High-voltage cable production process parameter management method and system

Abstract

The invention relates to the field of data processing, in particular to a high-voltage cable production process parameter management method and system, and the method comprises the steps: obtaining a measurement value of the same process parameter at the current moment through a plurality of sensors, and correcting the measurement values of the plurality of sensors at the current moment to obtain an optimal measurement value for the management of a production process; the method for correcting the measured value at the current moment comprises the following steps of: constructing an optimal measurement matrix by utilizing the optimal measured value at the previous moment, taking the product of the optimal measurement matrix and a preset first state transition matrix as a predicted value of the technological parameter at the current moment, and calculating the adaptive Kalman gain of each sensor. The method has the effect of improving the accuracy of process parameter acquisition.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for managing process parameters of high-voltage cable production. Background Art

[0002] In the production process of high-voltage cables, it is necessary to accurately manage and monitor the production process parameters to ensure the production quality of the cables. In order to monitor and control various parameters in the production process, such as temperature, pressure, tension, etc., it is usually necessary to rely on sensors for real-time data collection. A single sensor is easily affected by external environmental changes during the production process of high-voltage cables, such as temperature fluctuations, vibrations, and equipment aging, resulting in error accumulation, which further reduces the accuracy of the measurement results. In the prior art, multi-sensor fusion technology is used to combine the measurement results of multiple sensors to reduce the error and noise of a single sensor.

[0003] The patent document with the announcement number CN114202025B discloses a multi-sensor data fusion method, which removes dirty data through coarse clustering and fine clustering and associates data with higher credibility, then performs spatiotemporal alignment on the target data in the same cluster, performs identity determination on the target data after spatiotemporal alignment, deletes the target data that does not meet the identity determination from the cluster, and finally fuses the target data in the cluster that passes the identity determination. Data fusion of multiple sensors and multiple targets is achieved.

[0004] However, in the data fusion process, when multiple sensors are affected by the same or similar environmental factors, the errors of the sensors may be correlated. Traditional multi-sensor fusion does not take into account the error correlation and noise characteristics of different sensors, which leads to the aggravation of error transmission, thus resulting in low accuracy of data fusion results. Summary of the invention

[0005] In order to improve the accuracy of data fusion in the production process of high-voltage cables, the present invention provides a method and system for managing process parameters of high-voltage cable production.

[0006] In a first aspect, the present invention provides a method for managing process parameters of high voltage cable production, which adopts the following technical solution: Using multiple sensors to obtain the measurement value of the same process parameter at the current moment, correcting the measurement values ​​of multiple sensors at the current moment to obtain the optimal measurement value for use in managing the production process; The method to correct the current measurement value is: Using multiple sensors to obtain the measurement value of the same process parameter at the current moment, and correcting the measurement values ​​of multiple sensors at the current moment to obtain the optimal measurement value; The method for correcting the measured value is: The optimal measurement matrix is ​​constructed using the optimal measurement value at the previous moment, and the product of the optimal measurement matrix and the preset first state transfer matrix is ​​used as the predicted value of the process parameter at the current moment; the adaptive Kalman gain of each sensor is calculated; The expression of the optimal measurement value is: ; In the formula, represents the optimal measurement value at time k, represents the predicted value of the process parameter at time k, represents the measured value of sensor i, represents the adaptive Kalman gain of sensor i at time k, and n represents the number of sensors.

[0007] The effect is that the process parameters are corrected based on the fusion weight of each sensor to obtain the optimal measurement value. During the correction process, the measurement error of each sensor is comprehensively considered, which achieves accurate correction of the process parameters and improves the measurement accuracy of production process parameters.

[0008] Preferably, the method further comprises: Calculate the error covariance of any two sensors and construct the sensor error covariance matrix; The mean of the difference between the sensor's measured value and the true value is taken as the measurement noise, the covariance of the measurement noise of any two sensors is calculated, and the covariance matrix of the measurement noise is constructed; Calculate the estimate of the error covariance matrix at the current moment, the expression is: ; In the formula, represents the estimate of the error covariance matrix at time k, represents the error covariance matrix at time k-1, is the preset second state transfer matrix, represents the covariance matrix of the measurement noise at time k-1.

[0009] The effect is that the error covariance at the current moment is calculated by the error covariance at the historical moment and the measurement noise covariance matrix, which avoids the error transmission phenomenon and improves the accuracy of the error covariance.

