An intelligent manufacturing process data acquisition method and system

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a method and system for collecting intelligent manufacturing process data. The method includes: collecting industrial data and processing industrial data in the intelligent manufacturing process; the industrial data collection includes: collecting the original data within a predetermined time period in the intelligent manufacturing process; the industrial data processing includes: determining the data information enrichment stage according to the original data; determining the collection authenticity of the data information enrichment stage; using the collection authenticity and the initial decomposition interval scale of the data information enrichment stage to determine the adaptive denoising decomposition interval scale of the data information enrichment stage; decomposing the original data by using the adaptive denoising decomposition interval scale to obtain all component data matrices; selecting a target component data matrix from all the component data matrices; adding the target component data matrices to obtain the target collected data. The present invention improves the accuracy and reliability of data collection in the intelligent manufacturing process.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for collecting intelligent manufacturing process data. Background Art

[0002] Intelligent manufacturing is the direction of the transformation and upgrading of the manufacturing industry. It realizes the intelligence and digitization of the production process through the deep integration of information technology and manufacturing technology. In the process of intelligent manufacturing, data collection is the basis for realizing intelligence and digitization. By monitoring various data in the production process in real time, enterprises can timely discover and solve problems, optimize the production process, and improve product quality and production efficiency.

[0003] In some scenarios, multiple sensors are often used to monitor the state of equipment, environmental parameters, and key indicators in the production process. As the number of intelligent manufacturing devices increases and the production line becomes more complex, the data collection environment becomes more complex. To cope with the more complex data collection environment, the number of sensors can be increased and the data collection frequency can be improved. With the increase in the number of sensors and the improvement of the data collection frequency, the data throughput increases sharply, resulting in a decrease in the timeliness of the data collection system. Moreover, due to the complexity of the data collection environment, data overlap and mutual influence are likely to occur, causing data disorder, thereby generating incorrect data and affecting the extraction of true information for subsequent analysis. Thus, the accuracy and reliability of data collection in the process of intelligent manufacturing are relatively low. Summary of the Invention

[0004] In order to solve the technical problem of relatively low accuracy and reliability of data collection in the process of intelligent manufacturing, the purpose of the present invention is to provide a method and system for collecting intelligent manufacturing process data, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for collecting intelligent manufacturing process data, including: collecting industrial data and processing industrial data in the intelligent manufacturing process; the industrial data collection includes: collecting original data within a predetermined time period in the intelligent manufacturing process; the industrial data processing includes: determining a data information rich stage according to the original data; determining the collection authenticity of the data information rich stage according to the data maximum points, data minimum points, and the window areas where each data information rich point in the data information rich stage is located; determining an adaptive denoising decomposition interval scale of the data information rich stage by using the collection authenticity and the initial decomposition interval scale of the data information rich stage; decomposing the original data of the data information rich stage by using the adaptive denoising decomposition interval scale to obtain all component data matrices; selecting a target component data matrix with a variance less than a first threshold from all the component data matrices; adding the target component data matrices to obtain target collection data.

[0006] Optionally, determining the data information enrichment stage based on the original data includes: fitting the original data to obtain a fitting curve; constructing a window centered on any original data in the fitting curve to obtain the target window where the original data is located; calculating the variance of the data within the target window where the original data is located, the maximum value of the data within the target window, the minimum value of the data within the target window, and the first time distance between the maximum value and the minimum value of the data; calculating the first difference between the maximum value and the minimum value of the data, and the first ratio between the first difference and the first time distance; determining the first product between the variance and the first ratio as the information detail degree of the original data; determining the original data with the information detail degree greater than the second threshold as the data information enrichment points; and marking the interval segment where the continuous data information enrichment points are located as the data information enrichment stage.

[0007] Optionally, determining the acquisition authenticity of the data information enrichment stage based on the data maximum points, data minimum points, and the window regions where each data information enrichment point in the data information enrichment stage is located includes: determining the data fluctuation time delay characteristic coefficient of the data information enrichment stage according to the data maximum points and data minimum points in the data information enrichment stage; determining the distortion coefficient of the data information enrichment stage according to the window regions where each data information enrichment point in the data information enrichment stage is located; and determining the acquisition authenticity of the data information enrichment stage according to the information detail degrees, data fluctuation time delay characteristic coefficients, and distortion coefficients of all data information enrichment points in the data information enrichment stage.

