Belt weigher measurement data denoising method and system based on Bezier curve
The belt scale measurement data is cleaned and smoothed by denoising method based on the Bezier curve, which solves the problem of random noise in the belt scale measurement data, and improves the data stability and the reliability of the automated control system.
Patent Information
- Application Number
- CN202411433078.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-15
- Publication Date
- 2025-04-15
AI Technical Summary
The measurement data of the belt scale is unevenly changed due to the uneven distribution of the mass distribution of the focal block in the vibrating feeder, which introduces random noise, affecting the normal operation of the automated control system.
The denoising method based on the Bezier curve is adopted, and the measurement data of the belt scale is cleaned and pre-processed at a set time interval. The data is smoothed by the Bezier curve formula to eliminate or reduce random noise.
It effectively reduces random noise in belt scale measurement data, improves data stability and accuracy, and enhances the stability and reliability of the automated control system.
Smart Images
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Abstract
Description
Technical Field
[0001] The present application relates to the field of industrial automation control technology, and in particular to a method and system for denoising belt scale measurement data based on Bezier curves. Background Art
[0002] In the CDQ system, the belt scale and the vibrating feeder cooperate with each other to achieve effective control of the coke discharge amount. The vibrating feeder is set at the bottom of the CDQ furnace, and discharges the cooled coke blocks continuously onto the belt scale through uniform vibration. Although the operator can control the amount of coke discharge by adjusting the amplitude of the vibrating feeder, the actual value of the coke discharge needs to be measured by the weighing sensor on the belt scale, usually expressed as an average value per hour.
[0003] Due to the uneven distribution of the mass of coke in the vibrating feeder, the unloading process of the vibrating feeder is not smooth, which also makes the amount of coke passing through the belt scale in unit time unstable, and it is easy to produce weight fluctuations. For example, when a large coke block falls on the belt scale, it will produce a large impact, causing the vibration and tension of the belt scale to increase instantly; when a small coke block falls on the belt scale, the impact is small, and the vibration and tension of the belt scale are not obvious. This uneven change seriously affects the accuracy of the belt scale's measurement data and introduces a certain amount of random noise to the belt scale's measurement data.
[0004] Because the automatic control system in the CDQ system mainly relies on the measurement data provided by the belt scale to make decisions and adjustments, once the measurement data fluctuates greatly, it will receive wrong information and make inappropriate control actions. Therefore, the fluctuation of the belt scale measurement data seriously affects the normal operation of the automatic control system. Summary of the invention
[0005] The purpose of this application is to provide a belt scale measurement data denoising method and system based on Bezier curve, which can effectively reduce the random noise in the belt scale measurement data and avoid the jitter of the belt scale measurement data.
[0006] To achieve the above objectives, this application provides the following solutions:
[0007] In a first aspect, the present application provides a belt scale measurement data denoising method based on a Bezier curve, and the belt scale measurement data denoising method based on a Bezier curve comprises:
[0008] Cleaning the stored belt scale measurement data at set time intervals to obtain a plurality of pre-processed data;
[0009] Count all preprocessed data within a set time period, and select a benchmark data and two preprocessed data adjacent to the benchmark data from all preprocessed data;
[0010] Based on the value range of the time variable in the Bezier curve algorithm, determining multiple test values of the time variable;
[0011] Based on the benchmark data and two pre-processed data adjacent to the benchmark data, using a Bezier curve formula to respectively calculate Bezier curve data corresponding to the benchmark data under each test value;
[0012] Based on all the benchmark data and the Bezier curve data corresponding to the benchmark data, selecting a test value from all the test values as a standard value of the time variable;
[0013] Based on the standard value of the time variable, each pre-processed data is smoothed using a Bezier curve formula to obtain smoothed data with random noise eliminated or reduced.
[0014] Optionally, the stored belt scale measurement data is cleaned at a set time interval to obtain a plurality of pre-processed data, specifically including:
[0015] The stored belt scale measurement data are screened at set time intervals, the maximum and minimum belt scale measurement data are removed, and the average value of the screened belt scale measurement data is calculated to obtain a plurality of pre-processed data.
[0016] Optionally, all preprocessed data within a set time period are counted, and a benchmark data and two preprocessed data adjacent to the benchmark data are selected from all preprocessed data, specifically including:
[0017] All pre-processed data within a set period are counted, and data stabilization stages are divided according to all pre-processed data; the difference between the pre-processed data in the data stabilization stage is less than 10%;
[0018] Randomly select a pre-processed data from the data stabilization stage as the benchmark data;
[0019] Two pre-processed data adjacent to the reference data are determined.