[0010] Preferably, the expression of the adaptive Kalman gain is: ; In the formula, represents the adaptive Kalman gain of sensor i at time k, represents the third state transfer matrix at the preset k time, represents the estimate of the error covariance matrix at time k, represents the variance of the measurement noise of sensor i at time k.

[0011] The effect is that the adaptive Coleman gain of each sensor is calculated by the error correlation and measurement noise correlation between sensors, thus avoiding the error transmission phenomenon. The accuracy of the correction result can be improved by the adaptive Kalman gain.

[0012] Preferably, the expression of the adaptive Kalman gain is: ; In the formula, represents the adaptive Kalman gain of sensor i at time k, represents the third state transfer matrix at the preset time j, represents the estimate of the error covariance matrix at time j, represents the variance of the measurement noise of sensor i at time j, and m represents the number of measurements from the initial time to time k.

[0013] Preferably, the expression of the error covariance matrix of the sensor is: ; In the formula, represents the error covariance matrix of the sensor, represents the independent error covariance of sensor n, represents the error covariance between sensors i and j.

[0014] The effect is that the error covariance can be used to understand the error correlation between sensors, making it easier to calculate the adaptive Kalman gain.

[0015] Preferably, the expression of the covariance matrix of the measurement noise is: ; Where Q represents the covariance matrix of the measurement noise, represents the independent covariance of the measurement noise of sensor i, represents the covariance between the measurement noise of sensor i and sensor j.

[0016] The effect is that the correlation between the sensor measurement noise can be understood through the covariance matrix of the measurement noise.

[0017] Preferably, the process parameters include wire drawing temperature, tension, pressure and acceleration during the production process of the high voltage cable.

[0018] In a second aspect, the present invention provides a high-voltage cable production process parameter management system, which adopts the following technical solution: A high-voltage cable production process parameter management system, a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a high-voltage cable production process parameter management method according to the above is implemented.

[0019] The beneficial effect is that the above-mentioned high-voltage cable production process parameter management method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0020] The present invention has the following technical effects: The present invention calculates the adaptive Kalman gains of different sensors based on the estimated error covariance matrix and the variance of the measurement noise, takes the adaptive Kalman gains as the fusion weights of different sensors, realizes the correction of production process parameters by improving the data fusion strategy, adaptively adjusts the fusion weights based on the error correlation of different sensors and the change of the measurement noise, improves the fusion strategy according to the adjusted fusion weights to realize the correction of production process parameters, and improves the acquisition accuracy of production process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 The present invention is a flowchart of a method for managing process parameters of high-voltage cable production. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The embodiment of the present invention discloses a method for managing process parameters of high voltage cable production, referring to Figure 1 , including the following steps, as follows: S1: Use multiple sensors to obtain the measurement value of the same process parameter at the current moment.

[0026] In the production process of high-voltage cables, multiple sensors are used to obtain the same process parameters, wherein the process parameters include wire drawing temperature, tension, pressure and acceleration. For example, five temperature sensors are used to measure the wire drawing temperature at the same position to obtain five temperature measurement values.

[0027] S2: Calculate the error covariance of any two sensors and construct the error covariance matrix of the sensors.

[0028] Due to the different characteristics of different sensors, different nonlinear errors will occur when using multiple sensors to measure production process parameters, resulting in large deviations in the measurement results. Therefore, it is necessary to calculate the error of the corresponding sensor based on the measured value and true value of the process parameters in the historical data.

[0029] The expression of sensor error is: ; In the formula, represents the measurement error of sensor i, represents the true value of the process parameter measured by sensor i, Represents the measured value of the production process parameter measured by sensor i.

[0030] Since the sensors in a multi-sensor system are in the same measurement environment, the errors of different sensors are not completely independent, which leads to the phenomenon that the errors of multiple sensors are correlated when the sensor data is fused, causing the measurement result errors to be large. Therefore, the independent error covariance of the sensor itself and the error covariance between different sensors are calculated through the sensor errors, and the joint covariance matrix is ​​constructed based on the error covariance and the independent error covariance to analyze the correlation of the errors between sensors.

[0031] The error covariance between different sensors is: ; In the formula, represents the error covariance between sensor i and sensor j, represents the expected value of the error of sensor i, represents the expected value of sensor j error, and The value of is set artificially according to the actual situation. The error covariance indicates the correlation between the measurement errors of the two sensors. A positive value indicates that the measurement errors of the two sensors are positively correlated, a negative value indicates that the measurement errors of the two sensors are negatively correlated, and a value of zero indicates that the measurement errors of the two sensors are uncorrelated.

