Material sampling inspection method and device, electronic equipment and storage medium

Abstract

The invention discloses a material sampling inspection method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the process capability index risk rating of a target machine according to the historical material sampling inspection data of the target machine; determining a single point location risk rating and a point location trend risk rating of the target machine according to the point location value of each point location of the target machine; determining an equipment risk rating of the target machine according to the equipment parameters of the target machine; and according to the process capability index risk rating, the single-time point location risk rating, the point location trend risk rating and the equipment risk rating of the target machine, judging whether to carry out spot check on the materials of the target machine. According to the invention, the casual inspection frequency of the machine table can be dynamically adjusted, the material inspection frequency of the low-risk machine table is effectively reduced, the material inspection frequency of the high-risk machine table is improved, the stability of the production process of the machine table is improved, and the production quality is ensured.

Description

Technical Field

[0001] This invention relates to the field of machine tool processing technology, and in particular to a material sampling inspection method, apparatus, electronic device, and storage medium. Background Technology

[0002] CNC (Computer Numerical Control) machining is one of the core technologies of modern manufacturing. It uses computer programs to control the movement of machine tools to achieve high-precision, high-efficiency, and automated parts processing, and is a fundamental technological support for intelligent manufacturing.

[0003] In CNC machining, to control production quality, materials are typically sampled at regular intervals on each machine. These sampled materials are then inspected at all or some points to determine compliance. However, this traditional sampling method involves high sampling frequency, a huge workload, and during the process of detecting abnormal materials and locating the machine, the quality of materials produced by the machine cannot be controlled in a timely manner. Summary of the Invention

[0004] This invention provides a material sampling inspection method, apparatus, electronic device, and storage medium to dynamically adjust the sampling inspection frequency of machines, effectively reduce the material inspection frequency of low-risk machines, increase the material inspection frequency of high-risk machines, improve the stability of machine production processes, and ensure production quality.

[0005] In a first aspect, embodiments of the present invention provide a material sampling inspection method, the method comprising:

[0006] Based on the historical material sampling data of the target machine, determine the process capability index risk rating of the target machine;

[0007] Based on the location values ​​of each point of the target machine, determine the single location risk rating and location trend risk rating of the target machine.

[0008] Determine the equipment risk rating of the target machine based on its equipment parameters;

[0009] Based on the process capability index risk rating, single-point risk rating, point trend risk rating, and equipment risk rating of the target machine, determine whether to conduct random inspections of the materials of the target machine.

[0010] Secondly, embodiments of the present invention also provide a material sampling inspection device, the device comprising:

[0011] The process capability index risk rating determination module is used to determine the process capability index risk rating of the target machine based on the historical material sampling inspection data of the target machine.

[0012] The location risk rating determination module is used to determine the single location risk rating and location trend risk rating of the target machine based on the location values ​​of each location of the target machine.

[0013] The equipment risk rating determination module is used to determine the equipment risk rating of the target machine based on its equipment parameters.

[0014] The target machine material sampling inspection judgment module is used to determine whether to conduct sampling inspection of the target machine's materials based on the target machine's process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating.

[0015] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the material sampling method as described in any of the embodiments of the present invention.

[0016] Fourthly, embodiments of the present invention also provide a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform the material sampling method as described in any of the embodiments of the present invention.

[0017] The technical solution of this invention determines the process capability index risk rating of the target machine by using historical material sampling data. It then determines the single-point risk rating and point trend risk rating of the target machine by using the point values ​​at each location. Finally, it determines the equipment risk rating of the target machine by using its equipment parameters. Based on these risk ratings, it determines whether to conduct material sampling inspections on the target machine. This invention solves the problems of high sampling frequency, massive measurement workload, and difficulty in tracing problematic materials in existing technologies. It enables dynamic adjustment of the machine sampling frequency by determining whether sampling inspections are necessary based on the risk rating of the target machine. This effectively reduces the material inspection frequency of low-risk machines and increases the material inspection frequency of high-risk machines, thereby improving the stability of the machine's production process and ensuring production quality.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a material sampling inspection method provided in Embodiment 1 of the present invention;

