index of the adjacent pair of similar information clusters, divide the average time deviation index by the timing of the previous information queue to obtain the corresponding quotient, thereby obtaining the timing estimation of the adjacent pair of similar information clusters, thereby obtaining the timing estimation of all adjacent pairs of similar information clusters;

[0085] A critical value Z2 is defined, and the similar information clusters whose timing estimated values ​​are not less than the critical value Z2 are selected, and the selected similar information clusters are defined as reference information clusters.

[0086] The critical amount of timing estimation is defined in advance It can be changed according to the specific situation.

[0087] Then, according to the information value of the motion information in the reference information cluster, the motion information is grouped to obtain driving information and static information.

[0088] When the vehicle is moving, the information value of the motion information of the on-board sensor is not small; when the vehicle is stationary, the motion information of the on-board sensor is not large.

[0089] In a preferred but non-limiting embodiment of the present invention, the method for obtaining driving information and static information specifically includes: obtaining the first segmentation point and the third segmentation point in the four-equal division points of the entire motion information in the entire reference information cluster, defining the motion information corresponding to the first segmentation point and the third segmentation point as the starting centroid, and using the K-medoids method to perform grouping on the entire motion information in the entire reference information cluster to obtain multiple groups;

[0090] Obtain the final group centroid of a pair of multiple groups, and define the motion information corresponding to the final group centroid with the highest information value as driving information; define the motion information corresponding to the final group centroid with the lowest information value as static information, and obtain multiple driving information and static information. The information values ​​of all motion information in the driving information are the same, and the information values ​​of all motion information in the static information are the same.

[0091] The motion information corresponding to the last group centroid with the highest information value is the motion information when the vehicle is driving, and the motion information corresponding to the last group centroid with the lowest information value is the motion information when the vehicle is stopped.

[0092] Therefore, driving information and static information are obtained through the above method.

[0093] Step 3, calculating the probability of information authenticity of the current motion information according to the similar amplitude of the current motion information and the past motion information, obtaining the calibration key of the current motion information through the information authenticity probability of the current motion information; constructing the correction coefficient of the current motion information according to the time interval between the similar past motion information and the current motion information; using the correction key and the correction coefficient to calibrate the current motion information to obtain the latest information;

[0094] When the vehicle is running, due to various electromagnetic disturbances in the scene, the information collected by the on-board sensors contains noise values ​​composed of disturbances, which causes the motion information transmitted by the on-board sensors to change, reducing the reliability and stability. Therefore, it is necessary to determine whether the motion information is driving information or static information based on the information value of the current motion information, and then calibrate the current latest information based on the corresponding past motion information of the current motion information.

[0095] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically includes: obtaining the probability of information authenticity of the current motion information according to the similar amplitudes of the current motion information and the past motion information;

[0096] The calculation equation of the probability of the current motion information being true is:

[0097]

[0098] In the equation, I represents the probability of the authenticity of the current motion information, y represents the information value of the current motion information, and y l represents the information value of the lth driving information, u represents the time point of the current motion information, and u l represents the last time point of the cluster of similar information where the lth driving information is located, U represents the timing of the previous information queue, P represents the number of driving information, and e represents the Euler number;

[0099] The lower the difference between the information values ​​of the current motion information and the driving information, the greater the probability that it belongs to the authenticity of the collected information; the lower the difference between the time distance and timing of the motion information between the current motion information b and the previous similar information, the greater the probability that the data belongs to the driving information.

[0100] The calibration criticality r of the current motion information is obtained through the probability of the information authenticity of the current motion information:

[0101]

[0102] In the equation, y represents the information value of the current motion information, I represents the probability that the current motion information is attributed to driving information, yh represents the information value of driving information, yg represents the information value of static information, e represents the Euler number, and Z3 represents the predefined indicator three, that is, the critical value for selecting the probability that the current motion information is attributed to driving information;

[0103] The critical value of the probability that the current motion information belongs to driving information is selected based on experience. It can be changed according to the specific situation.

[0104] The equation for constructing the correction coefficients of the current motion information is:

[0105]

[0106] In the equation, f represents the correction coefficient of the current motion information, y d represents the dth past motion information that is similar to the current motion information; Δu d represents the time interval between the dth past motion information and the current motion information; N represents the total amount of similar information of the current motion information, and e represents the Euler number.

[0107] Using the calibration key and the calibration coefficient, the current motion information is calibrated to obtain the latest information;

[0108] The specific equation for obtaining the most recent information is:

[0109] Y=f*(1-r)+y*r

[0110] In the equation, Y represents the most recent information, f represents the calibration coefficient of the current motion information, r represents the calibration criticality of the current motion information, and y represents the information value of the current motion information.

[0111] Therefore, the latest information after calibration is obtained through the above method.

[0112] Step 4, display and store the latest information on the first terminal.

[0113] like Figure 2 As shown, the present invention provides a multifunctional information collection vehicle networking platform, including:

[0114] The first terminal (the first terminal may be a computer or an industrial computer) is connected to a vehicle-mounted sensor provided on the vehicle for communication. The vehicle-mounted sensor is used to collect the motion information of the current vehicle and transmit it to the first terminal for processing and storage. The motion information includes the current speed, acceleration or turning angular velocity of the vehicle.

