A method and system for managing process parameters in high-voltage cable production

Abstract

The present invention relates to the field of data processing, and in particular to a method and system for managing process parameters in the production of high-voltage cables. The method includes: using multiple sensors to obtain measurement values of the same process parameter at the current moment, and correcting the measurement values of the multiple sensors at the current moment to obtain an optimal measurement value for managing the production process; the method for correcting the measurement value at the current moment is: constructing an optimal measurement matrix using the optimal measurement value at the previous moment, taking the product of the optimal measurement matrix and a preset first state transition matrix as the predicted value of the process parameter at the current moment, and calculating the adaptive Kalman gain of each sensor. The present invention 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 particularly to a method and system for managing production process parameters of high-voltage cables. Background Art

[0002] During the production process of high-voltage cables, it is necessary to precisely manage and monitor production process parameters to ensure the production quality of the cables. In order to monitor and control various parameters during the production process, such as temperature, pressure, tension, etc., it is usually necessary to rely on sensors for real-time data acquisition. A single sensor is prone to being 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 and further lower accuracy of the measurement results. In the prior art, the measurement results of multiple sensors are combined through multi-sensor fusion technology to reduce the errors and noise of a single sensor.

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

[0004] However, during 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 consider the error correlation and noise characteristics of different sensors, resulting in the exacerbation of error transmission. Therefore, the accuracy of the data fusion result is relatively low. Summary of the Invention

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

[0006] In a first aspect, the present invention provides a method for managing production process parameters of high-voltage cables, adopting the following technical solution:

[0007] Obtain the measured values of the same process parameter at the current moment by using multiple sensors, and correct the measured values of the multiple sensors at the current moment to obtain the optimal measured value for managing the production process;

[0008] The method for correcting the measured value at the current moment is:

[0009] Obtain the measured values of the same process parameter at the current moment by using multiple sensors, and correct the measured values of the multiple sensors at the current moment to obtain the optimal measured value;

[0010] The method for correcting the measurement value is as follows:

[0011] Construct an optimal measurement matrix using the optimal measurement value at the previous moment, and take the product of the optimal measurement matrix and a preset first state transition matrix as the predicted value of the process parameter at the current moment; calculate the adaptive Kalman gain of each sensor;

[0012] The expression for the optimal measurement value is:

[0013] ;

[0014] In the formula, represents the optimal measurement value at time k, represents the predicted value of the process parameter at time k, represents the measurement value of sensor i, represents the adaptive Kalman gain of sensor i at time k, and n represents the number of sensors.

[0015] The effect is that the process parameter is corrected based on the fusion weight of each sensor to obtain the optimal measurement value. During the correction process, the measurement errors of each sensor are comprehensively considered, achieving precise correction of the process parameter and improving the measurement accuracy of the production process parameter.

[0016] Preferably, the method further includes:

[0017] Calculate the error covariance between any two sensors and construct an error covariance matrix of the sensors;

[0018] Take the mean value of the difference between the measurement value of the sensor and the true value as the measurement noise, calculate the covariance of the measurement noise between any two sensors, and construct a covariance matrix of the measurement noise;

[0019] Calculate the estimation of the error covariance matrix at the current moment, and the expression is:

[0020] ;

[0021] In the formula, represents the estimation of the error covariance matrix at time k, represents the error covariance matrix at time k - 1, is a preset second state transition matrix, represents the covariance matrix of the measurement noise at time k - 1.

[0022] The effect is that the error covariance at the current moment is calculated through the error covariance and the measurement noise covariance matrix at the historical moment, avoiding the phenomenon of error transmission and improving the accuracy of the error covariance.

[0023] Preferably, the expression for the adaptive Kalman gain is:

[0024] ;

[0025] wherein, represents the adaptive Kalman gain of sensor i at time k, represents the preset third state transition matrix at time k, represents the estimation of the error covariance matrix at time k, represents the variance of the measurement noise of sensor i at time k.

[0026] Its effect is that by calculating the adaptive Kalman gain of each sensor through the error correlation and measurement noise correlation between sensors, the phenomenon of error transmission is avoided, and the accuracy of the correction result can be improved through the adaptive Kalman gain.