[0021] Figure 2 This is a flowchart of a material sampling inspection method provided in Embodiment 2 of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of a material sampling inspection device provided in Embodiment 3 of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. In the embodiments of this application, certain software, components, models, and other existing industry solutions may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0026] The acquisition, transmission, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0027] Example 1

[0028] Figure 1 The present invention provides a flowchart of a material sampling inspection method according to Embodiment 1. This embodiment is applicable to the situation of material sampling inspection during CNC machine tool processing. The method can be executed by a material sampling inspection device, which can be implemented in hardware and / or software and can be configured in a server or electronic device.

[0029] like Figure 1 As shown, the method includes:

[0030] S110. Based on the historical material sampling data of the target machine, determine the process capability index risk rating of the target machine.

[0031] The target machine refers to the machine that is in the material production or processing state during CNC machining and which needs to be inspected at present. Historical material sampling data refers to the dimensional data of each historical material sampled during a preset time period prior to the current moment when the target machine was sampled. Historical material sampling data may include the dimensional data of historical materials at all or some points.

[0032] The Process Capability Index (CPK) is a core indicator that measures whether a production process can consistently meet product specifications. It quantifies the degree of matching between process output and specification limits. Correspondingly, the Process Capability Index Risk Rating describes the fluctuations in the production process of the target machine indicated by the Process Capability Index, and the corresponding risk of the target machine exceeding specifications. The Process Capability Index Risk Rating can be represented by high risk, medium risk, and low risk, etc. This embodiment does not limit the form of expression for the Process Capability Index Risk Rating.

[0033] Specifically, the process capability index of the target machine can be calculated using the following formula: Among them, USL (Upper Specification Limit) and LSL (Lower Specification Limit) are the upper and lower limits of material specifications that meet the production requirements. This represents the average value of historical material sampling data for the target machine. This represents the standard deviation of historical material sampling data for the target machine.

[0034] Furthermore, for the historical materials produced by the target machine, the CPK of each sampling point can be calculated based on the historical material sampling data for all or some of its locations. For example, for 100 historical material sampling data points of the target machine, each historical material sampling data point contains historical measurement data for 32 points. Then, for each point, the CPK is calculated based on the 100 historical measurement data points, resulting in the CPK of the 32 points.

[0035] Furthermore, according to the CPK calculation formula, the smaller the CPK value, the lower the target machine's ability to meet specifications. Generally speaking, when CPK ≥ 1.67, it indicates that the target machine's production process fluctuates very little and is far from meeting the material specification requirements; when 1.33 ≤ CPK < 1.67, it indicates that the target machine's production process capacity is sufficient and can basically stably meet the material specification requirements; when 1.0 ≤ CPK < 1.33, it indicates that the target machine's production process capacity is insufficient, although it may currently meet the material specification requirements, the fluctuations are slightly larger; when CPK < 1.0, it indicates that the target machine's production process capacity is severely insufficient, and the output materials are highly likely to exceed specifications, requiring production shutdown and anomaly investigation.

[0036] In this embodiment, the process capability index risk rating of the target machine is determined based on the historical material sampling data of the target machine. The process capability index of each point of the material can be calculated separately, and the process capability index risk rating of the target machine is determined based on the process capability index of each point.

[0037] Taking the process capability index risk rating as represented by high risk, medium risk and low risk as an example, in an optional embodiment, the process capability index risk rating of the target machine is determined according to the process capability index of each point. This can be that the process capability index of at least one point is less than 1.0, that is, the process capability index risk rating of the target machine is determined to be high risk.

[0038] In another optional embodiment, the process capability index risk rating of the target machine is determined based on the process capability index of each point. Alternatively, based on the above embodiment, if the process capability index of each point is greater than or equal to 1.0, the ratio of the number of points with 1.0 ≤ CPK < 1.33 to the total number of points is calculated. If the ratio of points is greater than or equal to a preset ratio threshold, the process capability index risk rating of the target machine is determined to be high risk.

