Formula construction method, system and device based on AI service

Abstract

The invention discloses a formula construction method, system and device based on an AI service, and the method comprises the steps: enabling a client to respond to a user operation, and starting a formula AI configuration service, so as to enable the client to establish communication connection with a server; the client sends formula editing information input by a user in the formula AI configuration window to the server; the server side calls an API interface and sends the formula editing information to an AI service; the AI service identifies the formula editing information to generate an operation instruction; the AI service sends the operation instruction to the server; the server sends the operation instruction to the client; and the client controls the formula editing software to run the operation instruction so as to generate a formula corresponding to the formula editing information. According to the method, the AI service is utilized to identify the operation instruction obtained by the formula editing information, so that the client controls the formula editing software to run the operation instruction to generate the corresponding formula, a manual formula configuration mode is replaced, and the formula configuration efficiency is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a recipe construction method, system and device based on AI services. Background Art

[0002] Batch (batch control) software is a type of application layer software based on the controller of the bottom control layer and DCS. It is mainly used in batch production in industries such as fine chemicals, pharmaceuticals, food and beverages. Engineers can use the recipe editing Batch software to build process recipes and then control the production of products. Batch production of products is based on a series of operation sequences and parameters specified by the recipe. The recipe contains all information related to the product in a specific batch production, including recipe raw materials, recipe parameters and recipe procedures.

[0003] The recipe parameters are composed of material parameters (such as feed amount, etc.) and process parameters (such as temperature control parameters, stirring frequency, etc.). The recipe procedure is the most important part of the recipe. According to the procedure model in ISA S88 (batch control standard), the recipe procedure is divided into four layers from top to bottom: Procedure, Unit, Operation and Phase. Figure 1 As shown. Phase is the smallest unit of Batch software, which is divided into two categories from the application perspective: automatic Phase (executed by DCS, involving related equipment control, such as stirring, temperature control, etc.) and interactive Phase (manual activity, not involving related equipment control, such as manual feeding, manual confirmation, etc.). Operation defines the order in which the processed materials undergo chemical or physical changes, which in turn constitutes the operation of batch production of products within the Unit equipment. Units are generally divided based on material storage or processing equipment such as kettles and tanks, which specify the operations and sequence required to complete a process. Procedure organizes Units according to the process flow to achieve a complete batch production process.

[0004] The preparation of recipes mainly involves the planning of process flow. This work is led by process engineers. Usually, recipe engineers will configure the production process recipes through recipe editing Batch software. The operation interface of recipe editing Batch software is as follows: Figure 2As shown. Currently, recipe configuration editing is done by opening recipe editing Batch software to manually configure raw materials, product BOM (Bill of Material), recipe header, recipe SFC (Sequential function chart) process, recipe formula, recipe parameter settings, etc. In the actual product production process, in order to pursue high-quality products, recipes are often variable, ranging from small parameter value adjustments such as temperature and stirring rate to large-scale redoing of the entire recipe. Using this traditional method in the past is highly complex, time-consuming and labor-intensive, which will undoubtedly increase resistance to the high productivity and high-quality product production of enterprises, and may cause misoperation, which may result in the scrapping of raw materials or unqualified product quality, or even damage to equipment and devices, or even personnel safety accidents. Summary of the invention

[0005] The present application provides a recipe construction method, system and device based on AI service, aiming to improve the efficiency of recipe configuration.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] A recipe construction method based on AI service is applied to a system composed of a client, a server and an AI service, wherein the server includes an API interface for connecting to the AI ​​service, and the method includes:

[0008] The client starts the recipe AI configuration service in response to the user operation, so that the client establishes a communication connection with the server;

[0009] The client sends the recipe editing information input by the user in the recipe AI configuration window to the server;

[0010] The server calls the API interface to send the recipe editing information to the AI ​​service;

[0011] The AI ​​service identifies the recipe editing information to generate corresponding operation instructions;

[0012] The AI ​​service sends the operation instruction to the server;

[0013] The server sends the operation instruction to the client;

[0014] The client controls the recipe editing software to run the operation instruction to generate a recipe corresponding to the recipe editing information.

