Process knowledge recommendation method and device based on part process design and medium

Abstract

The invention discloses a process knowledge recommendation method and device based on part process design and a medium. The method comprises the steps of obtaining part description information of a to-be-recommended part; according to standard part description information corresponding to each standard part in a pre-constructed process knowledge base and the part description information of the to-be-recommended part, calculating to obtain the total similarity of each part, and determining a target recommended part; obtaining at least one piece of part feature information corresponding to the target recommended part, and determining at least one piece of target part feature information; and according to the feature information of each target part, the feature similarity of each piece of process knowledge is calculated through the associated feature information of each piece of process knowledge in the process knowledge base, and target recommended process knowledge corresponding to the feature information of each target part is determined and fed back. The problems of low efficiency and low accuracy caused by manual determination of the parts and the process knowledge matched with the parts are solved, and the efficiency and accuracy of process knowledge recommendation of part process design are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and medium for recommending process knowledge based on part process design. Background Art

[0002] In the process of development, manufacturing enterprises will accumulate a large amount of process data, mainly including design examples, engineering drawings, standard specifications and technical documents, etc. These data are stored in the database of the data management system. Generally speaking, when process personnel perform process design tasks, they often rely on their own experience due to lack of knowledge reference, which seriously affects the work efficiency of the enterprise; the imperfection of process knowledge structure will lead to the limitation of retrieval scope, so word-based knowledge retrieval can no longer meet the knowledge needs of process personnel for knowledge management systems.

[0003] In the domestic aviation field, in recent years, with the increasing complexity of aviation weapons and equipment, higher requirements have been put forward for the development methods, technical capabilities, development cycles, costs and quality of civil aircraft. Faced with unprecedented system scale, high reliability and high-quality application requirements and increasingly fierce international competition, civil aircraft development technology is required to be leading and the cycle is shortened, while quality reliability is greatly improved. In the life cycle of civil aircraft development, process design spans the design finalization stage and the production finalization stage. With the frequent iteration of design digital models, the frequency of process changes also increases. It is one of the most labor-intensive and time-consuming links in the process development cycle. Improving the design efficiency of the process is a necessary prerequisite for achieving rapid response development of aircraft. The number of machined parts in aircraft processing is huge and the structure is complex, which makes the development of processing technology difficult and labor-intensive.

[0004] In the process of realizing the present invention, the inventors found that the prior art has the following defects: At present, in the process of civil aircraft parts process design and manufacturing, the object of process design is the parts, and the parts are the decisive dimension that determines the demand for process knowledge. However, the accumulation of process knowledge consumes a lot of manual effort, the efficiency of summarizing mature process planning rules and methods is low, and the accuracy cannot meet the application requirements of actual part process design. Summary of the invention

[0005] The present invention provides a method, device and medium for recommending process knowledge based on part process design, so as to improve the efficiency and accuracy of recommending process knowledge for part process design.

[0006] According to one aspect of the present invention, a process knowledge recommendation method based on part process design is provided, which includes:

[0007] Obtain part description information of the part to be recommended;

[0008] The total similarity of each part is calculated by using the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended, and the target recommended part is determined according to the total similarity of each part;

[0009] Acquire at least one piece of part feature information corresponding to the target recommended part, and determine at least one piece of target part feature information from each piece of part feature information;

[0010] According to the feature information of each target part, the feature similarity of the process knowledge corresponding to the feature information of each target part is calculated by associating the feature information of each process knowledge in the process knowledge base;

[0011] According to the process knowledge feature similarity, the target recommended process knowledge corresponding to each target part feature information is determined, and each target recommended process knowledge is fed back to the user.

[0012] According to another aspect of the present invention, a process knowledge recommendation device based on part process design is provided, which includes:

[0013] A parts description information acquisition module is used to acquire the parts description information of the parts to be recommended;

[0014] A target recommended part determination module is used to calculate the total similarity of each part through the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended, and determine the target recommended part according to the total similarity of each part;

[0015] A target part feature information determination module, used to obtain at least one part feature information corresponding to the target recommended part, and determine at least one target part feature information from each of the part feature information;

[0016] A process knowledge feature similarity calculation module is used to calculate the process knowledge feature similarity corresponding to each target part feature information respectively through each process knowledge associated feature information in the process knowledge base according to each target part feature information;

[0017] The target recommended process knowledge feedback module is used to determine the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and to feed back each target recommended process knowledge to the user.

