Dynamic metrological accuracy estimation method based on weight loss weighing data

Abstract

The application provides a dynamic measurement precision estimation method based on weight loss weighing data. The dynamic measurement precision estimation method based on weight loss weighing data comprises the following steps: extracting and segmenting the weight loss weighing data; performing linear regression analysis on the weight data to obtain residual data sequences outside the material supplementing stage; performing linear regression analysis on the residual data sequences to obtain error data sequences of the material supplementing stage; and calculating the periodic dynamic precision according to the error data sequences of the material supplementing stage. The dynamic measurement precision estimation method based on weight loss weighing data can solve the problem of low weight loss dynamic measurement precision and influence on the accuracy of material supplementing.

Description

Technical Field

[0001] This invention relates to the field of loss-in-weight weighing technology, and more specifically, to a method for estimating dynamic measurement accuracy based on loss-in-weight weighing data. Background Technology

[0002] In industrial applications, to ensure product consistency, material proportions must strictly adhere to the formula, necessitating precise material measurement, as seen in lithium battery manufacturing. In continuous production equipment, the feeding system employs a loss-in-weight scale to continuously feed material at a set flow rate. Current technology operates in volumetric mode during the loss-in-weight scale replenishment and post-replenishment stabilization phases. This means the discharge screw operates at the speed prior to replenishment or the average speed over a previous period, recording only weight data without closed-loop feedback; the replenishment phase remains a measurement blind spot.

[0003] In the prior art known to the inventor, dynamic metering accuracy is calculated based on the weight loss during the non-replenishment stage, which cannot truly reflect the actual dynamic metering accuracy and greatly affects the accuracy of batching. Summary of the Invention

[0004] The main objective of this invention is to provide a dynamic measurement accuracy estimation method based on loss-in-weight weighing data, which can solve the problem of low dynamic measurement accuracy of existing loss-in-weight weighing systems, affecting the accuracy of ingredient dispensing.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for estimating dynamic metrological accuracy based on loss-in-weight weighing data is provided, comprising:

[0006] Extract and segment the weight loss weighing data;

[0007] Linear regression analysis was performed on the weight data to obtain the residual data sequence outside the feeding stage;

[0008] Linear regression analysis was performed on the residual data sequence to obtain the error data sequence during the feeding stage;

[0009] The periodic dynamic accuracy is calculated based on the error data sequence during the feeding stage.

[0010] Furthermore, the step of extracting and segmenting the weight loss weighing data includes:

[0011] The weight change period of the loss-of-weight scale is defined based on the changes in the weight data.

[0012] The weight loss weighing data within a period is segmented.

[0013] Furthermore, the steps for dividing the weight data change period of the loss-in-weight scale based on the weight data changes include:

[0014] The interval between the start of two consecutive material replenishments is defined as one cycle;

[0015] The steps for segmenting loss-in-weight data within a period include:

[0016] The weight loss and weighing data collected within one cycle are divided into three weight data sequences G1, G2, and G3, which correspond to the feeding stage, the stabilization stage, and the closed-loop control stage, respectively.

[0017] Furthermore, the step of performing linear regression analysis on the residual data sequence to obtain the error data sequence during the replenishment stage includes:

[0018] Linear regression analysis was performed on the weight data sequence G3 of the closed-loop control segment to obtain the residual data sequences E2 and E3 of the weight loss weighing data of the stable stage and the closed-loop control stage relative to the linear regression line.

[0019] Further, the steps of performing linear regression analysis on the weight data sequence G3 of the closed-loop control phase to obtain the residual data sequences E2 and E3 of the weight loss and weighing data of the stable phase and the closed-loop control phase relative to the linear regression line include:

[0020] Based on the weight data sequence G3 of the closed-loop control stage, obtain the residual data sequence E3 of the weight loss weighing data relative to the linear regression line during the closed-loop control stage;

[0021] Based on the residual data sequence E3 of the closed-loop control section, the residual data sequence E2 of the weight loss and weighing data in the steady stage relative to the linear regression line is obtained through linear regression analysis.

[0022] Furthermore, the feeding stage and the stabilization stage are the screw constant speed stage, and the closed-loop control stage is the screw variable speed stage.

[0023] Furthermore, the step of performing linear regression analysis on the residual data sequence to obtain the error data sequence during the replenishment stage includes:

[0024] Linear regression analysis was performed on the residual data sequence E2 of the weight loss data during the steady-state phase to obtain the error data sequence E1 during the feeding phase.

[0025] Furthermore, the step of calculating the periodic dynamic accuracy based on the error data sequence of the feeding stage includes:

[0026] The periodic dynamic measurement accuracy e is calculated using the following formula:

[0027]

[0028] Where E1[1] is the first value of the E1 sequence, L is the set flow rate of the weightlessness scale, and T is the cycle time.

