阅读说明：本技术 车辆载重数据处理方法及装置 (Vehicle load data processing method and device ) 是由 付诚 许柏玮 吴鹏 郭凯 王德志 刘林 于 2021-08-13 设计创作，主要内容包括：本发明提供一种车辆载重数据处理方法及装置,该方法包括：将预设时间段内重型重力传感器获取的车辆的原始测量值按照获取时刻的先后顺序进行排序,计算排序结果中每个原始测量值减去每个原始测量值之后第n个原始测量值；其中,n为预设正整数；在连续m个原始测量值对应的差值均位于预设范围内的情况下,将所述连续m个原始测量值作为关键点,根据所述关键点的获取时刻将所述预设时间段划分为多个子时间段；其中,m为预设正整数；根据所述排序结果中每个子时间段内的原始测量值计算每个子时间段内所述车辆的载重测量值,对每个子时间段对应的载重测量值进行曲线拟合,获取每个子时间段内所述车辆的载重拟合值。本发明提高载重数据的准确性。(The invention provides a vehicle load data processing method and a vehicle load data processing device, wherein the method comprises the following steps: sequencing original measurement values of the vehicle acquired by the heavy gravity sensor within a preset time period according to the sequence of the acquisition time, and calculating the nth original measurement value after subtracting each original measurement value from each original measurement value in a sequencing result; wherein n is a preset positive integer; under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, taking the continuous m original measurement values as key points, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer; and calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period in the sequencing result, and performing curve fitting on the load measurement value corresponding to each sub-time period to obtain the load fitting value of the vehicle in each sub-time period. The invention improves the accuracy of the load data.)

1. A vehicle load data processing method, characterized by comprising:

sequencing original measurement values of the vehicle acquired by the heavy gravity sensor within a preset time period according to the sequence of the acquisition time, and calculating the nth original measurement value after subtracting each original measurement value from each original measurement value in a sequencing result; wherein n is a preset positive integer;

under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, taking the continuous m original measurement values as key points, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer;

and calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period in the sequencing result, and performing curve fitting on the load measurement value corresponding to each sub-time period to obtain the load fitting value of the vehicle in each sub-time period.

2. The vehicle load data processing method according to claim 1, wherein the preset range includes a first preset range and a second preset range;

the maximum value and the minimum value in the first preset range are negative numbers;

the maximum value and the minimum value in the second preset range are positive numbers;

the key points comprise a first type key point and a second type key point;

correspondingly, when the difference values corresponding to the m consecutive original measurement values are all within the preset range, taking the m consecutive original measurement values as key points includes:

under the condition that the difference values corresponding to the m continuous original measurement values are all located in the first preset range, taking a first measurement value in the m continuous original measurement values as a first type key point;

and under the condition that the difference values corresponding to the continuous m original measurement values are all located in the second preset range, taking the first measurement value in the continuous m original measurement values as a second type key point.

3. The vehicle load data processing method according to claim 2, wherein the sub-periods include a loading period, a transportation period, and an unloading period;

the dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key point includes:

acquiring the loading time period according to the acquisition time of the first type key point;

acquiring the unloading time period according to the acquisition time of the second type key point;

and taking the time period between the adjacent loading time period and unloading time period in the preset time period as the transportation time period.

4. The vehicle load data processing method according to claim 3, wherein the curve fitting the load measurement value corresponding to each sub-period to obtain the load fitting value of the vehicle in each sub-period comprises:

carrying out curve fitting on the load measurement value corresponding to the first key point in the loading time period to obtain a load fitting value of the vehicle in the loading time period;

and carrying out curve fitting on the load measurement value corresponding to the second type key point in the unloading time period to obtain the load fitting value of the vehicle in the unloading time period.

5. The vehicle load data processing method according to any one of claims 2 to 4, wherein said dividing the preset time period into a plurality of sub-time periods according to the acquisition timing of the key point includes:

when the ceiling of the vehicle is in a closed state and/or the ACC state of the vehicle is in an open state at the moment when the key point is acquired, adjusting n and m, and re-determining the key point according to the adjusted n and m until the ceiling of the vehicle is in the closed state and/or the ACC state of the vehicle is in the open state at the moment when the re-determined key point is not acquired;

and dividing the preset time period into a plurality of sub-time periods according to the last determined acquisition time of the key point.

