A method and system for intelligently assisting in the acquisition of design requirements for manufacturing equipment

Abstract

The application discloses a kind of manufacturing equipment design requirement intelligent auxiliary acquisition method and system, belong to the technical field of industrial design;Among them, the hierarchical relationship of manufacturing equipment design requirement is established, and then the hierarchical relationship database of manufacturing equipment historical design requirement is constructed;Design requirement intelligent auxiliary acquisition model is constructed, and the design requirement intelligent auxiliary acquisition model is trained based on the hierarchical relationship database of manufacturing equipment historical design requirement;The original design requirement information of new manufacturing equipment is collected, and the design requirement information is obtained using the design requirement intelligent auxiliary acquisition model, and the design requirement information includes design requirement information hierarchical relationship and labeled abnormal requirement;Modification, confirmation and output calculated design requirement information.The manufacturing equipment design requirement intelligent auxiliary acquisition method and system provided in the application not only improve the accuracy and completeness of equipment design requirement acquisition, but also greatly improve the acquisition efficiency and reduce the acquisition cost.

Description

Technical Field

[0001] This application belongs to the technical field of industrial design, and in particular relates to a method and system for intelligently assisting in the acquisition of design requirements for manufacturing equipment. Background Technology

[0002] Large-scale product customization has spurred the customized development of manufacturing equipment, leading to a growing market demand for customized manufacturing equipment design. Customer needs are the starting point and the ultimate goal of the design process. Design requirements are a key factor in determining the correctness of the design outcome; comprehensively and accurately obtaining design requirements can reduce errors and significantly improve design efficiency.

[0003] The invention patent CN118839006B, entitled "Method and System for Obtaining Design Requirements of Manufacturing Equipment Based on Product Processing Actions," describes a method that involves constructing three databases: a database of equipment design requirement feature elements and a database of equipment design requirement feature elements, a database of equipment design requirement feature elements, a database of equipment design requirement answer information, and a database of equipment design requirement feature elements and design requirement relationships. The method then uses these databases to train an algorithm model and uses the trained model to conduct question-and-answer dialogues to obtain design requirements. However, during the process of obtaining design requirements, due to differences in the knowledge background of the personnel conveying the requirements, and the diverse, complex, and uncertain characteristics of design requirements, even with intelligent questioning, the human response method still results in inaccurate, repetitive, or missing design requirement information, poor quality, and an inability to effectively judge and verify the quality and internal relationships of the collected information. Summary of the Invention

[0004] To address the problems in related technologies, this application provides an intelligent auxiliary method and system for obtaining manufacturing equipment design requirements, which not only improves the accuracy and completeness of equipment design requirement acquisition, but also greatly improves acquisition efficiency and reduces acquisition costs.

[0005] The technical solution is as follows:

[0006] On the one hand, a method for intelligently assisting in obtaining design requirements for manufacturing equipment is provided, including the following steps:

[0007] Establish a hierarchical relationship of manufacturing equipment design requirements, and then construct a database of the hierarchical relationship of historical design requirements for manufacturing equipment.

[0008] A design requirement intelligent assistance acquisition model is constructed, and the design requirement intelligent assistance acquisition model is trained based on the historical design requirement hierarchical relationship database of the manufacturing equipment. The design requirement intelligent assistance acquisition model is a multimodal fusion neural network model. The multimodal fusion neural network model is used to process multimodal design requirements and fill them into the corresponding layers of design requirements in the form of structured text, and construct the relationship between the layers. At the same time, the constructed design hierarchy relationship is checked and abnormal requirements are marked.

[0009] Collect the original design requirements information of the new manufacturing equipment, and use the intelligent auxiliary acquisition model of the design requirements to obtain the design requirements information, which includes the hierarchical relationship of the design requirements information and the marked abnormal requirements.

[0010] Modify, confirm, and output the calculated design requirements information.

[0011] A further technical solution is that the design requirements for the manufacturing equipment include a functional layer, an action layer, a structural layer, a layout layer, and a performance layer. The functional layer includes one or more functional modules, the action layer includes several action modules, and the structural layer includes several structural modules.

[0012] The system establishes mapping relationships between functional modules and action modules, action modules and structural modules, functional layers and layout layers, structural layers and layout layers, and layout layers and performance layers of the equipment's historical design requirements information, thereby constructing a hierarchical relationship database of the manufacturing equipment's historical design requirements.