[0039] Furthermore, based on the above embodiments, if the process capability index risk rating of the target machine is not determined to be high risk, then it is further determined that if the CPK of each point is greater than or equal to 1.33, the process capability index risk rating of the target machine is determined to be low risk; otherwise, for all other cases, the process capability index risk rating of the target machine is determined to be medium risk.

[0040] In this embodiment, the production process capability of the target machine is quantitatively and qualitatively determined from the perspective of the process capability index, thereby providing a data basis for subsequent judgment on whether the target machine needs to undergo material sampling inspection. Understandably, a higher process capability index risk rating indicates a poorer production process capability of the target machine, and a greater need for material sampling inspection of that machine.

[0041] S120. Based on the location values ​​of each point of the target machine, determine the single-point risk rating and the point trend risk rating of the target machine.

[0042] The point values ​​of each location on the target machine refer to the dimensional measurements of the material produced by the target machine at each location. The single-point risk rating is used to determine whether the point values ​​of the latest material produced by the target machine are within the specification range, while the point trend risk rating is used to determine whether the point values ​​of the material produced by the target machine exhibit a trend of change.

[0043] Correspondingly, when determining the risk rating of a single point, the point values ​​of each point for the latest material produced by the target machine are required; when determining the risk rating of a point trend, the point values ​​of each point for the recent materials produced by the target machine are required.

[0044] Similarly, both single-point risk rating and point trend risk rating can be represented by high-risk, medium-risk, and low-risk levels.

[0045] In this embodiment, the risk rating of a single point of the target machine is determined based on the point values ​​of each point of the target machine. This can be achieved by determining the latest current material produced by the target machine, and then determining the risk rating of a single point of the target machine based on the point values ​​of each point of the current material and the material specification range (USL and LSL) corresponding to each point.

[0046] Specifically, if at least one point has a value less than the corresponding LSL or greater than the USL, it indicates that the point value has exceeded the material specification range and the material quality is seriously poor. The risk rating of a single point can be set to high risk.

[0047] Furthermore, if the value of each point is within the material specification range, the relative offset ratio of each point value is calculated. The single-point risk rating of the target machine is determined based on the relative offset ratio of each point value.

[0048] Specifically, for each point, the relative offset ratio can be calculated as the ratio of the difference between the point's value and its target value to the target value. Alternatively, when the point's value is greater than or equal to its target value, the relative offset ratio can be calculated using the following formula: ,in, Indicates the point value. This indicates the target value at that point. This represents the USL value for that location. When the location value is less than the target value for that location, the relative offset ratio is calculated using the following formula: ,in, This indicates the LSL value at that point.

[0049] The single-point risk rating of the target machine is determined based on the relative offset ratio of the point values ​​at each location. This can be achieved by: 1) Determining the single-point risk rating of the target machine as high-risk if the relative offset ratio of at least one location is greater than or equal to a first offset ratio threshold; 2) Determining the single-point risk rating of the target machine as high-risk if the relative offset ratio of all locations is less than the first offset ratio threshold, and 3) If the ratio of this number to the total number of locations is greater than or equal to a preset ratio threshold; 4) Determining the single-point risk rating of the target machine as low-risk if the relative offset ratio of all locations is less than the second offset ratio threshold, and classifying all other cases as medium-risk. For example, the first offset ratio threshold could be 80%, and the second offset ratio threshold could be 50%.

[0050] In this embodiment, the proportion of the single-time deviation of the latest material produced by the target machine relative to the material specification range is used to assess whether the latest material produced by the target machine meets the material specification requirements. This assessment, which evaluates the current production capacity of the target machine, is an immediate judgment method and provides a data basis for subsequent judgments on whether material sampling inspection of the target machine is necessary. It is understandable that single-point risk rating, as an immediate risk assessment method, allows for rapid decision-making on the production site. For example, if the single-point risk rating for a consecutive preset number of materials at the target machine is high, it indicates that material sampling inspection of the target machine is required.