[0027] Preferably, the expression of the adaptive Kalman gain is:

[0028] ;

[0029] wherein, represents the adaptive Kalman gain of sensor i at time k, represents the preset third state transition matrix at time j, represents the estimation 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.

[0030] Preferably, the expression of the error covariance matrix of the sensor is:

[0031] ;

[0032] wherein, 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.

[0033] Its effect is that through the error covariance, the error correlation between sensors can be understood, which is convenient for calculating the adaptive Kalman gain.

[0034] Preferably, the expression of the covariance matrix of the measurement noise is:

[0035] ;

[0036] wherein Q represents the covariance matrix of the measurement noise, represents the independent covariance of the measurement noise of sensor i, Denotes the covariance between the measurement noises of sensor i and sensor j.

[0037] The effect is that through the covariance matrix of the measurement noise, the correlation between the measurement noises of the sensors can be understood.

[0038] Preferably, the process parameters include the drawing temperature, drawing force, pressure and acceleration during the production of high-voltage cables.

[0039] In a second aspect, the present invention provides a management system for process parameters in the production of high-voltage cables, adopting the following technical solution:

[0040] A management system for process parameters in the production of high-voltage cables, a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a management method for process parameters in the production of high-voltage cables according to the above is implemented.

[0041] The beneficial effect is: generating a computer program for the above management method for process parameters in the production of high-voltage cables and storing it in the memory to be loaded and executed by the processor. Thus, making a system according to the memory and the processor is convenient to use.

[0042] The present invention has the following technical effects:

[0043] The present invention calculates the adaptive Kalman gain of different sensors based on the estimated error covariance matrix and the variance of the measurement noise, takes the adaptive Kalman gain as the fusion weight of different sensors, corrects the process parameters by improving the data fusion strategy, adaptively adjusts the fusion weight based on the error correlation of different sensors and the change of the measurement noise, and improves the acquisition accuracy of the process parameters by improving the fusion strategy according to the adjusted fusion weight. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0045] Figure 1 Is a flowchart of a management method for process parameters in the production of high-voltage cables according to the present invention. DETAILED DESCRIPTION

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] It should be understood that when terms such as "first" and "second" are used in the claims, the description, and the drawings of the present invention, they are only used to distinguish different objects and not to describe a specific order. The terms "including" and "comprising" 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 combinations.

[0048] An embodiment of the present invention discloses a method for managing process parameters in the production of high-voltage cables. Referring to Figure 1 , the following steps are included, specifically as follows:

[0049] S1: Use multiple sensors to obtain the measured values of the same process parameter at the current moment.

[0050] During the production process of high-voltage cables, multiple sensors are used to obtain the same process parameter. Among them, the process parameters include drawing temperature, tensile force, pressure, and acceleration. Exemplarily, 5 temperature sensors are used to measure the drawing temperature at the same position, and 5 measured values of temperature are obtained.

[0051] S2: Calculate the error covariance between any two sensors and construct an error covariance matrix of the sensors.

[0052] Since the characteristics of different sensors are different, when multiple sensors are used to measure production process parameters, different non-linear errors will occur, resulting in a large deviation in the measurement results. Therefore, it is necessary to calculate the errors of the corresponding sensors based on the measured values and true values of the process parameters in the historical data.

[0053] The expression for the error of the sensor is:

[0054] ;

[0055] 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.

[0056] Since the sensors in a multi-sensor system are in the same measurement environment, the errors of different sensors are not completely independent. As a result, when fusing sensor data, there will be a phenomenon that the measurement results have a large error due to the correlation of the errors of multiple sensors. Therefore, the independent error covariance of the sensor itself and the error covariance between different sensors are calculated through the errors of the sensors, and a joint covariance matrix is constructed based on the error covariance and the independent error covariance to analyze the correlation of the errors between sensors.

