CN115129008A - Process quality detection method, equipment and storage medium based on industrial process - Google Patents
Process quality detection method, equipment and storage medium based on industrial process Download PDFInfo
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Abstract
The embodiment of the application provides a process quality detection method, equipment and a storage medium based on an industrial process so as to be capable of detecting process quality. The method comprises the following steps: determining key process parameters corresponding to the process nodes; analyzing the key process parameters corresponding to the process nodes, and determining a stability result; analyzing a process capability index corresponding to the process node based on the stability result; determining a process detection result based on the process capability index. Aiming at complex procedures and processes, key process parameters can be identified, process capability evaluation is realized, and daily quality control level is optimized.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method for detecting process quality based on an industrial process, an electronic device, and a storage medium.
Background
The process refers to a method and a process for processing or treating various raw materials and semi-finished products by using various production tools. In different industrial production processes, the process may have different steps, operation methods and the like, for example, in the ceramic production process of ceramic production, the processes comprise processes of drawing, drying, glazing, firing and the like, and for example, the coating process of automobiles comprises processes of electrophoresis, gluing, spraying finish paint and the like.
In the industrial production process, the quality of products can be affected by the working procedures of each process, for example, in the coating process of automobiles, welding slag adheres to the automobile body to form particles, secondary flow marks are formed due to incomplete draining of electrophoresis flowing out of a tank, the production quality of vehicles can be affected, and if the process is unqualified, the products can be re-manufactured, and the manufacturing efficiency of the products is affected.
Disclosure of Invention
The embodiment of the application provides a method for detecting the process quality of an industrial process so as to be capable of detecting the process quality.
Correspondingly, the embodiment of the application also provides electronic equipment and a storage medium, which are used for ensuring the realization and the application of the system.
In order to solve the above problems, the embodiment of the present application discloses a process quality detection method based on an industrial process, the method including:
determining a key process parameter corresponding to a process node, wherein the key process parameter corresponding to the process node is determined by identifying an identification model;
analyzing the key process parameters corresponding to the process nodes, and determining a stability result;
analyzing a process capability index corresponding to the process node based on the stability result;
determining a process detection result based on the process capability index.
Optionally, the analyzing the key process parameters corresponding to the process nodes to determine the stability result includes:
carrying out statistical analysis on the key process parameters corresponding to the process nodes to determine controlled state information; and performing stability analysis according to the controlled state information to determine a stability result of the process node.
Optionally, the method further includes: and if the stability result is poor stability, analyzing the quality problem corresponding to the process node and determining an improvement suggestion.
Optionally, the determining that the process node corresponds to the key process parameter includes:
determining a plurality of process nodes of a target process flow and production data thereof in the production process;
and inputting the generated data into a recognition model aiming at each process node, and determining corresponding key process parameters, wherein the process parameters comprise process measurement data and/or statistical data of the process measurement data.
Optionally, performing statistical analysis on the key process parameters corresponding to the process node, and determining controlled state information, including:
drawing a statistical process control chart based on the key process parameters aiming at the metering type key process parameters, and determining controlled state information based on the statistical process control chart;
and aiming at the percentage type key process parameters, drawing a percentile control chart based on the key process parameters, and determining controlled state information based on the percentile control chart.
Optionally, the drawing a statistical process control chart based on the key process parameters includes:
performing normality test on the key process parameters corresponding to the process nodes to determine a normal distribution result;
and drawing a statistical process control chart according to the normal distribution result.
Optionally, the drawing a statistical process control chart according to the normal distribution result includes:
if the normal distribution result meets the normal distribution, drawing a process control chart based on a statistical process control mode;
and if the normal distribution result is that the normal distribution is not satisfied, converting the key process parameters to satisfy the normal distribution, and drawing a process control diagram based on a statistical process control mode under the condition of satisfying the normal distribution.
Optionally, the analyzing the process capability index corresponding to the process node based on the stability result includes:
and if the stability result is good in stability, determining a process capability index corresponding to the process node based on the type of the key process parameter.
Optionally, the determining the process capability index corresponding to the process node based on the type of the key process parameter includes:
aiming at the metering type key process parameters, determining a process capacity index corresponding to the process node based on a normal distribution result;
and determining the process capability index corresponding to the process node based on a percentile mode aiming at the percentage type key process parameters.
Optionally, determining a process detection result based on the process capability index includes:
and determining the capacity grade corresponding to the process capacity index, and determining a process detection result based on the capacity grade.
Optionally, the determining a process detection result based on the capability level includes:
judging whether the capacity grade meets the process production conditions of the process node;
if the process production conditions are not met, analyzing the reasons of insufficient process capability and generating a process detection result of the process to be improved;
and if the process production conditions are met, generating a process detection result with qualified process.
