CN112858725A - Vehicle speed consistency detection method, terminal equipment and storage medium - Google Patents
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Abstract
The invention relates to a vehicle speed consistency detection method, a terminal device and a storage medium, which are used for detecting whether vehicle speed data of a second device are consistent when the vehicle speed data of a first device are confirmed to be consistent, wherein the method comprises the following steps: s1: collecting speed data and mileage data of two devices; s2: calculating the speed error of the two devices; when the standard deviation of the speed errors at multiple moments is smaller than a preset standard deviation threshold value, the step S3 is carried out; s3: calculating a plurality of speeds of each device in a plurality of different time periods; s4: taking the speed of the two devices in the same time period as a group of data, performing linear fitting on a plurality of groups of data obtained by the two devices in a plurality of different time periods through a least square method, and entering S5 when the slope of a fitting straight line meets a preset slope range; s5: and when the correlation coefficient of the multiple sets of data is larger than the correlation coefficient threshold value, judging that the vehicle speed data of the second device are consistent. The method and the device can quickly and accurately judge the vehicle speed consistency of the newly added equipment according to the existing equipment.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a vehicle speed consistency detection method, terminal equipment and a storage medium.
Background
The national six-emission standard of heavy vehicles is about to be comprehensively implemented, the national heavy vehicle remote emission service and management platform is built and completed, more and more local cities require the remote online networking monitoring of heavy diesel vehicles, and vehicles which are not networked online and pass the environmental protection inspection cannot be sold. In order to meet the national regulatory requirements of the whole vehicle production enterprises, an enterprise platform for monitoring and managing six vehicles in China needs to be established.
The newly established enterprise platform for monitoring and managing the six state vehicles needs to perform offline detection on each vehicle, wherein the offline detection includes vehicle speed consistency detection, the existing enterprise platform adopting the standard of the department of transportation already performs vehicle speed consistency detection, and how to detect the vehicle speed consistency of the newly established platform by using the existing enterprise platform through the vehicle speed consistency detection becomes a problem to be solved.
Disclosure of Invention
In order to solve the above problems, the present invention provides a vehicle speed consistency detection method, a terminal device and a storage medium.
The specific scheme is as follows:
a vehicle speed consistency detection method is used for detecting whether vehicle speed data of second equipment are consistent when the vehicle speed data of first equipment are confirmed to be consistent, wherein the first equipment and the second equipment are used for collecting the vehicle speed data of the same vehicle, and the method comprises the following steps:
s1: acquiring speed data and mileage data of two devices at different moments in a fixed time period;
s2: calculating a speed error corresponding to the moment according to the difference value of the speed data corresponding to the two devices at the same moment; when the standard deviation of the speed errors at multiple moments is smaller than a preset standard deviation threshold value, the step S3 is carried out; otherwise, judging that the vehicle speed data of the second equipment are inconsistent;
s3: respectively calculating a plurality of speeds of each device in a plurality of different time periods according to the acquired mileage data of the two devices at different moments, wherein the time difference of the different time periods is greater than a time difference threshold value;
s4: taking the speed of the two devices in the same time period calculated in the step S3 as a group of data, performing linear fitting on a plurality of groups of data obtained by the two devices in a plurality of different time periods through a least square method, judging whether the slope of a fitting straight line meets a preset slope range, and if so, entering S5; otherwise, judging that the vehicle speed data of the second equipment are inconsistent;
s5: whether the correlation coefficient of the multiple groups of calculated data is larger than the correlation coefficient threshold value or not is judged, and if the correlation coefficient of the multiple groups of calculated data is larger than the correlation coefficient threshold value, the speed data of the second equipment are judged to be consistent; otherwise, the vehicle speed data of the second device is determined to be inconsistent.
Further, in step S1, when acquiring speed data and mileage data of two devices at different times within a fixed time period, it is determined whether a total duration of speeds greater than 0 in all the acquired speed data is greater than a preset duration threshold, and if not, the total duration of speeds greater than 0 in all the acquired speed data is greater than the preset duration threshold.