[0018] According to another aspect of the present invention, there is provided an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for recommending process knowledge based on part process design as described in any embodiment of the present invention is implemented.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the process knowledge recommendation method based on part process design described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention is to obtain the part description information of the part to be recommended; calculate the total similarity of each part by comparing the standard part description information corresponding to each standard part in the pre-constructed process knowledge base with the part description information of the part to be recommended, and determine the target recommended part according to the total similarity of each part; obtain at least one part feature information corresponding to the target recommended part, and determine at least one target part feature information in each part feature information; calculate the process knowledge feature similarity corresponding to each target part feature information according to each target part feature information through each process knowledge associated feature information in the process knowledge base; determine the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and feedback each target recommended process knowledge to the user. The present invention solves the problems of low efficiency and low accuracy caused by manually determining parts and process knowledge matching parts, and improves the efficiency and accuracy of process knowledge recommendation for part process design.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for recommending process knowledge based on part process design according to the first embodiment of the present invention;

[0024] Figure 2is a structural schematic diagram of a process knowledge recommendation device based on part process design provided according to a second embodiment of the present invention;

[0025] Figure 3 It is a structural schematic diagram of an electronic device provided according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target", "current", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 A flowchart of a method for recommending process knowledge based on part process design is provided for Embodiment 1 of the present invention. This embodiment is applicable to situations where process knowledge for part process design is recommended. The method can be performed by a process knowledge recommendation device based on part process design. The process knowledge recommendation device based on part process design can be implemented in the form of hardware and / or software.

[0030] Correspondingly, such as Figure 1 As shown, the method includes:

[0031] S110. Obtain part description information of the part to be recommended.

[0032] The part description information may be information used to describe the part to be recommended.

[0033] Specifically, the part description information includes not only basic information such as the project to which the part belongs, the part type, the part material, key parts and symmetrical parts; it also includes geometric information such as the longest dimension of the part, the widest dimension of the part and the thickest dimension of the part.

[0034] In this embodiment, it is generally used at the beginning stage of a process design task, and it is necessary to obtain corresponding recommended parts from a process knowledge base based on part description information.

[0035] For the process knowledge base, the standard part description information and part feature information corresponding to each standard part are stored in association. The process knowledge base also includes different process knowledge associated feature information.

[0036] S120, calculating the total similarity of each part by using the standard part description information corresponding to each standard part in the pre-constructed process knowledge base and the part description information of the part to be recommended, and determining the target recommended part according to the total similarity of each part.

[0037] The standard part description information may be information describing the standard part in the process knowledge base, and the total part similarity may be information describing the similarity between two parts.

[0038] Specifically, the greater the total similarity of parts, the higher the matching degree of the two parts, and vice versa, the lower the matching degree of the two parts.

[0039] Among them, the target recommended part can be the recommended part, that is, the recommended part with the largest total part similarity.

[0040] For example, suppose there are 10 standard parts in the process knowledge base (respectively: standard part 1, standard part 2, standard part 3, ..., standard part 10), and it is necessary to obtain the standard part description information corresponding to these 10 standard parts, and further calculate the total part similarity with the part description information of the recommended part. Accordingly, the total part similarity corresponding to the 10 standard parts can be obtained; further, the target recommended part is determined according to the total similarity of each part.

[0041] Optionally, the total similarity of each part is calculated by using the standard part description information corresponding to each standard part in the pre-constructed process knowledge base and the part description information of the part to be recommended, including: calculating the similarity of part basic information and the similarity of part geometric information by using the standard part description information corresponding to each standard part in the pre-constructed process knowledge base and the part description information of the part to be recommended; and calculating the total similarity of each part based on the similarity of the part basic information and the similarity of the part geometric information.

[0042] Among them, the similarity of basic information of parts can be used to describe the magnitude of the similarity calculated from the basic information of two parts. The similarity of geometric information of parts can be used to describe the magnitude of the similarity calculated from the geometric information of two parts.