[0008] Optionally, determining the data fluctuation time delay characteristic coefficient of the data information enrichment stage according to the data maximum points and data minimum points in the data information enrichment stage includes: determining a continuous plurality of data sampling points after the data maximum points in the data information enrichment stage to form a data sampling point set of the data maximum points; determining the time delay coefficient of the data maximum points according to the slope of any maximum point in the data sampling point set of the data maximum points on the fitting curve of the original data and the second time distance between any maximum point and the nearest minimum point in the data sampling point set; and averaging the time delay coefficients of all data maximum points within the data information enrichment nodes to obtain the data fluctuation time delay characteristic coefficient of the data information enrichment stage.

[0009] Optionally, determining the time delay coefficient of the data maximum points according to the slope of any maximum point in the data sampling point set of the data maximum points on the fitting curve of the original data and the second time distance between any maximum point and the nearest minimum point in the data sampling point set includes: calculating the second ratio of the slope of any maximum point on the fitting curve of the original data to the corresponding second time distance; and multiplying the second ratios corresponding to each maximum point to obtain the time delay coefficient of the data maximum points.

[0010] Optionally, determining the distortion coefficient of the data information enrichment stage according to the window area where each data information enrichment point is located in the data information enrichment stage includes: obtaining the data maximum value, data minimum value, and the third time distance between the data maximum value and the data minimum value within the window area where the data information enrichment point is located; determining the second difference between the means of the original data corresponding to the data information enrichment points on the left and right sides of the data information enrichment point; and determining the distortion coefficient of the data information enrichment stage according to the data maximum value, data minimum value, third time distance, and second difference of each data information enrichment point.

[0011] Optionally, determining the distortion coefficient of the data information enrichment stage according to the data maximum value, data minimum value, third time distance, and second difference of each data information enrichment point includes: calculating the third difference between the data maximum value and the data minimum value of each data information enrichment point, and the third ratio between the third difference and the third time distance; calculating the second product between the third ratio and the absolute value of the second difference, and calculating the superimposed value of the second product of each data information enrichment point; and determining the fourth ratio between the superimposed value of the second product and the number of all data information enrichment points in the data information enrichment stage as the distortion coefficient of the data information enrichment stage.

[0012] Optionally, determining the acquisition authenticity of the data information enrichment stage according to the information detail level, data fluctuation time-delay characteristic coefficient, and distortion coefficient of all data information enrichment points in the data information enrichment stage includes: determining the information detail level of all data information enrichment points in the data information enrichment stage as the data information richness of the data information enrichment stage; and determining the third product of the data information richness, data fluctuation time-delay characteristic coefficient, and distortion coefficient as the acquisition authenticity of the data information enrichment stage.

[0013] Optionally, determining the adaptive denoising decomposition interval scale of the data information enrichment stage by using the acquisition authenticity and the initial decomposition interval scale of the data information enrichment stage includes: determining the ratio of the initial decomposition interval scale to the acquisition authenticity as the adaptive denoising decomposition interval scale.

[0014] In a second aspect, an embodiment of the present invention provides an intelligent manufacturing process data acquisition system, including: a processor and a memory; wherein, the memory is used to store a computer program that can run on the processor; the processor is used to execute the program stored in the memory to implement the steps of the intelligent manufacturing process data acquisition method mentioned in the first aspect.

[0015] The present invention has the following beneficial effects: In the embodiments of the present invention, industrial data collection and industrial data processing are performed on the intelligent manufacturing process; industrial data collection includes: collecting the raw data within a predetermined time period in the intelligent manufacturing process; industrial data processing includes: determining the data information enrichment stage according to the raw data; determining the acquisition authenticity of the data information enrichment stage according to the data maximum points, data minimum points within the data information enrichment stage, and the window area where each data information enrichment point in the data information enrichment stage is located; determining the adaptive denoising decomposition interval scale of the data information enrichment stage by using the acquisition authenticity and the initial decomposition interval scale of the data information enrichment stage; decomposing the raw data of the data information enrichment stage by using the adaptive denoising decomposition interval scale to obtain all component data matrices; selecting the target component data matrices with variances less than the first threshold from all the component data matrices; and adding the target component data matrices to obtain the target acquisition data.

