A quality inspection and traceability method and device based on food mixer manufacturing

Abstract

The present invention relates to a quality inspection and traceability method and device based on food mixer manufacturing, the method comprising the following steps: collecting various production data in the food mixer manufacturing process; using the production data, combined with expert knowledge in the field of food mixer manufacturing, to construct a knowledge graph dedicated to the food mixer, the knowledge graph comprising food mixer component information, food mixer product parameters and historical fault records; annotating nodes in the knowledge graph to obtain annotated results, and using Transformer to perform entity recognition and relationship extraction on the annotated results; using the entity recognition results, combined with a trained food mixer quality inspection model, to predict and trace the potential faults of the food mixer, to obtain analysis results, and to provide a fault diagnosis report, the fault diagnosis report comprising a fault process, a fault cause, a response range and suggested rectification measures.

Description

Technical Field

[0001] The invention relates to the field of food mixer manufacturing, and in particular to a quality inspection and traceability method and device based on food mixer manufacturing. Background Art

[0002] As consumers' requirements for the quality of food processing equipment increase, food mixers, as one of the commonly used kitchen appliances, have become increasingly important for quality control and fault tracing during their manufacturing process. Traditional quality inspection methods often rely on manual inspection and empirical judgment, which is not only inefficient but also difficult to ensure the consistency and accuracy of the inspection results.

[0003] In the production process of food mixers, our company mainly has the following main components that need quality control: injection molded base shell, transmission components, mixing blade holder and mixing cup body, etc., all of which require strict quality control. However, due to the multiple components and processes involved in the production process, once a quality problem occurs, it is difficult to quickly and accurately locate the source of the fault, resulting in delays in maintenance and improvement measures.

[0004] As a structured data representation method, knowledge graph can effectively store and organize various data and knowledge in the food mixer manufacturing process. However, the existing knowledge graph construction method is not optimized specifically for the needs of the food mixer manufacturing field, and lacks adaptability to professional terminology and context.

[0005] In view of the shortcomings of the existing technology, it is necessary to propose new technical solutions. Summary of the invention

[0006] The present invention provides a quality inspection and traceability method and device based on the manufacture of a food mixer, aiming to solve at least one of the technical problems existing in the prior art.

[0007] The technical solution of the present invention is a quality inspection and traceability method based on food mixer manufacturing, and the method comprises the following steps: collecting various production data in the food mixer manufacturing process; using the production data, combined with expert knowledge in the field of food mixer manufacturing, to construct a knowledge graph dedicated to the food mixer, the knowledge graph including food mixer component information, food mixer product parameters and historical fault records; annotating the nodes in the knowledge graph to obtain annotated results, and using Transformer to perform entity recognition and relationship extraction on the annotated results; using the entity recognition results, combined with a trained food mixer quality inspection model, to predict and trace the potential faults of the food mixer, to obtain analysis results, and to provide a fault diagnosis report, the fault diagnosis report including the fault process, fault cause, response range and recommended rectification measures.

[0008] Furthermore, the production data includes food mixer component type, component type key production parameters, and component type historical failure data.

[0009] Furthermore, the component types of the food mixer include an injection-molded base shell, a transmission component, a stirring blade seat and a stirring cup body; the component type production parameters corresponding to the injection-molded base shell include raw material type, size specifications of the injection-molded shell, manufacturing date of the injection-molded shell, temperature of the injection-molded shell corresponding to the injection mold operation and pressure of the injection-molded shell corresponding to the injection mold operation; the component type historical failure data corresponding to the injection-molded base shell includes appearance defect data, size defect data and physical performance defect data; the component type production parameters corresponding to the transmission component include raw material type, size specifications of the transmission component and gear transmission ratio of the transmission component; the component type historical failure data corresponding to the transmission component includes appearance defect data, size defect data and operating performance defect data; the component type production parameters corresponding to the stirring blade seat include raw material type, size specifications of the stirring blade seat and blade assembly angle data of the stirring blade seat; the component type historical failure data corresponding to the stirring blade seat includes appearance defect data, size defect data and operating performance defect data; the component type production parameters corresponding to the stirring cup body include raw material type, size specifications of the stirring cup body and manufacturing date of the stirring cup body; the component type historical failure data corresponding to the stirring cup body includes appearance defect data, size defect data and physical performance defect data.

