A vehicle speed correction method, electronic device and storage medium

Abstract

This invention provides a vehicle speed correction method, electronic device, and storage medium. The method obtains a first weighted average vehicle speed based on a first historical vehicle speed list and a first historical time point list corresponding to the target vehicle; obtains a second weighted average vehicle speed based on a second historical vehicle speed list and a second historical time point list; obtains a third weighted average vehicle speed based on a key vehicle speed list and a key vehicle distance list; and obtains the target vehicle speed corresponding to the target vehicle based on the first weighted average vehicle speed, the second weighted average vehicle speed, the third weighted average vehicle speed, and the vehicle speed of the target vehicle collected by the target server at the current time point. By employing weighted averaging and multi-source data fusion, the method corrects the target vehicle speed in real time, effectively reducing the impact of sensor errors and environmental factors, and making full use of historical data and information from surrounding vehicles, which is beneficial to improving the accuracy and robustness of vehicle speed correction.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a vehicle speed correction method, electronic device, and storage medium. Background Technology

[0002] In the fields of intelligent transportation systems, vehicle-to-everything (V2X) and autonomous driving, the accuracy of vehicle speed data is crucial for traffic safety, traffic management, and vehicle control. However, due to various reasons such as sensor errors, environmental factors, and data transmission delays, the raw vehicle speed data collected by vehicle sensors may be biased or inaccurate. To ensure the authenticity and reliability of vehicle speed data, it is necessary to correct the vehicle speed data. Existing vehicle speed correction methods mainly rely on a single or limited data source (such as wheel speed sensors or GPS). They typically reduce errors through sensor calibration, data smoothing, and simple mathematical models, thereby achieving vehicle speed correction.

[0003] However, the above method also has the following technical problems:

[0004] Relying on a single or limited data source to correct vehicle speed is easily affected by specific factors, resulting in limited correction effects. Furthermore, the above methods ignore the temporal and spatial characteristics of vehicle speed data and fail to make full use of historical vehicle speed data and driving information of surrounding vehicles, thus failing to fully reflect the actual driving status of the vehicle. Summary of the Invention

[0005] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0006] According to a first aspect of the present invention, a vehicle speed correction method is provided. The method is used to correct the vehicle speed of a target vehicle collected by a target server at a current time point to obtain the target vehicle speed. The method includes the following steps:

[0007] S1. Based on the first historical vehicle speed list A corresponding to the target vehicle and the first historical time point list B corresponding to A, obtain the first weighted average vehicle speed C corresponding to the target vehicle, where A = {A1, A2, ..., A...} i , ..., A m}, A i Let B be the i-th first historical speed corresponding to the target vehicle, where i ranges from 1 to m, and m is the number of first historical speeds corresponding to the target vehicle. Let B = {B1, B2, ..., B...} i , ..., B m}, B i For A iThe corresponding first historical time point, the first historical vehicle speed of the target vehicle is the speed of the target vehicle collected by the target server within the preset time period, the end time point of the preset time period is the current time point, and the first historical time point corresponding to the first historical vehicle speed is the time point when the first historical vehicle speed was collected.

[0008] S2. Based on the second historical vehicle speed list set D and the corresponding second historical time point list set E, obtain the second weighted average vehicle speed F corresponding to the target vehicle, where D = {D1, D2, ..., D...} j , ..., D n}, D j ={D j1 D j2 , ..., D je , ..., D jf(j)}, D j This is the list of second historical vehicle speeds corresponding to the j-th historical time slice, where j ranges from 1 to n, and n is the number of historical time slices. je Let f(j) be the e-th second historical speed corresponding to the j-th historical time slice, where e takes values ​​from 1 to f(j), and f(j) is the number of second historical speeds corresponding to the j-th historical time slice. E = {E1, E2, ..., E...} j , ..., E n}, E j ={E j1 E j2 , ..., E je , ..., E jf(j)}, E j D j The corresponding second historical time point list, E je D je The corresponding second historical time point, the second historical vehicle speed is the vehicle speed of the vehicle traveling on the target road collected by the target server within the historical time slice, the target road is the road where the target vehicle is located at the current time point, and the second historical time point is the time point at which the second historical vehicle speed was collected.