[0016] In this way, the embodiments of the present invention can extract the data information enrichment stage in the raw data collected in the intelligent manufacturing process, avoiding the problem of reduced timeliness of the data acquisition system caused by the sharp increase in data throughput. And analyze the acquisition authenticity of the data in this stage for the data information enrichment stage. Finally, determine the adaptive denoising decomposition interval scale of the data information enrichment stage, decompose the raw data of the data information enrichment stage, select the target component data matrices with variances less than the first threshold in the component data matrices and add them to obtain the target acquisition data, so as to achieve the purpose of denoising and reducing error data, and improve the accuracy and reliability of data collection in the intelligent manufacturing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of a method for collecting data in the intelligent manufacturing process provided by an embodiment of the present invention;

[0019] Figure 2 It is a flowchart of industrial data processing in the intelligent manufacturing process provided by an embodiment of the present invention;

[0020] Figure 3 It is a schematic structural diagram of a data acquisition system in the intelligent manufacturing process provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method and system for collecting intelligent manufacturing process data proposed according to the present invention, including its specific implementation manner, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0023] The following specifically describes the specific solution of a method and system for collecting intelligent manufacturing process data provided by the present invention in conjunction with the accompanying drawings.

[0024] Embodiment 1:

[0025] Please refer to Figure 1 , which shows a flowchart of a method for collecting intelligent manufacturing process data provided by an embodiment of the present invention, including:

[0026] S101, Collect industrial data and process industrial data in the intelligent manufacturing process.

[0027] Specifically, industrial data collection includes: collecting the original data within a predetermined time period in the intelligent manufacturing process. Among them, in the embodiments of the present invention, various sensors are used to collect data in the intelligent manufacturing process, and the types of sensors include but are not limited to temperature sensors, pressure sensors, humidity sensors, vibration sensors, and smoke sensors, etc. These sensors are fixed at key parts of the intelligent manufacturing machine tool to collect the original data in real time. The data collected by the sensors can be wirelessly connected to a data acquisition controller, such as an embedded controller and a PLC controller, etc. The data acquisition controller is responsible for reading the sensor signals and performing preliminary processing on the original signals, such as filtering and amplification, to ensure the accuracy of the data. Further, the data acquisition controller can also set a data sampling frequency of once per second, and store the collected original data after removing duplicates, missing values, and outliers. This is convenient for industrial data processing in subsequent steps. The predetermined time period can be determined according to the actual situation, and in the embodiments of the present invention, the value is 1 hour.

[0028] Further, as Figure 2 shown, Figure 2 is a flowchart of industrial data processing in an intelligent manufacturing process provided by an embodiment of the present invention, and the process of industrial data processing includes:

[0029] S201, Determine the data information enrichment stage according to the original data.

[0030] Specifically, in the process of intelligent manufacturing, the main purpose of data collection is to ensure the accuracy and completeness of the data, so attention needs to be paid to the disordered data in the original data in the intelligent manufacturing process. In actual operation, the more disordered and complex the data in the intelligent manufacturing process is, the more the data in the current stage contains more manufacturing process information or the data in this stage is subject to serious external interference, resulting in inaccurate data. Therefore, when the data fluctuations in a certain stage are more drastic and the amplitude of the change is larger, it means that the intelligent manufacturing process in this stage contains rich information, such as drastic fluctuations may indicate equipment failure, wear and tear, and other abnormal interference conditions, which are very important for the maintenance and timely repair of equipment. Therefore, in the embodiment of the present invention, it is necessary to determine the stage of rich data information in the original data.

[0031] Further, when determining the data information rich stage, as an optional embodiment of the present invention, the original data is first fitted to obtain a fitting curve; then a window is constructed with any original data in the fitting curve as the center to obtain a target window where the original data is located; secondly, the variance of the data in the target window where the original data is located, the maximum value of the data in the target window, the minimum value of the data in the target window, and the first time distance between the maximum value of the data and the minimum value of the data are calculated; and the first difference between the maximum value of the data and the minimum value of the data, and the first ratio between the first difference and the first time distance are calculated; then the first product between the variance and the first ratio is determined to be the information detail level of the original data; and the original data with an information detail level greater than a second threshold is determined to be a data information rich point; finally, the interval where the continuous data information rich points are located is marked as a data information rich stage.