[0013] The mapping relationship includes:

[0014] The mapping relationship between functional modules and action modules can be one-to-one or one-to-many.

[0015] The mapping relationship between action modules and structure modules is one-to-one.

[0016] The mapping relationships between layers include the mapping relationships between functional layers and layout layers, structural layers and layout layers, and layout layers and performance layers.

[0017] A further technical solution is that the multimodal fusion neural network model includes an input layer, a hidden layer, and an output layer;

[0018] A multimodal natural language model is incorporated into the input layer to process multimodal design requirements and populate the corresponding layers of design requirements in the form of structured text;

[0019] The hidden layer is used to learn the mapping relationships between functional modules and action modules, action modules and structural modules, functional layers and layout layers, structural layers and layout layers, and layout layers and performance layers.

[0020] The output layer includes a first output layer and a second output layer. The first output layer outputs the requirements content and hierarchical relationship of the design requirements layer. The second output layer checks the hierarchical relationship of the design requirements output by the first output layer and outputs the check results.

[0021] A further technical solution is that the requirement content in the design requirement layer of the first output layer is the requirement content at each level obtained by processing the original design requirements of the new manufacturing equipment through the multimodal natural language processing model in the input layer, and the missing information in the original requirements is supplemented by the hierarchical mapping relationship learned by the hidden layer.

[0022] A further technical solution is that the abnormal requirements include weak abnormal requirements and strong abnormal requirements;

[0023] Weakly abnormal requirements are those that are incomplete in the original requirements and are automatically completed by a multimodal fusion neural network model.

[0024] Strongly abnormal requirements are those that are checked to determine the abnormality of the requirement hierarchy relationship between the output of the second output layer and the output of the first output layer.

[0025] In a further technical solution, the original design requirement information is unprocessed requirement information, and the modalities of the original design requirement information include text, speech, and requirement information fused from text and speech.

[0026] In a further technical solution, the design requirement information is output in the form of a requirement hierarchy mapping diagram.

[0027] On the other hand, a smart auxiliary acquisition system for manufacturing equipment design requirements is provided, employing the aforementioned smart auxiliary acquisition method for manufacturing equipment design requirements. The system includes:

[0028] The historical design requirement hierarchy data storage module is used to store historical design requirement hierarchy data based on the hierarchical relationship annotation of manufacturing equipment design requirements;

[0029] The design requirements acquisition module is used to collect the original design requirements information of the equipment.

[0030] The design requirement matching and reasoning module is used to match and organize the original design requirements into the corresponding layers of design requirements, and to construct the hierarchical relationship of design requirements through calculation, supplement the missing design requirement information, and check whether the design requirements are abnormal through the hierarchical relationship.

[0031] The storage module is used to store the original design requirements collected, the hierarchical relationship of requirements obtained through calculation and reasoning, and the results of checking whether the requirements are abnormal.

[0032] The design requirement output module is used to output the design requirement information obtained by the intelligent assisted design requirement acquisition model. This technical solution includes at least the following technical effects:

[0033] This application provides an intelligent auxiliary method and system for acquiring design requirements of manufacturing equipment. It processes historical equipment design requirement data and constructs a database of historical design requirement hierarchies by building a hierarchical structure of requirements and mapping relationships between them. A multimodal fusion neural network model is used to learn the structured representation of requirements at each level and the mapping relationships between them. This allows for the breakdown of new equipment design requirements into different requirement levels and their clear, structured expression. Furthermore, by constructing mapping relationships between requirement levels, it supplements obviously missing requirements and identifies inherent errors in the requirements. This avoids problems such as inaccurate, repetitive, missing, and poorly accurate design requirement collection caused by the complexity, ambiguity, diversity of design requirements themselves, differences in personnel knowledge backgrounds, and human error during the design requirement acquisition process.