6. The vehicle load data processing method according to any one of claims 1 to 4, wherein the step of sorting the original measured values of the vehicle acquired by the heavy-duty gravity sensor within the preset time period according to the sequence of the acquisition time comprises the steps of:

deleting each original measurement value under the condition that the original measurement value is smaller than a first preset threshold value;

calculating the average running speed of the vehicle corresponding to each original measured value according to the running speed of the vehicle in a time period of preset duration with the acquisition time of each original measured value as the center;

deleting the original measured values under the condition that the absolute value of the difference between the running speed of the vehicle at the moment of acquiring each original measured value and the average running speed of the vehicle corresponding to each original measured value is greater than a second preset threshold value;

and sequencing the deleted original measured values according to the sequence of the acquisition time.

7. A vehicle load data processing apparatus, comprising:

the calculation module is used for sequencing original measured values of the vehicle acquired by the heavy gravity sensor in a preset time period according to the sequence of the acquisition moments, and calculating the nth original measured value after each original measured value is subtracted from each original measured value in a sequencing result; wherein n is a preset positive integer;

the dividing module is used for taking the continuous m original measurement values as key points under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer;

and the fitting module is used for calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period, performing curve fitting on the load measurement value corresponding to each sub-time period, and acquiring the load fitting value of the vehicle in each sub-time period.

8. An electronic device comprising a memory, a processor and a computer program stored on said memory and executable on said processor, characterized in that said processor, when executing said program, carries out the steps of a vehicle load data processing method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the vehicle load data processing method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the vehicle load data processing method according to any one of claims 1 to 6 when executed by a processor.

Technical Field

The invention relates to the technical field of data processing, in particular to a vehicle load data processing method and device.

Background

At present, heavy-duty vehicles such as muck vehicles generally adopt heavy gravity sensors to acquire load data so as to prevent overload. The serious overload not only harms the safety of the automobile, but also influences the road safety, the driving safety and the like.

The measurement principle of the existing heavy gravity sensor is that load data is measured according to the deformation of a steel wire when a heavy object pulls the steel wire. In the actual use process of the heavy gravity sensor, due to factors such as road bump, high vehicle running speed and failure of the heavy gravity sensor, load data measured by the heavy gravity sensor is inaccurate.

Disclosure of Invention

The invention provides a vehicle load data processing method and device, which are used for solving the defect that vehicle load data measured by a heavy gravity sensor in the prior art is inaccurate, and improving the accuracy of vehicle load data measurement.

The invention provides a vehicle load data processing method, which comprises the following steps:

sequencing original measurement values of the vehicle acquired by the heavy gravity sensor within a preset time period according to the sequence of the acquisition time, and calculating the nth original measurement value after subtracting each original measurement value from each original measurement value in a sequencing result; wherein n is a preset positive integer;

under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, taking the continuous m original measurement values as key points, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer;

and calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period in the sequencing result, and performing curve fitting on the load measurement value corresponding to each sub-time period to obtain the load fitting value of the vehicle in each sub-time period.

According to the vehicle load data processing method provided by the invention, the preset range comprises a first preset range and a second preset range;

the maximum value and the minimum value in the first preset range are negative numbers;

the maximum value and the minimum value in the second preset range are positive numbers;

the key points comprise a first type key point and a second type key point;

correspondingly, when the difference values corresponding to the m consecutive original measurement values are all within the preset range, taking the m consecutive original measurement values as key points includes:

under the condition that the difference values corresponding to the m continuous original measurement values are all located in the first preset range, taking a first measurement value in the m continuous original measurement values as a first type key point;

and under the condition that the difference values corresponding to the continuous m original measurement values are all located in the second preset range, taking the first measurement value in the continuous m original measurement values as a second type key point.