[0020] Optionally, the Bezier curve formula is a second-order Bezier curve formula.
[0021] Optionally, the second-order Bezier curve formula is specifically:
[0022] B(t)=(1-t) 2 ×P0+2×t×(1-t)×P1+t 2 ×P2;
[0023] Wherein, B(t) is the Bezier curve data, t is the time variable, and P0, P1, and P2 are three adjacent pre-processed data.
[0024] Optionally, based on the value range of the time variable in the Bezier curve formula, multiple test values of the time variable are determined, specifically including:
[0025] Determine the maximum boundary value and the minimum boundary value of the value range of the time variable;
[0026] Set time change interval;
[0027] Taking the minimum boundary value as a reference, multiple test values are obtained by sequentially increasing according to the time variation interval until the maximum boundary value is reached.
[0028] Optionally, the value range of the time variable is [0, 1].
[0029] Optionally, based on all the benchmark data and Bezier curve data corresponding to the benchmark data, selecting a test value from all the test values as the standard value of the time variable specifically includes:
[0030] Take the benchmark data and Bezier curve data under each test value as an array;
[0031] Calculate the difference between the reference data and the Bezier curve data in each array;
[0032] The test value corresponding to the array with the difference value less than 10% is used as the standard value of the time variable.
[0033] Optionally, based on the standard value of the time variable, each pre-processed data is smoothed using a Bezier curve formula to obtain smoothed data with random noise eliminated or reduced, specifically including:
[0034] Select a preprocessed data as a target;
[0035] Determining two pre-processed data adjacent to the target;
[0036] Based on the standard value of the time variable, the target and two pre-processed data adjacent to the target are substituted into a Bezier curve formula to obtain smooth data corresponding to the target.
[0037] In a second aspect, the present application also provides a computer system, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the Bezier curve-based belt scale measurement data denoising method.
[0038] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0039] First, the present application cleans the stored belt scale measurement data at set intervals, and the data cleaning process can remove abnormal values or erroneous values in the belt scale measurement data. Secondly, the present application also adopts the principle of the Bezier curve and uses its smoothing characteristics to further calculate the pre-processed data, thereby eliminating or reducing the random noise in the belt scale measurement data, so that the belt scale measurement data is closer to the true value, and the stability and reliability of the automatic control system are improved. In addition, the present application also uses the benchmark data in the pre-processed data to further determine the standard value of the time variable in the Bezier curve formula, ensuring the effectiveness of the calculation using the Bezier curve formula. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.
[0041] Figure 1 A flowchart of a belt scale measurement data denoising method based on a Bezier curve provided in an embodiment of the present application;
[0042] Figure 2 A first process diagram for determining a Bezier curve provided in an embodiment of the present application;
[0043] Figure 3 A second process diagram for determining a Bezier curve provided in an embodiment of the present application;
[0044] Figure 4 A third process diagram for determining a Bezier curve provided in an embodiment of the present application;
[0045] Figure 5 This is a diagram of the internal structure of a computer system provided in an embodiment of the present application. DETAILED DESCRIPTION
[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0047] The purpose of this application is to provide a belt scale measurement data denoising method and system based on Bezier curve, which can effectively reduce the random noise in the belt scale measurement data and avoid the jitter of the belt scale measurement data.
[0048] This application improves the statistical method of the amount of coke discharged in the dry coke quenching system. In the actual working process of the belt scale, due to environmental factors, equipment accuracy limitations, material characteristics and other reasons, the measurement data often contains random noise and jitter. If these jitters are not processed, they will seriously affect the accuracy and reliability of the data. Using the Bezier curve algorithm, these random noises can be eliminated or reduced through its smoothing characteristics, making the data closer to the true value. In the automatic control system, stable measurement data is also the basis for the stable operation of the system. The use of the Bezier curve algorithm to process the belt scale measurement data reduces the uncertainty caused by data jitter and improves the stability and reliability of the automatic control system.
[0049] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.
[0050] Example 1
[0051] like Figure 1 As shown, this embodiment provides a belt scale measurement data denoising method based on Bezier curve, which specifically includes:
[0052] Step S1: Clean the stored belt scale measurement data at a set time interval to obtain multiple pre-processed data. The weight signal of the coke block on the belt scale and the speed signal of the belt are transmitted to the weighing sensor together. The weighing sensor is a force conversion device that converts the force acting on the scale frame into an electrical signal output, and can automatically calculate the instantaneous flow rate and cumulative output of the coke block, and collect and store the belt scale measurement data in real time as basic data.