[0034] This application provides an intelligent auxiliary method and system for obtaining manufacturing equipment design requirements, which not only improves the accuracy and completeness of equipment design requirement acquisition, but also greatly improves acquisition efficiency and reduces acquisition costs. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0036] Figure 1 A flowchart of a method for intelligently assisting in obtaining design requirements for manufacturing equipment, provided in a preferred embodiment of this application;

[0037] Figure 2 A schematic diagram of a manufacturing equipment design requirement intelligent auxiliary acquisition system module provided in a preferred embodiment of this application;

[0038] Figure 3 This is a schematic diagram illustrating the hierarchical relationship of design requirements provided for a preferred embodiment of this application;

[0039] Figure 4 An abnormal requirement output display diagram marked in the design requirement information of "a certain new energy battery pack and equipment" provided in a preferred embodiment of this application;

[0040] Figure 5 This is a diagram showing the final design requirements output after modification and confirmation of a "new energy battery pack and equipment" provided in a preferred embodiment of this application. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0042] like Figure 1 As shown, this application provides an intelligent auxiliary method for obtaining design requirements of manufacturing equipment, including:

[0043] A hierarchical relationship of manufacturing equipment design requirements is established, leading to the construction of a historical database of manufacturing equipment design requirements. This database comprises four layers: functional, operational, structural, layout, and performance. Each functional layer includes one or more functional modules, each operational layer includes several operational modules, and each structural layer includes several structural modules. The historical database maps functional modules to operational modules, operational modules to structural modules, functional layers to layout layers, structural layers to layout layers, and layout layers to performance layers. The mappings between functional layers and layout layers, structural layers and layout layers, and layout layers and performance layers are layer-to-layer mappings. Preferably, the mapping between functional modules and operational modules is one-to-one or one-to-many; the mapping between operational modules and structural modules is one-to-one.

[0044] It should be noted that the historical design requirement hierarchy database for manufacturing equipment constructed in this application represents the inherent relationships between equipment design requirements at the functional, operational, structural, layout, and performance levels. The labeled dataset represents the requirement relationships between these levels. Essentially, it is used to train an algorithm model to construct relationships between requirements at each level and identify anomalies in these relationships.

[0045] A design requirement intelligent assisted acquisition model is constructed and trained based on the historical design requirement hierarchy database of the manufacturing equipment. The design requirement intelligent assisted acquisition model is a multimodal fusion neural network model. This model processes multimodal design requirements and populates them into the corresponding layers of the design requirements in structured text form, constructs relationships between layers, and checks the constructed hierarchical relationships, marking abnormal requirements.

[0046] The multimodal fusion neural network model incorporates a multimodal natural language model in its input layer to process multimodal design requirements and fill them into the corresponding layers of the design requirements in structured text form. The hidden layers of the model learn the mapping relationships between functional modules and action modules, action modules and structural modules, functional layers and layout layers, structural layers and layout layers, and layout layers and performance layers. The model has two output layers. The first output layer outputs the requirement content and hierarchical relationships of the design requirements layer. The requirement content consists of the requirements at each level obtained by processing the original design requirements of the new manufacturing equipment through the multimodal natural language processing model in the input layer, and the missing information in the original requirements is supplemented by the hierarchical mapping relationships learned in the hidden layers. The second output layer checks the hierarchical relationships of the design requirements output by the first output layer and outputs the check results.

[0047] The system collects original design requirements information for new manufacturing equipment, utilizes an intelligent design requirement acquisition model to obtain design requirement information, modifies, confirms, and outputs calculated design requirement information. The original design requirement information is unprocessed and includes text, speech, and a fusion of text and speech modalities. The design requirement information includes hierarchical relationships and annotated abnormal requirements. Abnormal requirements are categorized as weakly abnormal or strongly abnormal. Weakly abnormal requirements are those incomplete in the original design requirements, automatically completed by a multimodal fusion neural network model; strongly abnormal requirements are those identified as abnormal by checking the hierarchical relationship of the requirements output from the first output layer through the second output layer. The design requirement information is output in the form of a requirement hierarchy mapping diagram.

[0048] In summary, this technical solution constructs a hierarchical structure of equipment design requirements and the mapping relationships between these hierarchical levels. It processes historical equipment design requirement data and builds a database of historical design requirement hierarchical relationships. By combining this with a multimodal fusion neural network model to learn the structured representation of requirements at each level and the mapping relationships between them, it effectively breaks down unstructured text, speech, and text-speech fusion requirements into different requirement levels and expresses them clearly in a structured manner. Furthermore, it utilizes a dual-output layer collaborative mechanism of an intelligent design requirement acquisition model. Simultaneously, by constructing mapping relationships between requirement levels, it supplements obviously missing requirements and corrects inherent errors in the labeled requirements, significantly improving the efficiency and accuracy of requirement processing. This solves the problems of missing requirements and poor accuracy caused by the complexity of the requirements themselves, the ambiguity of multi-source information, and reliance on human experience in traditional methods. It provides a highly reliable and collaborative requirement analysis foundation for the development of intelligent manufacturing equipment.