[0029] Furthermore, the dynamic measurement accuracy estimation method also includes:

[0030] Obtain dynamic measurement accuracy for multiple consecutive cycles;

[0031] The dynamic measurement accuracy of loss-in-weight data is calculated based on the dynamic measurement accuracy of multiple consecutive cycles.

[0032] Furthermore, the feeding stage is a stage where feeding and discharging coexist, and the stabilization stage is a stage where discharging occurs only.

[0033] The present invention provides a method for estimating dynamic measurement accuracy based on loss-in-weight weighing data, comprising: extracting and segmenting loss-in-weight weighing data; performing linear regression analysis on the weight data to obtain residual data sequences outside the replenishment stage; performing linear regression analysis on the residual data sequences to obtain error data sequences for the replenishment stage; and calculating the periodic dynamic accuracy based on the error data sequences for the replenishment stage. This method segments the loss-in-weight weighing data, then uses the determined segmented data to obtain data for other segments, and predicts the error data sequences for the replenishment stage by performing linear regression on the obtained segmented data. This allows the measurement accuracy of the replenishment stage, which is a measurement blind zone, to be represented by a linear function. Furthermore, the linear function obtained from the linear regression of the replenishment stage can be used to determine the dynamic measurement accuracy of the replenishment stage, calculate the periodic dynamic measurement accuracy, and achieve accurate estimation of dynamic measurement accuracy for continuous production. This enables the dynamic accuracy measurement of the loss-in-weight weighing system to more accurately reflect the true accuracy, greatly improving the accuracy of batching. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0035] Figure 1 A flowchart illustrating a dynamic metrological accuracy estimation method based on loss-in-weight weighing data according to an embodiment of the present invention is shown; and

[0036] Figure 2 The diagram illustrates the relationship between weight data and weight loss over a period of time using a dynamic metrological accuracy estimation method based on weight loss weighing data according to an embodiment of the present invention. Detailed Implementation

[0037] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] See also Figures 1 to 2As shown, this invention provides a dynamic measurement accuracy estimation method based on loss-in-weight weighing data, comprising:

[0039] Extract and segment the weight loss weighing data;

[0040] Linear regression analysis was performed on the weight data to obtain the residual data sequence outside the feeding stage;

[0041] Linear regression analysis was performed on the residual data sequence to obtain the error data sequence during the feeding stage;

[0042] The periodic dynamic accuracy is calculated based on the error data sequence during the feeding stage.

[0043] This dynamic measurement accuracy estimation method based on loss-in-weight weighing data segments the loss-in-weight weighing data, then uses the determined segmented data to obtain data for other segments, and predicts the error data sequence of the replenishment stage by performing linear regression on the obtained segmented data. This allows the measurement accuracy of the replenishment stage, which is a measurement blind zone, to be represented by a linear function. Furthermore, the dynamic measurement accuracy of the replenishment stage can be determined using the linear function obtained from the linear regression, and the periodic dynamic measurement accuracy can be calculated. This enables accurate estimation of the dynamic measurement accuracy of continuous production, allowing the dynamic accuracy measurement of the loss-in-weight weighing system to more accurately reflect the true accuracy and greatly improving the accuracy of batching.

[0044] In one embodiment, the step of extracting and segmenting the weight loss weighing data includes:

[0045] The weight change period of the loss-of-weight scale is defined based on the changes in the weight data.

[0046] The weight loss weighing data within a period is segmented.

[0047] By periodically dividing the loss-in-weight weighing data and selecting reasonable division criteria, the difficulty of linear regression analysis can be reduced, making it easier to obtain accurate and reliable linear regression lines. Furthermore, it facilitates the division of segments where weight data is readily available versus those where it is difficult to obtain, resulting in more rational segmentation. This allows for the determination of linear regression lines based on available weight data segments. By utilizing these linear regression lines and the relationships between different data segments, linear regression analysis can be used to obtain linear regression lines for segments where weight data is difficult to obtain. This helps determine the error data sequence for these segments, providing a basis for dynamic accuracy measurement during the replenishment stage. This, in turn, allows for the calculation of the weight change accuracy of the loss-in-weight weighing system, enabling the adjustment of material proportions based on the dynamic measurement accuracy of the data. This makes the material proportions controllable and more accurate throughout the entire cycle, effectively improving the accuracy and reliability of material batching.

[0048] In one embodiment, the step of dividing the weight data change period of the loss-of-weight scale based on the weight data change includes:

[0049] The interval between the start of two consecutive material replenishments is defined as one cycle;

[0050] The steps for segmenting loss-in-weight data within a period include:

[0051] The weight loss and weighing data collected within one cycle are divided into three weight data sequences G1, G2, and G3, which correspond to the feeding stage, the stabilization stage, and the closed-loop control stage, respectively.