[0009] S3. Based on the key vehicle speed list G and the corresponding key vehicle distance list H, obtain the third weighted average vehicle speed R of the target vehicle, where G = {G1, G2, ..., G...} r , ..., G s}, G r Let H be the critical speed corresponding to the r-th critical vehicle, where r ranges from 1 to s, and s is the number of critical vehicles. H = {H1, H2, ..., H} r H s}, H r For G rThe corresponding key vehicle distance is defined as follows: key vehicle refers to the vehicle on the target road at the current time point; target road refers to the road where the target vehicle is located at the current time point; key vehicle speed refers to the speed of the key vehicle collected by the target server at the current time point; and key vehicle distance refers to the road distance between the key vehicle and the target vehicle.

[0010] S4. Based on C, F, R, and V, obtain the target vehicle speed J corresponding to the target vehicle, where V is the vehicle speed of the target vehicle collected by the target server at the current time point, and J meets the following conditions:

[0011] J = ((C+F+V) / 3+(R+V) / 2) / 2.

[0012] According to a second aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and the computer program is loaded and executed by a processor to implement the aforementioned method.

[0013] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned method.

[0014] The present invention has at least the following beneficial effects:

[0015] This invention provides a vehicle speed correction method, electronic device, and storage medium. The method is used to correct the vehicle speed of a target vehicle collected by a target server at the current time point to obtain the target vehicle speed. The method obtains a first weighted average vehicle speed corresponding to the target vehicle based on a first historical vehicle speed list and a first historical time point list; obtains a second weighted average vehicle speed corresponding to the target vehicle based on a second historical vehicle speed list and a second historical time point list; obtains a third weighted average vehicle speed corresponding to the target vehicle based on a key vehicle speed list and a key vehicle distance list; and obtains the target vehicle speed corresponding to the target vehicle based on the first weighted average vehicle speed, the second weighted average vehicle speed, the third weighted average vehicle speed, and the vehicle speed of the target vehicle collected by the target server at the current time point. It can be seen that this invention combines historical vehicle speeds and key vehicle speeds, using weighted averaging and multi-source data fusion to correct the vehicle speed of the target vehicle in real time. This effectively reduces the influence of sensor errors and environmental factors, and fully utilizes historical data and information from surrounding vehicles, which is beneficial to improving the accuracy and robustness of vehicle speed correction. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a vehicle speed correction method provided in an embodiment of the present invention. Detailed Implementation

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

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar tasks and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] An embodiment of the present invention provides a vehicle speed correction method. The method is used to correct the vehicle speed of a target vehicle collected by a target server at the current time point to obtain the target vehicle speed. The method includes the following steps: Figure 1 As shown:

[0021] S1. Based on the first historical vehicle speed list A corresponding to the target vehicle and the first historical time point list B corresponding to A, obtain the first weighted average vehicle speed C corresponding to the target vehicle, where A = {A1, A2, ..., A...} i , ..., A m}, A i Let B be the i-th first historical speed corresponding to the target vehicle, where i ranges from 1 to m, and m is the number of first historical speeds corresponding to the target vehicle. Let B = {B1, B2, ..., B...} i , ..., B m}, Bi For A i The corresponding first historical time point, the first historical vehicle speed of the target vehicle is the vehicle speed of the target vehicle collected by the target server in the preset time period, the end time point of the preset time period is the current time point, the length of the preset time period is set by those skilled in the art according to actual needs, and the first historical time point corresponding to the first historical vehicle speed is the time point when the first historical vehicle speed is collected.

[0022] Specifically, step S1 includes the following steps S11-S13:

[0023] S11, according to B i Given the current time point DQ, obtain the average time difference ΔB corresponding to B, where ΔB satisfies the following condition:

[0024] △B=∑ m i＝1 |B i -DQ| / m.

[0025] S12, according to B i △B and DQ, obtain A i The corresponding first weight K i The first weight is used to represent the importance of the first historical vehicle speed, K. i Meets the following conditions:

[0026] When |B i When -DQ|≤△B, K i = a × (1 / △B) (1 / |Bi-DQ|) 'a' represents the preset impact factor corresponding to the preset time period. As those skilled in the art know, the preset impact factor is a factor set by those skilled in the art according to actual needs, and will not be elaborated here.