[0032] Specifically, first obtain the original data x within 1 hour of the history of the intelligent manufacturing process, and then use linear fitting and least squares method to obtain the fitting curve f(x) of the original data x. Build a window with any original data in the fitting curve f(x) as the center, and the step length is 5 data points adjacent to the left and right of any original data to obtain the target window of any original data. If there are not enough remaining data points, all the remaining data points are used as a target window. Get the maximum value H of the data in the target window max , data minimum value H min and the first time distance D between the two t . Calculate the variance S of all data points in each target window.

[0033] Furthermore, in the embodiment of the present invention, the following formula is used to calculate the information detail level:

[0034]

[0035] In the above formula, F i represents the degree of information detail of the i-th original data, and i represents the i-th original data in the fitting curve f(x). S i represents the variance of the data within the target window corresponding to the i-th original data. represents the maximum value of the data within the target window corresponding to the i-th original data. represents the minimum value of the data within the target window corresponding to the i-th original data. represents within the target window corresponding to the i-th original data and the first time distance between them, where t represents time and has no other special meaning.

[0036] Obtain the degree of information detail of all original data in the above manner.

[0037] Furthermore, the second threshold can be determined according to the actual situation. In the embodiment of the present invention, the second threshold T1 = 0.5 is set for threshold judgment, and the data sampling points corresponding to the original data with the degree of information detail greater than 0.5 are regarded as data information-rich points.

[0038] Furthermore, all adjacent and continuous data information-rich points are regarded as data in the same stage, marked as the data information-rich stage.

[0039] S202. Determine the acquisition authenticity of the data information-rich stage according to the data maximum points, data minimum points within the data information-rich stage, and the window area where each data information-rich point is located in the data information-rich stage.

[0040] Specifically, during the operation of the intelligent manufacturing equipment, the state will be adjusted due to changes in load, temperature, or operation mode, resulting in obvious fluctuations in the collected data and accompanied by a certain time-delay change, reflecting the time required for the intelligent manufacturing equipment to return to a stable state. In contrast, the noise interference from the external environment will cause random fluctuations but usually does not cause time-delay changes. Therefore, in the embodiment of the present invention, the acquisition authenticity of the data information-rich stage is determined by analyzing the data fluctuation time delay and distortion coefficient in the data information-rich stage.

[0041] Furthermore, in the embodiment of the present invention, when obtaining all data information enrichment stages in the original acquisition data of the intelligent manufacturing process, for the acquisition authenticity of each data information enrichment stage, first, the data fluctuation time-delay characteristic coefficient of the data information enrichment stage is determined according to the data maximum point and the data minimum point in the data information enrichment stage; then, the distortion coefficient of the data information enrichment stage is determined according to the window area where each data information enrichment point is located in the data information enrichment stage; finally, the acquisition authenticity of the data information enrichment stage is determined according to the information detail degree, the data fluctuation time-delay characteristic coefficient, and the distortion coefficient of all data information enrichment points in the data information enrichment stage.

[0042] Specifically, when determining the data fluctuation time-delay in the data information enrichment stage, as an optional embodiment of the present invention, first, a continuous plurality of data sampling points after the data maximum point in the data information enrichment stage are determined to form a data sampling point set of the data maximum point; then, according to the slope of any maximum point in the data sampling point set of the data maximum point on the fitting curve of the original data and the second time distance between any maximum point and the nearest minimum point in the data sampling point set, the time-delay coefficient of the data maximum point is determined; finally, the average of the time-delay coefficients of all data maximum points in the data information enrichment node is calculated to obtain the data fluctuation time-delay characteristic coefficient of the data information enrichment stage.

[0043] Among them, in the embodiment of the present invention, 19 continuous data sampling points after each data maximum point in each data information enrichment stage are statistically counted to form a data sampling point set of each data maximum point, and each data sampling point set contains at least one maximum point and at least one minimum point. The slope k of each maximum point in each data sampling point set on the fitting curve f(x) is obtained, and the time distance L between the maximum point corresponding to the data sampling point set and the nearest minimum point is obtained. When determining the time-delay coefficient of the data maximum point in the embodiment of the present invention, first, the second ratio of the slope of any maximum point on the fitting curve of the original data to the corresponding second time distance is calculated; then, the second ratios corresponding to each maximum point are multiplied continuously to obtain the time-delay coefficient of the data maximum point.

