Data filtering method, device and storage medium for truck weighing sensor

Abstract

The present invention discloses a data filtering method, device, and storage medium for a truck weighing sensor. The method comprises: obtaining the current weighing data Xi and initial weighing data Xo of the weighing sensor, as well as the current speed Vi and initial speed Vo of the truck; determining whether the truck is in a single motion state or a two-motion switching state within the current sampling period based on the current speed Vi and initial speed Vo of the truck; when the truck is in a single motion state, smoothing and filtering the current weighing data Xi and initial weighing data Xo of the weighing sensor using a sampling data length n preset for the single motion state; and when the truck is in a two-motion switching state, smoothing and filtering the current weighing data Xi and initial weighing data Xo of the weighing sensor using a combination of sampling data lengths n preset for the two motion states. The present application switches filtering modes according to different truck states and maintains data smoothing and filtering during the switching process, thereby ensuring the accuracy and real-time performance of data acquisition.

Description

Technical Field

[0001] The present invention relates to the technical field of truck state detection, and in particular to a data filtering method, device and storage medium for a truck weighing sensor. Background Art

[0002] With the rapid development of society and the economy, people's living and production needs are constantly increasing, and the demand for logistics is skyrocketing. Logistics trucks play an extremely important and irreplaceable role in this process. To detect and monitor truck overloading, it is often necessary to weigh the cargo on trucks. Existing truck cargo weighing methods are mainly divided into two categories: ground-based vehicle weighing and on-board online weighing. Ground-based vehicle weighing involves measuring the weight of the entire truck at a fixed or mobile location using a scale or other device. On-board online weighing involves directly measuring the weight of the cargo on the truck using a weighing device on the truck, without relying on a fixed or mobile scale or other device on the ground. Existing on-board online weighing methods mainly include the following: 1. Indirectly calculating cargo weight by measuring the change in the distance between the truck's leaf spring and the axle using a laser rangefinder or ultrasonic rangefinder; 2. Directly measuring cargo weight by placing a load cell between the truck frame and the truck bed; 3. Indirectly measuring cargo weight by measuring the change in the lateral deformation of the truck's leaf spring.

[0003] With technological advancements, on-board online weighing is becoming increasingly popular. Vehicle-mounted sensors enable a wider range of sensing and monitoring capabilities. Logistics scheduling management systems can significantly improve overall scheduling efficiency by assessing the loading status of transport trucks in real time. By using appropriate weighing or pressure sensors to capture and analyze vehicle load data, the current truck load status can be determined. Sensor data can be processed using methods such as median filtering, mean filtering, and Kalman filtering to eliminate noise during sampling and obtain more accurate load data.

[0004] In actual load measurement, sensors often operate under complex conditions. While stationary vehicles typically acquire data with less noise, driving vehicles, due to terrain, bumps, and other factors, often generate much greater noise than in stationary conditions. Using a filtering method with lower smoothness can significantly impact data accuracy due to noise during driving. Using a filtering method with higher smoothness can increase data latency during stationary conditions and easily miss changes in load (e.g., rapid loading and unloading of a stationary vehicle). Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a data filtering method, device and storage medium for a truck weighing sensor, aiming to solve the technical problems of unstable data filtering accuracy and poor real-time performance when the vehicle is in different states.

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0007] A first aspect of the present invention provides a data filtering method for a truck weighing sensor, the method comprising:

[0008] Obtain the current weighing data Xi and initial weighing data Xo of the weighing sensor, as well as the current speed Vi and initial speed Vo of the truck;

[0009] Determine whether the truck is in a single motion state or a two-motion switching state during the current sampling period according to the current speed Vi and the initial speed Vo of the truck;

[0010] When the truck is in a single motion state, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using the sampling data length n preset in the single motion state;

[0011] When the truck is in two motion switching states, the preset sampling data lengths n of the two motion states are combined to perform smoothing filtering on the current weighing data Xi and the initial weighing data Xo of the weighing sensor.

[0012] Furthermore, before the step of determining whether the truck is in a single motion state or a switching state between two motion states within a current sampling period based on the current speed and initial speed of the truck, the method further includes:

[0013] The preset speed threshold is Vt. When Vi is 0, the truck is at rest; when 0<Vi≤Vt, the truck is at a low speed; when Vi>Vt, the truck is at a high speed.