According to the vehicle load data processing method provided by the invention, the sub-time periods comprise a loading time period, a transportation time period and an unloading time period;

the dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key point includes:

acquiring the loading time period according to the acquisition time of the first type key point;

acquiring the unloading time period according to the acquisition time of the second type key point;

and taking the time period between the adjacent loading time period and unloading time period in the preset time period as the transportation time period.

According to the vehicle load data processing method provided by the invention, the curve fitting is carried out on the load measurement value corresponding to each sub-time period, and the load fitting value of the vehicle in each sub-time period is obtained, and the method comprises the following steps:

carrying out curve fitting on the load measurement value corresponding to the first key point in the loading time period to obtain a load fitting value of the vehicle in the loading time period;

and carrying out curve fitting on the load measurement value corresponding to the second type key point in the unloading time period to obtain the load fitting value of the vehicle in the unloading time period.

According to the vehicle load data processing method provided by the invention, dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key point comprises the following steps:

when the ceiling of the vehicle is in a closed state and/or the ACC state of the vehicle is in an open state at the moment when the key point is acquired, adjusting n and m, and re-determining the key point according to the adjusted n and m until the ceiling of the vehicle is in the closed state and/or the ACC state of the vehicle is in the open state at the moment when the re-determined key point is not acquired;

and dividing the preset time period into a plurality of sub-time periods according to the last determined acquisition time of the key point.

According to the vehicle load data processing method provided by the invention, the original measured values of the vehicle acquired by the heavy gravity sensor in the preset time period are sequenced according to the sequence of the acquisition moments, and the method comprises the following steps:

deleting each original measurement value under the condition that the original measurement value is smaller than a first preset threshold value;

calculating the average running speed of the vehicle corresponding to each original measured value according to the running speed of the vehicle in a time period of preset duration with the acquisition time of each original measured value as the center;

deleting the original measured values under the condition that the absolute value of the difference between the running speed of the vehicle at the moment of acquiring each original measured value and the average running speed of the vehicle corresponding to each original measured value is greater than a second preset threshold value;

and sequencing the deleted original measured values according to the sequence of the acquisition time.

The present invention also provides a vehicle load data processing apparatus comprising:

the calculation module is used for sequencing original measured values of the vehicle acquired by the heavy gravity sensor in a preset time period according to the sequence of the acquisition moments, and calculating the nth original measured value after each original measured value is subtracted from each original measured value in a sequencing result; wherein n is a preset positive integer;

the dividing module is used for taking the continuous m original measurement values as key points under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer;

and the fitting module is used for calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period, performing curve fitting on the load measurement value corresponding to each sub-time period, and acquiring the load fitting value of the vehicle in each sub-time period.

The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of the vehicle load data processing method as described in any one of the above when executing the program.

The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the vehicle load data processing method as described in any one of the above.

The invention also provides a computer program product comprising a computer program which, when executed by a processor, carries out the steps of the vehicle load data processing method as described in any one of the above.

According to the vehicle load data processing method and device, the key point is determined by judging whether the difference value between the original measured values is within the preset range, the time period obtained by the original measured values is divided into a plurality of sub-time periods according to the key point, and then the load measured values corresponding to the original measured values are fitted in a segmented mode, so that the accuracy of load data is improved.

Drawings

In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

FIG. 1 is a schematic flow chart of a vehicle load data processing method provided by the present invention;

FIG. 2 is a second schematic flow chart of a vehicle load data processing method according to the present invention;

FIG. 3 is a schematic diagram of raw measurements in a vehicle load data processing method provided by the present invention;

FIG. 4 is a schematic representation of raw measurements after cleaning in a vehicle load data processing method provided by the present invention;

FIG. 5 is a schematic structural diagram of a vehicle load data processing apparatus provided by the present invention;

fig. 6 is a schematic structural diagram of an electronic device provided in the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

A vehicle load data processing method of the present invention is described below with reference to fig. 1, and includes: 101, sequencing original measurement values of a vehicle acquired by a heavy gravity sensor in a preset time period according to the sequence of acquisition moments, and calculating the nth original measurement value after each original measurement value is subtracted from each original measurement value in a sequencing result; wherein n is a preset positive integer;

the original measured value of the vehicle obtained by the heavy weight sensor refers to the deformation quantity generated by the steel wire when the heavy weight sensor pulls the steel wire during measurement.