[0049] In one embodiment, a method for intelligently assisting in obtaining design requirements for manufacturing equipment is provided for assisting in obtaining design requirements for "equipment for a certain new energy battery pack". The method includes the following steps:

[0050] Step S10: Establish the hierarchical relationship of manufacturing equipment design requirements, and then construct a database of historical design requirements for manufacturing equipment. Specifically, this includes the following steps:

[0051] Step S11: Collect the original design requirements information of 1652 sets of historical manufacturing equipment, and process the original design requirements of 1652 sets of historical manufacturing equipment according to the constructed manufacturing equipment requirement hierarchy, and fill them into the functional layer, action layer, structural layer, layout layer and performance layer in a structured description form.

[0052] Step S12: For the 1652 sets of historical requirements that have been processed and filled into each layer, construct the hierarchical relationship of requirements. Specifically, construct one-to-one or one-to-many relationships between functions and actions, one-to-one relationships between actions and structures, and mapping relationships between functional layers and layout layers, structural layers and layout layers, and layout layers and performance layers. For example, a dispensing function in the XY plane corresponds to a dispensing action, an X-axis motion action, and a Y-axis motion action, forming a one-to-three mapping relationship, indicating that the dispensing function in the XY plane requires three corresponding actions to achieve. In addition, the dispensing action in the action layer corresponds to the dispensing working system in the structure layer, the X-axis motion action in the action layer corresponds to a module in the structure layer, and the Y-axis motion action in the action layer corresponds to a module in the structure layer. See details. Figure 3 Hierarchical relationship diagram.

[0053] Step S20: Construct an intelligent auxiliary acquisition model for design requirements, and train the intelligent auxiliary acquisition model for design requirements based on the hierarchical relationship database of historical design requirements of the manufacturing equipment. Specifically, this includes the following steps:

[0054] Step S21: Construct an intelligent design requirement acquisition model. This model is a multimodal fusion neural network model with an overall architecture of a multi-layer neural network. A multimodal natural language model (BERT) is integrated into the input layer to process the original multimodal design requirements. The processed design requirements are then organized into structured text and distributed to the functional layer, action layer, structure layer, layout layer, and performance layer. The multimodal natural language model (BERT) supports modalities including text, speech, and combinations of both. The hidden layers of the multimodal fusion neural network model learn and construct one-to-one or one-to-many mappings between functions and actions, one-to-one mappings between actions and structures, and inter-layer mappings between the functional layer and the layout layer, the structure layer and the layout layer, and the layout layer and the performance layer. The multimodal fusion neural network model contains two output layers. The first output layer outputs the requirement content and hierarchical relationships from the design requirement layer, and can supplement missing information in the requirements through these hierarchical relationships. The second output layer outputs the results of checking the hierarchical relationships of the requirements output by the first output layer.

[0055] Step S22: Construct a computing server cluster, use 1400 sets of the processed 1652 sets of historical design requirement hierarchy relationship data for model training, use 200 sets for validation, evaluate model performance, and finally use 52 sets of historical design requirement hierarchy relationship data to test the model and obtain the final trained model.

[0056] Step S30: Collect the original design requirements information of the new manufacturing equipment, and use the intelligent auxiliary acquisition model of the design requirements to obtain the design requirements information. The design requirements information includes the hierarchical relationship of the design requirements information and the marked abnormal requirements. Specifically, it includes the following steps:

[0057] Step S31: Construct a program to collect original design requirements for manufacturing equipment, and collect the original design requirements for "equipment for a certain new energy battery pack". The collected content is as follows:

[0058] Equipment Name: Battery Packaging Equipment

[0059] Industry sector: New energy industry

[0060] Application: Battery packaging

[0061] Functional action: First, apply glue to all four sides of the connection, then tighten the 8 screws distributed in the XY plane.

[0062] Structure: One adhesive application system, one fastening system, and one XY planar motion module.