[0014] Furthermore, before the step of smoothing and filtering the current weighing data Xi and the initial weighing data Xo of the weighing sensor using the sampling data length n preset in the single motion state, the method further includes:

[0015] The sampling data lengths n1, n2, n3 are set for the stationary state, low speed state, and high speed state of the truck, and n1 <n2<n3。

[0016] Optionally, the smoothing filter is one of a box filter (boxFilter), a mean filter (blur), and a Gaussian filter (GaussianBlur).

[0017] Furthermore, when the truck is in two motion switching states, the combination of the preset sampling data lengths n of the two motion states performs smooth filtering on the current weighing data Xi and the initial weighing data Xo of the weighing sensor, including:

[0018] According to the proportions of the two motion states in the current sampling period, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using a combination of sampling data lengths n preset for the two motion states.

[0019] Furthermore, after the step of smoothing and filtering the current weighing data Xi and the initial weighing data Xo of the weighing sensor, the method further includes:

[0020] The filtering result after smoothing and filtering the current weighing data Xi replaces and updates the initial weighing data Xo; the current speed Vi of the truck replaces and updates the initial speed Vo.

[0021] Furthermore, the smoothing filter method is mean filtering, and its calculation method is:

[0022]

[0023] Among them, E(x) is the filtering result, Xi is the current weighing data, and n is the sampling data length.

[0024] Furthermore, the smoothing filtering of the current weighing data Xi and the initial weighing data Xo of the weighing sensor by using a combination of preset sampling data lengths n of the two motion states according to the proportions of the two motion states in the current sampling period includes:

[0025] Obtain the data length m that maintains the current speed state, and compare the data length m with the preset sampling data length n of the current speed state. If m≥n, set the current filtering sampling data length n;

[0026] If m≤n, and the current speed state is static, the current sampling time point is m sampling points before the high-speed state, and the length of the current filtering sampling data n is composed of the m sampling data of the static state and the m sampling data of the high-speed state. The sampling data is composed of sampling data, and the sampling data length is

[0027] If m≤n, and the current speed state is static, the current sampling time point is m sampling points before the low speed state, the length n of the current filtering sampling data is composed of the m sampling data of the static state and the m sampling data of the low speed state. The sampling data is composed of sampling data, and the sampling data length is

[0028] If m≤n, and the current speed state is low speed, the current sampling time point is m sampling points before the high speed state, the length n of the current filtering sampling data is composed of the m sampling data of the low speed state and the m sampling data of the high speed state. The sampling data is composed of sampling data, and the sampling data length is

[0029] If m≤n, and the current speed state is low speed, the current sampling time point is m sampling points before the static state, and the length n of the current filtering sampling data is composed of the m sampling data of the low speed state and the m sampling data of the static state. The sampling data is composed of sampling data, and the sampling data length is

[0030] If m≤n, and the current speed state is high speed, the current sampling time point is m sampling points before the low speed state, the length n of the current filtering sampling data is composed of the m sampling data of the high speed state and the m sampling data of the low speed state. The sampling data is composed of sampling data, and the sampling data length is

[0031] If m≤n, and the current speed state is high speed, the current sampling time point is m sampling points before the static state, and the length of the current filtering sampling data n is composed of the m sampling data of the high speed state and the m sampling data of the static state. The sampling data is composed of sampling data, and the sampling data length is

[0032] According to the calculated sampling data length n, mean filtering is used Calculate the result after this filtering.

[0033] Based on the same inventive concept, another aspect of the present invention provides a data filtering device for a truck weighing sensor. The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the following is achieved:

[0034] Obtain the current weighing data Xi and initial weighing data Xo of the weighing sensor, as well as the current speed Vi and initial speed Vo of the truck;

[0035] Determine whether the truck is in a single motion state or a two-motion switching state during the current sampling period according to the current speed Vi and the initial speed Vo of the truck;

[0036] When the truck is in a single motion state, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using the sampling data length n preset in the single motion state;

[0037] When the truck is in two motion switching states, the preset sampling data lengths n of the two motion states are combined to perform smoothing filtering on the current weighing data Xi and the initial weighing data Xo of the weighing sensor.