The acquired raw measurement values AD are sorted. The difference σ corresponding to the ith original measurement value in the sorting result_i＝AD_i-AD_i+n。

Step 102, under the condition that the difference values corresponding to m continuous original measurement values are all located in a preset range, taking the m continuous original measurement values as key points, and dividing a preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer;

and judging whether the difference values corresponding to the continuous m original measurement values are all within a preset range, if so, indicating that the original measurement values are relatively stable at the stage, and taking the original measurement values as key points.

And obtaining a critical point for dividing the preset time period according to the critical point, so that the preset time period is divided into a plurality of sub-time periods according to the critical point. Each sub-period represents one work phase of the vehicle.

Step 103, calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period in the sequencing result, and performing curve fitting on the load measurement value corresponding to each sub-time period to obtain the load fitting value of the vehicle in each sub-time period.

And calculating the load measurement value corresponding to each original measurement value in the sequencing result. Alternatively, the calculation formula is as follows:

f(AD)＝aAD²+b；

where f (AD) represents the measured load corresponding to the raw measurement AD in tons, and a and b are constants.

And establishing an equation through the load value weighed when the vehicle is unloaded and meets the requirement and the original measurement value measured by the heavy gravity sensor to obtain the values of a and b.

And according to the acquisition time of each original measurement value, carrying out curve fitting on the load measurement value corresponding to the original measurement value acquired in each sub-time period, and acquiring the load fitting value of the vehicle in each sub-time period.

On one hand, the load measurement value can be modified through fitting, and on the other hand, the load measurement missing value can be supplemented, so that more accurate load data can be obtained.

In the embodiment, the key point is determined by judging whether the difference value between the original measurement values is within the preset range, the time period obtained by the original measurement values is divided into a plurality of sub-time periods according to the key point, and then the load measurement values corresponding to the original measurement values are fitted in a segmented manner, so that the accuracy of the load data is improved.

On the basis of the above embodiment, the preset range in this embodiment includes a first preset range and a second preset range; the maximum value and the minimum value in the first preset range are negative numbers; the maximum value and the minimum value in the second preset range are positive numbers;

for example, the first predetermined range is [ -2 × S, -S ], and S takes the value 2000. The second predetermined range is [ S,2 × S ].

The key points comprise a first type key point and a second type key point;

correspondingly, when the difference values corresponding to the m consecutive original measurement values are all within the preset range, taking the m consecutive original measurement values as key points includes:

under the condition that the difference values corresponding to the m continuous original measurement values are all located in the first preset range, taking a first measurement value in the m continuous original measurement values as a first type key point; and under the condition that the difference values corresponding to the continuous m original measurement values are all located in the second preset range, taking the first measurement value in the continuous m original measurement values as a second type key point.

For example, if m is 3, the difference σ corresponding to 3 consecutive original measurement values_i,σ_i+1,σ_i+2,∈[-2*S,-S]Then AD will be_iMarking the key points as first class key points and representing the key points in the loading stage; if the difference value sigma corresponding to 3 continuous original measurement values_i,σ_i+1,σ_i+2,∈[S,2*S]Then AD will be_iThe mark is a second type key point, which represents the key point of the unloading stage. The specific labeling formula is as follows:

on the basis of the above embodiment, the sub-period in this embodiment includes a loading period, a transportation period, and an unloading period;

the dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key point includes: acquiring the loading time period according to the acquisition time of the first type key point;

optionally, the duration between two adjacent first-class key points is obtained according to the obtaining time of the two adjacent first-class key points. If the time length between the two key points is short, the two adjacent first-class key points belong to the same operation stage, namely the same sub-time period.

And taking the time period between the earliest acquisition time and the latest acquisition time in the acquisition times of the first-class key points belonging to the same sub-time period as a loading time period.

Acquiring the unloading time period according to the acquisition time of the second type key point;

optionally, the duration between two adjacent second-class key points is obtained according to the obtaining time of the two adjacent second-class key points. If the time length between the two key points is short, it indicates that two adjacent second-class key points belong to the same operation stage, namely the same sub-time period.