[0010] Furthermore, the process of constructing a knowledge graph dedicated to food mixers further includes the following steps: defining the classification system and attributes of food mixer components, establishing assembly relationships and functional relationships between components, and extracting failure modes and causes from historical failure records based on expert knowledge in the field of food mixer manufacturing.

[0011] Furthermore, based on the expert knowledge in the field of food mixer manufacturing and in combination with historical fault records, nodes are assigned to each process in which each of the food mixer components appears, and possible faults are marked for each node.

[0012] Furthermore, in the process of labeling the nodes in the knowledge graph, entities and relationships are extracted from the constructed knowledge graph dedicated to food mixers, potential fault propagation paths between entities are defined, and probability weights are assigned to each fault propagation path using historical fault data and expert knowledge to form a fault propagation probability graph. An importance ranking algorithm is used to analyze the key paths and nodes in the fault propagation probability graph, and based on the key paths and nodes, a food mixer quality detection model is constructed. The food mixer quality detection model is used to simulate the propagation process of faults in the food mixer manufacturing process to obtain the fault diagnosis report, wherein, in the process of using Transformer for entity recognition and relationship extraction, the Transformer model is pre-trained to adapt to the professional terminology and context in the field of food mixer manufacturing.

[0013] Furthermore, for the food mixer quality detection model, historical production data is used as input into the model for supervised learning, and the model is learned and updated online through the continuously collected production data.

[0014] A computer device comprises a memory and a processor, wherein the processor implements the above method when executing a computer program stored in the memory.

[0015] A computer-readable storage medium stores program instructions, which implement the above-mentioned method when executed by a processor.

[0016] The beneficial effects of the present invention are as follows.

[0017] The above-mentioned quality inspection and traceability method based on food mixer manufacturing improves the quality inspection efficiency and accuracy in the production process of food mixers. At the same time, it uses knowledge graphs and deep learning technologies to achieve accurate prediction and traceability of potential faults. In addition, the continuously updated quality inspection model ensures the long-term effectiveness and adaptability of the method.

[0018] In addition, additional aspects and advantages of the present invention will be given in part in the following description, and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is an overall flow chart of a quality inspection and traceability method based on food mixer manufacturing according to an embodiment of the present invention.

[0020] Figure 2 It is a flow chart of application objects of a quality inspection and traceability method based on food mixer manufacturing according to an embodiment of the present invention.

[0021] Figure 3It is a detailed flow chart of a quality inspection and traceability method based on food mixer manufacturing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The concept, specific structure and technical effects of the present invention will be clearly and completely described below in combination with the embodiments and drawings to fully understand the purpose, scheme and effect of the present invention.

[0023] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to another feature, or it may be indirectly fixed or connected to another feature. The singular forms "a", "said" and "the" used herein are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art. The terms used in this specification are intended only to describe specific embodiments and are not intended to limit the invention. The term "and / or" used herein includes any combination of one or more of the related listed items.

[0024] It should be understood that, although the term first, second, third etc. may be adopted to describe various elements in the present disclosure, these elements should not be limited to these terms. These terms are only used to distinguish the same type of elements from each other. For example, without departing from the scope of the present disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element. The use of any and all examples or exemplary language ("for example", "such as" etc.) provided herein is only intended to better illustrate embodiments of the present invention, and unless otherwise required, the scope of the present invention will not be limited.

[0025] Reference Figures 1 to 3 In some embodiments, the present invention discloses a quality inspection and traceability method based on food mixer manufacturing, the method comprising the following steps:

[0026] Collect various production data during the food mixer manufacturing process. On the food mixer production line, various production data are collected in real time through installed sensors, databases and operator records. These data include but are not limited to: component types: such as injection molded base shells, transmission components, mixing blade holders and mixing cup bodies; key production parameters: such as raw material types, size specifications, manufacturing dates, operating temperatures, pressures, gear ratios and blade assembly angles; historical failure data: covering appearance defects, size defects, physical performance defects and operating performance defects.