The embodiment also provides a process quality detection device for the industrial process, which is applied to the electronic equipment of the server.
The parameter determination module is used for determining that the process node corresponds to a key process parameter, and the process node corresponds to the key process parameter and is determined by the identification of the identification model;
the stability analysis module is used for analyzing the key process parameters corresponding to the process nodes and determining stability results;
the capability index determining module is used for analyzing the process capability index corresponding to the process node based on the stability result;
a detection module to determine a process detection result based on the process capability index.
The embodiment of the application also discloses an electronic device, which comprises: a processor; and a memory having executable code stored thereon that, when executed by the processor, performs a method as described in embodiments of the present application.
One or more machine-readable media having stored thereon executable code that, when executed by a processor, performs a method as described in embodiments of the present application are also disclosed.
Compared with the prior art, the embodiment of the application has the following advantages:
in the embodiment of the application, process nodes of different processes are determined, key process parameters are analyzed based on the process nodes, stability results of the process nodes are analyzed based on the key process parameters, whether the processes are stable or not is determined, process capability indexes corresponding to the process nodes are analyzed based on the stability results, process detection results are determined based on the process capability indexes, the key process parameters can be identified for complex processes and processes, process capability evaluation is achieved, and daily quality control level is optimized.
Drawings
FIG. 1 is a flow chart of steps of an embodiment of a method of process quality inspection of an industrial process of the present application;
FIG. 2 is a schematic diagram of an example coating process flow according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of steps of another embodiment of a method of process quality detection of an industrial process of the present application;
fig. 4 is a schematic structural diagram of an exemplary apparatus provided in an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
The method and the device can be applied to scenes such as detection, evaluation and improvement aiming at the process in industrial production. The process can be a process of various industrial productions, and can be specifically set based on requirements. The process node can be understood as a node corresponding to a process method in the process flow, so that the process node can be determined according to the process flow of different processes, the process node can be determined according to each process method in the process flow, specifically, the process node can be determined according to the process flow and the process method, and the process method can comprise a process manufacturing method, a process inspection method and the like. For example, in the coating process flow of the vehicle, the process nodes may include electrophoresis process nodes, finish process nodes, and the like, and the electrophoresis process and the finish process may be combined with specific process methods to determine the sub-nodes. After the process node is determined, analyzing key process parameters based on the process node, analyzing the stability result of the process node based on the key process parameters, continuing production if the stability is good, and analyzing the quality problem corresponding to the process node and determining an improvement suggestion if the stability is poor. According to the method and the device, for complex procedures and processes, key process parameters can be identified, process capability evaluation is achieved, and daily quality control level is optimized.
Referring to FIG. 1, a flow chart of steps of an embodiment of a method of process quality inspection of an industrial process of the present application is shown.
According to the method and the device, the recognition model can be trained in advance, so that the recognition model is adopted for recognition processing, and the key process parameters corresponding to the process nodes are determined. The method comprises the steps of acquiring various production data of a process in the process production process as historical data, constructing a training sample set based on the historical data, and training an identification model based on the training sample set. The identification model can be based on classification models such as a logic (logistic) regression model, an eXtreme Gradient Boosting (XGDoost) model, a Gradient Boosting Decision Tree (GBDT) model, a Random Forest (Random Forest) model and the like, the identification model is built by adopting the classification models, and model training is carried out based on a supervised machine learning algorithm, so that important feature identification can be realized based on the classification problem and is used as a key process parameter of the process node. And collecting production data in the production process, and processing the production data through the identification model to obtain corresponding key process parameters.
Taking a coating process of a vehicle as an example, the coating process mainly comprises the following steps: pre-processing electrophoresis, polishing lines, finishing lines, inspecting and modifying lines and the like. In an example of a part of a process node of a coating process flow as shown in fig. 2, an electrophoresis process node includes: pretreatment electrophoresis sub-node, vehicle bottom gluing sub-node, welding seam gluing sub-node and electrophoresis inspection sub-node, wherein the finish paint process node comprises: a paint spraying sub-node, a finish paint checking sub-node, a finish paint repairing sub-node and the like.
By taking the former electrophoresis processing process node as an example, a feature project is constructed by collecting historical data of key indexes of pretreatment and electrophoresis, and a supervised machine learning algorithm is constructed by using vehicle body off-line indexes in an electrophoresis inspection process, and key process parameters are analyzed. Therefore, the key process parameters can be determined based on the indexes of the sub-nodes in the production process. Similarly, taking the finish paint process node as an example, the key process parameters can be determined by analyzing key factors of the offline vehicle body.