And taking the time period between the earliest acquisition time and the latest acquisition time in the acquisition times of the second type key points belonging to the same sub-time period as an unloading time period.

And taking the time period between the adjacent loading time period and unloading time period in the preset time period as the transportation time period.

According to the working rule of the vehicle, the vehicle is loaded, then transported and then unloaded. And taking the time period between the adjacent loading time period and unloading time period as the transportation time period.

On the basis of the foregoing embodiment, in this embodiment, the performing curve fitting on the load measurement value corresponding to each sub-period to obtain the load fitted value of the vehicle in each sub-period includes: carrying out curve fitting on the load measurement value corresponding to the first key point in the loading time period to obtain a load fitting value of the vehicle in the loading time period;

other raw measurement values in the loading time period except the first-type key are abnormal jitter points, and influence is caused on curve fitting. Therefore, the curve fitting is only carried out on the load measurement value corresponding to the first type key point in the loading time period, and a more accurate load fitting value is obtained.

And carrying out curve fitting on the load measurement value corresponding to the second type key point in the unloading time period to obtain the load fitting value of the vehicle in the unloading time period.

Other raw measurements in the unloading period, except for the second type of key, are abnormal jitter points, which may affect the curve fitting. According to the method, only the load measurement value corresponding to the second type key point in the unloading time period is subjected to curve fitting, so that a more accurate load fitting value is obtained.

On the basis of the foregoing embodiments, in this embodiment, the dividing the preset time period into a plurality of sub-time periods according to the time of acquiring the key point includes: when the ceiling of the vehicle is in a closed state and/or an ACC (Adaptive Cruise Control) state of the vehicle is on at the moment when key point acquisition exists, adjusting n and m, and re-determining the key points according to the adjusted n and m until the ceiling of the vehicle is in the closed state and/or the ACC state of the vehicle is on at the moment when the re-determined key point acquisition does not exist; and dividing the preset time period into a plurality of sub-time periods according to the last determined acquisition time of the key point.

The state of the vehicle roof comprises a closed state and an unsealed state, and can be monitored through a sensor.

The ACC state of the vehicle includes 1 and 0. Wherein 1 represents on and 0 represents off.

The first type of key points are original measurement values obtained when the vehicle is in a loading stage, and the second type of key points are original measurement values obtained when the vehicle is in an unloading stage. The ceiling of the vehicle is in an unsealed state in the loading stage and the unloading stage, and the ACC state of the vehicle is off. If at least one of the two conditions is not met, it is indicated that the determined key point is not accurate or that the closed system of the roof and/or the control system of the vehicle is malfunctioning.

Optionally, in the case of eliminating faults of the ceiling and/or the vehicle, it is stated that the obtained key points are inaccurate due to the influence of parameters, so that the division of the operation stage is inaccurate, and the accuracy of the load data fitting is influenced.

The parameter n is too small and is easily interfered by noise; the parameter n is too large, and effective data is easily lost. If m is too small, the abnormal jitter cannot be determined; and if m is too large, key points are lost. And (4) finding appropriate n values and m values by optimizing m and n.

In the embodiment, the parameters n and m are corrected, and the corrected values of m and n are used for determining the key points until the time when the key points are acquired meets the two conditions, so that the accuracy of the acquired load data is improved.

On the basis of the foregoing embodiments, in this embodiment, the sorting, according to the order of the acquisition times, the original measurement values of the vehicle acquired by the heavy gravity sensor in the preset time period includes: deleting each original measurement value under the condition that the original measurement value is smaller than a first preset threshold value;

and after each original measurement value in a preset time period is obtained, cleaning the original measurement values. As shown in fig. 2, each raw measurement is first compared to a first preset value, such as 1000. And unloading the original measurement value smaller than the first preset threshold value into an exception table.