Further, the calculation formula of the standard deviation in step S2 is:
wherein s represents a standard deviation, DiWhich is indicative of the i-th speed error,
the average of n speed errors is shown, i indicates the number of speed errors, and n indicates the total number of speed errors.
Further, in step S2, dividing all the acquired speed data into a plurality of groups according to a time sequence, calculating a standard deviation of each group of speed data, and entering S3 when a ratio of the number of groups having a standard deviation smaller than a preset standard deviation threshold to all the groups is larger than a ratio threshold; otherwise, the vehicle speed data of the second device is determined to be inconsistent.
Further, in step S3, the calculation method of the speeds of each device in the different time periods includes:
randomly acquiring multiple groups of mileage data from the acquired mileage data, wherein each group of mileage data comprises at least two mileage data, and the interval duration between the maximum mileage data and the minimum mileage data in each group of mileage data is greater than a preset duration threshold;
calculating the corresponding speed within the selected duration according to the maximum mileage data and the minimum mileage data in each group of mileage data;
calculating the average value of the speeds in all selected time lengths according to the multiple groups of mileage data;
and judging whether the difference between the speed in the selected time length corresponding to each group of mileage data and the average value of the speeds in all the selected time lengths is greater than a preset speed difference threshold value, if so, discarding the speed in the selected time length corresponding to the group of mileage data, and otherwise, keeping the speed in the selected time length corresponding to the group of mileage data.
Further, the least squares fitting formula in step S4 is:
y＝kx+l
wherein y represents the velocity of the second device over the selected duration, x represents the velocity of the first device over the selected duration, k represents the slope of the fitted line, and l represents the intercept of the fitted line on the y-axis.
Further, the preset slope range is 0.9-1.1.
Further, the calculation formula of the correlation coefficient r in step S5 is:
wherein, VakIndicating the velocity, V, of the first device a in the kth group of databkIndicating the velocity of the second device b in the kth group of data,
and
representing the average, σ, of the velocities of the first and second devices a, b, respectivelyaAnd σbThe standard deviation of the velocities of the first device a and the second device b, respectively, is indicated, k indicates the serial number of each set of data, and m indicates the number of all sets.
A vehicle speed consistency detection terminal device comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above for an embodiment of the invention.
By adopting the technical scheme, the method and the device can quickly and accurately judge the vehicle speed consistency of the newly added equipment according to the existing equipment.
Drawings
Fig. 1 is a flowchart illustrating a first embodiment of the present invention.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
the embodiment of the invention provides a vehicle speed consistency detection method, which is used for detecting whether vehicle speed data of second equipment are consistent or not when the vehicle speed data of first equipment are confirmed to be consistent. The first equipment and the second equipment are used for collecting the speed data of the same vehicle, wherein the first equipment is originally installed on the vehicle and adopts the standard of the department of transportation, the speed consistency is already confirmed, and the second equipment is newly installed and is used for the national six standard and needs to be confirmed.
As shown in fig. 1, the method comprises the steps of:
s1: and acquiring speed data and mileage data of the two devices at different moments in a fixed time period.
In order to improve the accuracy of subsequent calculation and avoid the influence on the judgment accuracy due to long-time stillness, the following steps are set in the embodiment: when speed data and mileage data of two devices at different moments in a fixed time period are collected, judging whether the total duration of the speed greater than 0 in all the collected speed data is greater than a preset duration threshold value or not, if not, collecting again until the total duration of the speed greater than 0 in all the collected speed data is greater than the preset duration threshold value.
S2: calculating a speed error corresponding to the moment according to the difference value of the speed data corresponding to the two devices at the same moment; when the standard deviation of the speed errors at multiple moments is smaller than a preset standard deviation threshold value, the step S3 is carried out; otherwise, the vehicle speed data of the second device is determined to be inconsistent.
It should be noted that the speed error is a difference between the speed data of the two devices at the same time.
In this embodiment, to avoid the influence of abnormal data, the accuracy of judgment is improved, and a multiple-time judgment mode is adopted, specifically: dividing all the acquired speed data into a plurality of groups according to a time sequence, respectively calculating the standard deviation of each group of speed data, and entering S3 when the ratio of the number of groups with the standard deviation smaller than a preset standard deviation threshold value to all the groups is larger than a ratio threshold value; otherwise, the vehicle speed data of the second device is determined to be inconsistent. The threshold ratio value in this embodiment is set to 98% by experimental results.