[0115] The modules running on the first terminal include:

[0116] A composition module is used to obtain motion information transmitted by the vehicle-mounted sensor during the operation of the vehicle, and to form a time point queue, which is defined as a past information queue;

[0117] A grouping module is used to obtain similar information clusters according to the similarity between the information of the motion information in the past information queue; select a reference information cluster from the similar information clusters by analyzing the arrangement rules of the similar information clusters in terms of time points and information values; group the motion information according to the information values ​​of the motion information in the reference information clusters to obtain driving information and static information;

[0118] A calibration module is used to calculate the probability of information authenticity of the current motion information based on the similar amplitudes of the current motion information and the past motion information, and obtain the calibration key of the current motion information through the information authenticity probability of the current motion information; construct the correction coefficient of the current motion information based on the time interval between the similar past motion information and the current motion information; and perform calibration on the current motion information using the calibration key and the correction coefficient to obtain the latest information;

[0119] The display module is used to display and store the latest information on the first terminal.

[0120] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0121] The disturbance of the scene elements in which the vehicle is located on the motion information collected by the on-board sensors is involved. Therefore, by analyzing the change law of past motion information and based on the similar amplitude between the current motion information and the past motion information, the current motion information is calibrated, which is suitable for weakening the influence of the scene elements in which the vehicle is located on the motion information collected by the on-board sensors, effectively reducing the error or loss of motion information belonging to driving information, and improving the accuracy and reliability of the displayed and stored registered motion information; this method improves the current motion information by calibrating it, so that the motion information of the on-board sensors is more stable and reliable, so that the platform can respond to the operation of the vehicle more stably, and reduce the poor performance of vehicle network information due to inaccurate or unstable motion information.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A control method for a multifunctional information collection vehicle networking platform, characterized in that: include: The vehicle-mounted sensor collects the current vehicle motion information and transmits it to the first terminal for processing and storage, and the motion information includes the current vehicle speed, acceleration or turning angular velocity; The method for transmitting motion information to a first terminal for processing includes: Step 1, obtain the motion information transmitted by the vehicle-mounted sensor during the operation of the vehicle, and form a time point queue, which is defined as a past information queue; Step 2, according to the similarity between the information of the motion information in the past information queue, obtain the similar information cluster; by analyzing the arrangement rules of the similar information clusters in terms of time points and information values, select the reference information cluster from the similar information cluster; according to the information value of the motion information in the reference information cluster, group the motion information to obtain the driving information and static information; Step 3, calculating the probability of information authenticity of the current motion information according to the similar amplitude of the current motion information and the past motion information, obtaining the calibration key of the current motion information through the information authenticity probability of the current motion information; constructing the correction coefficient of the current motion information according to the time interval between the similar past motion information and the current motion information; using the correction key and the correction coefficient to calibrate the current motion information to obtain the latest information; Step 4, display and store the latest information on the first terminal.

2. The control method of the vehicle networking platform for multifunctional information collection according to claim 1 is characterized in that: Step 1 specifically includes: The motion information collected by the vehicle-mounted sensor is obtained, and the collected motion information is arranged in the order of the collection time points to form a time point queue, which is defined as a past information queue; The acquisition rate of the vehicle-mounted sensor is 1 time / S, which collects continuous motion information within a working day and arranges the collected data into a past information queue according to the detection time.

3. The control method of the vehicle networking platform for multifunctional information collection according to claim 2 is characterized in that: Step 2 specifically includes: In the past information queue, the similarity between information is calculated according to the information value difference of the motion information, and the motion information whose similarity between information is higher than the pre-defined critical value Z1 is regarded as similar information, and a queue consisting of continuously adjacent similar information is obtained, which is defined as a similar information cluster; Then, the timing of the past information queue is calculated, and the benchmark information cluster within the similar information cluster is selected according to the arrangement rules of the similar information clusters in terms of time points and information values; Then, according to the information value of the motion information in the reference information cluster, the motion information is grouped to obtain driving information and static information.

4. The control method of the vehicle networking platform for multifunctional information collection according to claim 3 is characterized in that: The calculation method of the similarity between information is: based on the modulus of the amount obtained by subtracting the information value of the bth motion information from the information value of the jth motion information in the previous information queue, the similarity between the bth motion information and the jth motion information is obtained, and the modulus of the amount obtained by subtraction is negatively correlated with the similarity between information.

5. The control method of the vehicle networking platform for multifunctional information collection according to claim 4 is characterized in that: The operational equation for the similarity between information is: In the equation, T b，j represents the similarity between the b-th motion information and the j-th motion information, T b Represents the information value of the b-th motion information, T j represents the information value of the jth motion information, and e represents the Euler number.