[0063] Layout: The base plate is used as the reference plane, with its center point as the origin of the XYZ axes. The surface of the base plate is located on the XY plane. An adhesive application system and a module that moves in the XY plane are installed on the base plate. The module is connected to the locking system.

[0064] Performance: Accuracy requirement ±0.01, cycle time 60 seconds / piece, yield rate 95%, stability required for continuous production of 50,000 pieces, overall budget 300,000 RMB / unit.

[0065] Step S32: Import the collected original design requirements into the trained intelligent design requirement acquisition model to obtain hierarchical and structured display of the requirement text information.

[0066] Step S33: In the obtained hierarchically structured display of requirement text information, requirements that are automatically and initially completed based on hierarchical relationships by the multimodal fusion neural network model are identified as weakly anomalous requirements and highlighted in yellow; requirement content whose hierarchical relationship with the output of the first output layer in the output layer of the multimodal fusion neural network model is determined to be anomalous is identified as strongly anomalous requirements and highlighted in red. See details. Figure 4 .

[0067] Equipment Name: Battery Packaging Equipment

[0068] Industry sector: New energy industry

[0069] Application: Battery packaging

[0070] Functional layer: Apply adhesive to the four sides of the component connection points and tighten the 8 screws of the battery pack in the XY plane.

[0071] Action layer: Glue application, XY plane motion (this information was missing in the original requirements and was completed by the algorithm model), locking, XY plane motion.

[0072] Structural layer: a glue application system, an XY plane motion module (this information was missing in the original requirements and was completed through the algorithm model), a locking system, and a set of XY plane motion modules.

[0073] Layout layer: With the base plate as the reference plane, the center point of the base plate is the origin of the XYZ axis, and the plane on which the base plate is located is the XY plane. Two sets of working systems and motion execution mechanisms are placed side by side on the base plate (this information was missing in the original requirements and was completed by the algorithm model). A glue application working system is installed on the base plate (this content was determined to be an abnormal requirement by the model calculation). A module for XY plane motion is installed on the YZ axis plane in the direction of the positive X axis, and a locking working system is installed on the module.

[0074] Performance level: Accuracy requirement ±0.01mm, cycle time 60 seconds / piece, yield rate 95%, stability requirement for continuous production of 50,000 pieces, overall budget of 300,000 RMB / unit.

[0075] Step S40: Modify, confirm, and output the calculated design requirements information:

[0076] Step S41: Modify the design requirement calculated and marked as a strong anomaly by the multimodal fusion neural network model. Change "a glue application system is installed on the base plate" in the layout layer to "a module with XY plane motion is installed in the YZ-axis plane in the direction of the negative X-axis, and a glue application system is installed on the module". Confirm the missing requirement calculated and completed by the multimodal fusion neural network model.

[0077] Step S42: Import the modified design requirement information back into the multimodal fusion neural network model and calculate to obtain the hierarchical structured display of the requirement text information again. At this time, no error messages are displayed, and the design requirement collection is confirmed to be complete.

[0078] Step S43: Display and output the confirmed design requirements for "a certain new energy battery pack and its equipment" in the form of a requirement hierarchy mapping diagram. See details below. Figure 5 .

[0079] The final step was to obtain the design requirements for "a certain new energy battery pack and its supporting equipment".

[0080] like Figure 2 As shown, in one embodiment, an intelligent auxiliary acquisition system for manufacturing equipment design requirements is provided, the system comprising:

[0081] The historical design requirement hierarchy data storage module 100 is used to store historical design requirement hierarchy data based on the hierarchical relationship annotation of manufacturing equipment design requirements;

[0082] The design requirements acquisition module 200 is used to collect the original design requirements information of the equipment.

[0083] The design requirement matching and reasoning module 300 is used to match and organize the original design requirements into the corresponding layers of design requirements, and to construct the hierarchical relationship of design requirements through calculation, supplement the missing design requirement information, and check whether the design requirements are abnormal through the hierarchical relationship.

[0084] Storage module 400 is used to store the original design requirements collected, the hierarchical relationship of requirements obtained after calculation and reasoning, and the results of checking whether the requirements are abnormal.

[0085] The design requirement output module 500 is used to output the design requirement information obtained by the intelligent auxiliary acquisition model.