The calculation formula of the standard deviation of each group of speed data is as follows:
wherein s represents a standard deviation, DiWhich is indicative of the i-th speed error,
the average of n speed errors is shown, i indicates the number of speed errors, and n indicates the total number of speed errors.
S3: according to the collected mileage data of the two devices at different moments, a plurality of speeds of each device in a plurality of different time periods are respectively calculated for each device, wherein the time difference of the different time periods is larger than a time difference threshold value.
The method for calculating the speeds of each device in different time periods comprises the following steps:
s31: randomly acquiring multiple groups of mileage data from the acquired mileage data, wherein each group of mileage data comprises at least two mileage data, the interval duration between the maximum mileage data and the minimum mileage data in each group of mileage data is greater than a preset duration threshold, and the interval duration between the two mileage data is set as a selected duration.
S32: and calculating the corresponding speed within the selected duration according to the maximum mileage data and the minimum mileage data in each group of mileage data, wherein the specific calculation process comprises the following steps: obtaining the mileage within the corresponding selected duration according to the difference value of the two mileage data in each group of mileage data; and obtaining the speed in the selected duration according to the ratio of the mileage in the selected duration to the selected duration.
S33: and calculating the average value of the speeds in all the selected time periods according to the multiple groups of mileage data.
S34: and judging whether the difference between the speed in the selected time length corresponding to each group of mileage data and the average value of the speeds in all the selected time lengths is greater than a preset speed difference threshold value, if so, discarding the speed in the selected time length corresponding to the group of mileage data, and otherwise, keeping the speed in the selected time length corresponding to the group of mileage data.
S4: taking the speed of the two devices in the same time period calculated in the step S3 as a group of data, performing linear fitting on a plurality of groups of data obtained by the two devices in a plurality of different time periods through a least square method, judging whether the slope of a fitting straight line meets a preset slope range, and if so, entering S5; otherwise, the vehicle speed data of the second device is determined to be inconsistent.
In this example, m sets of data are finally obtained, each being (V)a1,Vb1),(Va2,Vb2),...,(Vam,Vbm) Each group of data comprises the speed data of the two devices in the same time period, wherein the same time period is the same as the starting time and the ending time of the time period.
The least square fitting formula is as follows:
y＝kx+l
wherein y represents the velocity of the second device over the selected duration, x represents the velocity of the first device over the selected duration, k represents the slope of the fitted line, and l represents the intercept of the fitted line on the y-axis.
In this embodiment, on the basis of the experimental data, the preset slope range is preferably set to be 0.9-1.1.
S5: whether the correlation coefficient of the multiple groups of calculated data is larger than the correlation coefficient threshold value or not is judged, and if the correlation coefficient of the multiple groups of calculated data is larger than the correlation coefficient threshold value, the speed data of the second equipment are judged to be consistent; otherwise, the vehicle speed data of the second device is determined to be inconsistent.
The correlation coefficient r is calculated by the formula:
wherein, VakIndicating the velocity, V, of the first device a in the kth group of databkIndicating the velocity of the second device b in the kth group of data,
and
representing the average, σ, of the velocities of the first and second devices a, b, respectivelyaAnd σbThe standard deviation of the velocities of the first device a and the second device b, respectively, is indicated, k indicates the serial number of each set of data, and m indicates the number of all sets.
In this embodiment, on the basis of experimental data, it is preferable to set the correlation coefficient threshold value to 0.9.
Through the mode, the vehicle speed consistency of the newly added equipment can be judged quickly and accurately according to the existing equipment.
Example two:
the invention also provides vehicle speed consistency detection terminal equipment, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the vehicle speed consistency detection terminal device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The vehicle speed consistency detection terminal device can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the constituent structure of the vehicle speed consistency detection terminal device is only an example of the vehicle speed consistency detection terminal device, and does not constitute a limitation on the vehicle speed consistency detection terminal device, and may include more or less components than the above, or combine some components, or different components, for example, the vehicle speed consistency detection terminal device may further include an input-output device, a network access device, a bus, and the like, which is not limited by the embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor is a control center of the vehicle speed consistency detection terminal device and connects various parts of the entire vehicle speed consistency detection terminal device by using various interfaces and lines.