[0053] In this embodiment, step S1 specifically includes:
[0054] In the first step, the stored belt scale measurement data is screened at a set time interval to remove the maximum and minimum belt scale measurement data.
[0055] The second step is to calculate the average value of the filtered belt scale measurement data to obtain multiple pre-processed data.
[0056] As a preferred implementation, 6 belt scale measurement data closest to the current moment are selected from the stored belt scale measurement data every 5 seconds for cleaning, a maximum value and a minimum value are removed, and the average value of the remaining 4 belt scale measurement data is calculated. This process is mainly to eliminate abnormal values or erroneous values to ensure the accuracy of the belt scale measurement data. The pseudo code of the data processing function is:
[0057] private static double calculateAverageExcludingMinMax(int[]nums){
[0058] If (nums.length <= 2) {
[0059] throw new IllegalArgumentException("Array must have at least 3
[0060]
[0061] Step S2: Count all pre-processed data within a set period of time, and select a benchmark data and two pre-processed data adjacent to the benchmark data from all pre-processed data.
[0062] In this embodiment, step S2 specifically includes:
[0063] In the first step, all pre-processed data within a set period are counted, and data stabilization stages are divided according to all pre-processed data; the difference between the pre-processed data in the data stabilization stage is less than 10%.
[0064] In the second step, a preprocessed data is randomly selected from the data stabilization stage as the benchmark data.
[0065] The third step is to determine two pre-processed data adjacent to the benchmark data.
[0066] In one example, the preprocessed data within 30 seconds are counted to obtain Table 1. By observing the data in Table 1, it is not difficult to find that the preprocessed data within the period of 0 to 15 seconds is relatively stable, and the difference between the preprocessed data is small, so a preprocessed data can be selected from this stage as the benchmark data. For example: taking 73t / h corresponding to 10 seconds as the benchmark data, the two adjacent preprocessed data can be 69t / h and 72t / h, 72t / h and 63t / h, 72t / h and 72t / h. In other words, it can be the preprocessed data corresponding to 0 seconds and 5 seconds, 15 seconds and 20 seconds, 5 seconds and 15 seconds, and these preprocessed data are adjacent to the benchmark data.
[0067] Table 1 Preprocessed data within 30 seconds
[0068] time 0 seconds 5 seconds 10 seconds 15 seconds 20 seconds 25 seconds 30 seconds Preprocessing Data 69t / h 72t / h 73t / h 72t / h 63t / h 59t / h 63t / h
[0069] Step S3: Based on the value range of the time variable in the Bezier curve algorithm, determine multiple test values of the time variable.
[0070] Before explaining step S3 in detail, let's first introduce the Bezier curve algorithm:
[0071] (1) Select three different points ABC in the two-dimensional plane and connect them with line segments in sequence. For details, see Figure 2 .
[0072] (2) Find two points D and E on line segments AB and BC such that AD / DB = BE / EC. Figure 3 .
[0073] (3) Connect DE and find point F on DE so that it satisfies the parabola tri-tangent theorem AD / DB=BE / EC=DF / FE. For details, see Figure 4 .
[0074] (4) Find all points that meet the above conditions. In addition, in the Bezier curve algorithm, the Bezier curve is divided into a first-order Bezier curve and a second-order Bezier curve, where the first-order Bezier curve formula is:
[0075] B(t)=(1-t)×P0+t×P1;
[0076] The formula for the second-order Bezier curve is:
[0077] B(t)=(1-t) 2 ×P0+2×t×(1-t)×P1+t 2 ×P2;
[0078] Wherein, B(t) is the Bezier curve data, t is the time variable, and P0, P1, and P2 are three adjacent pre-processed data.
[0079] In this embodiment, step S3 specifically includes:
[0080] The first step is to determine the maximum and minimum boundary values of the value range of the time variable.
[0081] The second step is to set the time change interval.
[0082] The third step is to use the minimum boundary value as a reference and increase multiple test values in sequence according to the time interval until the maximum boundary value is reached.
[0083] As a preferred implementation method, the value range of the time variable in the Bezier curve algorithm is determined to be [0,1], with a maximum boundary value of 1 and a minimum boundary value of 0. With 0.1 as the time change interval, the test values of the time variable are set to 0.1, 0.2, 0.3, 0.4...0.9 respectively.