[0044] Specifically, the embodiment of the present invention calculates the time-delay coefficient of the data maximum point by using the following formula:

[0045]

[0046] In the above formula, E represents the time-delay coefficient of the data maximum point. M represents a set of 19 data sampling points after a certain data maximum point in the data information enrichment stage. m represents the m-th maximum point in the M set. k m represents the slope of the m-th maximum point on the data fitting curve f(x). L mrepresents the second time distance between the m-th maximum point and the nearest minimum point.

[0047] Furthermore, after obtaining the delay coefficients of all data maximum points in the data information rich phase, obtain the set N of all data maximum points in the data information rich phase, and the delay coefficient corresponding to each data maximum point. The embodiment of the present invention calculates the data fluctuation delay characteristic coefficient of the data information rich phase by the following formula:

[0048]

[0049] In the above formula, R j represents the data fluctuation delay characteristic coefficient of the j-th data information rich phase. j represents the j-th data information rich phase in the original data of the intelligent manufacturing process. N j represents the set of all data maximum points in the j-th data information rich phase. n j represents N j the n-th j data maximum point in the set. represents the delay coefficient of the n-th j data maximum point.

[0050] Furthermore, the data fluctuation delay characteristic coefficient of the data information rich phase is effectively measured by the mean value of the delay coefficients of all data maximum points in the data information rich phase. If the data fluctuation delay characteristic coefficient of the data information rich phase is less than the threshold, it indicates that the data information rich phase is caused by external noise. If the data fluctuation delay characteristic coefficient of the phase is greater than the threshold, it indicates that the data information rich phase is caused by the intelligent manufacturing equipment itself.

[0051] Furthermore, in the data information rich phase caused by the change of the state of the intelligent manufacturing equipment itself, strong external environmental noise interference may cause the sensor input data to exceed its measurement range. In this case, the output data of the sensor cannot be restored to the normal value range in time after reaching the maximum value. This abnormal output will cause pulse trains to appear in the data, that is, a series of abnormally high values appear in a short time. When similar pulse trains frequently appear in the fluctuation mode, it indicates that the input data of the surface sensor has been saturated, resulting in the output data unable to return to the normal range in time, thus making the overall output data too high and exacerbating the distortion of the collected data. Therefore, analysis is required in the subsequent decomposition process.

[0052] Further, when determining the distortion coefficient in the data information enrichment stage, as an alternative embodiment of the present invention, first obtain the data maximum value, data minimum value, and the third time distance between the data maximum value and the data minimum value within the window area where the data information enrichment point is located; then determine the second difference between the means of the original data corresponding to the data information enrichment points on the left and right sides of the data information enrichment point; finally, determine the distortion coefficient in the data information enrichment stage according to the data maximum value, data minimum value, third time distance, and second difference of each data information enrichment point.

[0053] Specifically, obtain all the data information enrichment points in the data information enrichment stage. Starting from any data information enrichment point in the data information enrichment stage, construct a window area for the data information enrichment point, and the step size of the window area is the subsequent 10 data information enrichment points. When the remaining data information enrichment points are insufficient, the remaining data information enrichment points are taken as a window area. Obtain the data maximum value G max , data minimum value G min and the third time distance t between the two. Respectively obtain the data means on the left and right sides of any data information enrichment point in the data information enrichment stage where it is located, and the absolute value μ(G) of the second difference between the data means on both sides a,b . The larger the value of the absolute value μ(G) a,b of this second difference, the greater the data difference on both sides of the data information enrichment point, that is, there may be a pulse train phenomenon in the original data collected at this information enrichment point, resulting in a larger subsequent data acquisition value.

[0054] Further, when determining the distortion coefficient in the data information enrichment stage, as an alternative embodiment of the present invention, first calculate the third difference between the data maximum value and the data minimum value of each data information enrichment point, and the third ratio between the third difference and the third time distance; then calculate the second product between the third ratio and the absolute value of the second difference, and calculate the superimposed value of the second product of each data information enrichment point; finally, determine that the fourth ratio of the superimposed value of the second product to the number of all data information enrichment points in the data information enrichment stage is the distortion coefficient in the data information enrichment stage.

[0055] Specifically, the embodiment of the present invention uses the following formula to calculate the distortion coefficient in the data information enrichment stage:

[0056]

[0057] In the above formula, P j represents the distortion coefficient of the jth data information enrichment stage. U j represents the set of all data information enrichment points in the jth data information enrichment stage. uj represents the u j -th data point in the U j set. represents the maximum value of the data within the window region corresponding to the u j -th data point. represents the minimum value of the data within the window region corresponding to the u j -th data point. represents the absolute value of the second difference between the means of the original data corresponding to the data information-rich points on the left and right sides of the u j -th data point in the j-th data information-rich phase. denotes the third time distance between the maximum value and the minimum value of the data within the window region corresponding to the u j -th data point.