[0052] In this embodiment, the start trigger interval between two adjacent feedings is defined as a cycle. The working cycle of the loss-in-weight scale can be divided according to its working principle, so that the cycle division of the loss-in-weight scale matches its working principle. The cycle division is more reasonable, which can obtain more accurate and reliable data in subsequent dynamic measurement accuracy estimation, and can also effectively reduce the difficulty of dynamic measurement accuracy estimation.

[0053] In one embodiment, the feeding stage and the stabilization stage are the screw constant speed stage, and the closed-loop control stage is the screw variable speed stage.

[0054] In one embodiment, the feeding stage is a stage in which feeding and discharging coexist, and the stabilization stage is a stage in which discharging occurs alone.

[0055] During the operation of the loss-in-weight weighing system, the screw operates in two phases: a uniform speed phase and a variable speed phase. The uniform speed phase includes both a feeding and discharging phase and a discharging phase alone. The variable speed phase is a discharging phase alone. The operating phases of the loss-in-weight weighing system are divided using the uniform speed and variable speed phases of the screw's actual rotational speed. The uniform speed phase of the screw's actual rotational speed is further subdivided based on the loss-in-weight weighing data from the closed-loop control section. This linear function can then be used to deduce the linear function of the loss-in-weight weighing data in the stable phase. Furthermore, the linear function of the stable phase loss-in-weight weighing data and the residual data sequence can be used to determine the linear function of the residual data sequence in the feeding phase through linear regression analysis. Finally, the linear function of the residual data sequence can be used to determine the error data sequence in the feeding phase, providing a basis for calculating the periodic dynamic measurement accuracy.

[0056] In one embodiment, the step of performing linear regression analysis on the residual data sequence to obtain the error data sequence during the replenishment stage includes:

[0057] Linear regression analysis was performed on the weight data sequence G3 of the closed-loop control segment to obtain the residual data sequences E2 and E3 of the weight loss weighing data of the stable stage and the closed-loop control stage relative to the linear regression line.

[0058] In one embodiment, the step of performing linear regression analysis on the weight data sequence G3 of the closed-loop control phase to obtain the residual data sequences E2 and E3 of the weight loss weighing data of the stable phase and the closed-loop control phase relative to the linear regression line includes:

[0059] Based on the weight data sequence G3 of the closed-loop control stage, obtain the residual data sequence E3 of the weight loss weighing data relative to the linear regression line during the closed-loop control stage;

[0060] Based on the residual data sequence E3 of the closed-loop control section, the residual data sequence E2 of the weight loss and weighing data in the steady stage relative to the linear regression line is obtained through linear regression analysis.

[0061] In this embodiment, the weight data sequence G3 of the closed-loop control segment can be directly obtained. By performing linear regression analysis on the weight data sequence G3, the residual data sequence E3 of the closed-loop control segment can be obtained. Based on this, by combining the weight data sequence G2 of the stable stage and the linear regression line of the stable stage data sequence obtained through linear regression analysis, the residual data sequence E2 of the weight data sequence G2 of the stable stage relative to the linear regression line can be obtained, which facilitates the subsequent calculation of the error data sequence of the feeding stage.

[0062] In one embodiment, the step of performing linear regression analysis on the residual data sequence to obtain the error data sequence during the replenishment stage includes:

[0063] Linear regression analysis was performed on the residual data sequence E2 of the weight loss data during the steady-state phase to obtain the error data sequence E1 during the feeding phase.

[0064] In this embodiment, the error data sequence E1 of the replenishment stage can be considered to be on the same linear regression line as the residual data sequence E2 of the stable stage. Therefore, after performing linear regression analysis on the residual data sequence E2 of the stable stage loss-in-weight weighing data using the above method and obtaining the linear regression line of the residual data sequence E2 of the stable stage loss-in-weight weighing data, the linear regression line of the residual data sequence E2 of the stable stage loss-in-weight weighing data is extended in the opposite direction to the replenishment stage. The error data sequence E1 of the replenishment stage can then be obtained through linear regression analysis, so that the error data of the replenishment stage can also be represented by a function, so that the measurement of the replenishment stage can also be carried out, so that the material ratio of the replenishment stage can also be accurately controlled, and the measurement accuracy of the entire working process of the loss-in-weight weigher can be calculated, thereby effectively solving the problem that the dynamic measurement accuracy calculation of the existing loss-in-weight weigher cannot reflect the true accuracy.

[0065] In one embodiment, the step of calculating the periodic dynamic accuracy based on the error data sequence of the feeding phase includes:

[0066] The periodic dynamic measurement accuracy e is calculated using the following formula:

[0067]

[0068] Where E1[1] is the first value of the E1 sequence, L is the set flow rate of the weightlessness scale, and T is the cycle time.