[0043] In this embodiment, it is necessary to further calculate the total similarity of the parts by respectively calculating the similarity of the basic information of the parts and the similarity of the geometric information of the parts.

[0044] Optionally, the similarity of basic part information and geometric part information is calculated by using the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended, including: according to the formula z1=∑ t n ＝1 ω t LD(a ti ,a tj ), to calculate the similarity z1 of the basic information of the parts; where n means that the part description information includes n part description fields; ω t Indicates the field weights corresponding to each part description field; t represents a variable, and the value of t is [1, n]; a ti Indicates the specific value of the part description field; a tj Indicates the specific value of the standard part description field in the standard part description information; LD(a ti ,a tj ) represents the edit distance between the specific values ​​of the part description field and the standard part description field; i represents the recommended part i; j represents the standard part j; according to the formula To calculate the geometric information Euclidean distance D(s i ,s j ); According to the formula To calculate the part geometry similarity z2; where s i Part geometry information representing part description information; j Part geometry information representing standard part description information; ix The longest dimension of the part that represents the part description information; s iy The widest dimension of the part shape that represents the part description information; s iz The thickest dimension of the part shape indicating the part description information; jx The longest dimension of the part that represents the description information of the standard part; s jy The widest dimension of the part shape that represents the standard part description information; jz The thickest dimension of the part outline that represents the standard part description information.

[0045] In this embodiment, the formula To calculate the similarity z1 of the basic information of the parts, assume that the calculated similarity of the basic information of the standard part 1 is 0.8.

[0046] Furthermore, according to the formula To calculate the part geometric information similarity z2, assume that the calculated part basic information similarity of standard part 1 is 0.6.

[0047] Optionally, the total similarity of each part is calculated based on the similarity of the basic information of the part and the similarity of the geometric information of the part, including: according to the formula sim(i,j)=ω ′ z1+ω″z2, to calculate the total similarity sim(i,j) of each part; where ω ′ represents the weight of the similarity of the basic information of the parts; ω″ represents the weight of the similarity of the geometric information of the parts.

[0048] In this embodiment, the sum of the weight of the similarity of the basic information of the parts and the weight of the similarity of the geometric information of the parts can be set to 1.

[0049] Continuing with the previous example, since the similarity of basic information of parts is 0.8, the similarity of basic information of parts is 0.6; assuming that the weight of similarity of basic information of parts is 0.6; the weight of similarity of geometric information of parts is 0.4. According to the formula sim(i,j)=ω ′ z1+ω″z2, to calculate the total part similarity sim(i,j) of standard part 1 is 0.72.

[0050] Optionally, determining the target recommended part according to the total similarities of the parts includes: sorting the total similarities of the parts from large to small, and determining the standard part corresponding to the largest total part similarity as the target recommended part.

[0051] Continuing with the previous example, since the total part similarities corresponding to 10 standard parts have been obtained, it is necessary to sort these 10 total part similarities from large to small, so as to determine the standard part corresponding to the largest total part similarity, and then determine it as the target recommended part.

[0052] S130, obtaining at least one piece of part feature information corresponding to the target recommended part, and determining at least one piece of target part feature information from each piece of part feature information.

[0053] The part feature information may be information describing different features of the part.

[0054] Specifically, the part feature information may include hole feature information, rib feature information, groove feature information, reference surface feature information, and boss feature information.

[0055] In detail, hole feature information may include parameter information such as hole diameter, hole depth, hole roughness, bottom angle radius, dimple depth, dimple angle, bottom tip angle, chamfer length and chamfer angle. Rib feature information may include straight top rib height, inclined top and curved top height, inclined top and curved top minimum height, minimum projection length, minimum width, fillet radius and cylindrical top. Groove feature information includes groove length, groove width, maximum groove depth, minimum groove depth, bottom angle radius, corner radius, angle and number of walls. Reference surface feature information may include total number of edges and roughness. Boss feature information may include boss height, boss bottom angle radius, boss width and length, minimum distance to other features, small hole diameter, large hole diameter, large hole depth, small hole diameter tolerance value, small hole diameter roughness and boss residual height.

[0056] The target part feature information may be one or more part feature information selected from hole feature information, rib feature information, groove feature information, reference surface feature information and boss feature information corresponding to the part feature information.