[0027] By using the production data and combining it with the expert knowledge in the field of food mixer manufacturing, a knowledge graph dedicated to food mixers is constructed, which includes food mixer component information, food mixer product parameters and historical fault records. In the actual production and supplied material processing of our company, the injection molded base shell, transmission component, mixing blade holder and mixing cup body.

[0028] The nodes in the knowledge graph are labeled to obtain labeling results, and Transformer is used to perform entity recognition and relationship extraction on the labeling results.

[0029] The production data includes food mixer component type, component type key production parameters, and component type historical failure data.

[0030] The nodes in the knowledge graph are carefully annotated, including defining possible fault types for components and process nodes. Then, a pre-trained Transformer model is used to perform entity recognition and relationship extraction on the annotated results to identify entities such as components, production parameters, and failure modes in the knowledge graph and their relationships.

[0031] Specifically, the component types of the food mixer include an injection-molded base shell, a transmission component, a stirring blade seat and a stirring cup body; the component type production parameters corresponding to the injection-molded base shell include raw material type, size specifications of the injection-molded shell, manufacturing date of the injection-molded shell, temperature of the injection-molded shell corresponding to the injection mold operation and pressure of the injection-molded shell corresponding to the injection mold operation; the component type historical failure data corresponding to the injection-molded base shell includes appearance defect data, size defect data and physical performance defect data; the component type production parameters corresponding to the transmission component include raw material type, size specifications of the transmission component and gear transmission ratio of the transmission component; the component type historical failure data corresponding to the transmission component includes appearance defect data, size defect data and operating performance defect data; the component type production parameters corresponding to the stirring blade seat include raw material type, size specifications of the stirring blade seat and blade assembly angle data of the stirring blade seat; the component type historical failure data corresponding to the stirring blade seat includes appearance defect data, size defect data and operating performance defect data; the component type production parameters corresponding to the stirring cup body include raw material type, size specifications of the stirring cup body and manufacturing date of the stirring cup body; the component type historical failure data corresponding to the stirring cup body includes appearance defect data, size defect data and physical performance defect data.

[0032] The process of constructing a knowledge graph dedicated to food mixers further includes the following steps: defining the classification system and attributes of food mixer components, establishing assembly relationships and functional relationships between components, extracting failure modes and causes from historical failure records based on expert knowledge in the field of expert food mixer manufacturing. Based on expert knowledge in the field of expert food mixer manufacturing and combined with historical failure records, nodes are assigned to each process in which each of the food mixer components appears, and possible failures are marked for each node.

[0033] In the process of labeling the nodes in the knowledge graph, entities and relationships are extracted from the constructed knowledge graph dedicated to food mixers, potential fault propagation paths between entities are defined, and probability weights are assigned to each fault propagation path using historical fault data and expert knowledge to form a fault propagation probability graph. The importance sorting algorithm is used to analyze the key paths and nodes in the fault propagation probability graph. Based on the key paths and nodes, the food mixer quality detection model is constructed. The propagation process of faults in the food mixer manufacturing process is simulated by the food mixer quality detection model to obtain the fault diagnosis report. In the process of using Transformer for entity recognition and relationship extraction, the Transformer model is pre-trained to adapt to the professional terms and context in the field of food mixer manufacturing. Using the entity recognition results, combined with the trained food mixer quality detection model, the potential faults of the food mixer are predicted and traced to obtain analysis results and provide a fault diagnosis report, which includes the fault process, fault cause, response range and recommended rectification measures.

[0034] For the food mixer quality detection model, historical production data is used as input to the model for supervised learning, and the model is learned and updated online through continuously collected production data. Based on the entity recognition results, a food mixer quality detection model is constructed. The model is trained with historical fault data to learn fault characteristics and propagation paths. The model training adopts a supervised learning algorithm and combines it with an online learning mechanism to update the model parameters in real time and improve the prediction accuracy.

[0035] The above-mentioned quality inspection and traceability method based on food mixer manufacturing automatically generates a fault diagnosis report based on the results of fault prediction and traceability analysis. The report content includes but is not limited to the fault process, fault cause, response scope and recommended corrective measures, providing decision support for production managers.

[0036] It should be appreciated that the method steps in the embodiments of the present invention can be implemented or implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level process or object-oriented programming language to communicate with a computer system. However, if necessary, the program can be implemented in an assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed ASIC for this purpose.