In the embodiment of the present application, the key process parameter may be a monitored value, or may be a statistical indicator of the monitored value, such as: aggregate values such as summation and averaging, or values that satisfy some affine transformation. In an optional embodiment, the determining that the process node corresponds to the key process parameter includes: determining a plurality of process nodes in a target process flow and production data thereof in the production process; and inputting the generated data into a recognition model aiming at each process node, and determining corresponding key process parameters, wherein the process parameters comprise process measurement data and/or statistical data of the process measurement data.
And 104, analyzing the key process parameters corresponding to the process nodes, and determining a stability result.
The statistical analysis can be performed on the key process parameters corresponding to the process nodes to determine the stability results of the process nodes. The stability results are used to characterize the stability of the process during production, and include stability (or good stability) and instability (or poor stability). In some examples, the stability results of the process nodes may be determined by analyzing controlled state information of the process nodes, which is information of process control states generated by the process. In the process of production, the process is kept in a certain controlled state to achieve the purpose of controlling quality, so that the sign of occurrence of systematic factors can be found in time by controlling the process in the process of production, measures are taken to eliminate the influence of the systematic factors, and the process is maintained in a controlled state influenced by random factors to achieve the purpose of controlling quality. The method and the device for analyzing the controlled state information of the process node can analyze the controlled state information of the process node corresponding to the process method based on the key process parameters, and further analyze the stability result of the process. Wherein the controlled state information includes: the controlled state can be understood as being in a statistical control state, such as conforming to normal distribution or other stable random distribution, and the process is only affected by random factors, and the fluctuation has statistical regularity. An out-of-control condition may be understood as a statistical out-of-control condition in which the process may be affected by other system factors to cause an out-of-control. The method and the device can analyze and control the process based on the fluctuating statistical regularity in the process production process, determine the controlled state information and analyze the stability of the process. Analyzing the key process parameters corresponding to the process nodes to determine the stability result, wherein the method comprises the following steps: analyzing the key process parameters corresponding to the process nodes to determine controlled state information; and performing stability analysis according to the controlled state information to determine a stability result of the process node.
In the embodiment of the application, the types of the key process parameters comprise a metering type and a percentage type, and for different types of key process parameters, the analysis of the controlled state information can be performed based on different modes. And analyzing the key process parameters according to the types to determine the controlled state information. The analysis may be based on some or all of the key process parameters, such as sampling the key process parameters, then drawing the corresponding control charts by type, and determining controlled state information based on the control charts. The control map is a map with control limits used for analyzing and judging whether the process is in a steady state. Aiming at metering type key process parameters, drawing a statistical process control chart based on the key process parameters, and determining controlled state information based on the statistical process control chart; and aiming at the percentage type key process parameters, drawing a percentile control chart based on the key process parameters, and determining controlled state information based on the percentile control chart.
For metrology-type key Process parameters, a SPC (Statistical Process Control) chart may be drawn, and then controlled state information may be analyzed based on the SPC chart. In an alternative embodiment, the drawing a SPC chart based on the key process parameters includes: performing normality test on the key process parameters corresponding to the process nodes to determine a normal distribution result; and drawing a control chart according to the normal distribution result. And performing normality test on the key process parameters corresponding to the process nodes, namely analyzing the distribution information of the process nodes corresponding to the key process parameters, comparing the distribution information with the set significance level information, and determining whether the process nodes conform to normal distribution. In the actual industrial production process, data are difficult to conform to standard normal distribution, so that significance level information can be preset, and whether normality inference is given based on the significance level information to obtain a normal distribution result. The significance level information is information representing significance level, and the significance level is the probability that the estimated overall parameters fall within a certain interval and errors are possibly made. Significance is the degree of difference, and a small probability criterion that is permissible as a decision limit is determined in advance when performing hypothesis testing. Control charts can then be drawn based on the normal distribution results. Wherein, drawing a statistical process control chart according to the normal distribution result, comprising: if the normal distribution result meets the normal distribution, drawing a process control chart based on a statistical process control mode; and if the normal distribution result is that the normal distribution is not satisfied, converting the key process parameters to satisfy the normal distribution, and drawing a process control diagram based on a statistical process control mode under the condition of satisfying the normal distribution.
If the normal distribution result meets the normal distribution, a process control chart can be drawn based on a statistical process control mode, then a controlled judgment rule can be obtained, and the controlled state information is judged based on the controlled judgment rule. If the normal distribution result is that the normal distribution is not satisfied, converting the key process parameters, converting the non-normal distribution data into normal distribution data, for example, through Box-Cox conversion, Johnson conversion and the like, and then judging the controlled state information based on the controlled judgment rule.