[0051] In this embodiment, the point trend risk rating of the target machine is determined based on the point values ​​of each point of the target machine. This can be done by determining the point values ​​of each point of multiple materials recently produced by the target machine, determining the changing trend of each point value over time based on the point values ​​of each material, and determining the point trend risk rating of the target machine based on the changing trend of each point value over time.

[0052] Specifically, the current material on the target machine and at least two historical materials preceding it can be identified. For example, six materials, including the current material, can be identified to determine the trend of their position changes. For each position, the trend of the position value of each material over time can be determined. Specifically, the difference between the position values ​​of two adjacent materials can be calculated. If all differences are positive, it indicates that the position value of that position is trending upwards; if all differences are negative, it indicates that the position value of that position is trending downwards.

[0053] Based on the trend of each location's value over time, the location trend risk rating of the target machine is determined. This can be defined as follows: if at least one location's value shows an upward or downward trend, the target machine's location trend risk rating is determined to be high risk. Otherwise, the target machine's location trend risk rating is determined to be low risk.

[0054] In this embodiment, the trend of changes in the point values ​​of multiple materials at various locations of the target machine in recent times is used to determine whether the target machine has a trend risk. As a long-term trend assessment method, this provides a data basis for subsequent judgments on whether the target machine needs to conduct material sampling inspections. It is understandable that when the point trend risk rating is high, it indicates that the target machine's processing of one or more points of the material shows a trend of increasingly higher or lower specifications, eventually leading to exceeding the specification range. Therefore, material sampling inspections of the target machine are necessary.

[0055] S130. Determine the equipment risk rating of the target machine based on its equipment parameters.

[0056] The equipment parameters may include standard feed rate, spindle speed, feed rate, standard spindle load, maximum spindle load, maximum spindle speed, tool life, fixture, and machining time, etc. This embodiment does not limit the number or type of equipment parameters.

[0057] Equipment risk rating is used to indicate the level of risk of equipment failure or malfunction in a target machine. Similarly, equipment risk ratings can include high risk, medium risk, and low risk.

[0058] In this embodiment, the equipment risk rating can be determined based on the latest equipment parameters of the target machine after each new material is produced. Alternatively, the equipment risk rating can be determined in real time or at preset intervals; this embodiment does not impose any restrictions on this.

[0059] In an optional embodiment, the equipment risk rating of the target machine is determined based on its equipment parameters. This can be achieved by determining empirical values ​​or ranges for each equipment parameter, and comparing each parameter to these empirical values ​​or ranges. If at least one parameter exceeds the empirical value range, or the ratio of the difference between the parameter and the empirical value is greater than or equal to a first ratio threshold, the equipment risk rating of the target machine is determined to be high-risk. If all parameters are within the empirical value range, or the ratio of the difference between the parameter and the empirical value is less than or equal to a second ratio threshold, the equipment risk rating of the target machine is determined to be low-risk; otherwise, it is set to medium-risk.

[0060] In another optional embodiment, the equipment risk rating of the target machine is determined based on its equipment parameters, and an equipment risk assessment model can be pre-trained. Specifically, a pre-set deep neural network model is trained based on the historical equipment parameters of at least two machines and risk labels matching the historical risk parameters to obtain the equipment risk assessment model. The equipment parameters of the target machine are then input into the equipment risk assessment model to obtain the equipment risk rating of the target machine output by the model.

[0061] In this embodiment, the operational status of the target machine is used to determine whether the machine is malfunctioning, potentially leading to materials exceeding specifications during production. This provides a data basis for subsequent determinations regarding whether material sampling inspection of the target machine is necessary. Understandably, a higher equipment risk rating indicates a higher likelihood of machine malfunction and the production of problematic materials, thus necessitating material sampling inspection.

[0062] S140. Based on the process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating of the target machine, determine whether to conduct random inspections of the materials of the target machine.