[0057] S140, according to each target part feature information, by associating feature information of each process knowledge in the process knowledge base, calculating the process knowledge feature similarity corresponding to each target part feature information.

[0058] The process knowledge feature similarity may be a feature similarity calculated based on target part feature information and process knowledge associated feature information.

[0059] Optionally, the process knowledge feature similarity corresponding to each target part feature information is calculated through each process knowledge-related feature information in the process knowledge base, including: obtaining a target part feature information in turn; determining the target process knowledge-related feature information in each process knowledge-related feature information corresponding to the process knowledge base according to the target part feature information; calculating the process knowledge feature similarity according to the target part feature information and the target process knowledge-related feature information; returning to execute the step of obtaining a target part feature information in turn until the traversal of each target part feature information is completed.

[0060] The process knowledge-related feature information may be feature information associated with process knowledge stored in a process knowledge base. Specifically, the process knowledge-related feature information may include process knowledge hole feature information, process knowledge rib feature information, process knowledge groove feature information, process knowledge reference surface feature information, and process knowledge boss feature information.

[0061] The target process knowledge-related feature information may be feature information selected from the process knowledge-related feature information.

[0062] For example, assuming that the selected target part feature information is hole feature information and rib feature information, then in each process knowledge associated feature information corresponding to the process knowledge base, the target process knowledge associated feature information is determined to be process knowledge hole feature information and process knowledge rib feature information. This is because the process knowledge feature similarity can only be calculated if the target part feature information and the target process knowledge associated feature information are of the same type of feature information.

[0063] Furthermore, the process knowledge feature similarity between the hole feature information and the process knowledge hole feature information is calculated; and the process knowledge feature similarity between the rib feature information and the process knowledge rib feature information is calculated.

[0064] Optionally, the process knowledge feature similarity is calculated based on the target part feature information and the target process knowledge associated feature information, including: according to the formula To calculate the process knowledge feature similarity; where p represents the target part feature information of the target recommended part; q represents the target process knowledge associated feature information; m represents that the target part feature information includes m part features; ω r represents the weight of each part feature; r represents a variable, and the value of r is [1,m]; b rp b) the specific value of the part feature representing the target part feature information; rq The specific value of the part feature that represents the target process knowledge associated feature information; LD(b rp ,b rq ) represents the specific value edit distance between the target part feature information and the target process knowledge associated feature information; r p Vectorized parameter representing the target part feature information; r q Vectorized parameters representing the associated feature information of target process knowledge.

[0065] The target part feature information may include multiple part features, each part feature corresponds to a different weight, a specific value of the part feature and a vectorized parameter. The target process knowledge associated feature information includes a specific value of the part feature and a vectorized parameter.

[0066] In this embodiment, the formula To respectively calculate the process knowledge feature similarity between the hole feature information and the process knowledge hole feature information, and to calculate the process knowledge feature similarity between the rib feature information and the process knowledge rib feature information.

[0067] S150, determining the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and feeding back each target recommended process knowledge to the user.

[0068] The target recommended process knowledge can be the process knowledge such as the entire process specification file, process, process step, equipment, tooling and cutting tool corresponding to each target part feature information corresponding to the target recommended part. The target recommended process knowledge can be used to describe the processing method and equipment tooling method of the target recommended part.

[0069] In this embodiment, for the process knowledge feature similarity calculation between the hole feature information and the process knowledge hole feature information, since the process knowledge base includes multiple process knowledge hole feature information, multiple process knowledge feature similarities can be calculated with the hole feature information respectively.

[0070] Furthermore, the similarities of multiple process knowledge features can be sorted from large to small, and the process knowledge feature with the largest similarity can be determined as the target recommended process knowledge corresponding to the hole feature information. The processing method and equipment tooling method corresponding to the target recommended part can be fed back to the user.

[0071] Similarly, for the calculation of the process knowledge feature similarity between the rib feature information and the process knowledge rib feature information, it is also possible to determine that the target recommended process knowledge corresponding to the rib feature information has the largest process knowledge feature similarity, and the processing method and equipment tooling method corresponding to the target recommended part can be fed back to the user.