In the process control analysis, each method has variation, and is influenced by time and space, and even a group of analysis results obtained under ideal conditions has a certain random error. However, when a certain result exceeds the allowable range of random error, the result can be judged to be abnormal and incredible by using a mathematical statistics method. Therefore, the controlled judgment rule can be set for judging the variation condition in the process control, for example, the error of the controlled judgment rule can be set to be not beyond the error range. The corresponding resulting controlled state information includes: a controlled state and/or an uncontrolled state.
And aiming at the percentage type key process parameters, drawing a percentile control chart based on the key process parameters, analyzing the percentile control chart by adopting a controlled judgment rule, and determining controlled state information.
In the embodiment of the application, stability analysis can be further performed according to the controlled state information to determine the stability result of the process node. Whether the controlled state information is stable or not can be judged, if the controlled state information is the controlled state, the stability result of the process node is determined to be stable or good in stability, and if the controlled state information is the out-of-control state, the stability result of the process node is determined to be unstable or poor in stability. If the stability result is poor stability, analyzing the quality problem corresponding to the process node and determining an improvement suggestion, determining uncontrolled process parameters, performing index diagnosis, determining the corresponding quality problem, checking out the reason of the runaway and analyzing the reconstruction suggestion. If the stability is good, the subsequent processing steps can be continued.
And 106, analyzing the process capability index corresponding to the process node based on the controlled state information.
For the key process parameters with good stability, the process capability index corresponding to the process node can be analyzed based on the key process parameters. The Process capability index (Process capability index) represents the degree to which the Process capability meets the technical standards (e.g., specifications, tolerances), and is generally referred to as CPK. The process capability index is the degree to which the process capability meets the product quality standard requirements (specification range, etc.), and is also called a process capability index. Refers to the actual processing capability of the process under a controlled condition for a certain period of time. It is the inherent capacity of the process or the capacity of the process to guarantee quality. The working procedure refers to the process of combining five basic quality factors, such as operators, machines, raw materials, process methods, production environment and the like.
In analyzing the process capability index, the process capability index corresponding to the process node may be determined based on the type of key process parameter. Determining a process capability index corresponding to the process node based on the type of the key process parameter includes: aiming at the metering type key process parameters, determining a process capability index corresponding to the process node based on a normal distribution result; and determining the process capability index corresponding to the process node based on a percentile mode aiming at the percentage type key process parameters.
Determining a process capability index by combining the normal distribution result of the last step according to the metering type key process parameters, and if the key process parameters meet the state distribution, taking the specification of two sides as an example, calculating according to the following formula:
Cp＝T/(6*σ)＝(Tu-Tl)/(6*σ)
TU and Tl are upper and lower tolerance limits, respectively, T is the maximum allowed value (TU) -the minimum allowed value (Tl), and σ is the total standard deviation of the process statistics, which can be obtained when the process is in a steady state. Therefore, the smaller σ, the larger its Cp value, the better the process technology capability.
It can also be calculated according to the following formula:
Cpk＝MIN(Tu-μ，μ-Tl)/(3*σ)
cpk is the deviation (epsilon) between the process average value and the product standard specification, wherein epsilon is | M- μ |, μ is the overall average value of the distribution, and M is the tolerance center. ε is the deviation of the overall mean μ of the distribution from the tolerance center M.
If the key process parameters do not meet normal distribution, converting the non-normal key process parameters into the key process parameters of normal distribution through a conversion algorithm for calculation, wherein the conversion algorithm is Box-Cox conversion, Johnson conversion and the like. The process capability index may then be calculated by fitting the actual distribution of the data and then estimating its mean, standard deviation, etc. from the actual distribution. The CPK is established under the condition that SPC is controllable, and a corresponding solution scheme is provided according to distribution inspection, so that the method is more scientific.
And calculating the process capability index based on a percentile method on the basis of a nonparametric statistical method aiming at the key process parameters of the percentage type.
Therefore, aiming at different types of key process parameters, the process capability index can be analyzed aiming at the stable process so as to determine whether the technical standard and the quality requirement are met.