[0038] Based on the same inventive concept, another aspect of the present invention provides a computer-readable storage medium, on which is stored a data filtering program for a truck weighing sensor. When the data filtering program for a truck weighing sensor is executed by a processor, the steps of the above-mentioned data filtering method for a truck weighing sensor are implemented.

[0039] By adopting the above technical solution, the data filtering method, device and storage medium for truck weighing sensors of the embodiments of the present invention obtain truck speed information while obtaining truck weighing data, and select different smoothing filtering methods according to the movement state of the truck; for the stationary state, a mean filter with a lower smoothing rate is selected; for the low-speed state, a mean filter with a medium smoothing rate is selected; for the high-speed state, a mean filter with a high smoothing rate is selected. In the switching state between stationary, low-speed and high-speed, a gradual adjustment of the smoothing rate is used to ensure that the data does not suddenly change due to changes in the smoothing coefficient of the filter during the state switching process. This method not only ensures the accuracy of the data but also minimizes the delay caused by data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a flowchart of a data filtering method for a truck weighing sensor according to an embodiment of the present invention;

[0042] Figure 2 A flowchart of smoothing and filtering the current weighing data Xi of a weighing sensor by combining two preset sampling data lengths n for the two motion states according to an embodiment of the present invention;

[0043] Figure 3 This is a structural block diagram of a data filtering device for a truck weighing sensor according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following is a further description of specific embodiments of the present invention in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0045] Example 1

[0046] like Figure 1 As shown, an embodiment of the present invention provides a data filtering method for a truck weighing sensor, the method comprising:

[0047] S101, obtaining the current weighing data Xi and the initial weighing data Xo of the weighing sensor, as well as the current speed Vi and the initial speed Vo of the truck;

[0048] S102: Determine whether the truck is in a single motion state or a two-motion switching state within a current sampling period based on the truck's current speed Vi and initial speed Vo;

[0049] Specifically, before the step of determining whether the truck is in a single motion state or two motion switching states within the current sampling period based on the current speed and initial speed of the truck, the method also includes: presetting a speed threshold value as Vt, when Vi is 0, the truck is in a stationary state; when 0<Vi≤Vt, the truck is in a low-speed state; when Vi>Vt, the truck is in a high-speed state.

[0050] S103, when the truck is in a single motion state, using a sampling data length n preset for the single motion state to smooth and filter the current weighing data Xi and the initial weighing data Xo of the weighing sensor;

[0051] Optionally, before the step of smoothing and filtering the current weighing data Xi and the initial weighing data Xo of the weighing sensor using the sampling data length n preset in the single motion state, the method further includes: setting the sampling data lengths n1, n2, and n3 for the stationary state, low speed state, and high speed state of the truck, respectively, and n1 < n2 <n3。

[0052] S104 , when the truck is in two motion switching states, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using a combination of sampling data lengths n preset in the two motion states.

[0053] Specifically, when the truck is in two motion switching states, the preset sampling data length n combination of the two motion states is used to smooth and filter the current weighing data Xi and the initial weighing data Xo of the weighing sensor, including: according to the proportion of the two motion states in the current sampling period, the preset sampling data length n combination of the two motion states is used to smooth and filter the current weighing data Xi and the initial weighing data Xo of the weighing sensor.

[0054] The method of smoothing and filtering the current weighing data Xi and the initial weighing data Xo of the weighing sensor by using a combination of preset sampling data lengths n of the two motion states according to the proportions of the two motion states in the current sampling period includes:

[0055] Obtain the data length m that maintains the current speed state, and compare the data length m with the preset sampling data length n of the current speed state. If m≥n, set the current filtering sampling data length n;

[0056] If m≤n, and the current speed state is static, the current sampling time point m sampling points before is high speed state, the length n of the current filtering sampling data is composed of the m sampling data of static state and the m sampling data of high speed state. The sampling data is composed of sampling data, and the sampling data length is

[0057] If m≤n, and the current speed state is static, the current sampling time point is m sampling points before the low speed state, and the length n of the current filtering sampling data is composed of the m sampling data of the static state and the m sampling data of the low speed state. The sampling data is composed of sampling data, and the sampling data length is

[0058] If m≤n, and the current speed state is low speed, the current sampling time point is m sampling points before the high speed state, the length n of the current filtering sampling data is composed of the m sampling data of the low speed state and the m sampling data of the high speed state. The sampling data is composed of sampling data, and the sampling data length is