[0072] The technical solution of the embodiment of the present invention is to obtain the part description information of the part to be recommended; calculate the total similarity of each part by comparing the standard part description information corresponding to each standard part in the pre-constructed process knowledge base with the part description information of the part to be recommended, and determine the target recommended part according to the total similarity of each part; obtain at least one part feature information corresponding to the target recommended part, and determine at least one target part feature information in each part feature information; calculate the process knowledge feature similarity corresponding to each target part feature information according to each target part feature information through each process knowledge associated feature information in the process knowledge base; determine the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and feedback each target recommended process knowledge to the user. The present invention solves the problems of low efficiency and low accuracy caused by manually determining parts and process knowledge matching parts, and improves the efficiency and accuracy of process knowledge recommendation for part process design.

[0073] Embodiment 2

[0074] Figure 2This is a schematic diagram of the structure of a process knowledge recommendation device based on part process design provided in the second embodiment of the present invention. The process knowledge recommendation device based on part process design provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal device or server to implement a process knowledge recommendation method based on part process design in the embodiment of the present invention. Figure 2 As shown, the device includes: a part description information acquisition module 210, a target recommended part determination module 220, a target part feature information determination module 230, a process knowledge feature similarity calculation module 240 and a target recommended process knowledge feedback module 250.

[0075] Wherein, the part description information acquisition module 210 is used to acquire the part description information of the part to be recommended;

[0076] The target recommended part determination module 220 is used to calculate the total similarity of each part through the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended, and determine the target recommended part according to the total similarity of each part;

[0077] A target part feature information determination module 230 is used to obtain at least one part feature information corresponding to the target recommended part, and determine at least one target part feature information from each of the part feature information;

[0078] The process knowledge feature similarity calculation module 240 is used to calculate the process knowledge feature similarity corresponding to each target part feature information respectively through each process knowledge associated feature information in the process knowledge base according to each target part feature information;

[0079] The target recommended process knowledge feedback module 250 is used to determine the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and to feed back each target recommended process knowledge to the user.

[0080] The technical solution of the embodiment of the present invention is to obtain the part description information of the part to be recommended; calculate the total similarity of each part by comparing the standard part description information corresponding to each standard part in the pre-constructed process knowledge base with the part description information of the part to be recommended, and determine the target recommended part according to the total similarity of each part; obtain at least one part feature information corresponding to the target recommended part, and determine at least one target part feature information in each part feature information; calculate the process knowledge feature similarity corresponding to each target part feature information according to each target part feature information through each process knowledge associated feature information in the process knowledge base; determine the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and feedback each target recommended process knowledge to the user. The present invention solves the problems of low efficiency and low accuracy caused by manually determining parts and process knowledge matching parts, and improves the efficiency and accuracy of process knowledge recommendation for part process design.

[0081] On the basis of the above-mentioned embodiments, the target recommended part determination module 220 includes: a part basic information similarity and part geometric information similarity calculation unit, which can be specifically used to calculate the part basic information similarity and part geometric information similarity through the standard part description information corresponding to each standard part in the pre-constructed process knowledge base and the part description information of the part to be recommended; a part total similarity calculation unit, which can be specifically used to calculate the total similarity of each part based on the part basic information similarity and the part geometric information similarity.

[0082] On the basis of the above embodiments, the parts basic information similarity and parts geometric information similarity calculation unit can be specifically used to: according to the formula To calculate the similarity z1 of the basic information of the part; where n means that the part description information includes n part description fields; ω t Indicates the field weights corresponding to each part description field; t represents a variable, and the value of t is [1, n]; a ti Indicates the specific value of the part description field; a tj Indicates the specific value of the standard part description field in the standard part description information; LD(a ti ,a tj ) represents the edit distance between the specific values ​​of the part description field and the standard part description field; i represents the recommended part i; j represents the standard part j; according to the formula To calculate the geometric information Euclidean distance D(s i ,s j ); According to the formula To calculate the part geometry similarity z2; where si Part geometry information representing part description information; j Part geometry information representing standard part description information; ix The longest dimension of the part that represents the part description information; s iy The widest dimension of the part shape that represents the part description information; s iz The thickest dimension of the part shape indicating the part description information; jx The longest dimension of the part that represents the description information of the standard part; s jy The widest dimension of the part shape that represents the standard part description information; jz The thickest dimension of the part outline that represents the standard part description information.