The process capability index may include a capability level, and the division of the level may be determined according to a process, and a process test result may be determined based on the capability level. Determining a process test result based on the process capability index, including: and determining the capability grade corresponding to the process capability index, and determining a process detection result based on the capability grade. The determining a process test result based on the capability level includes: judging whether the capacity grade meets the process production conditions of the process node; if the process production conditions are not met, analyzing the reasons of insufficient process capability and generating a process detection result of the improved process; and if the process production conditions are met, generating a process detection result with qualified process. Process production conditions, which are conditions for detecting technical standards and quality of the process, may be set based on the process and the process nodes, and then it may be determined whether the capability level satisfies the process production conditions of the process nodes. If the process production condition is not met, the reason of the insufficient capacity of the process is analyzed, wherein the capacity grade can correspond to the reason of the insufficient capacity, so that the reason of the insufficient capacity can also be determined based on the capacity grade, and the mode of the improved process is analyzed based on the reason of the insufficient capacity to obtain the process detection result of the improved process. And if the process production conditions are met, generating a process detection result with qualified process.
In one example, the calculated process capability index is acted upon accordingly based on process production criteria, one common criteria being as follows:
a + + level: cpk is more than or equal to 2.0, the capacity rating is particularly excellent and is described as: cost reduction can be considered;
a + level: 2.0> Cpk ≧ 1.67, the competence rating is excellent, described as: should remain;
a level: 1.67> Cpk ≧ 1.33, the capability rating is good, described as: the capacity is good, the state is stable, but the grade A + is improved as much as possible;
b, stage: 1.33> Cpk ≧ 1.0, the capability rating is general, described as: the state is general, the risk of generating bad factors due to slight variation of factors in the production process is high, and various resources and methods are utilized to promote the bad factors to be A grade;
c level: 1.0> Cpk ≧ 0.67, the capability rating is poor, described as: the production process has more defects, and the capability of the production process needs to be improved.
D stage: 0.67> Cpk, capability rating unacceptable, described as: the capability is too poor, and the design process should be re-adjusted.
The description corresponding to each capability grade can be obtained in the process detection result.
Therefore, a method for providing adaptive key capacity process capability in a multi-process complex process scene can be provided, the process level evaluation rationality is improved, and the production process is improved in an auxiliary manner.
In summary, the process nodes of different processes are determined, key process parameters are analyzed based on the process nodes, the stability results of the process nodes are analyzed based on the key process parameters, whether the processes are stable or not is determined, process capability indexes corresponding to the process nodes are analyzed based on the stability results, process detection results are determined based on the process capability indexes, the key process parameters can be identified for complex processes and processes, process capability evaluation is achieved, and daily quality control levels are optimized.
On the basis of the above embodiment, the embodiment of the present application further discloses a process quality detection method for an industrial process, which can perform SPC control and calculation of process capability indexes for data of different data types.
Referring to FIG. 3, a flow chart of steps of another industrial process quality inspection method embodiment of the present application is shown.
And step 304, inputting the generated data into a recognition model for each process node, and determining corresponding key process parameters.
The process parameters include process measurement data and/or statistical data of the process measurement data.
And step 306, drawing a statistical process control chart based on the key process parameters aiming at the metering type key process parameters, and determining controlled state information based on the statistical process control chart.
Wherein, said drawing a statistical process control chart based on key process parameters comprises: performing normality test on the key process parameters corresponding to the process nodes to determine a normal distribution result; and drawing a statistical process control chart according to the normal distribution result. The drawing of the statistical process control chart according to the normal distribution result comprises the following steps: if the normal distribution result meets the normal distribution, drawing a process control chart based on a statistical process control mode; and if the normal distribution result is that the normal distribution is not satisfied, converting the key process parameters to satisfy the normal distribution, and drawing a process control diagram based on a statistical process control mode under the condition of satisfying the normal distribution.
And 308, drawing a percentile control chart based on the key process parameters aiming at the percentile type key process parameters, and determining controlled state information based on the percentile control chart.
In step 310, it is determined whether the stability is stable.
And performing stability analysis according to the controlled state information to determine a stability result of the process node. If so, the stability result is stable (good stability), go to step 314, if not, the stability result is unstable (poor stability), go to step 312,
and step 312, if the stability result is poor stability, analyzing the quality problem corresponding to the process node and determining an improvement suggestion.
And step 314, determining a process capability index corresponding to the process node based on the normal distribution result aiming at the metering type key process parameter.
And step 316, determining a process capability index corresponding to the process node based on a percentile mode aiming at the percentage type key process parameter.
And step 320, judging whether the capability level meets the process production conditions of the process node.
If so, go to step 324, otherwise go to step 320.
At step 322, the cause of the process step deficiency is analyzed and a process test result of the improved process is generated.
If the process production conditions are not met, analyzing the reasons of insufficient process capacity and generating a process detection result of the improved process.
And step 324, generating a process detection result with qualified process.
If the process production conditions are met, a process detection result with qualified process is generated, and the follow-up process can be based on production.