[0057] The error covariance between different sensors is:

[0058] ;

[0059] 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 the error of sensor j, and The values of

[0060] The expression of the error covariance matrix of the sensor is:

[0061] ;

[0062] In the formula, represents the error covariance matrix of the sensor, 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 sensor i and j, and the matrix size of the error covariance is n×n.

[0063] S3: Take the mean value of the difference between the measured value of the sensor 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.

[0064] Based on the measured values and true values of the process parameters in the historical data, calculate the measurement noise of the corresponding sensors. The expression of the measurement noise is:

[0065] ;

[0066] represents the measurement noise of sensor i, represents the measured value of sensor i for the production process parameters at time t, Denote the true value of the production process parameters at time \(t\), \(T\) denote the number of historical production process parameters, and the measurement noise represents the average measurement error of sensor \(i\).

[0067] The expression for the covariance matrix of the measurement noise is:

[0068]

[0069] In the formula, \(Q\) 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 \(n\times n\), and the elements in the covariance matrix of the measurement noise represent the correlation between the measurement noises of two sensors.

[0070] S4: Calculate the estimate of the error covariance matrix at the current moment.

[0071] In one embodiment, the expression for the estimate of the error covariance matrix is:

[0072] ;

[0073] 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 transition matrix, represents the covariance matrix of the measurement noise at time \(k - 1\). represents estimating the error covariance matrix at time \(k\) 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 between sensors and the correlation of the measurement noise at the previous moment are considered, and the error covariance matrix at time \(k\) is adaptively adjusted, improving the accuracy of the data elements of the error covariance matrix at time \(k\). The second state transition matrix is set manually according to the implementation situation. Exemplarily:

[0074] ;

[0075] S5: Calculate the adaptive Kalman gain of the sensor.

[0076] In one embodiment, the expression for the adaptive Kalman gain is:

[0077] ;

[0078] In the formula, Denote the adaptive Kalman gain of sensor \(i\) at time \(k\). Denote the preset third state transition matrix at time \(k\), and the size of the third state transition matrix is \(1\times n\). Denote the estimation of the error covariance matrix at time \(k\). Denote the variance of the measurement noise of sensor \(i\) at time \(k\). From the initial time to time \(k\), there is a measurement noise corresponding to each time. Calculate the variance based on the measurement noises at historical times and time \(k\), and use the obtained variance as the variance of the measurement noise at time \(k\). The third state transition matrix is set manually according to the actual situation. Exemplarily, the third state transition matrix is: . Denote the error correlation between sensor \(i\) and the other sensors.

[0079] When calculating the adaptive Kalman gain, multiple factors such as the correlation of the measurement errors of the sensors and the correlation of the measurement noises are comprehensively considered, effectively reducing the phenomenon of error transmission. The weight of the sensor measurement value can be adaptively adjusted through the adaptive Kalman gain.

[0080] In one embodiment, the expression of the adaptive Kalman gain is:

[0081] ;

[0082] In the formula, Denote the adaptive Kalman gain of sensor \(i\) at time \(k\). Denote the preset third state transition matrix at time \(j\). Denote the estimation of the error covariance matrix at time \(j\). Denote the variance of the measurement noise at time \(j\), and \(m\) represents the number of measurements from the initial time to time \(k\).

[0083] S6: Correct the measurement values of multiple sensors at the current time to obtain the optimal measurement value.

[0084] The method for correcting the measurement value at the current time is:

[0085] S61: Use the optimal measurement value at the previous time to construct an optimal measurement matrix, and take the product of the optimal measurement matrix and the preset first state transition matrix as the predicted value of the process parameter at the current time.

[0086] The expression of the predicted value of the process parameter at the current time is: ;

[0087] In the formula, Denote the predicted value of the process parameter at time \(k\). Denote the optimal measurement value matrix of the process parameter at time \(k - 1\), and the size of the matrix is \(n\times1\). is a preset first state transition matrix with a size of 1×n.

[0088] Exemplarily, is , 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; is , and all matrix elements except those in the first column of the matrix are 0.

[0089] S62: Calculate the optimal measured value at the current time.

[0090] The expression for the optimal measured value at the current time is:

[0091] ;

[0092] In the formula, represents the optimal measured 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.