[0027] When |B i When -DQ|>△B, K i = a × (1 / △B) (1 / △B) .

[0028] S13, According to A i and K i Obtain C, where C meets the following conditions:

[0029] C=∑ m i＝1 (A i ×K i ) / ∑ m i＝1 K i .

[0030] Specifically, the higher the weight, the more important the first historical speed, and the more important the first historical speed.

[0031] Through the above steps, the first weight corresponding to the first historical vehicle speed is dynamically adjusted according to the interval between the first historical time point and the current time point, so that the more recent first historical vehicle speed is given a higher weight and the more distant first historical vehicle speed is given a lower weight. This can avoid the excessive influence of long-term data on vehicle speed correction and improve the robustness of vehicle speed correction. The first weighted average vehicle speed is obtained by weighted averaging the first historical vehicle speed and the first weight corresponding to the first historical vehicle speed, which can effectively smooth short-term fluctuations and reduce errors caused by instantaneous acceleration, deceleration or sensor noise.

[0032] S2. Based on the second historical vehicle speed list set D and the corresponding second historical time point list set E, obtain the second weighted average vehicle speed F corresponding to the target vehicle, where D = {D1, D2, ..., D...} j , ..., D n}, D j ={D j1 D j2 , ..., D je , ..., D jf(j)}, D j This is the list of second historical vehicle speeds corresponding to the j-th historical time slice, where j ranges from 1 to n, and n is the number of historical time slices. je Let f(j) be the e-th second historical speed corresponding to the j-th historical time slice, where e takes values ​​from 1 to f(j), and f(j) is the number of second historical speeds corresponding to the j-th historical time slice. E = {E1, E2, ..., E...} j , ..., E n}, E j ={E j1 E j2 , ..., E je , ..., E jf(j)}, E j D j The corresponding second historical time point list, E je D je The corresponding second historical time point, the second historical vehicle speed is the vehicle speed of the vehicle traveling on the target road collected by the target server within the historical time slice, the target road is the road where the target vehicle is located at the current time point, and the second historical time point is the time point at which the second historical vehicle speed was collected.

[0033] Specifically, the historical time slice is the time slice before the current time slice, and the length of the time slice is 1 day. The current time point is the time slice in which the current time point is located.

[0034] Specifically, the (j+1)th historical time slice is earlier than the jth historical time slice.

[0035] Specifically, step S2 includes the following steps S21-S23:

[0036] S21, according to E je And DQ, obtain E j The corresponding average time difference △P j , △P j Meets the following conditions:

[0037] △P j =∑ f(j) e＝1 Q je / f(j), Q je For E je The duration of the interval between DQ and DQ.

[0038] S22, according to Q je and △P j , obtain D je The corresponding second weight L je The second weight is used to represent the importance of the second historical vehicle speed, L. je Meets the following conditions:

[0039] When Q je ≤△P j At that time, L je =b j ×(1 / △P j ) (1 / Qje) b j The preset influence factor corresponding to the j-th historical time slice;

[0040] When Q je >△P j At that time, L je =b j ×(1 / △P j ) (1 / △Pj) .

[0041] Specifically, when j=1, b j <a, when j≠1, b j <b j-1 b j-1 This is the preset influence factor corresponding to the (j-1)th historical time slice.

[0042] S23, According to D je and L je Obtain F, where F satisfies the following conditions:

[0043] F = ∑ n j＝1 (∑ f(j) e＝1 (D je ×L je ) / ∑f(j) e＝1 L je ) / n.

[0044] Specifically, the greater the weight of the second historical speed, the more important the second historical speed is.

[0045] Through the above steps, the first weight corresponding to the second historical vehicle speed is dynamically adjusted according to the interval between the second historical time point and the current time point, so that the more recent second historical vehicle speed is given a higher weight and the more distant second historical vehicle speed is given a lower weight. This can avoid the excessive influence of long-term data on vehicle speed correction and improve the robustness of vehicle speed correction. The second weighted average vehicle speed is obtained by weighting and averaging the second historical vehicle speed and the first weight corresponding to the second historical vehicle speed. This can effectively smooth short-term fluctuations and reduce errors caused by instantaneous acceleration, deceleration or sensor noise.