The method and the device for locating the key process parameters can quickly locate the key process parameters based on a machine learning algorithm. Taking a coating process as an example, the coating process mainly comprises the following steps: pre-processing electrophoresis, polishing lines, finishing lines and inspecting and modifying lines. In the coating process, each step may be a source of coating quality problems, such as: welding slag is attached to a vehicle body to form particles, secondary flow marks are formed due to incomplete draining of electrophoresis discharged from a tank, the sealant cannot be wiped cleanly to form glue, the flow of a spraying robot is unstable, and the like, and at least 30 core procedures are involved, and at least 2000 process indexes are involved. According to the method and the device, a key factor recognition algorithm can be designed, the problem of overload of process index information is solved, and the key indexes of the process nodes are rapidly positioned.
An adaptive algorithm module is provided to allow calculation of SPC control and process capability indices for different data types, such as normal/non-normal data, percentage type data, etc. In a multi-process complex process scene, a self-adaptive key capacity process capacity calculation method is provided, the process level evaluation rationality is improved, and the production process is improved in an auxiliary mode.
In the embodiments of the application, the information related to the user is collected, used and stored after being authorized and allowed by the user, and various operations based on the user information are executed after being authorized and allowed by the user.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.
On the basis of the above embodiment, the embodiment further provides a process quality detection device for an industrial process, which is applied to electronic equipment at a server.
The parameter determination module is used for determining that the process node corresponds to a key process parameter, and the process node corresponds to the key process parameter and is determined by the identification of the identification model;
the stability analysis module is used for analyzing the key process parameters corresponding to the process nodes and determining stability results;
the capability index determining module is used for analyzing the process capability index corresponding to the process node based on the stability result;
a detection module to determine a process detection result based on the process capability index.
In summary, the process nodes of different processes are determined, key process parameters are analyzed based on the process nodes, the stability results of the process nodes are analyzed based on the key process parameters, so that whether the processes are stable is determined, the process capability indexes corresponding to the process nodes are analyzed based on the stability results, the process detection results are determined based on the process capability indexes, the key process parameters can be identified for complex processes and processes, the process capability evaluation is realized, and the daily quality control level is optimized.
The stability analysis module is used for carrying out statistical analysis on key process parameters corresponding to the process nodes and determining controlled state information; and performing stability analysis according to the controlled state information to determine a stability result of the process node. And the stability analysis module is also used for analyzing the quality problem corresponding to the process node and determining an improvement suggestion under the condition that the stability result is poor in stability.
The acquisition module is used for determining a plurality of process nodes of a target process flow and production data thereof in the production process; and inputting the generated data into a recognition model aiming at each process node, and determining corresponding key process parameters, wherein the process parameters comprise process measurement data and/or statistical data of the process measurement data.
The controlled analysis module is used for drawing a statistical process control chart based on the key process parameters aiming at the metering type key process parameters and determining controlled state information based on the statistical process control chart; and aiming at the percentage type key process parameters, drawing a percentile control chart based on the key process parameters, and determining controlled state information based on the percentile control chart.
The controlled analysis module is used for performing normality test on the key process parameters corresponding to the process nodes and determining a normal distribution result; and drawing a statistical process control chart according to the normal distribution result.
The controlled analysis module is used for drawing a process control chart based on a statistical process control mode if the normal distribution result meets the normal distribution; and if the normal distribution result is that the normal distribution is not satisfied, converting the key process parameters to satisfy the normal distribution, and drawing a process control diagram based on a statistical process control mode under the condition of satisfying the normal distribution.
And the capability index determining module is used for determining the process capability index corresponding to the process node based on the type of the key process parameter under the condition that the stability result is good in stability.
The capacity index determining module is used for determining a process capacity index corresponding to the process node based on a normal distribution result aiming at the metering type key process parameter; and determining the process capability index corresponding to the process node based on a percentile mode aiming at the percentage type key process parameters.
And the detection module is used for determining the capability grade corresponding to the process capability index and determining a process detection result based on the capability grade.
The detection module is used for judging whether the capability level meets the process production condition of the process node; if the process production conditions are not met, analyzing the reasons of insufficient process capability and generating a process detection result of the improved process; and if the process production conditions are met, generating a process detection result with qualified process.
The method and the device for locating the key process parameters can quickly locate the key process parameters based on a machine learning algorithm. And a key factor identification algorithm is designed, so that the problem of overload of process index information is solved, and the key indexes of key processes are rapidly positioned. And, an adaptive algorithm module is provided, which can realize SPC control and process capability index calculation for different data types, such as normal/non-normal data, percentage type data, etc. In a multi-process complex process scene, a self-adaptive key capacity process capacity calculation method is provided, the process level evaluation rationality is improved, and the production process is improved in an auxiliary mode.