[0037] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that may be executed by one or more processors.

[0038] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention can also include the computer itself.

[0039] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on the display.

[0040] The above is only a preferred embodiment of the present invention. The present invention is not limited to the above implementation. As long as the technical effect of the present invention is achieved by the same means, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of protection of the present invention. Within the scope of protection of the present invention, its technical scheme and / or implementation method may have various modifications and changes.

Claims

1. A quality inspection and traceability method based on food mixer manufacturing, characterized in that: The method comprises the following steps: Collect various production data during the food mixer manufacturing process; Using the production data and combining it with expert knowledge in the field of food mixer manufacturing, a knowledge graph dedicated to food mixers is constructed, wherein the knowledge graph includes food mixer component information, food mixer product parameters, and historical fault records; Labeling the nodes in the knowledge graph to obtain labeling results, and using Transformer to perform entity recognition and relationship extraction on the labeling results; By using the entity recognition results and combining with the trained food mixer quality detection model, the potential faults of the food mixer are predicted and traced back to obtain analysis results and provide a fault diagnosis report, which includes the fault process, fault cause, response range and recommended rectification measures. The production data includes food mixer component type, component type key production parameters, component type historical failure data, In the process of labeling the nodes in the knowledge graph, entities and relationships are extracted from the constructed knowledge graph dedicated to food mixers, potential fault propagation paths between entities are defined, and probability weights are assigned to each fault propagation path using historical fault data and expert knowledge to form a fault propagation probability graph. An importance ranking algorithm is used to analyze the key paths and nodes in the fault propagation probability graph. Based on the key paths and nodes, a quality detection model for food mixers is constructed. The propagation process of faults in the manufacturing process of food mixers is simulated through the quality detection model for food mixers to obtain the fault diagnosis report. In the process of using Transformer for entity recognition and relationship extraction, the Transformer model is pre-trained to adapt to the professional terminology and context in the field of food mixer manufacturing.

2. The method according to claim 1, characterized in that The process of building a knowledge graph dedicated to food mixers further includes the following steps: The classification system and properties of food mixer components are defined, the assembly relationship and functional relationship between components are established, and the failure modes and causes are extracted from the historical failure records based on the expert knowledge in the field of food mixer manufacturing.

3. The method according to claim 2, characterized in that Based on the expert knowledge in the field of food mixer manufacturing and combined with historical fault records, each process in which each food mixer component appears is assigned a node, and each node is marked with possible faults.

4. The method according to claim 1, characterized in that: For the food mixer quality detection model, historical production data is used as input into the model for supervised learning, and the model is learned and updated online through continuously collected production data.

5. The method according to claim 1, characterized in that The food mixer component types include an injection-molded base housing, a transmission component, a mixing blade holder, and a mixing cup body; The key production parameters of the component type corresponding to the injection molded base shell include the raw material type, the size specification of the injection molded shell, the manufacturing date of the injection molded shell, the temperature of the injection molded shell corresponding to the injection molded operation, and the pressure of the injection molded shell corresponding to the injection molded operation; The component type historical failure data corresponding to the injection molded base shell includes appearance defect data, size defect data and physical performance defect data; The key production parameters of the component type corresponding to the transmission component include the raw material type, the size specification of the transmission component and the gear ratio of the transmission component; The component type historical fault data corresponding to the transmission component includes appearance defect data, size defect data and operation performance defect data; The key production parameters of the component type corresponding to the stirring blade seat include the raw material type, the size specification of the stirring blade seat and the blade assembly angle data of the stirring blade seat; The component type historical fault data corresponding to the stirring blade seat includes appearance defect data, size defect data and operation performance defect data; The key production parameters of the component type corresponding to the mixing cup body include the raw material type, the size specification of the mixing cup body and the manufacturing date of the mixing cup body; The component type historical failure data corresponding to the mixing cup body includes appearance defect data, size defect data and physical performance defect data.

6. A computer device comprising a memory and a processor, characterized in that: The method according to any one of claims 1 to 5 is implemented when the processor executes the computer program stored in the memory. 7 . A computer-readable storage medium having program instructions stored thereon, wherein the program instructions are executed by a processor to implement the method according to claim 1 .

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