[0046] S3. Based on the key vehicle speed list G and the corresponding key vehicle distance list H, obtain the third weighted average vehicle speed R of the target vehicle, where G = {G1, G2, ..., G...} r , ..., G s}, G r Let H be the critical speed corresponding to the r-th critical vehicle, where r ranges from 1 to s, and s is the number of critical vehicles. H = {H1, H2, ..., H} r H s}, H r For G r The corresponding key vehicle distance is defined as follows: key vehicle refers to the vehicle on the target road at the current time point; target road refers to the road where the target vehicle is located at the current time point; key vehicle speed refers to the speed of the key vehicle collected by the target server at the current time point; and key vehicle distance refers to the road distance between the key vehicle and the target vehicle.

[0047] Specifically, the road distance between the key vehicle and the target vehicle is the distance traveled between the locations of the key vehicle and the target vehicle, not the straight-line distance between them.

[0048] Specifically, step S3 includes the following steps S31-S32:

[0049] S31. For H1, H2, ..., H r H s Perform normalization to obtain the normalized value list T = {T1, T2, ..., T} corresponding to H. r , ..., T s}, T r For H rThe corresponding normalized values, as those skilled in the art will know, any normalization method in the prior art is within the protection scope of this invention, and will not be elaborated further here.

[0050] Specifically, T r The value range is [0, 1].

[0051] S32, according to G r and T r Obtain R, which satisfies the following conditions:

[0052] R = ∑ s r＝1 (G r ×T r ) / ∑ s r＝1 T r .

[0053] Through the above steps, the distances of all key vehicles in the key vehicle distance list are normalized to obtain a list of normalized values ​​corresponding to the key vehicle distance list, eliminating the difference in dimensions. The smaller the key vehicle distance, the larger its corresponding normalized value. Based on the key vehicle speed and the normalized value corresponding to the key vehicle distance, a weighted average is performed to obtain the third weighted average vehicle speed corresponding to the target vehicle. This can effectively reduce the impact of outliers (such as vehicles that suddenly accelerate or decelerate) on the final result. Even if the speed of some key vehicles fluctuates greatly, its impact on speed correction will be small due to its low weight, thereby improving the robustness of speed correction.

[0054] S4. Based on C, F, R, and V, obtain the target vehicle speed J corresponding to the target vehicle, where V is the vehicle speed of the target vehicle collected by the target server at the current time point, and J meets the following conditions:

[0055] J = ((C+F+V) / 3+(R+V) / 2) / 2.

[0056] In one specific embodiment, the following steps S10-S40 are included after step S3:

[0057] S10. Obtain the first weighted speed list U = {U1, U2, ..., U...} corresponding to A. i , ..., U m}, U i For A i The corresponding first weighted speed, U i Meets the following conditions:

[0058] U i =A i ×K i .

[0059] S20. Obtain the second weighted speed list set W = {W1, W2, ..., W...} corresponding to D. j , ..., W n}, W j ={W j1 W j2 , ..., W je , ..., W jf(j)}, W j D j The corresponding second weighted speed list, W je D je The corresponding second weighted speed, W je Meets the following conditions:

[0060] W je =D je ×L je .

[0061] S30. Obtain the third weighted speed list set X = {X1, X2, ..., X...} corresponding to G. r ..., X s}, X r For G r The corresponding third weighted speed, X r Meets the following conditions:

[0062] X r =G r ×T r .

[0063] S40. Input the working condition information corresponding to U, W, G, and the target road, and the weather information corresponding to each historical time slice into the target Transformers model to obtain J. The target Transformers model is a Transformers model based on time attention and spatial attention.

[0064] Specifically, the working condition information corresponding to the target road is information describing the current state of the road and environmental conditions, such as: road surface type, road surface smoothness, and congestion status.

[0065] Specifically, weather information includes data and indicators describing atmospheric conditions, such as wind speed and precipitation.