[0069] In one embodiment, the dynamic measurement accuracy estimation method further includes:

[0070] Obtain dynamic measurement accuracy for multiple consecutive cycles;

[0071] The dynamic measurement accuracy of loss-in-weight data is calculated based on the dynamic measurement accuracy of multiple consecutive cycles.

[0072] When calculating the dynamic measurement accuracy of loss-in-weight weighing data, it can be done by averaging the dynamic measurement accuracy over multiple consecutive periods, or by using the variance method.

[0073] This invention proposes a method for estimating dynamic measurement accuracy based on loss-in-weight weighing data. The interval between two adjacent replenishment triggers is defined as a cycle. Each cycle is divided into a replenishment stage, a stabilization stage, and a closed-loop control stage. Linear regression analysis is performed on the loss-in-weight weighing data in the closed-loop control stage, and the residuals in the stabilization stage and the closed-loop control stage are calculated. Then, linear regression analysis is performed on the residuals in the stabilization stage to calculate the error in the replenishment stage. Thus, the cycle measurement accuracy can be calculated, and the dynamic measurement accuracy for continuous production can be estimated.

[0074] After adopting the embodiments of the present invention, in conjunction with see [link to previous document] Figure 2 As shown, the absolute measurement error of the cycle is 2kg, that is, E1[1]=2, the cycle time is 278s, and the theoretical feed of the cycle is 115kg based on the flow rate, and the cycle measurement accuracy is 1.7%.

[0075] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0076] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating dynamic metrological accuracy based on loss-in-weight weighing data, characterized in that, include: Extract and segment the weight loss weighing data; Linear regression analysis was performed on the weight data to obtain the residual data sequence outside the feeding stage; Linear regression analysis was performed on the residual data sequence to obtain the error data sequence during the feeding stage; The cycle dynamic accuracy is calculated based on the error data sequence during the feeding phase; The steps for extracting and segmenting the weight loss weighing data include: The weight change period of the loss-of-weight scale is defined based on the changes in the weight data. Segment the weight loss weighing data within a single cycle; The steps for dividing the weight change period of a loss-of-weight scale based on the weight data changes include: The interval between the start of two consecutive material replenishments is defined as one cycle; The steps for segmenting loss-in-weight data within a period include: The weight loss weighing data collected within one cycle is divided into three weight data sequences G1, G2, and G3, which correspond to the feeding stage, stabilization stage, and closed-loop control stage, respectively. The steps of performing linear regression analysis on the residual data sequence to obtain the error data sequence during the replenishment stage include: Linear regression analysis was performed on the weight data sequence G3 of the closed-loop control phase to obtain the residual data sequences E2 and E3 of the weight loss and weighing data of the stable phase and the closed-loop control phase relative to the linear regression line. The steps for performing linear regression analysis on the weight data sequence G3 of the closed-loop control phase to obtain the residual data sequences E2 and E3 of the weight loss weighing data relative to the linear regression line during the stable phase and the closed-loop control phase include: Based on the weight data sequence G3 of the closed-loop control stage, obtain the residual data sequence E3 of the weight loss weighing data relative to the linear regression line during the closed-loop control stage; Based on the residual data sequence E3 of the closed-loop control section, the residual data sequence E2 of the weight loss and weighing data in the steady stage relative to the linear regression line is obtained through linear regression analysis. The steps of performing linear regression analysis on the residual data sequence to obtain the error data sequence during the replenishment stage include: Linear regression analysis was performed on the residual data sequence E2 of the weight loss data during the steady-state phase to obtain the error data sequence E1 during the feeding phase.

2. The dynamic measurement accuracy estimation method according to claim 1, characterized in that, The feeding stage and the stabilization stage are the screw constant speed stage, and the closed-loop control stage is the screw variable speed stage.

3. The dynamic measurement accuracy estimation method according to claim 1, characterized in that, The step of calculating the periodic dynamic accuracy based on the error data sequence of the feeding stage includes: The periodic dynamic measurement accuracy e is calculated using the following formula: ； in The first value of the E1 sequence. L Set the flow rate for the weightlessness scale. T The periodic time.

4. The dynamic measurement accuracy estimation method according to claim 1, characterized in that, The dynamic measurement accuracy estimation method also includes: Obtain dynamic measurement accuracy for multiple consecutive cycles; The dynamic measurement accuracy of loss-in-weight data is calculated based on the dynamic measurement accuracy of multiple consecutive cycles.

5. The dynamic measurement accuracy estimation method according to claim 2, characterized in that, The feeding stage is a stage in which feeding and discharging coexist, and the stabilization stage is a stage in which discharging occurs alone.

Images