[0058] During the data information-rich phase, the frequent occurrence of a large number of pulse trains may indicate serious distortion of the data in the information-rich phase. The fluctuations in these pulse trains will mask many important changes, resulting in key events not being recorded, thus significantly increasing the distortion of the data collected by the sensor. In addition, since the pulse trains prevent the output data from quickly returning to the normal range after reaching the maximum value, a stronger data decomposition scale needs to be adopted in the subsequent data decomposition process to eliminate the influence of the gradually increasing trend of the data collection fluctuations. In the embodiments of the present invention, the acquisition authenticity of the data information-rich phase and the scale of the initial decomposition interval are used to determine the final denoising decomposition interval scale.

[0059] Furthermore, when determining the acquisition authenticity of the data information-rich phase, as an optional embodiment of the present invention, first determine the information detail degree of all data information-rich points in the data information-rich phase as the data information richness of the data information-rich phase; then determine the third product of the data information richness, the data fluctuation time-delay characteristic coefficient, and the distortion coefficient as the acquisition authenticity of the data information-rich phase.

[0060] Specifically, in the embodiments of the present invention, first calculate the mean value ε(F) j of the information detail degrees of all data information-rich points within the data information-rich phase, and use it as the data information richness of the data information-rich phase. The greater this data information richness, the more detailed information is included in the data information-rich phase, and analysis is required when analyzing the accuracy of the subsequent collected data. Finally, combine the data fluctuation time-delay characteristic coefficient, the distortion coefficient, and the data information richness of the data information-rich phase to evaluate the acquisition authenticity of the data information-rich phase.

[0061] Furthermore, the embodiments of the present invention use the following formula to calculate the acquisition authenticity of the data information-rich phase:

[0062] KPI j = ε(F) j × R j × P j

[0063] In the above formula, KPI j represents the acquisition authenticity of the j-th data information enrichment stage. ε(F) j represents the data information richness of the j-th data information enrichment stage. R j represents the data fluctuation time delay characteristic coefficient of the j-th data information enrichment stage. P j represents the distortion coefficient of the j-th data information enrichment stage.

[0064] Among them, when obvious time delay characteristics appear in the data of the data information enrichment stage, that is, the data fluctuation time delay characteristic coefficient is large, it indicates that the data fluctuation is caused by the change of the state of the intelligent manufacturing equipment itself, rather than caused by external noise interference. By analyzing the distortion situation of the data in this data information enrichment stage, the data in the data information enrichment stage can be corrected, so as to more accurately evaluate the acquisition authenticity of the data in the data information enrichment stage.

[0065] S203. Determine the adaptive denoising decomposition interval scale of the data information enrichment stage by using the acquisition authenticity and the initial decomposition interval scale of the data information enrichment stage.

[0066] Specifically, in the traditional matrix decomposition algorithm (Robust Low-rank Matrix Decomposition, RLMD), calculate the local mean within the sliding window of each original data in the intelligent manufacturing process. The length of the sliding window can be set to 20 sampling points. Determine the initial decomposition interval scale according to the local mean within the sliding window of each original data. It should be noted that the initial decomposition interval scale can refer to the RLMD decomposition algorithm in the prior art, and the embodiments of the present invention will not elaborate here.

[0067] Furthermore, when determining the adaptive denoising decomposition interval scale of the data information enrichment stage, as an optional embodiment of the present invention, determine that the ratio of the initial decomposition interval scale to the acquisition authenticity is the adaptive denoising decomposition interval scale.

[0068] Specifically, the embodiments of the present invention use the following formula to calculate the adaptive denoising decomposition interval scale:

[0069]

[0070] In the above formula, Q j represents the adaptive denoising decomposition interval scale of the j-th data information enrichment stage. represents the initial decomposition interval scale of the j-th data information enrichment stage. KPI j represents the acquisition authenticity of the j-th data information enrichment stage.

[0071] S204. Decompose the original data in the data information enrichment stage using the adaptive denoising decomposition interval scale to obtain all component data matrices.