[0093] Based on the measured values and true values of the process parameters in the historical data, the present invention calculates the errors of each sensor, 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 values and measured values of the parameters at the historical moments, estimates the error covariance matrix of the process parameter at the current time through the error covariance matrix of the process parameter collected at the previous time, calculates the adaptive Kalman gains of different sensors based on the estimated error covariance matrix and the variance of the measurement noise, uses the adaptive Kalman gains as the fusion weights of different sensors, the present invention improves the data fusion strategy to correct the production process parameters, adaptively adjusts the fusion weights based on the error correlation of different sensors and the change of the measurement noise, and improves the fusion strategy according to the adjusted fusion weights to correct the production process parameters, thereby improving the acquisition accuracy of the production process parameters.

[0094] The embodiment of the present invention also discloses a management system for high-voltage cable production process parameters, including a processor and a memory, and the memory stores computer program instructions, which implement a method for managing high-voltage cable production process parameters according to the present invention when the computer program instructions are executed by the processor.

[0095] 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 settings and functions are known in the art, so they will not be described in detail here.

[0096] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high bandwidth memory (HBM), a hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0097] Although this specification has shown and described multiple 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. Many variations, changes, and alternative approaches will occur to those skilled in the art without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0098] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A method for managing process parameters of high-voltage cable production, characterized in that, Including the steps: Obtain the measured values of the same process parameter at the current moment by using multiple sensors; Calculate the error covariance between any two sensors and construct the error covariance matrix of the sensors; Take the mean value of the difference between the measured value of the sensor and the true value as the measurement noise, calculate the covariance of the measurement noise between any two sensors, and construct the covariance matrix of the measurement noise; Calculate the estimation of the error covariance matrix at the current moment, and the expression is: In the formula, represents the estimation of the error covariance matrix at time k, represents the error covariance matrix at time k-1, and A R is a preset second state transition matrix, and Q k-1 represents the covariance matrix of the measurement noise at time k-1; Correct the measured values of multiple sensors at the current moment to obtain the optimal measured values; The method for correcting the measured values is: Construct an optimal measurement matrix by using the optimal measured value of the previous moment, and take the product of the optimal measurement matrix and the preset first state transition matrix as the predicted value of the process parameter at the current moment; calculate the adaptive Kalman gain of each sensor; the expression of the adaptive Kalman gain is: In the formula, represents the adaptive Kalman gain of sensor i at time k, and A j represents the preset third state transition matrix at time j, represents the estimation of the error covariance matrix at time j, and R 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; The expression of the optimal measured 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 measurement value of sensor i, represents the adaptive Kalman gain of sensor i at time k, and n represents the number of sensors.

2. The method for managing process parameters of a high-voltage cable production according to claim 1, wherein, The expression of the adaptive Kalman gain is: In the formula, represents the adaptive Kalman gain of sensor i at time k, and A k represents the preset third state transition matrix at time k, represents the estimation of the error covariance matrix at time k, and R k represents the variance of the measurement noise of sensor i at time k.

3. A method for managing process parameters of high-voltage cable production according to claim 1, characterized in that, The expression of the error covariance matrix of the sensor is: where, R total represents the error covariance matrix of the sensor, and R n represents the independent error covariance of sensor n, and cov(∈ i , ∈ j ) represents the error covariance between sensors i and j.

4. A method for managing process parameters of high-voltage cable production according to claim 1, characterized in that, The expression of the covariance matrix of the measurement noise is: where Q represents the covariance matrix of the measurement noise, and σ i represents the independent covariance of the measurement noise of sensor i, and cov(σ i , σ j ) represents the covariance between the measurement noises of sensor i and sensor j.

5. A method for managing process parameters of high-voltage cable production according to claim 1, characterized in that, The process parameters include the drawing temperature, tensile force, pressure and acceleration in the production process of high-voltage cables.

6. A management system for high-voltage cable production process parameters, characterized in that, Including: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for managing process parameters in the production of high-voltage cables according to any one of claims 1-5 is implemented.

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