The present application further provides a non-transitory, readable storage medium, where one or more modules (programs) are stored, and when the one or more modules are applied to a device, the device may execute instructions (instructions) of method steps in this application.
Embodiments of the present application provide one or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an electronic device to perform the methods as described in one or more of the above embodiments. In the embodiment of the application, the electronic device includes a server, a terminal device and other devices.
Embodiments of the present disclosure may be implemented as an apparatus, which may comprise a server (cluster), a terminal, etc., electronic device, using any suitable hardware, firmware, software, or any combination thereof, in a desired configuration. Fig. 4 schematically illustrates an example apparatus 400 that may be used to implement various embodiments described herein.
For one embodiment, fig. 4 illustrates an example apparatus 400 having one or more processors 402, a control module (chipset) 404 coupled to at least one of the processor(s) 402, memory 406 coupled to the control module 404, non-volatile memory (NVM)/storage 408 coupled to the control module 404, one or more input/output devices 410 coupled to the control module 404, and a network interface 412 coupled to the control module 404.
In some embodiments, the apparatus 400 may include one or more computer-readable media (e.g., the memory 406 or the NVM/storage 408) having instructions 414 and one or more processors 402 in combination with the one or more computer-readable media and configured to execute the instructions 414 to implement modules to perform the actions described in this disclosure.
For one embodiment, control module 404 may include any suitable interface controllers to provide any suitable interface to at least one of processor(s) 402 and/or any suitable device or component in communication with control module 404.
The control module 404 may include a memory controller module to provide an interface to the memory 406. The memory controller module may be a hardware module, a software module, and/or a firmware module.
The memory 406 may be used, for example, to load and store data and/or instructions 414 for the apparatus 400. For one embodiment, memory 406 may comprise any suitable volatile memory, such as suitable DRAM. In some embodiments, the memory 406 may comprise a double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).
For one embodiment, control module 404 may include one or more input/output controllers to provide an interface to NVM/storage 408 and input/output device(s) 410.
For example, NVM/storage 408 may be used to store data and/or instructions 414. NVM/storage 408 may include any suitable non-volatile memory (e.g., flash memory) and/or may include any suitable non-volatile storage device(s) (e.g., one or more hard disk drive(s) (HDD (s)), one or more Compact Disc (CD) drive(s), and/or one or more Digital Versatile Disc (DVD) drive (s)).
NVM/storage 408 may include storage resources that are part of the device on which apparatus 400 is installed, or it may be accessible by the device and may not necessarily be part of the device. For example, NVM/storage 408 may be accessible over a network via input/output device(s) 410.
Input/output device(s) 410 may provide an interface for apparatus 400 to communicate with any other suitable device, and input/output devices 410 may include communication components, audio components, sensor components, and the like. The network interface 412 may provide an interface for the apparatus 400 to communicate over one or more networks, and the apparatus 400 may wirelessly communicate with one or more components of a wireless network according to any of one or more wireless network standards and/or protocols, such as access to a communication standard-based wireless network, such as WiFi, 2G, 3G, 4G, 5G, etc., or a combination thereof.
For one embodiment, at least one of the processor(s) 402 may be packaged together with logic for one or more controller(s) (e.g., memory controller module) of the control module 404. For one embodiment, at least one of the processor(s) 402 may be packaged together with logic for one or more controller(s) of the control module 404 to form a System In Package (SiP). For one embodiment, at least one of the processor(s) 402 may be integrated on the same die with logic for one or more controller(s) of the control module 404. For one embodiment, at least one of the processor(s) 402 may be integrated on the same die with logic of one or more controllers of the control module 404 to form a system on a chip (SoC).
In various embodiments, the apparatus 400 may be, but is not limited to being: a server, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet, a netbook, etc.), among other terminal devices. In various embodiments, apparatus 400 may have more or fewer components and/or different architectures. For example, in some embodiments, device 400 includes one or more cameras, a keyboard, a Liquid Crystal Display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an Application Specific Integrated Circuit (ASIC), and speakers.
The detection device can adopt a main control chip as a processor or a control module, sensor data, position information and the like are stored in a memory or an NVM/storage device, a sensor group can be used as an input/output device, and a communication interface can comprise a network interface.
An embodiment of the present application further provides an electronic device, including: a processor; and a memory having executable code stored thereon, which when executed, causes the processor to perform a method as described in one or more of the embodiments of the application. In the embodiment of the present application, various data, such as various data of a target file, a file and application associated data, and the like, may be stored in the memory, and user behavior data may also be included, so as to provide a data basis for various processing.