[0063] In this embodiment, four different risk ratings are used to determine whether material sampling inspection of the target machine is necessary. The advantage of this approach is that it comprehensively assesses the risk of the target machine producing abnormal materials from different perspectives, thereby accurately identifying high-risk and low-risk machines. Material sampling inspection is performed on high-risk machines, and the sampling frequency can be adjusted based on the machine's risk level. For low-risk machines, material sampling inspection can be omitted or reduced. Through this dynamic material sampling adjustment process, the overall sampling frequency can be reduced, material measurement costs can be saved, the stability of the machine's production process can be improved, and production quality can be guaranteed.

[0064] In an optional embodiment, a determination is made as to whether material sampling inspection of the target machine is required based on four different risk ratings of the target machine. This can be done as long as at least one risk rating is high risk.

[0065] In another optional embodiment, based on the four different risk ratings of the target machine, it is determined whether material sampling inspection of the target machine is necessary. Alternatively, if none of the risk ratings are high-risk, the number of medium-risk ratings is counted. If the number of medium-risk ratings is greater than or equal to a preset quantity threshold, then material sampling inspection of the target machine is performed. For example, the preset quantity threshold can be 3, that is, material sampling inspection of the target machine is performed when at least three risk ratings are medium-risk.

[0066] In another optional embodiment, based on four different risk ratings of the target machine, it is determined whether material sampling inspection of the target machine is necessary. Alternatively, different risk ratings can be represented numerically; for example, high risk can be represented by 3, medium risk by 2, and low risk by 1. Different risk ratings can be assigned different weights based on their role in determining whether the materials produced by the target machine are abnormal. The weighted sums of the values ​​corresponding to each risk rating are then obtained to obtain the final comprehensive score. If the comprehensive score is greater than or equal to a preset score threshold, material sampling inspection of the target machine is performed.

[0067] The technical solution of this invention determines the process capability index risk rating of the target machine by using historical material sampling data. It then determines the single-point risk rating and point trend risk rating of the target machine by using the point values ​​at each location. Finally, it determines the equipment risk rating of the target machine by using its equipment parameters. Based on these risk ratings, it determines whether to conduct material sampling inspections on the target machine. This invention solves the problems of high sampling frequency, massive measurement workload, and difficulty in tracing problematic materials in existing technologies. It enables dynamic adjustment of the machine sampling frequency by determining whether sampling inspections are necessary based on the risk rating of the target machine. This effectively reduces the material inspection frequency of low-risk machines and increases the material inspection frequency of high-risk machines, thereby improving the stability of the machine's production process and ensuring production quality.

[0068] Example 2

[0069] Figure 2 This is a flowchart of a material sampling inspection method provided in Embodiment 2 of the present invention. Based on the above embodiments, the present invention further specifies the process of determining the process capability index risk rating, single point risk rating, point trend risk rating and equipment risk rating, as well as the process of determining whether to sample materials of the target machine based on each risk rating.

[0070] like Figure 2 As shown, the method includes:

[0071] S210. Determine the process capability index of each point based on the historical point values ​​of at least two points corresponding to at least two historical materials of the target machine.

[0072] In this embodiment, the historical materials sampled by the target machine within a preset time period prior to the current moment are first determined, for example, 100 historical materials within one month. Based on the historical point values ​​of each historical material at each point, the CPK of each point is calculated. The calculation process has been described in the previous embodiment and will not be repeated here.

[0073] S220. If the process capability index of at least one point is determined to be less than or equal to a pre-set first index threshold, then the process capability index risk rating of the target machine is determined to be high risk.

[0074] The first index threshold can be 1.0. As long as the CPK of at least one point is less than or equal to 1.0, the process capability index risk rating of the target machine is determined to be high risk.

[0075] Furthermore, it can be set that if the CPK of each point is greater than or equal to 1.33, then the process capability index risk rating of the target machine is determined to be low risk, and for other cases, it is set to medium risk.