[0015] Optionally, the AI ​​service identifies the recipe editing information to generate corresponding operation instructions, including:

[0016] The AI ​​service identifies the recipe editing information to obtain corresponding semantic keywords;

[0017] The AI ​​service generates corresponding operation instructions according to the semantic keywords.

[0018] Optionally, the AI ​​service generates corresponding operation instructions according to the semantic keywords, including:

[0019] If the semantic keyword is related to raw material management, the AI ​​service generates a first operation instruction; the first operation instruction is used to configure the corresponding raw material in the raw material management window of the recipe editing software;

[0020] If the semantic keyword is related to product management, the AI ​​service generates a second operation instruction; the second operation instruction is used to configure the corresponding product and bill of materials in the product management window of the recipe editing software;

[0021] If the semantic keyword is related to the recipe configuration, the AI ​​service generates a third operation instruction; the third operation instruction is used to configure the corresponding recipe configuration in the recipe configuration window of the recipe editing software;

[0022] If the semantic keyword is related to recipe modification, the AI ​​service generates a fourth operation instruction; the fourth operation instruction is used to modify corresponding configurations in the raw material management window, the product management window, and the recipe configuration window, respectively.

[0023] Optionally, the AI ​​service identifies the recipe editing information to obtain corresponding semantic keywords, including:

[0024] If the recipe editing information is voice data, the AI ​​service performs voice recognition on the recipe editing information to obtain corresponding text data;

[0025] The AI ​​service performs semantic recognition on the text data to obtain corresponding semantic keywords.

[0026] Optionally, the AI ​​service identifies the recipe editing information to obtain corresponding semantic keywords, including:

[0027] If the recipe editing information is image data, the AI ​​service performs optical character recognition on the recipe editing information to obtain corresponding text data;

[0028] The AI ​​service performs semantic recognition on the text data to obtain corresponding semantic keywords.

[0029] Optionally, the method further includes:

[0030] The client generates an instruction text corresponding to the operation instruction, and displays the instruction text in the recipe AI configuration window.

[0031] A recipe building system based on AI services, including:

[0032] Client, server and AI service, and the server includes an API interface for connecting to the AI ​​service;

[0033] The client is used to: start the recipe AI configuration service in response to user operation, so that the client establishes a communication connection with the server; send the recipe editing information input by the user in the recipe AI configuration window to the server; control the recipe editing software to run the operation instruction to generate a recipe corresponding to the recipe editing information;

[0034] The server is used to: call the API interface to send the recipe editing information to the AI ​​service; send the operation instruction to the client;

[0035] The AI ​​service is used to: identify the recipe editing information to generate corresponding operation instructions; and send the operation instructions to the server.

[0036] A recipe building device based on AI service, comprising:

[0037] An AI service startup unit, used to start a recipe AI configuration service in response to a user operation, so as to establish a communication connection with a server;

[0038] An information sending unit, used to send the recipe editing information input by the user in the recipe AI configuration window to the server, so that the server calls the API interface to send the recipe editing information to the AI ​​service; the AI ​​service is used to identify the recipe editing information to generate a corresponding operation instruction, and feed the operation instruction back to the server;

[0039] An instruction receiving unit, used for receiving the operation instruction sent by the server;

[0040] The instruction execution unit is used to control the recipe editing software to execute the operation instruction to generate a recipe corresponding to the recipe editing information.

[0041] Optionally, the AI ​​service-based recipe building device further includes:

[0042] A text display unit is used to generate an instruction text corresponding to the operation instruction and display the instruction text in the recipe AI configuration window.