[0059] If m≤n, and the current speed state is low speed, the current sampling time point is m sampling points before the static state, and the length n of the current filtering sampling data is composed of the m sampling data of the low speed state and the m sampling data of the static state. The sampling data is composed of sampling data, and the sampling data length is

[0060] If m≤n, and the current speed state is high speed, the current sampling time point is m sampling points before the low speed state, the length n of the current filtering sampling data is composed of the m sampling data of the high speed state and the m sampling data of the low speed state. The sampling data is composed of sampling data, and the sampling data length is

[0061] If m≤n, and the current speed state is high speed, the current sampling time point is m sampling points before the static state, and the length of the current filtering sampling data n is composed of the m sampling data of the high speed state and the m sampling data of the static state. The sampling data is composed of sampling data, and the sampling data length is

[0062] According to the calculated sampling data length n, mean filtering is used Calculate the result after this filtering.

[0063] The technical solution of the real-time example of the present invention processes the weighing sensor data of the truck during operation by a filtering method that automatically adjusts the smoothing rate according to the running status of the truck, so as to reduce the filtering delay as much as possible while reducing the impact of data fluctuations, and no data is lost in the process.

[0064] Smoothing filtering can effectively reduce the noise of continuous data, but it will also bring a certain delay. According to the actual scenario, several corresponding motion states can be set. During the operation of a truck, it will inevitably be in a single motion state or a switching state between two states. For a single motion state, select the corresponding smoothing filter parameters (select high smoothing and high delay filter parameters for a motion state with large noise, and select low smoothing and low delay parameters for a motion state with low noise); for a motion switching state, different smoothing parameters can be used for the data before and after the switching and combined (the combination weight can be adjusted according to the time after the switching state). In this way, the delay can be reduced as much as possible while maintaining smoothness, and at the same time, no jump points will be generated due to the switching of states.

[0065] Optionally, the smoothing filter is one of a box filter (boxFilter), a mean filter (blur), and a Gaussian filter (GaussianBlur).

[0066] Among them, mean filtering is a typical linear digital filtering method. The method is to continuously sample the signal within a certain period of time and take the arithmetic average of the sampled values ​​as the signal value at that moment. The calculation method is:

[0067]

[0068] Where E(x) is the filtering result, Xi is the current weighing data, and n is the sampling data length. For vehicle-mounted load sensors, n data points are acquired within a period of time prior to the current time point and the arithmetic average is performed to obtain the signal value at the current time point. For the data length n, when the device is actually operating, a low value can be set when stationary, a medium value at low speeds, and a large value at high speeds. Gradually adjust the value at intersections of these states; this ensures good noise consistency in the output signal under varying data noise conditions.

[0069] Optionally, after the step of smoothing and filtering the current weighing data Xi and the initial weighing data Xo of the weighing sensor, the method further includes:

[0070] The filtering result after smoothing and filtering the current weighing data Xi replaces and updates the initial weighing data Xo; the current speed Vi of the truck replaces and updates the initial speed Vo.

[0071] like Figure 2 As shown in the figure, the whole method process is described in detail using a truck carrying a weighing sensor as a scenario, where the lengths of the mean filter sampling data at rest, low speed, and high speed are n1, n2, and n3, and n1 < n2 <n3。

[0072] S201: Each time data filtering starts, first read the current load sensor data x i .

[0073] S202: While collecting sensor data, obtain the current speed information v of the truck.

[0074] S203: Based on the read speed information value, the current truck state is divided into three states: stationary, low speed, and high speed. In these three states, the filtering algorithm processes are similar. The following takes the current stationary state as an example.

[0075] S204: Based on the speed and sensor data obtained at the previous sampling time, obtain the data length m for maintaining the current speed state (stationary in this case), that is, the number of sampling points that continuously maintain the current speed state from the current sampling time point forward; and compare it with the designed sampling length n1 of the current speed state. If m≥n1, jump to step S205, otherwise jump to step S206.

[0076] S205: Set the length n of the filtered sampling data to n1, and jump to step S209.

[0077] S206: Check the speed status m sampling points before the current sampling time point. If it is high speed, jump to step S207; if it is low speed, jump to step S208.

[0078] S207: The filtered sampling data of this time is composed of m sampling data in the static state and m sampling data in the high-speed state. The sampling data is composed of sampling data, and the sampling data length is Jump to step S209.