[0076] S230. Based on the current point values ​​of each point of the current material on the target machine and the pre-set point value thresholds, determine the single point risk rating of the target machine.

[0077] The single-point risk rating is determined based on the current point values ​​of the latest material produced by the target machine at each point. The point location threshold is the USL and LSL corresponding to that point. For each point, if the point value is within the range of LSL and USL, the point is considered low-risk; otherwise, the relative offset ratio of the point value is calculated based on the point value, LSL, USL, and the target value nominal for that point, and the risk level of that point is determined based on the relative offset ratio.

[0078] After obtaining the risk level of each location, if at least one location is considered high-risk, the single-location risk rating of the target machine is determined to be high-risk. Otherwise, the risk level of each location is calculated as the ratio of the number of medium-risk locations to the total number of locations, and a ratio threshold is applied. If the ratio is greater than or equal to the threshold, the single-location risk rating of the target machine is determined to be high-risk. If all locations are considered low-risk, the single-location risk rating of the target machine is determined to be low-risk; otherwise, it is determined to be medium-risk.

[0079] S240. Based on the current point value of each point of the current material of the target machine and the historical point values ​​of each point of at least two historical materials preceding the current material, determine the point trend risk rating of each point of the target machine.

[0080] In this embodiment, the location trend risk rating only includes two types: high risk and low risk. The difference between two adjacent location values ​​is calculated. If both differences are positive or both are negative, the location trend of that location is upward or downward. When at least one location shows an upward or downward trend, the target machine's location trend risk rating is determined to be high risk; otherwise, the target machine's location trend risk rating is determined to be low risk.

[0081] S250. Input the equipment parameters of the target machine into the pre-trained equipment risk assessment model to obtain the equipment risk rating of the target machine output by the equipment risk assessment model.

[0082] The equipment risk assessment model is obtained by training a pre-set deep neural network model based on the historical equipment parameters of at least two machines and risk labels that match the historical risk parameters.

[0083] In this embodiment, a deep neural network model is trained based on the historical equipment parameters of each machine and the risk labels attached to these historical parameters to obtain an equipment risk assessment model. The current equipment parameters of the target machine are then input into the equipment risk assessment model to obtain the equipment risk rating of the target machine output by the model.

[0084] S260. Determine the risk rating of the target machine based on the process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating.

[0085] Similarly, the risk rating of a target machine can be represented by high risk, medium risk, and low risk. It can also be represented by different colors, such as red, orange, and green, in the user interface. Alternatively, each machine can be equipped with a corresponding warning light, controlled by the material sampling device in this embodiment, with different colors used to indicate the risk. This embodiment does not limit the form in which the risk rating of the target machine is presented.

[0086] Furthermore, S260 may include: if at least one of the process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating of the target machine is determined to be high risk, then the risk rating of the target machine is determined to be high risk; or, if the process capability index risk rating, point trend risk rating, and equipment risk rating of the target machine are all determined to be medium risk, then the risk rating of the target machine is determined to be high risk.

[0087] In this embodiment, if at least one of the four risk ratings is high risk, then the target machine's risk rating is high risk. Similarly, if the process capability index risk rating, location trend risk rating, and equipment risk rating are all medium risk, then the target machine's risk rating is also determined to be high risk. In this case, since a single location risk rating is an evaluation of a single production process and has high instability, the target machine's risk rating is determined based on the other three risk ratings.

[0088] S270. If the target machine is determined to be of high risk, then the materials of the target machine shall be sampled and inspected.

[0089] In this embodiment, when the target machine's risk rating is high, material sampling inspection is performed on the target machine. Furthermore, different material sampling inspection frequencies can be set based on the risk level of the target machine. For example, the corresponding material sampling inspection frequency can be determined based on the number of high-risk ratings among the four risk ratings when determining the target machine's risk rating. The more high-risk ratings there are, the higher the material sampling inspection frequency.