[0043] The technical solution provided by the present application is that the client starts the recipe AI configuration service in response to the user operation, so that the client and the server establish a communication connection. The client sends the recipe editing information entered by the user in the recipe AI configuration window to the server. The server calls the API interface to send the recipe editing information to the AI ​​service. The AI ​​service identifies the recipe editing information to generate an operation instruction. The AI ​​service sends the operation instruction to the server. The server sends the operation instruction to the client. The client controls the recipe editing software to run the operation instruction to generate a recipe corresponding to the recipe editing information. The present application utilizes the AI ​​service to identify the operation instruction obtained by the recipe editing information, so that the client controls the recipe editing software to run the operation instruction to generate the corresponding recipe, replacing the manual configuration method of the recipe, greatly improving the efficiency of the recipe configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic diagram of a recipe procedure provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of an operation interface of a recipe editing software provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the architecture of a recipe building system based on AI services provided in an embodiment of the present application;

[0048] Figure 4 A flowchart of a recipe construction method based on AI service provided in an embodiment of the present application;

[0049] Figure 5 A schematic diagram of a process for identifying recipe editing information provided in an embodiment of the present application;

[0050] Figure 6 A flowchart of a recipe construction provided in an embodiment of the present application;

[0051] Figure 7 A schematic diagram of a window interface provided in an embodiment of the present application;

[0052] Figure 8 A schematic diagram of a system architecture provided for an embodiment of the present application;

[0053] Fig. 9A schematic diagram of a system execution provided in an embodiment of the present application;

[0054] Fig.10 A schematic diagram of the architecture of an AI service-based recipe building device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0057] like Figure 3 As shown, it is a schematic diagram of the architecture of a recipe building system based on AI service provided in an embodiment of the present application, including the units shown below.

[0058] The client 100 , the server 200 and the AI ​​service 300 , and the server 200 includes an API interface for connecting to the AI ​​service 300 .

[0059] The interaction process between the client 100, the server 200 and the AI ​​service 300 can be seen in Figure 4 The recipe construction method based on AI service shown includes the following steps.

[0060] S401: The client starts the recipe AI configuration service in response to the user operation, so that the client establishes a communication connection with the server.

[0061] Among them, when the client responds to the user operation to start the recipe AI configuration service, it also needs to start the service related to the recipe AI configuration service. The service uses the enabled recipe editing software and opens the recipe AI configuration window.

[0062] S402: The client sends the recipe editing information entered by the user in the recipe AI configuration window to the server.

[0063] Among them, after the client and the server establish a communication connection, the client can exchange data with the server.

[0064] In some examples, the types of recipe editing information include, but are not limited to: text data, voice data, and image data.

[0065] In a possible implementation, the recipe editing information may be "add a raw material, coded as A001", "add a recipe, named TestRecipe, version V1.0, the product name is test product, including an automatic Phase (named automatic feeding Phase, the associated raw material is AutoMaterial) and an interactive Phase (named manual feeding Phase, the associated raw material is InavMaterial)", "modify the unit of raw material A001 to Kg, modify the standard output of product Product001 to 100, modify the recipe TestRecipe, add an automatic Phase (named stirring Phase, including a parameter named stirring rate)".

[0066] S403: The server calls the API interface and sends the recipe editing information to the AI ​​service.

[0067] S404: The AI ​​service identifies the recipe editing information to generate corresponding operation instructions.

[0068] Optionally, the AI ​​service recognizes the recipe editing information to generate the corresponding operation instructions. For details, see Figure 5 The steps are shown and the corresponding explanations.

[0069] S405: The AI ​​service sends the operation instruction to the server.

[0070] S406: The server sends the operation instruction to the client.

[0071] S407: The client controls the recipe editing software to execute the operation instruction to generate a recipe corresponding to the recipe editing information.

[0072] Optionally, after receiving the operation instruction, the client may also generate an instruction text corresponding to the operation instruction, and display the instruction text in the recipe AI configuration window.

[0073] In some examples, the recipe AI configuration window and the recipe editing software window can be displayed together, such as Figure 7 shown.