[0079] S208: The filtered sampling data consists of 1 sampling data in the static state and 1 sampling data in the low speed state. The sampling data is composed of sampling data, and the sampling data length is Jump to step S209.

[0080] S209: According to the calculated sampling data length n, use mean filtering Calculate the result after this filtering.

[0081] Example 2

[0082] like Figure 3 As shown, an embodiment of the present invention also provides a hardware structure diagram of a data filtering device for a truck weighing sensor. The device includes at least a processor 11, a memory 12, and a data bus 13. The data bus 13 is used to realize the connection and communication between the processor 11 and the memory 12. The memory 12 is a computer-readable storage medium that can store at least one computer program. These computer programs can be read, compiled, and executed by the processor 11 to implement the corresponding processing flow. In this embodiment, the memory 11 is a computer-readable storage medium, wherein the data filtering program for the truck weighing sensor is executed by the processor 11 and is implemented as follows:

[0083] Obtain the current weighing data Xi and initial weighing data Xo of the weighing sensor, as well as the current speed Vi and initial speed Vo of the truck;

[0084] Determine whether the truck is in a single motion state or a two-motion switching state during the current sampling period according to the current speed Vi and the initial speed Vo of the truck;

[0085] When the truck is in a single motion state, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using the sampling data length n preset in the single motion state;

[0086] When the truck is in two motion switching states, the preset sampling data lengths n of the two motion states are combined to perform smoothing filtering on the current weighing data Xi and the initial weighing data Xo of the weighing sensor.

[0087] The data filtering device for a truck weighing sensor in an embodiment of the present invention dynamically adjusts the filtering parameters according to the current motion state of the truck during the truck load sensor data processing, so that the truck load data always maintains low noise, has low latency, and does not jump during the switching of motion states.

[0088] Example 3

[0089] An embodiment of the present invention also provides a computer-readable storage medium, which stores a data filtering program for a truck weighing sensor. When the data filtering program for a truck weighing sensor is executed by a processor, the steps of the above-mentioned data filtering method for a truck weighing sensor are implemented.

[0090] The data filtering method, device, and storage medium for a truck weighing sensor according to an embodiment of the present invention simultaneously obtains truck weighing data and truck speed information, and selects different smoothing filtering methods according to the truck's motion state: for a stationary state, a mean filter with a relatively low smoothing rate is selected; for a low-speed state, a mean filter with a medium smoothing rate is selected; for a high-speed state, a mean filter with a high smoothing rate is selected. In the switching state between stationary, low-speed, and high-speed, a gradual adjustment of the smoothing rate is used to ensure that the data does not suddenly change due to changes in the smoothing coefficient of the filter during the state switching process. This method not only ensures data accuracy but also minimizes the delay caused by data processing.

[0091] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It is apparent to those skilled in the art that various changes, modifications, substitutions, and variations to these embodiments may be made without departing from the principles and spirit of the present invention, and the changes still fall within the scope of protection of the present invention.

Claims

1. A data filtering method for a truck weighing sensor, characterized in that: The method comprises: Obtain the current weighing data Xi and initial weighing data Xo of the weighing sensor, as well as the current speed Vi and initial speed Vo of the truck; Determine whether the truck is in a single motion state or a two-motion switching state during the current sampling period according to the current speed Vi and the initial speed Vo of the truck; When the truck is in a single motion state, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using the sampling data length n preset in the single motion state; When the truck is in two motion switching states, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered by the combination of the sampling data lengths n preset in the two motion states; When the truck is in two motion switching states, the preset sampling data length combination of the two motion states is used to smooth and filter the current weighing data Xi and the initial weighing data Xo of the weighing sensor, including: According to the proportions of the two motion states in the current sampling period, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using a combination of sampling data lengths n preset for the two motion states.

2. The data filtering method for a truck weighing sensor according to claim 1, characterized in that: Before the step of determining whether the truck is in a single motion state or a two-motion switching state within the current sampling period based on the current speed and the initial speed of the truck, the method further includes: The preset speed threshold is Vt. When Vi is 0, the truck is at rest; when 0<Vi≤Vt, the truck is at a low speed; when Vi>Vt, the truck is at a high speed.