[0090] The technical solution in this embodiment quantitatively and qualitatively represents the risk of problematic materials produced by a target machine through four aspects: process capability index, single-point performance, recent point trends, and equipment risk. This allows for accurate differentiation between high-risk and low-risk machines. High-risk machines undergo material sampling inspection, and the sampling frequency can be adjusted based on the machine's risk level. Low-risk machines may not require material sampling or the sampling frequency can be reduced. Through this dynamic material sampling adjustment process, the overall sampling frequency is reduced, material measurement costs are saved, the stability of the machine's production process is improved, and production quality is guaranteed.

[0091] Example 3

[0092] Figure 3 This is a schematic diagram of a material sampling inspection device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0093] The process capability index risk rating determination module 310 is used to determine the process capability index risk rating of the target machine based on the historical material sampling data of the target machine.

[0094] The location risk rating determination module 320 is used to determine the single location risk rating and location trend risk rating of the target machine based on the location values ​​of each location of the target machine.

[0095] The equipment risk rating determination module 330 is used to determine the equipment risk rating of the target machine based on the equipment parameters of the target machine.

[0096] The target machine material sampling judgment module 340 is used to determine whether to sample the materials of the target machine based on the process capability index risk rating, single point risk rating, point trend risk rating and equipment risk rating of the target machine.

[0097] The technical solution of this invention determines the process capability index risk rating of the target machine by using historical material sampling data. It then determines the single-point risk rating and point trend risk rating of the target machine by using the point values ​​at each location. Finally, it determines the equipment risk rating of the target machine by using its equipment parameters. Based on these risk ratings, it determines whether to conduct material sampling inspections on the target machine. This invention solves the problems of high sampling frequency, massive measurement workload, and difficulty in tracing problematic materials in existing technologies. It enables dynamic adjustment of the machine sampling frequency by determining whether sampling inspections are necessary based on the risk rating of the target machine. This effectively reduces the material inspection frequency of low-risk machines and increases the material inspection frequency of high-risk machines, thereby improving the stability of the machine's production process and ensuring production quality.

[0098] Based on the above embodiments, optionally, the process capability index risk rating determination module 310 includes:

[0099] The process capability index determination unit is used to determine the process capability index of each point based on the historical point values ​​of at least two points corresponding to at least two historical materials of the target machine.

[0100] The process capability index risk rating unit is used to determine the process capability index risk rating of the target machine as high risk if the process capability index of at least one point is less than or equal to a pre-set first index threshold.

[0101] Based on the above embodiments, optionally, the site risk rating determination module 320 includes:

[0102] The single-point risk rating determination unit is used to determine the single-point risk rating of the target machine based on the current point value of each point of the current material of the target machine and the pre-set point value threshold.

[0103] Based on the above embodiments, optionally, the site risk rating determination module 320 includes:

[0104] The point trend risk rating determination unit is used to determine the point trend risk rating of each point of the target machine based on the current point value of each point of the current material and the historical point values ​​of each point of at least two historical materials preceding the current material.

[0105] Based on the above embodiments, optionally, the equipment risk rating determination module 330 includes:

[0106] The equipment risk rating determination unit is used to input the equipment parameters of the target machine into the pre-trained equipment risk assessment model to obtain the equipment risk rating of the target machine output by the equipment risk assessment model.

[0107] The equipment risk assessment model is obtained by training a pre-set deep neural network model based on the historical equipment parameters of at least two machines and risk labels that match the historical risk parameters.

[0108] Based on the above embodiments, optionally, the target machine material sampling and judgment module 340 includes:

[0109] The target machine risk rating unit is used to determine the risk rating of the target machine based on the process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating.

[0110] The target machine material sampling and judgment unit is used to sample and inspect the materials of the target machine if the risk rating of the target machine is determined to be high risk.

[0111] Based on the above embodiments, optionally, the target machine risk rating unit is specifically used for:

[0112] If at least one of the following is determined to be of high risk: process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating of the target machine, then the target machine is determined to be of high risk.

[0113] Alternatively, if the process capability index risk rating, location trend risk rating, and equipment risk rating of the target machine are all determined to be of medium risk, then the risk rating of the target machine is determined to be of high risk.