[0074] In some examples, the recipe building system based on AI services can have other variant types. For details, see Figure 8 In the InPlant Batch system shown, the third-party AI system is equivalent to another form of AI service, the BatchAiClient client is equivalent to another form of client, and the BatchAiSever server is another form of server. Generally speaking, the BatchAIClient client is used to provide an HMI (Human Machine Interface) for users to input recipe building information in the form of text, images, and videos on the HMI. The BatchAIServer server is responsible for forwarding and calling related data to complete the automatic construction of the recipe. In addition, Figure 8 The InPlant Batch system shown also includes a gateway (or firewall), an information network C, a control network A, a control king B, an operation station, an engineer station, a recipe configuration station, a Web server, and a recipe server.

[0075] In a possible implementation, Figure 8 For information interaction between the BatchAiClient client, third-party AI system, and BatchAiSever server, see Fig. 9 shown.

[0076] Combination Figure 5 The method shown is a recipe building system based on AI services, which can provide a general API interface and can be connected with AI services to realize intelligent recipe building. By using AI services to recognize various types of recipe editing information such as voice, images, text, and files, it is possible to automatically build the recipe configuration that the user wants. It supports integration with third-party AI system platforms and cohesive AI services to complete the automatic construction of recipe configurations. It has a wide range of applications, is not restricted by time and region, and is convenient and efficient in building and modifying recipes.

[0077] The process shown in S401-S407 above utilizes AI services to identify the operating instructions obtained from the recipe editing information, so that the client controls the recipe editing software to run the operating instructions to generate a recipe corresponding to the recipe editing information, reducing the complexity of the enterprise in building recipe configuration. It is quick to get started and simple and convenient, replacing the manual configuration method of the recipe, automatically building and modifying the recipe, and greatly improving the efficiency of the recipe configuration.

[0078] like Figure 5 As shown, it is a flow chart of a recipe editing information identification method provided in an embodiment of the present application, which includes the following steps.

[0079] S501: The AI ​​service identifies the recipe editing information to obtain corresponding semantic keywords.

[0080] Since the type of recipe editing information is not limited to text data, corresponding measures need to be taken to identify other types of recipe editing information.

[0081] Optionally, if the recipe editing information is voice data, the AI ​​service performs voice recognition on the recipe editing information to obtain corresponding text data, and the AI ​​service performs semantic recognition on the text data to obtain corresponding semantic keywords.

[0082] Optionally, if the recipe editing information is image data, the AI ​​service performs optical character recognition on the recipe editing information to obtain corresponding text data, and the AI ​​service performs semantic recognition on the text data to obtain corresponding semantic keywords.

[0083] S502: The AI ​​service generates corresponding operation instructions according to the semantic keywords.

[0084] Among them, if the semantic keyword is related to raw material management, the AI ​​service generates a first operation instruction, and the first operation instruction is used to configure the corresponding raw material in the raw material management window of the recipe editing software.

[0085] In some examples, building a recipe first requires maintaining the raw material information required for the recipe. The semantic keywords (such as raw materials) shown in the recipe editing information "Add a new raw material, coded as A001" are related to raw material management. For this reason, the AI ​​service generates a first operation instruction that can be used to configure the raw material coded as A001 in the raw material management window.

[0086] Optionally, if the semantic keyword is related to product management, the AI ​​service generates a second operation instruction, and the second operation instruction is used to configure the corresponding product and bill of materials in the product management window of the recipe editing software.

[0087] In some examples, building a recipe also requires maintaining the product information required for the recipe. The semantic keywords (such as product) shown in the recipe editing information "A new product is added, coded as Product001, and its BOM contains an input raw material coded as A001 and an output raw material coded as B001" are related to product management. For this reason, the AI ​​service generates a second operation instruction that can be used to configure the product coded as Produtc001 and its BOM information in the product management window.

[0088] Optionally, if the semantic keyword is related to the recipe configuration, the AI ​​service generates a third operation instruction, and the third operation instruction is used to configure the corresponding recipe configuration in the recipe configuration window of the recipe editing software.