The memory may be configured to store the computer program and/or the module, and the processor may implement various functions of the vehicle speed consistency detection terminal device by executing or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The vehicle speed consistency detection terminal device integrated module/unit can be stored in a computer readable storage medium if it is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A vehicle speed consistency detection method is characterized in that when the vehicle speed data of a first device is confirmed to be consistent, whether the vehicle speed data of a second device is consistent or not is detected, wherein the first device and the second device are used for collecting the vehicle speed data of the same vehicle, and the method comprises the following steps:
s1: acquiring speed data and mileage data of two devices at different moments in a fixed time period;
s2: calculating a speed error corresponding to the moment according to the difference value of the speed data corresponding to the two devices at the same moment; when the standard deviation of the speed errors at multiple moments is smaller than a preset standard deviation threshold value, the step S3 is carried out; otherwise, judging that the vehicle speed data of the second equipment are inconsistent;
s3: respectively calculating a plurality of speeds of each device in a plurality of different time periods according to the acquired mileage data of the two devices at different moments, wherein the time difference of the different time periods is greater than a time difference threshold value;
s4: taking the speed of the two devices in the same time period calculated in the step S3 as a group of data, performing linear fitting on a plurality of groups of data obtained by the two devices in a plurality of different time periods through a least square method, judging whether the slope of a fitting straight line meets a preset slope range, and if so, entering S5; otherwise, judging that the vehicle speed data of the second equipment are inconsistent;
s5: whether the correlation coefficient of the multiple groups of calculated data is larger than the correlation coefficient threshold value or not is judged, and if the correlation coefficient of the multiple groups of calculated data is larger than the correlation coefficient threshold value, the speed data of the second equipment are judged to be consistent; otherwise, the vehicle speed data of the second device is determined to be inconsistent.
2. The vehicle speed consistency detection method according to claim 1, characterized in that: in step S1, when acquiring speed data and mileage data of two devices at different times within a fixed time period, it is determined whether the total duration of speeds greater than 0 in all the acquired speed data is greater than a preset duration threshold, and if not, the total duration of speeds greater than 0 in all the acquired speed data is again acquired until the total duration of speeds greater than 0 in all the acquired speed data is greater than the preset duration threshold.
3. The vehicle speed consistency detection method according to claim 1, characterized in that: the calculation formula of the standard deviation in step S2 is:
wherein s represents a standard deviation, DiWhich is indicative of the i-th speed error,
the average of n speed errors is shown, i indicates the number of speed errors, and n indicates the total number of speed errors.
4. The vehicle speed consistency detection method according to claim 1, characterized in that: in the step S2, dividing all the acquired speed data into a plurality of groups according to the time sequence, respectively calculating the standard deviation of each group of speed data, and entering into S3 when the ratio of the number of groups with the standard deviation smaller than a preset standard deviation threshold value to all the groups is larger than a ratio threshold value; otherwise, the vehicle speed data of the second device is determined to be inconsistent.
5. The vehicle speed consistency detection method according to claim 1, characterized in that: the method for calculating the speeds of each device in the step S3 in different time periods includes:
randomly acquiring multiple groups of mileage data from the acquired mileage data, wherein each group of mileage data comprises at least two mileage data, and the interval duration between the maximum mileage data and the minimum mileage data in each group of mileage data is greater than a preset duration threshold;
calculating the corresponding speed within the selected duration according to the maximum mileage data and the minimum mileage data in each group of mileage data;
calculating the average value of the speeds in all selected time lengths according to the multiple groups of mileage data;
and judging whether the difference between the speed in the selected time length corresponding to each group of mileage data and the average value of the speeds in all the selected time lengths is greater than a preset speed difference threshold value, if so, discarding the speed in the selected time length corresponding to the group of mileage data, and otherwise, keeping the speed in the selected time length corresponding to the group of mileage data.