[0114] The material sampling inspection device provided in the embodiments of the present invention can execute the material sampling inspection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0115] Example 4

[0116] Figure 4A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0117] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0118] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0119] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as material sampling methods.

[0120] In some embodiments, the material sampling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the material sampling method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the material sampling method by any other suitable means (e.g., by means of firmware).

[0121] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable material sampling device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0125] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0126] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0127] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0128] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for material sampling inspection, characterized in that, include: Based on the historical material sampling data of the target machine, determine the process capability index risk rating of the target machine; Based on the location values ​​of each point of the target machine, determine the single location risk rating and location trend risk rating of the target machine. Determine the equipment risk rating of the target machine based on its equipment parameters; Based on the process capability index risk rating, single-point risk rating, point trend risk rating, and equipment risk rating of the target machine, determine whether to conduct random inspections of the materials of the target machine.

2. The method according to claim 1, characterized in that, Based on the historical material sampling data of the target machine, the process capability index risk rating of the target machine is determined, including: The process capability index of each point is determined based on the historical point values ​​of at least two points corresponding to at least two historical materials of the target machine. If the process capability index of at least one point is determined to be less than or equal to a pre-set first index threshold, then the process capability index risk rating of the target machine is determined to be high risk.

3. The method according to claim 1, characterized in that, Based on the location values ​​of each point on the target machine, determine the single-point risk rating of the target machine, including: Based on the current point values ​​of each point of the current material on the target machine and the pre-set point value thresholds, determine the single point risk rating of the target machine.

4. The method according to claim 1, characterized in that, Based on the location values ​​of each point on the target machine, determine the location trend risk rating of the target machine, including: Based on the current point value of each point of the current material on the target machine, and the historical point values ​​of each point of at least two historical materials preceding the current material, determine the point trend risk rating of each point on the target machine.

5. The method according to claim 1, characterized in that, Based on the equipment parameters of the target machine, determine the equipment risk rating of the target machine, including: The equipment parameters of the target machine are input into the pre-trained equipment risk assessment model to obtain the equipment risk rating of the target machine output by the equipment risk assessment model. The equipment risk assessment model is obtained by training a pre-set deep neural network model based on the historical equipment parameters of at least two machines and risk labels that match the historical risk parameters.

6. The method according to claim 1, characterized in that, Based on the target machine's process capability index risk rating, single-point risk rating, point trend risk rating, and equipment risk rating, determine whether to conduct random sampling inspections of the target machine's materials, including: The risk rating of the target machine is determined based on the process capability index risk rating, single point risk rating, point trend risk rating and equipment risk rating of the target machine. If the target machine is determined to be of high risk, then the materials of the target machine will be sampled and inspected.

7. The method according to claim 6, characterized in that, Based on the target machine's process capability index risk rating, single-point risk rating, point trend risk rating, and equipment risk rating, the target machine's risk rating is determined, including: If at least one of the following is determined to be of high risk: process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating of the target machine, then the target machine is determined to be of high risk. Alternatively, if the process capability index risk rating, location trend risk rating, and equipment risk rating of the target machine are all determined to be of medium risk, then the risk rating of the target machine is determined to be of high risk.

8. A material sampling inspection device, characterized in that, include: The process capability index risk rating determination module is used to determine the process capability index risk rating of the target machine based on the historical material sampling inspection data of the target machine. The location risk rating determination module is used to determine the single location risk rating and location trend risk rating of the target machine based on the location values ​​of each location of the target machine. The equipment risk rating determination module is used to determine the equipment risk rating of the target machine based on its equipment parameters. The target machine material sampling inspection judgment module is used to determine whether to conduct sampling inspection of the target machine's materials based on the target machine's process capability index risk rating, single point risk rating, point trend risk rating, and equipment risk rating.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the material sampling method as described in any one of claims 1-7.

10. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the material sampling method as described in any one of claims 1-7.

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