[0089] In some examples, after maintaining the raw material information and product information, you can perform recipe configuration operations. The semantic keywords (such as Phase) shown in the recipe editing information "Add a new recipe named TestRecipe, version V1.0, product name test product, including an automatic Phase (named automatic feeding Phase, the associated raw material is AutoMaterial) and an interactive Phase (named manual feeding Phase, the associated raw material is InavMaterial)" are related to the recipe configuration. For this purpose, the AI ​​service generates a third operation instruction that can be used to configure the corresponding recipe configuration in the recipe configuration window (including recipe header, recipe SFC program, recipe parameters, recipe formula, recipe audit, recipe exception handling and other information).

[0090] Optionally, if the semantic keyword is related to the recipe modification, the AI ​​service generates a fourth operation instruction, and the fourth operation instruction is used to modify the corresponding configuration in the raw material management window, the product management window and the recipe configuration window respectively.

[0091] In some examples, the recipe will be updated from time to time, and the updated content includes but is not limited to one or more of the raw material information, product information and recipe configuration. The semantic keywords (such as modify) shown in the recipe editing information "Change the unit of raw material A001 to Kg, change the standard output of product Product001 to 100, modify the recipe TestRecipe, and add an automatic Phase (named stirring Phase, including a parameter named stirring rate)" are related to the recipe modification. For this purpose, the AI ​​service generates a fourth operation instruction that can be used to modify the corresponding configurations in the raw material management window, the product management window and the recipe configuration window respectively.

[0092] In a possible implementation, the workflow for implementing recipe building in the recipe editing software can be found in Figure 6 As shown, specifically, the product management process includes product configuration and material configuration, and the recipe configuration process at least includes creating a new recipe, recipe header attributes, editing processes (including configuring Unit instances, configuring Operation instances, configuring Phase instances, configuring material parameters and process parameters), binding production lines and equipment, exception handling configuration, editing formulas, and configuring electronic signatures.

[0093] The process shown in S501-S502 above can enable the AI ​​service to effectively identify the recipe editing information to obtain the corresponding operation instructions.

[0094] like Fig.10 As shown, it is a schematic diagram of the architecture of a recipe building device based on AI service provided in an embodiment of the present application, including the units shown below.

[0095] The AI ​​service starting unit 1001 is used to start the recipe AI configuration service in response to the user operation so as to establish a communication connection with the server.

[0096] The information sending unit 1002 is used to send the recipe editing information input by the user in the recipe AI configuration window to the server, so that the server calls the API interface and sends the recipe editing information to the AI ​​service; the AI ​​service is used to identify the recipe editing information to generate corresponding operation instructions and feed back the operation instructions to the server.

[0097] The instruction receiving unit 1003 is used to receive an operation instruction sent by the server.

[0098] The instruction execution unit 1004 is used to control the recipe editing software to execute the operation instruction to generate a recipe corresponding to the recipe editing information.

[0099] The text display unit 1005 is used to generate an instruction text corresponding to the operation instruction and display the instruction text in the recipe AI configuration window.

[0100] Each unit shown above uses AI services to identify the operating instructions obtained from the recipe editing information, so that the client controls the recipe editing software to run the operating instructions to generate a recipe corresponding to the recipe editing information, reducing the complexity of the enterprise's recipe configuration construction. It is quick to get started and simple and convenient, replacing the manual configuration of recipes, automatically constructing and modifying recipes, and greatly improving the efficiency of recipe configuration.

[0101] Although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present application. Certain features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0102] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A recipe construction method based on AI service, characterized in that: Applied to a system consisting of a client, a server, and an AI service, the server including an API interface for docking with the AI ​​service, the method comprising: The client starts the recipe AI configuration service in response to the user operation, so that the client establishes a communication connection with the server; The client sends the recipe editing information input by the user in the recipe AI configuration window to the server; The server calls the API interface to send the recipe editing information to the AI ​​service; The AI ​​service identifies the recipe editing information to generate corresponding operation instructions; The AI ​​service sends the operation instruction to the server; The server sends the operation instruction to the client; The client controls the recipe editing software to run the operation instruction to generate a recipe corresponding to the recipe editing information.