[0084] Step S4: Based on the benchmark data and two pre-processed data adjacent to the benchmark data, the Bezier curve data corresponding to the benchmark data under each test value are calculated using the Bezier curve formula.
[0085] In this example, the Bezier curve formula is a second-order Bezier curve formula, that is, B(t) = (1-t) 2 ×P0+2×t×(1-t)×P1+t 2 ×P2.
[0086] As a preferred implementation, the reference data 75 t / h and the two pre-processed data adjacent to the reference data are kept unchanged, and B(t)=(1-t) 2 ×P0+2×t×(1-t)×P1+t 2 Substitute 0.1, 0.2, 0.3, 0.4, ..., 0.9 into t of ×P2 respectively, and calculate the Bezier curve data corresponding to the benchmark data 75 t / h under different t values. For specific data, see Table 2.
[0087] Table 2 Bezier curve data table under different t values
[0088]
[0089] Step S5: Based on all the benchmark data and the Bezier curve data corresponding to the benchmark data, one test value is selected from all the test values as the standard value of the time variable.
[0090] In this embodiment, step S5 specifically includes:
[0091] The first step is to take the benchmark data and Bezier curve data under each test value as an array.
[0092] The second step is to calculate the difference between the benchmark data and the Bezier curve data in each array.
[0093] In the third step, the test values corresponding to the arrays with difference values less than 10% are taken as the standard values of the time variables.
[0094] As a preferred implementation, according to the data in Table 2, in the 10 arrays, the corresponding benchmark data and the Bezier curve data when t=0.4 are almost the same, and the difference between the two is small, so the time variable in the second-order Bezier curve formula is set to 0.4.
[0095] Step S6: Based on the standard value of the time variable, each pre-processed data is smoothed using the Bezier curve formula to obtain smoothed data with random noise eliminated or reduced; the smoothed data eliminates or reduces the random noise in the belt scale measurement data.
[0096] In this embodiment, step S6 specifically includes:
[0097] In the first step, a preprocessed data is selected as a target.
[0098] In the second step, two pre-processed data adjacent to the target are determined.
[0099] In the third step, based on the standard value of the time variable, the target and the two pre-processed data adjacent to the target are substituted into the Bezier curve formula to obtain the smoothed data corresponding to the target.
[0100] Based on the above analysis, the smoothed data obtained by the above-mentioned belt scale measurement data denoising method based on Bezier curve can be used as the actual value of the coke discharge amount. During use, the data can smoothly display the changing trend of the coke discharge amount, which helps operators better understand the changes in the data.
[0101] Example 2
[0102] This embodiment provides a computer system, which may be a server or a terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer system includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer system is used to provide computing and control capabilities. The memory of the computer system includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer system is used to store video tag processing data. The input / output interface of the computer system is used to exchange information between the processor and an external device. The communication interface of the computer system is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a belt scale measurement data denoising method based on a Bezier curve is implemented.
[0103] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer system to which the scheme of the present application is applied. The specific computer system may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
[0104] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0105] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.
[0106] In summary, this application uses the Bezier curve algorithm to process belt scale measurement data, which has the following advantages:
[0107] 1. High smoothness
[0108] Reduce data jitter: With its smooth characteristics, the Bezier curve can effectively reduce the random fluctuations and noise in the belt scale measurement data, making the processed data more stable, which is conducive to subsequent data analysis and processing.
[0109] Improve data quality: Through the Bezier curve algorithm, the originally chaotic data points can be connected into a smooth curve, thereby improving the overall quality of the data and making the data closer to the changing trend of the actual physical process.
[0110] 2. Good controllability
[0111] Flexible adjustment: The shape of the Bezier curve can be flexibly controlled by adjusting the position of the control point. When processing the measurement data of the belt scale, the position of the control point can be appropriately adjusted according to the specific jitter of the data to achieve the best smoothing effect.
[0112] Precise control: Compared with some simple smoothing algorithms, the Bezier curve algorithm can provide more precise control to ensure that the original characteristics of the data are not overly distorted while smoothing the data.
[0113] 3. Simple and effective calculation
[0114] Efficiency: Although the order of the Bezier curve can be very high, the algorithm for calculating its curve is relatively simple, which means that when processing a large amount of belt scale measurement data, the Bezier curve algorithm can maintain a high computational efficiency and meet real-time requirements.