[0032] The expression of the sensor error covariance matrix is: ; In the formula, represents the error covariance matrix of the sensor, It represents the independent error covariance of sensor n, which can also be understood as the variance of the error when sensor n measures the process parameters. represents the error covariance between sensors i and j, and the matrix size of the error covariance is n×n.

[0033] S3: Take the mean of the difference between the sensor's measured value and the true value as the measurement noise, calculate the covariance of the measurement noise of any two sensors, and construct the covariance matrix of the measurement noise.

[0034] The measurement noise of the corresponding sensor is calculated based on the measured value and true value of the process parameter in the historical data. The expression of the measurement noise is: ; represents the measurement noise of sensor i, represents the measurement value of the production process parameter by sensor i at time t, represents the true value of the production process parameter at time t, T represents the number of historical production process parameters, and the measurement noise represents the average measurement error of sensor i.

[0035] The expression of the covariance matrix of the measurement noise is:

[0036] Where Q represents the covariance matrix of the measurement noise, represents the independent covariance of the measurement noise of sensor i, which can also be understood as the variance of the measurement noise of sensor i. represents the covariance between the measurement noises of sensor i and sensor j. The calculation method of the covariance between the measurement noises is the same as the calculation method of the error covariance between different sensors in step S2. The size of the covariance matrix of the measurement noise is n×n. The elements in the covariance matrix of the measurement noise represent the correlation between the measurement noises of the two sensors.

[0037] S4: Calculate the estimate of the error covariance matrix at the current moment.

[0038] In one embodiment, the estimated expression of the error covariance matrix is: ; In the formula, represents the estimate of the error covariance matrix at time k, represents the error covariance matrix at time k-1, is the preset second state transfer matrix, represents the covariance matrix of the measurement noise at time k-1. It means that the error covariance matrix at time k is estimated based on the error covariance matrix at time k-1. When estimating the error covariance matrix at time k, the correlation of the measurement errors and the correlation of the measurement noise between the sensors at the previous moment are considered, and the error covariance matrix at time k is adaptively adjusted to improve the accuracy of the data elements of the error covariance matrix at time k. The second state transfer matrix is ​​artificially set according to the implementation situation, exemplarily: ; S5: Calculate the adaptive Kalman gain of the sensor.

[0039] In one embodiment, the expression of the adaptive Kalman gain is: ; In the formula, represents the adaptive Kalman gain of sensor i at time k, represents the third state transfer matrix at the preset k time, the size of the third state transfer matrix is ​​1×n, represents the estimate of the error covariance matrix at time k, It represents the variance of the measurement noise of sensor i at time k. From the initial moment to time k, each moment corresponds to a measurement noise. The variance is calculated based on the measurement noise at the historical moment and time k, and the obtained variance is used as the variance of the measurement noise at time k. The third state transfer matrix is ​​artificially set according to the actual situation. For example, the third state transfer matrix is: . represents the error correlation between sensor i and the rest of the sensors.

[0040] When calculating the adaptive Kalman gain, multiple factors such as the correlation of the sensor's measurement error and the correlation of the measurement noise are comprehensively considered, which effectively reduces the phenomenon of error transmission. The adaptive Kalman gain can adaptively adjust the weight of the sensor's measurement value.

[0041] In one embodiment, the expression of the adaptive Kalman gain is: ; In the formula, represents the adaptive Kalman gain of sensor i at time k, represents the third state transfer matrix at the preset time j, represents the estimate of the error covariance matrix at time j, represents the variance of the measurement noise at time j, and m represents the number of measurements from the initial time to time k.

[0042] S6: Correct the measurement values ​​of multiple sensors at the current moment to obtain the optimal measurement value.

[0043] The method to correct the current measurement value is: S61: construct an optimal measurement matrix using the optimal measurement value at the previous moment, and use the product of the optimal measurement matrix and the preset first state transfer matrix as the predicted value of the process parameter at the current moment.

[0044] The expression of the predicted value of the process parameter at the current moment is: ; In the formula, represents the predicted value of the process parameters at time k, Represents the optimal measurement value matrix of the process parameters at time k-1, the matrix size is n×1, It is the preset first state transfer matrix, with a size of 1×n.

[0045] For example, for , the elements in the first row of the matrix are the optimal measured values ​​of the process parameters at time k-1, and the values ​​of the remaining rows are all 0; for , except for the elements in the first column of the matrix, all other matrix elements are 0.