Embodiments of the present application also provide one or more machine-readable media having executable code stored thereon that, when executed, causes a processor to perform a method as described in one or more of the embodiments of the present application.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The above detailed description is provided for a process quality detection method based on an industrial process, an electronic device and a storage medium, and specific examples are applied herein to explain the principles and embodiments of the present application, and the description of the above embodiments is only used to help understand the method and its core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (13)
1. A method of process quality detection for an industrial process, the method comprising:
determining key process parameters corresponding to the process nodes;
analyzing the key process parameters corresponding to the process nodes, and determining a stability result;
analyzing a process capability index corresponding to the process node based on the stability result;
determining a process detection result based on the process capability index.
2. The method of claim 1, wherein analyzing the process node for a corresponding key process parameter to determine a stability result comprises:
carrying out statistical analysis on the key process parameters corresponding to the process nodes to determine controlled state information;
and performing stability analysis according to the controlled state information to determine a stability result of the process node.
3. The method of claim 2, wherein determining that the process node corresponds to a key process parameter comprises:
determining a plurality of process nodes of a target process flow and production data thereof in the production process;
and inputting the generated data into a recognition model aiming at each process node, and determining corresponding key process parameters, wherein the process parameters comprise process measurement data and/or statistical data of the process measurement data.
4. The method of claim 3, wherein statistically analyzing key process parameters corresponding to the process nodes to determine controlled state information comprises:
drawing a statistical process control chart based on the key process parameters aiming at the metering type key process parameters, and determining controlled state information based on the statistical process control chart;
and aiming at the percentage type key process parameters, drawing a percentile control chart based on the key process parameters, and determining controlled state information based on the percentile control chart.
5. The method of claim 4, wherein said plotting a SPC control chart based on key process parameters comprises:
performing normality test on the key process parameters corresponding to the process nodes to determine a normal distribution result;
and drawing a statistical process control chart according to the normal distribution result.
6. The method of claim 5, wherein said plotting a SPC chart based on said normal distribution result comprises:
if the normal distribution result meets the normal distribution, drawing a process control chart based on a statistical process control mode;
and if the normal distribution result is that the normal distribution is not satisfied, converting the key process parameters to satisfy the normal distribution, and drawing a process control diagram based on a statistical process control mode under the condition of satisfying the normal distribution.
7. The method of claim 6, wherein analyzing the process capability index corresponding to the process node based on the stability result comprises:
and if the stability result is good in stability, determining a process capability index corresponding to the process node based on the type of the key process parameter.
8. The method of claim 2, further comprising:
and if the stability result is poor stability, analyzing the quality problem corresponding to the process node and determining an improvement suggestion.
9. The method of claim 7, wherein determining the process capability index corresponding to the process node based on the type of key process parameter comprises:
aiming at the metering type key process parameters, determining a process capability index corresponding to the process node based on a normal distribution result;
and determining the process capability index corresponding to the process node based on a percentile mode aiming at the percentage type key process parameters.
10. The method of claim 1, wherein determining a process test result based on the process capability index comprises:
and determining the capacity grade corresponding to the process capacity index, and determining a process detection result based on the capacity grade.
11. The method of claim 10, wherein said determining a process test result based on said capability level comprises:
judging whether the capacity grade meets the process production conditions of the process node;
if the process production condition is not met, analyzing the reason of insufficient process capability and generating a process detection result of the process to be improved;
and if the process production conditions are met, generating a process detection result with qualified process.
12. An electronic device, comprising: a processor;
and a memory having stored thereon executable code which, when executed by the processor, performs the method of any of claims 1-11.
13. One or more machine-readable media having executable code stored thereon that, when executed by a processor, performs the method of any of claims 1-11.
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Cited By (2)
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| CN115879826A (en) * | 2023-02-20 | 2023-03-31 | 深圳普菲特信息科技股份有限公司 | Fine chemical process quality inspection method, system and medium based on big data |
| CN116756683A (en) * | 2023-06-15 | 2023-09-15 | 瑞莱谱(杭州)医疗科技有限公司 | Method and device for determining stability of mass spectrometer, storage medium and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115879826A (en) * | 2023-02-20 | 2023-03-31 | 深圳普菲特信息科技股份有限公司 | Fine chemical process quality inspection method, system and medium based on big data |
| CN116756683A (en) * | 2023-06-15 | 2023-09-15 | 瑞莱谱(杭州)医疗科技有限公司 | Method and device for determining stability of mass spectrometer, storage medium and electronic equipment |
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