[0072] Specifically, use the RLMD decomposition algorithm to decompose the original data in the data information enrichment stage using the adaptive denoising decomposition interval scale to obtain all component data matrices. In the RLMD decomposition algorithm, each component data matrix represents a specific data component of the original data, and they are usually sorted from high to low according to the importance of the component data matrices.

[0073] S205. Select the target component data matrices with variances less than the first threshold from all the component data matrices.

[0074] Specifically, select the component data matrices with smaller variances in all the component data matrices. The first threshold can be determined according to the actual situation, and it is not limited in this embodiment of the present invention.

[0075] S206. Add the target component data matrices to obtain the target acquisition data.

[0076] Specifically, add all the target component data matrices to finally obtain the acquisition data with accuracy and reliability. Finally, transmit the processed acquisition data to the user interface, enabling relevant personnel to effectively adjust and optimize the intelligent manufacturing process according to the acquired data. The user interface can provide intuitive data display and operation functions for the user, helping them to monitor the production status in real time and make timely decisions based on the data analysis results, thereby improving the manufacturing efficiency and product quality.

[0077] The embodiment of the present invention can extract the data information enrichment stage in the original data collected during the intelligent manufacturing process, avoiding the problem of reduced timeliness of the data acquisition system caused by the sharp increase in data throughput. And analyze the acquisition authenticity of the data in this stage for the data information enrichment stage. Finally, determine the adaptive denoising decomposition interval scale of this data information enrichment stage, decompose the original data of the data information enrichment stage, select the target component data matrices with variances less than the first threshold in the component data matrices and add them to obtain the target acquisition data, thereby achieving the purpose of denoising and reducing error data, and improving the accuracy and reliability of data acquisition in the intelligent manufacturing process.

[0078] Embodiment Two:

[0079] Corresponding to the intelligent manufacturing process data collection method provided in the above embodiments, based on the same technical concept, the embodiments of the present invention also provide an intelligent manufacturing process data collection system, which is used to execute the above intelligent manufacturing process data collection method. Figure 3 FIG. 222 is a schematic structural diagram of an intelligent manufacturing process data collection system provided by an embodiment of the present invention, as Figure 3 shown. The intelligent manufacturing process data collection system may vary greatly due to configuration or performance differences, and may include one or more processors 301 and a memory 302. The memory 302 is used to store computer programs that can run on the processor 301. The processor 301 is used to execute the programs stored in the memory 302 to implement the above Figure 1 or Figure 2 each step in the method embodiments. Among them, the memory 302 can be short-term storage or persistent storage. The application programs stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the intelligent manufacturing process data collection system.

[0080] Furthermore, the processor 301 can be set to communicate with the memory 302 and execute a series of computer-executable instructions in the memory 302 on the intelligent manufacturing process data collection system. The intelligent manufacturing process data collection system may further include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0081] Specifically, in this embodiment, the intelligent manufacturing process data collection system includes a processor, a communication interface, a memory, and a communication bus; among them, the processor, the communication interface, and the memory complete mutual communication through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to implement the above Figure 1 or Figure 2 each step in the method embodiments, and has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0082] It should be noted that the intelligent manufacturing process data collection system provided by the embodiments of the present invention and the intelligent manufacturing process data collection method provided by the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the foregoing intelligent manufacturing process data collection method, and has the same or similar beneficial effects. The repeated parts will not be described again.