[0115] Error control: When processing continuous data streams, traditional smoothing algorithms may cause large deviations in results due to the accumulation of errors. However, the Bezier curve algorithm can reduce the accumulation of errors and improve the accuracy of data to a certain extent due to its smoothness and good controllability.
[0116] 4. Data Visualization
[0117] In the field of data visualization, Bezier curves can be used to display data trends more smoothly and reduce visual interference caused by data jitter. By drawing Bezier curves, the data change trend of the coke discharge can be presented in a more intuitive and smoother way, which helps operators better understand the changes in the data.
[0118] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.
[0119] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A belt scale measurement data denoising method based on Bezier curve, characterized in that: The belt scale measurement data denoising method based on Bezier curve includes: Cleaning the stored belt scale measurement data at set time intervals to obtain a plurality of pre-processed data; Count all preprocessed data within a set time period, and select a benchmark data and two preprocessed data adjacent to the benchmark data from all preprocessed data; Based on the value range of the time variable in the Bezier curve algorithm, determining multiple test values of the time variable; Based on the benchmark data and two pre-processed data adjacent to the benchmark data, using a Bezier curve formula to respectively calculate Bezier curve data corresponding to the benchmark data under each test value; Based on all the benchmark data and the Bezier curve data corresponding to the benchmark data, selecting a test value from all the test values as a standard value of the time variable; Based on the standard value of the time variable, each pre-processed data is smoothed using a Bezier curve formula to obtain smoothed data with random noise eliminated or reduced.
2. The belt scale measurement data denoising method based on Bezier curve according to claim 1, characterized in that: The stored belt scale measurement data is cleaned at set intervals to obtain multiple pre-processed data, including: The stored belt scale measurement data are screened at set time intervals, the maximum and minimum belt scale measurement data are removed, and the average value of the screened belt scale measurement data is calculated to obtain a plurality of pre-processed data.
3. The belt scale measurement data denoising method based on Bezier curve according to claim 1, characterized in that: Counting all preprocessed data within a set period, and selecting a benchmark data and two preprocessed data adjacent to the benchmark data from all preprocessed data, specifically including: All pre-processed data within a set period are counted, and data stabilization stages are divided according to all pre-processed data; the difference between the pre-processed data in the data stabilization stage is less than 10%; Randomly select a pre-processed data from the data stabilization stage as the benchmark data; Two pre-processed data adjacent to the reference data are determined.
4. The belt scale measurement data denoising method based on Bezier curve according to claim 1, characterized in that: The Bezier curve formula is a second-order Bezier curve formula.
5. The belt scale measurement data denoising method based on Bezier curve according to claim 4, characterized in that: The second-order Bezier curve formula is specifically: B(t)=(1-t) 2 ×P0+2×t×(1-t)×P1+t 2 ×P2; Wherein, B(t) is the Bezier curve data, t is the time variable, and P0, P1, and P2 are three adjacent pre-processed data.
6. The belt scale measurement data denoising method based on Bezier curve according to claim 1, characterized in that: Based on the value range of the time variable in the Bezier curve formula, multiple test values of the time variable are determined, specifically including: Determine the maximum boundary value and the minimum boundary value of the value range of the time variable; Set time change interval; Taking the minimum boundary value as a reference, multiple test values are obtained by sequentially increasing according to the time variation interval until the maximum boundary value is reached.
7. The belt scale measurement data denoising method based on Bezier curve according to claim 1, characterized in that: The value range of the time variable is [0,1].
8. The belt scale measurement data denoising method based on Bezier curve according to claim 1, characterized in that: Based on all the benchmark data and the Bezier curve data corresponding to the benchmark data, selecting a test value from all the test values as the standard value of the time variable specifically includes: Take the benchmark data and Bezier curve data under each test value as an array; Calculate the difference between the reference data and the Bezier curve data in each array; The test value corresponding to the array with the difference value less than 10% is used as the standard value of the time variable.
9. The belt scale measurement data denoising method based on Bezier curve according to claim 1, characterized in that: Based on the standard value of the time variable, each pre-processed data is smoothed using the Bezier curve formula to obtain smoothed data with random noise eliminated or reduced, specifically including: Select a preprocessed data as a target; Determining two pre-processed data adjacent to the target; Based on the standard value of the time variable, the target and two pre-processed data adjacent to the target are substituted into a Bezier curve formula to obtain smooth data corresponding to the target.
10. A computer system comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the belt scale measurement data denoising method based on Bezier curve according to any one of claims 1 to 9.