3. The data filtering method for a truck weighing sensor according to claim 2, characterized in that: Before the step of smoothing and filtering the current weighing data Xi and the initial weighing data Xo of the weighing sensor using the sampling data length n preset in the single motion state, the method further includes: The sampling data lengths n1, n2, and n3 are set corresponding to the stationary state, low-speed state, and high-speed state of the truck, respectively, and n1 < n2 < n3.

4. The data filtering method for a truck weighing sensor according to claim 1, characterized in that: The smoothing filter is one of box filtering, mean filtering, and Gaussian filtering.

5. The data filtering method for a truck weighing sensor according to claim 1, characterized in that: After the step of smoothing and filtering the current weighing data Xi and the initial weighing data Xo of the weighing sensor, the method further comprises: The filtering result after smoothing and filtering the current weighing data Xi replaces and updates the initial weighing data Xo; the current speed Vi of the truck replaces and updates the initial speed Vo.

6. The data filtering method for a truck weighing sensor according to claim 1, characterized in that: The smoothing filter method is mean filtering, and its calculation method is: Among them, E(x) is the filtering result, Xi is the current weighing data, and n is the sampling data length.

7. The data filtering method for a truck weighing sensor according to claim 6, characterized in that: The smoothing filtering of the current weighing data Xi and the initial weighing data Xo of the weighing sensor by respectively using the preset sampling data length n combination of the two motion states according to the proportion of the two motion states in the current sampling period includes: Obtain the data length m that maintains the current speed state, and compare the data length m with the preset sampling data length n of the current speed state. If m≥n, set the current filtering sampling data length n; If m≤n, and the current speed state is static, the current sampling time point m sampling points before is high speed state, the length n of the current filtering sampling data is composed of the m sampling data of static state and the m sampling data of high speed state. The sampling data is composed of sampling data, and the sampling data length is If m≤n, and the current speed state is static, the current sampling time point is m sampling points before the low speed state, and the length n of the current filtering sampling data is composed of the m sampling data of the static state and the m sampling data of the low speed state. The sampling data is composed of sampling data, and the sampling data length is If m≤n, and the current speed state is low speed, the current sampling time point is m sampling points before the high speed state, the length n of the current filtering sampling data is composed of the m sampling data of the low speed state and the m sampling data of the high speed state. The sampling data is composed of sampling data, and the sampling data length is If m≤n, and the current speed state is low speed, the current sampling time point is m sampling points before the static state, and the length n of the current filtering sampling data is composed of the m sampling data of the low speed state and the m sampling data of the static state. The sampling data is composed of sampling data, and the sampling data length is If m≤n, and the current speed state is high speed, the current sampling time point is m sampling points before the low speed state, the length n of the current filtering sampling data is composed of the m sampling data of the high speed state and the m sampling data of the low speed state. The sampling data is composed of sampling data, and the sampling data length is If m≤n, and the current speed state is high speed, the current sampling time point is m sampling points before the static state, and the length of the current filtering sampling data n is composed of the m sampling data of the high speed state and the m sampling data of the static state. The sampling data is composed of sampling data, and the sampling data length is According to the calculated sampling data length n, mean filtering is used Calculate the result after this filtering.

8. A data filtering device for a truck weighing sensor, characterized in that: The apparatus includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the computer program implements the following: Obtain the current weighing data Xi and initial weighing data Xo of the weighing sensor, as well as the current speed Vi and initial speed Vo of the truck; Determine whether the truck is in a single motion state or a two-motion switching state during the current sampling period according to the current speed Vi and the initial speed Vo of the truck; When the truck is in a single motion state, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using the sampling data length n preset in the single motion state; When the truck is in two motion switching states, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered by the combination of the sampling data lengths n preset in the two motion states; When the truck is in two motion switching states, the preset sampling data length combination of the two motion states is used to smooth and filter the current weighing data Xi and the initial weighing data Xo of the weighing sensor, including: According to the proportions of the two motion states in the current sampling period, the current weighing data Xi and the initial weighing data Xo of the weighing sensor are smoothed and filtered using a combination of sampling data lengths n preset for the two motion states.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a data filtering program for a truck weighing sensor. When the data filtering program for a truck weighing sensor is executed by a processor, the steps of the data filtering method for a truck weighing sensor according to any one of claims 1 to 7 are implemented.

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