6. The control method of the vehicle networking platform for multifunctional information collection according to claim 5 is characterized in that: According to the interval between the corresponding time points of the motion information with the same sequence number in a pair of adjacent similar information clusters, the timing of the past information queue is calculated; the equation for obtaining the timing of the past information queue is: In the equation, U represents the timing of the previous information queue, u h，l represents the time point corresponding to the lth motion information in the hth similar information cluster, u h-1，l represents the time point corresponding to the lth motion information in the h-1th similar information cluster, n h represents the number of motion information in the hth similar information cluster, and p represents the number of similar information clusters in the previous information queue.

7. The control method of the vehicle networking platform for multifunctional information collection according to claim 6 is characterized in that: Calculate the timing estimation between the adjacent information clusters based on the variance of the time span of each adjacent information cluster and the variance of the time interval of the motion information with the same sequence number in the adjacent adjacent information clusters; The timing estimate of the close information cluster is obtained by: Select a random pair of adjacent similar information clusters, obtain the modulus of the amount obtained by subtracting the corresponding time points of the motion information under the same sequence number in the adjacent pair of similar information clusters, define it as the time deviation coefficient, calculate the average of the corresponding time deviation coefficients of all the motion information under the same sequence number in the adjacent pair of similar information clusters, define it as the average time deviation index of the adjacent pair of similar information clusters, divide the average time deviation index by the timing of the previous information queue to obtain the corresponding quotient value, thereby obtaining the timing estimation of the adjacent pair of similar information clusters, thereby obtaining the timing estimation of all adjacent pairs of similar information clusters; A critical value Z2 is defined, and the similar information clusters whose timing estimated values ​​are not less than the critical value Z2 are selected, and the selected similar information clusters are defined as reference information clusters.

8. The control method of the vehicle networking platform for multifunctional information collection according to claim 7 is characterized in that: Step 3 specifically includes: obtaining the probability of information authenticity of the current motion information based on the similar amplitudes of the current motion information and the past motion information; The calculation equation of the probability of the current motion information being true is: In the equation, I represents the probability of the authenticity of the current motion information, y represents the information value of the current motion information, and y l represents the information value of the lth driving information, u represents the time point of the current motion information, and u l represents the last time point of the cluster of similar information where the lth driving information is located, U represents the timing of the previous information queue, P represents the number of driving information, and e represents the Euler number; The calibration criticality r of the current motion information is obtained through the probability of the information authenticity of the current motion information: In the equation, y represents the information value of the current motion information, I represents the probability that the current motion information is attributed to driving information, yh represents the information value of driving information, yg represents the information value of static information, e represents the Euler number, and Z3 represents the predefined indicator three, that is, the critical value for selecting the probability that the current motion information is attributed to driving information; The equation for constructing the correction coefficients of the current motion information is: In the equation, f represents the correction coefficient of the current motion information, y d represents the dth past motion information that is similar to the current motion information; Δu d represents the time interval between the dth past motion information and the current motion information; N represents the total amount of similar information of the current motion information, and e represents the Euler number. Using the calibration key and the calibration coefficient, the current motion information is calibrated to obtain the latest information; The specific equation for obtaining the most recent information is: Y=f*(1-r)+y*r In the equation, Y represents the most recent information, f represents the calibration coefficient of the current motion information, r represents the calibration criticality of the current motion information, and y represents the information value of the current motion information.

9. The control method of the vehicle networking platform for multifunctional information collection according to claim 8 is characterized in that: The method for obtaining driving information and static information specifically includes: obtaining the first segmentation point and the third segmentation point in the four-equal division points of the entire motion information in the entire reference information cluster, defining the motion information corresponding to the first segmentation point and the third segmentation point as the starting mass center, and using the K-medoids method to perform grouping on the entire motion information in the entire reference information cluster to obtain multiple groups; Obtain the final group centroid of a pair of multiple groups, and define the motion information corresponding to the final group centroid with the highest information value as driving information; define the motion information corresponding to the final group centroid with the lowest information value as static information, and obtain multiple driving information and static information. The information values ​​of all motion information in the driving information are the same, and the information values ​​of all motion information in the static information are the same.

10. A multifunctional information collection vehicle networking platform, characterized in that: include: The first terminal is connected to a vehicle-mounted sensor on the vehicle, and the vehicle-mounted sensor is used to collect the current vehicle motion information and transmit it to the first terminal for processing and storage, and the motion information includes the current vehicle speed, acceleration or turning angular velocity; The modules running on the first terminal include: A composition module is used to obtain motion information transmitted by the vehicle-mounted sensor during the operation of the vehicle, and to form a time point queue, which is defined as a past information queue; A grouping module is used to obtain similar information clusters according to the similarity between the information of the motion information in the past information queue; select a reference information cluster from the similar information clusters by analyzing the arrangement rules of the similar information clusters in terms of time points and information values; group the motion information according to the information values ​​of the motion information in the reference information clusters to obtain driving information and static information; A calibration module is used to calculate the probability of information authenticity of the current motion information based on the similar amplitudes of the current motion information and the past motion information, and obtain the calibration key of the current motion information through the information authenticity probability of the current motion information; construct the correction coefficient of the current motion information based on the time interval between the similar past motion information and the current motion information; and perform calibration on the current motion information using the calibration key and the correction coefficient to obtain the latest information; The display module is used to display and store the latest information on the first terminal.

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