[0066] Through the above steps, a first weighted speed list, a second weighted speed list set, and a third weighted speed list are obtained. The first weighted speed list, the second weighted speed list set, the third weighted speed list, the road condition information corresponding to the target road, and the weather information corresponding to each historical time slice are input into the target Transformers model to obtain the target speed corresponding to the target vehicle. This not only considers historical speeds and key speeds, but also combines road condition information and weather information, providing a more comprehensive and accurate speed correction result, which is conducive to improving the accuracy and robustness of speed correction.

[0067] In one specific embodiment, the following steps S100-S300 are further included after step S30:

[0068] S100. Based on C, F, R, and V, obtain the first candidate vehicle speed HX1 corresponding to the target vehicle. HX1 meets the following conditions:

[0069] HX1=((C+F+V) / 3+(R+V) / 2) / 2.

[0070] S200. Input the U, W, G, working condition information corresponding to the target road and the weather information corresponding to each historical time slice into the target Transformers model to obtain the second candidate vehicle speed HX2 corresponding to the target vehicle.

[0071] S300. When |HX1-HX2|<△t, take HX1 as J or take HX2 as J, where △t is the preset speed difference.

[0072] Through the above steps, when the absolute value of the difference between the first candidate vehicle speed and the second candidate vehicle speed is less than the preset vehicle speed difference, it indicates that the calculated first candidate vehicle speed and the second candidate vehicle speed output by the target Transformers model have a high degree of consistency. At this time, selecting any one of the first and second candidate vehicle speeds as the target vehicle speed can ensure that the obtained target vehicle speed is more accurate and reliable.

[0073] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store a computer program related to implementing a method in the method embodiments, the computer program being loaded and executed by the processor to implement the method provided in the above embodiments.

[0074] Embodiments of the present invention also provide an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method provided in the above embodiments.

[0075] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0076] This invention provides a vehicle speed correction method, electronic device, and storage medium. The method is used to correct the vehicle speed of a target vehicle collected by a target server at the current time point to obtain the target vehicle speed. The method obtains a first weighted average vehicle speed corresponding to the target vehicle based on a first historical vehicle speed list and a first historical time point list; obtains a second weighted average vehicle speed corresponding to the target vehicle based on a second historical vehicle speed list and a second historical time point list; obtains a third weighted average vehicle speed corresponding to the target vehicle based on a key vehicle speed list and a key vehicle distance list; and obtains the target vehicle speed corresponding to the target vehicle based on the first weighted average vehicle speed, the second weighted average vehicle speed, the third weighted average vehicle speed, and the vehicle speed of the target vehicle collected by the target server at the current time point. It can be seen that this invention combines historical vehicle speeds and key vehicle speeds, using weighted averaging and multi-source data fusion to correct the vehicle speed of the target vehicle in real time. This effectively reduces the influence of sensor errors and environmental factors, and fully utilizes historical data and information from surrounding vehicles, which is beneficial to improving the accuracy and robustness of vehicle speed correction.