[0086] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0087] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligently assisting in obtaining design requirements for manufacturing equipment, comprising: The method comprises the following steps: ​ establishing a hierarchical relationship of manufacturing equipment design requirements, and then constructing a manufacturing equipment historical design requirement hierarchical relationship database; wherein the hierarchy comprises a function layer, an action layer, a structure layer, a layout layer, and a performance layer, the function layer comprises one or several function modules, the action layer comprises several action modules, the structure layer comprises several structure modules, and the hierarchical relationship comprises a mapping relationship between the function modules and the action modules, a mapping relationship between the action modules and the structure modules, a mapping relationship between the function layer and the layout layer, a mapping relationship between the structure layer and the layout layer, and a mapping relationship between the layout layer and the performance layer; wherein the mapping relationship between the function modules and the action modules is one-to-one or one-to-many, and the mapping relationship between the action modules and the structure modules is one-to-one; constructing a design requirement intelligent auxiliary acquisition model, and training the design requirement intelligent auxiliary acquisition model based on the manufacturing equipment historical design requirement hierarchical relationship database; wherein the design requirement intelligent auxiliary acquisition model is a multi-modal fusion neural network model, the multi-modal fusion neural network model is used for processing multi-modal design requirements and filling into the corresponding layer of the design requirement in the form of structured text, and constructing the hierarchical relationship, and checking the constructed design hierarchical relationship and marking abnormal requirements; wherein the multi-modal fusion neural network model comprises an input layer, a hidden layer, and an output layer; the output layer comprises a first output layer and a second output layer, the first output layer is used for outputting the requirement content and the hierarchical relationship of the design requirement layer, the second output layer is used for checking the hierarchical relationship of the design requirement output by the first output layer and outputting the checking result, marking abnormal requirements, the abnormal requirements include weak abnormal requirements and strong abnormal requirements; the weak abnormal requirements are incomplete requirement contents in the original design requirements, which are obtained by automatic completion through the multi-modal fusion neural network model; the strong abnormal requirements are requirement contents that are checked by the second output layer and are abnormal requirement contents of the hierarchical relationship of the design requirement output by the first output layer; collecting the original design requirement information of the new manufacturing equipment, and acquiring the design requirement information by using the design requirement intelligent auxiliary acquisition model, wherein the design requirement information comprises the hierarchical relationship of the design requirement information and the marked abnormal requirements; modifying, confirming, and outputting the calculated design requirement information.

2. The manufacturing equipment design requirement intelligent auxiliary acquisition method according to claim 1, wherein a multi-modal natural language model is integrated into the input layer, which is used for processing multi-modal design requirements and filling into the corresponding layer of the design requirement in the form of structured text; the hidden layer is used for learning the mapping relationship of the function modules and the action modules, the action modules and the structure modules, the function layer and the layout layer, the structure layer and the layout layer, and the layout layer and the performance layer.

3. The manufacturing equipment design requirement intelligent auxiliary acquisition method according to claim 2, wherein ​ The first output layer outputs the requirement content in the design requirement layer, which is obtained by processing the original design requirement of the new manufacturing equipment through the multi-modal natural language processing model in the input layer and filling the missing information in the original requirement through the inter-level mapping relationship learned by the hidden layer. 4.The method of claim 1, wherein, The original design requirement information is unprocessed requirement information, and the modalities thereof include text, voice, and requirement information fused from text and voice. 5.The method for intelligently assisting in obtaining design requirements of manufacturing equipment according to claim 1, wherein, The design requirement information is output in the form of a requirement level mapping relationship diagram.

6. A system for intelligently assisting in acquiring manufacturing equipment design requirements, employing the method for intelligently assisting in acquiring manufacturing equipment design requirements according to any one of claims 1-5. The system comprises: a historical design requirement level relationship data storage module configured to store historical design requirement level relationship data annotated based on the level relationship of the design requirement of the manufacturing equipment; a design requirement acquisition module configured to acquire original design requirement information of the equipment; a design requirement matching and reasoning module configured to match and arrange the original design requirement into the corresponding level of the design requirement, construct the design requirement level relationship through calculation, supplement the missing design requirement information, and check whether the design requirement is abnormal through the level relationship; a storage module configured to store the acquired original design requirement, the requirement level relationship obtained after calculation and reasoning, and the checking result of whether the requirement is abnormal; and a design requirement output module configured to output the design requirement information obtained by the design requirement intelligent auxiliary acquisition model.

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