6. The vehicle speed consistency detection method according to claim 1, characterized in that: the least squares fitting formula in step S4 is:
y＝kx+l
wherein y represents the velocity of the second device over the selected duration, x represents the velocity of the first device over the selected duration, k represents the slope of the fitted line, and l represents the intercept of the fitted line on the y-axis.
7. The vehicle speed consistency detection method according to claim 1, characterized in that: the predetermined slope is in the range of 0.9-1.1.
8. The vehicle speed consistency detection method according to claim 1, characterized in that: the calculation formula of the correlation coefficient r in step S5 is:
wherein, VakIndicating the velocity, V, of the first device a in the kth group of databkIndicating the velocity of the second device b in the kth group of data,
and
representing the average, σ, of the velocities of the first and second devices a, b, respectivelyaAnd σbThe standard deviation of the velocities of the first device a and the second device b, respectively, is indicated, k indicates the serial number of each set of data, and m indicates the number of all sets.
9. The utility model provides a speed of a motor vehicle uniformity detection terminal equipment which characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implementing the steps of the method as claimed in any one of claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040194532A1 (en) * | 2001-04-27 | 2004-10-07 | Oceana Sensor Technologies, Inc. | Transducer in-situ testing apparatus and method |
| US6980923B1 (en) * | 2003-06-27 | 2005-12-27 | Honda Giken Kogyo Kabushiki Kaisha | Method to prevent odometer fraud |
| CN104569501A (en) * | 2014-12-23 | 2015-04-29 | 郑州宇通客车股份有限公司 | Beidou system-based vehicle speed ratio calibration system and method |
| CN104678128A (en) * | 2013-11-29 | 2015-06-03 | 北汽福田汽车股份有限公司 | Automobile speedometer and odometer inspection-correction test system and method |
| US20180306836A1 (en) * | 2017-04-19 | 2018-10-25 | Ford Global Technologies, Llc | Gnss statistically derived abs speedometer calibration |
| CN110220535A (en) * | 2019-05-29 | 2019-09-10 | 中国第一汽车股份有限公司 | Vehicle instrument table test method, device, system, equipment and storage medium |
| CN110254436A (en) * | 2018-03-12 | 2019-09-20 | 深圳鼎然信息科技有限公司 | Driving behavior analysis method, apparatus, equipment and medium based on motion sensor |
| CN111337051A (en) * | 2020-03-17 | 2020-06-26 | 北京百度网讯科技有限公司 | Method and device for calibrating forward axis of vehicle accelerometer |
-
2021
- 2021-01-15 CN CN202110054358.2A patent/CN112858725B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040194532A1 (en) * | 2001-04-27 | 2004-10-07 | Oceana Sensor Technologies, Inc. | Transducer in-situ testing apparatus and method |
| US6980923B1 (en) * | 2003-06-27 | 2005-12-27 | Honda Giken Kogyo Kabushiki Kaisha | Method to prevent odometer fraud |
| CN104678128A (en) * | 2013-11-29 | 2015-06-03 | 北汽福田汽车股份有限公司 | Automobile speedometer and odometer inspection-correction test system and method |
| CN104569501A (en) * | 2014-12-23 | 2015-04-29 | 郑州宇通客车股份有限公司 | Beidou system-based vehicle speed ratio calibration system and method |
| US20180306836A1 (en) * | 2017-04-19 | 2018-10-25 | Ford Global Technologies, Llc | Gnss statistically derived abs speedometer calibration |
| CN110254436A (en) * | 2018-03-12 | 2019-09-20 | 深圳鼎然信息科技有限公司 | Driving behavior analysis method, apparatus, equipment and medium based on motion sensor |
| CN110220535A (en) * | 2019-05-29 | 2019-09-10 | 中国第一汽车股份有限公司 | Vehicle instrument table test method, device, system, equipment and storage medium |
| CN111337051A (en) * | 2020-03-17 | 2020-06-26 | 北京百度网讯科技有限公司 | Method and device for calibrating forward axis of vehicle accelerometer |
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| CN114756825A (en) * | 2022-06-15 | 2022-07-15 | 合肥安迅精密技术有限公司 | Method and system for identifying instantaneous speed of discrete measurement location |
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