[0077] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A vehicle speed correction method, characterized in that, The method is used to correct the vehicle speed of the target vehicle collected by the target server at the current time point to obtain the target vehicle speed. The method includes the following steps: S1. Based on the first historical vehicle speed list A corresponding to the target vehicle and the first historical time point list B corresponding to A, obtain the first weighted average vehicle speed C corresponding to the target vehicle, where A = {A1, A2, ..., A...} i , ..., A m }, A i Let B be the i-th first historical speed corresponding to the target vehicle, where i ranges from 1 to m, and m is the number of first historical speeds corresponding to the target vehicle. Let B = {B1, B2, ..., B...} i , ..., B m }, B i For A i The corresponding first historical time point, the first historical vehicle speed of the target vehicle is the speed of the target vehicle collected by the target server within a preset time period, the end time point of the preset time period is the current time point, and the first historical time point corresponding to the first historical vehicle speed is the time point at which the first historical vehicle speed was collected; including: S11, according to B i Given the current time point DQ, obtain the average time difference ΔB corresponding to B, where ΔB satisfies the following condition: △B=∑ m i=1 |B i -DQ| / m; S12, according to B i △B and DQ, obtain A i The corresponding first weight K i The first weight is used to represent the importance of the first historical vehicle speed, K. i Meets the following conditions: When |B i When -DQ|≤△B, K i =a×(1 / △B) (1 / |Bi-DQ|) 'a' represents the preset influencing factor corresponding to the preset time period; When |B i When -DQ|>△B, K i =a×(1 / △B) (1 / △B) ; S13, According to A i and K i Obtain C, where C meets the following conditions: C=∑ m i=1 (A i ×K i ) / ∑ m i=1 K i ; S2. Based on the second historical vehicle speed list set D and the corresponding second historical time point list set E, obtain the second weighted average vehicle speed F corresponding to the target vehicle, where D = {D1, D2, ..., D...} j , ..., D n }, D j ={D j1 D j2 , ..., D je , ..., D jf(j) }, D j This is the list of second historical vehicle speeds corresponding to the j-th historical time slice, where j ranges from 1 to n, and n is the number of historical time slices. je Let f(j) be the e-th second historical speed corresponding to the j-th historical time slice, where e takes values ​​from 1 to f(j), and f(j) is the number of second historical speeds corresponding to the j-th historical time slice. E = {E1, E2, ..., E...} j , ..., E n }, E j ={E j1 E j2 , ..., E je , ..., E jf(j) }, E j D j The corresponding second historical time point list, E je D je The corresponding second historical time point, the second historical vehicle speed is the speed of vehicles traveling on the target road collected by the target server within the historical time slice, the target road is the road where the target vehicle is located at the current time point, and the second historical time point is the time point at which the second historical vehicle speed was collected; including: S21, according to E je And DQ, obtain E j The corresponding average time difference △P j , △P j Meets the following conditions: △P j =∑ f(j) e=1 Q je / f(j), Q je For E je The duration of the interval between the DQ and the DQ; S22, according to Q je and △P j , obtain D je The corresponding second weight L je The second weight is used to represent the importance of the second historical vehicle speed, L. je Meets the following conditions: When Q je ≤△P j hour, b j The preset influence factor corresponding to the j-th historical time slice; When Q je >△P j hour, ; S23, According to D je and L je Obtain F, where F satisfies the following conditions: F=∑ n j=1 (∑ f(j) e=1 (D je ×L je ) / ∑ f(j) e=1 L je ) / n; S3. Based on the key vehicle speed list G and the corresponding key vehicle distance list H, obtain the third weighted average vehicle speed R of the target vehicle, where G = {G1, G2, ..., G...} r , ..., G s }, G r Let H be the critical speed corresponding to the r-th critical vehicle, where r ranges from 1 to s, and s is the number of critical vehicles. H = {H1, H2, ..., H} r H s }, H r For G r The corresponding critical vehicle distance, where critical vehicle refers to a vehicle on the target road at the current time point, target road refers to the road where the target vehicle is located at the current time point, critical vehicle speed refers to the speed of the critical vehicle collected by the target server at the current time point, and critical vehicle distance refers to the road distance between the critical vehicle and the target vehicle; including: S31. For H1, H2, ..., H r H s Perform normalization to obtain the normalized value list T = {T1, T2, ..., T} corresponding to H. r , ..., T s }, T r For H r The corresponding normalized value; S32, according to G r and T r Obtain R, which satisfies the following conditions: R=∑ s r=1 (G r ×T r ) / ∑ s r=1 T r ; S4. Based on C, F, R, and V, obtain the target vehicle speed J corresponding to the target vehicle, where V is the vehicle speed of the target vehicle collected by the target server at the current time point, and J meets the following conditions: J=((C+F+V) / 3+(R+V) / 2) / 2.

2. The vehicle speed correction method according to claim 1, characterized in that, The historical time slice is the time slice before the current time slice, and the length of the time slice is 1 day. The current time point is the time slice in which the current time point is located.

3. The vehicle speed correction method according to claim 2, characterized in that, The (j+1)th historical time slice is earlier than the jth historical time slice.

4. The vehicle speed correction method according to claim 1, characterized in that, When j=1, b j <a, when j≠1, b j <b j-1 b j-1 This is the preset influence factor corresponding to the (j-1)th historical time slice.

5. The vehicle speed correction method according to claim 1, characterized in that, T r The value range is [0, 1].

6. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer program, which is loaded and executed by a processor to implement the vehicle speed correction method as described in any one of claims 1-5.

7. An electronic device, comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the vehicle speed correction method as described in any one of claims 1-5.

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