Calculating the average running speed of the vehicle corresponding to each original measured value according to the running speed of the vehicle in a time period of preset duration with the acquisition time of each original measured value as the center;

for example, if the acquisition time t of a certain original measurement value is 3:00 and the preset time duration is 2 minutes, the average running speed V of the vehicle is calculated according to the running speed of the vehicle measured in the time period of 3:01 to 3:02₀。

Deleting the original measured values under the condition that the absolute value of the difference between the running speed of the vehicle at the moment of acquiring each original measured value and the average running speed of the vehicle corresponding to each original measured value is greater than a second preset threshold value; and sequencing the deleted original measured values according to the sequence of the acquisition time.

The acquisition time of the raw measurement value is 3:00, and the running speed V of the vehicle is₁Minus the average running speed V₀And obtaining the absolute value of the difference between the two. And under the condition that the absolute value is larger than the second preset threshold value of 10km/h, the vehicle is suddenly accelerated or suddenly decelerated, interference is caused on the original measured value, and the obtained original measured value is transferred to the abnormal table. The raw measurements are shown in fig. 3 and the cleaned raw measurements are shown in fig. 4.

The following describes the vehicle load data processing apparatus provided by the present invention, and the vehicle load data processing apparatus described below and the vehicle load data processing method described above may be referred to in correspondence with each other.

As shown in fig. 5, the apparatus includes a calculation module 501, a division module 502, and a fitting module 503, wherein:

the calculation module 501 is configured to sort the original measurement values of the vehicle acquired by the heavy gravity sensor in a preset time period according to the order of the acquisition times, and calculate an nth original measurement value after subtracting each original measurement value from each original measurement value in a sorting result; wherein n is a preset positive integer;

the dividing module 502 is configured to, when difference values corresponding to m consecutive raw measurement values are all within a preset range, take the m consecutive raw measurement values as key points, and divide the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer;

the fitting module 503 is configured to calculate a load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period, perform curve fitting on the load measurement value corresponding to each sub-time period, and obtain a load fitting value of the vehicle in each sub-time period.

In the embodiment, the key point is determined by judging whether the difference value between the original measurement values is within the preset range, the time period obtained by the original measurement values is divided into a plurality of sub-time periods according to the key point, and then the load measurement values corresponding to the original measurement values are fitted in a segmented manner, so that the accuracy of the load data is improved.

Fig. 6 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 6: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform a vehicle load data processing method comprising: sequencing original measurement values of the vehicle acquired by the heavy gravity sensor within a preset time period according to the sequence of the acquisition time, and calculating the nth original measurement value after subtracting each original measurement value from each original measurement value in a sequencing result; wherein n is a preset positive integer; under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, taking the continuous m original measurement values as key points, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer; and calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period in the sequencing result, and performing curve fitting on the load measurement value corresponding to each sub-time period to obtain the load fitting value of the vehicle in each sub-time period.

In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product comprising a computer program, the computer program being storable on a non-transitory computer-readable storage medium, the computer program, when executed by a processor, being capable of executing the vehicle load data processing method provided by the above methods, the method comprising: sequencing original measurement values of the vehicle acquired by the heavy gravity sensor within a preset time period according to the sequence of the acquisition time, and calculating the nth original measurement value after subtracting each original measurement value from each original measurement value in a sequencing result; wherein n is a preset positive integer; under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, taking the continuous m original measurement values as key points, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer; and calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period in the sequencing result, and performing curve fitting on the load measurement value corresponding to each sub-time period to obtain the load fitting value of the vehicle in each sub-time period.

In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a vehicle load data processing method provided by the above methods, the method comprising: sequencing original measurement values of the vehicle acquired by the heavy gravity sensor within a preset time period according to the sequence of the acquisition time, and calculating the nth original measurement value after subtracting each original measurement value from each original measurement value in a sequencing result; wherein n is a preset positive integer; under the condition that the difference values corresponding to the continuous m original measurement values are all located in a preset range, taking the continuous m original measurement values as key points, and dividing the preset time period into a plurality of sub-time periods according to the acquisition time of the key points; wherein m is a preset positive integer; and calculating the load measurement value of the vehicle in each sub-time period according to the original measurement value in each sub-time period in the sequencing result, and performing curve fitting on the load measurement value corresponding to each sub-time period to obtain the load fitting value of the vehicle in each sub-time period.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.