[0083] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A method for collecting intelligent manufacturing process data, characterized in that, The intelligent manufacturing process data acquisition method includes: Performing industrial data acquisition and industrial data processing on the intelligent manufacturing process; The industrial data acquisition includes: acquiring the raw data within a predetermined time period in the intelligent manufacturing process; The industrial data processing includes: determining the data information enrichment stage according to the raw data; Determining the acquisition authenticity of the data information enrichment stage according to the data maximum points, data minimum points within the data information enrichment stage, and the window areas where each data information enrichment point in the data information enrichment stage is located; Determining the adaptive denoising decomposition interval scale of the data information enrichment stage by using the acquisition authenticity and the initial decomposition interval scale of the data information enrichment stage; Decomposing the raw data of the data information enrichment stage by using the adaptive denoising decomposition interval scale to obtain all component data matrices; Selecting the target component data matrix with a variance less than the first threshold from all the component data matrices; Adding the target component data matrices to obtain the target acquisition data; The method for obtaining the acquisition authenticity includes: Determining the data fluctuation time delay characteristic coefficient of the data information enrichment stage according to the data maximum points and data minimum points within the data information enrichment stage; Determining the distortion coefficient of the data information enrichment stage according to the window areas where each data information enrichment point in the data information enrichment stage is located; Determining the acquisition authenticity of the data information enrichment stage according to the information detail degree of all data information enrichment points, the data fluctuation time delay characteristic coefficient, and the distortion coefficient in the data information enrichment stage; Among them, the method for obtaining the distortion coefficient includes: Obtaining the data maximum value, data minimum value within the window area where the data information enrichment point is located, and the third time distance between the data maximum value and the data minimum value; Determining the second difference between the means of the raw data corresponding to the data information enrichment points on the left and right sides of the data information enrichment point; Calculating the third difference between the data maximum value and the data minimum value of each data information enrichment point, and the third ratio between the third difference and the third time distance; Calculating the second product between the third ratio and the absolute value of the second difference, and calculating the superimposed value of the second products of each data information enrichment point; Determining the fourth ratio of the superimposed value of the second products to the number of all data information enrichment points in the data information enrichment stage as the distortion coefficient of the data information enrichment stage.

2. The intelligent manufacturing process data acquisition method according to claim 1, characterized in that The determining the data information enrichment stage according to the raw data includes: Performing fitting on the raw data to obtain a fitting curve; Constructing a window with any raw data in the fitting curve as the center to obtain the target window where the raw data is located; Calculating the variance of the data within the target window where the raw data is located, the data maximum value within the target window, the data minimum value within the target window, and the first time distance between the data maximum value and the data minimum value; Calculate a first difference between the maximum value and the minimum value of the data, and a first ratio between the first difference and the first time distance; Determine a first product between the variance and the first ratio as the information detail level of the original data; Determine the original data with the information detail level greater than a second threshold as data information rich points; Mark the interval segment where continuous data information rich points are located as a data information rich stage; 3. The intelligent manufacturing process data acquisition method according to claim 1, wherein The determining of the data fluctuation time delay characteristic coefficient of the data information rich stage according to the data maximum value points and data minimum value points in the data information rich stage includes: Determine a continuous plurality of data sampling points after the data maximum value points in the data information rich stage to form a data sampling point set of the data maximum value points; According to the slope of any maximum value point in the data sampling point set of the data maximum value points on the fitting curve of the original data and a second time distance between any such maximum value point and the nearest minimum value point in the data sampling point set, determine the time delay coefficient of the data maximum value point; Average the time delay coefficients of all data maximum value points in the data information rich nodes to obtain the data fluctuation time delay characteristic coefficient of the data information rich stage; 4. The intelligent manufacturing process data acquisition method according to claim 3, wherein The determining of the time delay coefficient of the data maximum value point according to the slope of any maximum value point in the data sampling point set of the data maximum value points on the fitting curve of the original data and a second time distance between any such maximum value point and the nearest minimum value point in the data sampling point set includes: Calculate a second ratio between the slope of any maximum value point on the fitting curve of the original data and the corresponding second time distance; Multiply the second ratios corresponding to each maximum value point to obtain the time delay coefficient of the data maximum value point; 5. The intelligent manufacturing process data acquisition method according to claim 1, characterized in that The determining of the acquisition authenticity of the data information rich stage according to the information detail levels, the data fluctuation time delay characteristic coefficient and the distortion coefficient of all data information rich points in the data information rich stage includes: Determine the information detail levels of all data information rich points in the data information rich stage as the data information richness of the data information rich stage; Determine a third product of the data information richness, the data fluctuation time delay characteristic coefficient and the distortion coefficient as the acquisition authenticity of the data information rich stage; 6. The intelligent manufacturing process data acquisition method according to claim 1, wherein The determining of the adaptive denoising decomposition interval scale of the data information rich stage by using the acquisition authenticity and the initial decomposition interval scale of the data information rich stage includes: Determine the ratio of the initial decomposition interval scale to the acquisition authenticity as the adaptive denoising decomposition interval scale; 7. An intelligent manufacturing process data acquisition system, characterized in that, The intelligent manufacturing process data acquisition system includes: a processor and a memory; wherein, the memory is used for storing a computer program that can run on the processor; the processor is used for executing the program stored in the memory to implement the steps of the intelligent manufacturing process data acquisition method according to any one of claims 1-6.

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