[0046] S62: Calculate the optimal measurement value at the current moment.

[0047] The expression of the optimal measurement value at the current moment is: ; In the formula, represents the optimal measurement value at time k, represents the predicted value of the process parameter at time k, represents the measured value of sensor i, represents the adaptive Kalman gain of sensor i at time k, and n represents the number of sensors.

[0048] The present invention calculates the error of each sensor based on the measured value and the true value of the process parameter in the historical data, constructs the error covariance matrix between different sensors according to the errors of different sensors, calculates the covariance matrix of the measurement noise according to the true value and the measured value of the parameter at the historical moment, estimates the error covariance matrix of the process parameter at the current moment through the error covariance matrix of the process parameter collected at the previous moment, calculates the adaptive Kalman gain of different sensors based on the estimated error covariance matrix and the variance of the measurement noise, and uses the adaptive Kalman gain as the fusion weight of different sensors. The present invention improves the data fusion strategy to realize the correction of the production process parameters, adaptively adjusts the fusion weight based on the error correlation of different sensors and the change of the measurement noise, improves the fusion strategy according to the adjusted fusion weight to realize the correction of the production process parameters, and improves the acquisition accuracy of the production process parameters.

[0049] An embodiment of the present invention further discloses a high-voltage cable production process parameter management system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a high-voltage cable production process parameter management method according to the present invention is implemented.

[0050] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0051] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0052] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0053] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing process parameters of high voltage cable production, characterized in that: Includes steps: Using multiple sensors to obtain the measurement value of the same process parameter at the current moment, and correcting the measurement values ​​of multiple sensors at the current moment to obtain the optimal measurement value; The method for correcting the measured value is: The optimal measurement matrix is ​​constructed using the optimal measurement value at the previous moment, and the product of the optimal measurement matrix and the preset first state transfer matrix is ​​used as the predicted value of the process parameter at the current moment; the adaptive Kalman gain of each sensor is calculated; The expression of the optimal measurement value is: ； In the formula, represents the optimal measurement value at time k, represents the predicted value of the process parameter at time k, represents the measured value of sensor i, represents the adaptive Kalman gain of sensor i at time k, and n represents the number of sensors.

2. A method for managing process parameters of high voltage cable production according to claim 1, characterized in that: The method also includes: Calculate the error covariance of any two sensors and construct the sensor error covariance matrix; The mean of the difference between the sensor's measured value and the true value is taken as the measurement noise, the covariance of the measurement noise of any two sensors is calculated, and the covariance matrix of the measurement noise is constructed; Calculate the estimate of the error covariance matrix at the current moment, the expression is: ； In the formula, represents the estimate of the error covariance matrix at time k, represents the error covariance matrix at time k-1, is the preset second state transfer matrix, represents the covariance matrix of the measurement noise at time k-1.

3. A method for managing process parameters of high voltage cable production according to claim 2, characterized in that: The expression of adaptive Kalman gain is: ； In the formula, represents the adaptive Kalman gain of sensor i at time k, represents the third state transfer matrix at the preset k time, represents the estimate of the error covariance matrix at time k, represents the variance of the measurement noise of sensor i at time k.

4. A method for managing process parameters of high voltage cable production according to claim 2, characterized in that: The expression of adaptive Kalman gain is: ； In the formula, represents the adaptive Kalman gain of sensor i at time k, represents the third state transfer matrix at the preset time j, represents the estimate of the error covariance matrix at time j, represents the variance of the measurement noise of sensor i at time j, and m represents the number of measurements from the initial time to time k.

5. A method for managing process parameters of high voltage cable production according to claim 2, characterized in that: The expression of the sensor error covariance matrix is: ； In the formula, represents the error covariance matrix of the sensor, represents the independent error covariance of sensor n, represents the error covariance between sensors i and j.

6. A method for managing process parameters of high voltage cable production according to claim 2, characterized in that: The expression of the covariance matrix of the measurement noise is: ； Where Q represents the covariance matrix of the measurement noise, represents the independent covariance of the measurement noise of sensor i, represents the covariance between the measurement noise of sensor i and sensor j.

7. A method for managing process parameters of high voltage cable production according to claim 1, characterized in that: Process parameters include wire drawing temperature, tension, pressure and acceleration during the production of high-voltage cables.

8. A high voltage cable production process parameter management system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for managing process parameters of high-voltage cable production according to any one of claims 1 to 7 is implemented.

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