2. The method according to claim 1, characterized in that The AI ​​service identifies the recipe editing information to generate corresponding operation instructions, including: The AI ​​service identifies the recipe editing information to obtain corresponding semantic keywords; The AI ​​service generates corresponding operation instructions according to the semantic keywords.

3. The method according to claim 2, characterized in that The AI ​​service generates corresponding operation instructions according to the semantic keywords, including: If the semantic keyword is related to raw material management, the AI ​​service generates a first operation instruction; the first operation instruction is used to configure the corresponding raw material in the raw material management window of the recipe editing software; If the semantic keyword is related to product management, the AI ​​service generates a second operation instruction; the second operation instruction is used to configure the corresponding product and bill of materials in the product management window of the recipe editing software; If the semantic keyword is related to the recipe configuration, the AI ​​service generates a third operation instruction; the third operation instruction is used to configure the corresponding recipe configuration in the recipe configuration window of the recipe editing software; If the semantic keyword is related to recipe modification, the AI ​​service generates a fourth operation instruction; the fourth operation instruction is used to modify corresponding configurations in the raw material management window, the product management window, and the recipe configuration window, respectively.

4. The method according to claim 2, characterized in that: The AI ​​service identifies the recipe editing information to obtain corresponding semantic keywords, including: If the recipe editing information is voice data, the AI ​​service performs voice recognition on the recipe editing information to obtain corresponding text data; The AI ​​service performs semantic recognition on the text data to obtain corresponding semantic keywords.

5. The method according to claim 2, characterized in that: The AI ​​service identifies the recipe editing information to obtain corresponding semantic keywords, including: If the recipe editing information is image data, the AI ​​service performs optical character recognition on the recipe editing information to obtain corresponding text data; The AI ​​service performs semantic recognition on the text data to obtain corresponding semantic keywords.

6. The method according to claim 1, characterized in that The method further comprises: The client generates an instruction text corresponding to the operation instruction, and displays the instruction text in the recipe AI configuration window.

7. A recipe building system based on AI service, characterized in that: include: Client, server and AI service, and the server includes an API interface for connecting to the AI ​​service; The client is used to: start the recipe AI configuration service in response to user operation, so that the client establishes a communication connection with the server; send the recipe editing information input by the user in the recipe AI configuration window to the server; control the recipe editing software to run the operation instruction to generate a recipe corresponding to the recipe editing information; The server is used to: call the API interface to send the recipe editing information to the AI ​​service; send the operation instruction to the client; The AI ​​service is used to: identify the recipe editing information to generate corresponding operation instructions; and send the operation instructions to the server.

8. The system according to claim 7, characterized in that The AI ​​service is also used to execute the recipe construction method based on the AI ​​service as described in any one of claims 2-5 above, and the client is also used to execute the recipe construction method based on the AI ​​service as described in claim 6 above.

9. A recipe building device based on AI service, characterized in that: include: An AI service startup unit, used to start a recipe AI configuration service in response to a user operation, so as to establish a communication connection with a server; An information sending unit, used to send the recipe editing information input by the user in the recipe AI configuration window to the server, so that the server calls the API interface to send the recipe editing information to the AI ​​service; the AI ​​service is used to identify the recipe editing information to generate a corresponding operation instruction, and feed the operation instruction back to the server; An instruction receiving unit, used for receiving the operation instruction sent by the server; The instruction execution unit is used to control the recipe editing software to execute the operation instruction to generate a recipe corresponding to the recipe editing information.

10. The device according to claim 9, characterized in that Also includes: A text display unit is used to generate an instruction text corresponding to the operation instruction and display the instruction text in the recipe AI configuration window.

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