[0083] On the basis of the above embodiments, the part total similarity calculation unit can be specifically used to: according to the formula sim(i,j)=ω ′ z1+ω″z2, to calculate the total similarity sim(i,j) of each part; where ω ′ represents the weight of the similarity of the basic information of the parts; ω″ represents the weight of the similarity of the geometric information of the parts.

[0084] Based on the above embodiments, the target recommended part determination module 220 can be specifically used to sort the total similarities of the parts from large to small, and determine the standard part corresponding to the largest total part similarity as the target recommended part.

[0085] On the basis of the above embodiments, the process knowledge feature similarity calculation module 240 can be specifically used to: obtain a target part feature information in sequence; determine the target process knowledge associated feature information in each process knowledge associated feature information corresponding to the process knowledge base according to the target part feature information; calculate the process knowledge feature similarity according to the target part feature information and the target process knowledge associated feature information; return to execute the step of obtaining a target part feature information in sequence until the traversal of each target part feature information is completed.

[0086] On the basis of the above embodiments, the process knowledge feature similarity calculation module 240 can also be specifically used to: To calculate the process knowledge feature similarity; where p represents the target part feature information of the target recommended part; q represents the target process knowledge associated feature information; m represents that the target part feature information includes m part features; ω r represents the weight of each part feature; r represents a variable, and the value of r is [1,m]; b rp b) the specific value of the part feature representing the target part feature information; rqThe specific value of the part feature that represents the target process knowledge associated feature information; LD(b rp ,b rq ) represents the specific value edit distance between the target part feature information and the target process knowledge associated feature information; r p Vectorized parameter representing the target part feature information; r q Vectorized parameters representing the associated feature information of target process knowledge.

[0087] The process knowledge recommendation device based on part process design provided in the embodiment of the present invention can execute the process knowledge recommendation method based on part process design provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0088] Embodiment 3

[0089] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement the third embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0090] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0091] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0092] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a process knowledge recommendation method based on part process design.

[0093] In some embodiments, the process knowledge recommendation method based on part process design may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the process knowledge recommendation method based on part process design described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the process knowledge recommendation method based on part process design in any other appropriate manner (e.g., by means of firmware).

[0094] The method includes: obtaining part description information of the part to be recommended; calculating the total similarity of each part by using the standard part description information corresponding to each standard part in a pre-constructed process knowledge base and the part description information of the part to be recommended, and determining the target recommended part according to the total similarity of each part; obtaining at least one part feature information corresponding to the target recommended part, and determining at least one target part feature information in each part feature information; calculating the process knowledge feature similarity corresponding to each target part feature information according to each target part feature information through each process knowledge associated feature information in the process knowledge base; determining the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and feeding back each target recommended process knowledge to the user.

[0095] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0097] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0100] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0101] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0102] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0103] Embodiment 4

[0104] Embodiment 4 of the present invention also provides a computer-readable storage medium, wherein the computer-readable instructions are used to execute a process knowledge recommendation method based on part process design when executed by a computer processor, the method comprising: obtaining part description information of the part to be recommended; calculating the total similarity of each part by using the standard part description information corresponding to each standard part in a pre-constructed process knowledge base and the part description information of the part to be recommended, and determining a target recommended part based on the total similarity of each part; obtaining at least one part feature information corresponding to the target recommended part, and determining at least one target part feature information in each part feature information; calculating the process knowledge feature similarity corresponding to each target part feature information by using the process knowledge associated feature information in the process knowledge base based on each target part feature information; determining the target recommended process knowledge corresponding to each target part feature information based on the process knowledge feature similarity, and feeding back each target recommended process knowledge to the user.

[0105] Of course, the computer-readable storage medium provided in an embodiment of the present invention has computer executable instructions that are not limited to the method operations described above, and can also execute related operations in the process knowledge recommendation method based on part process design provided in any embodiment of the present invention.

[0106] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk or an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0107] It is worth noting that in the above-mentioned embodiment of the process knowledge recommendation device based on part process design, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0108] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A process knowledge recommendation method based on part process design, characterized in that: include: Obtain part description information of the part to be recommended; The total similarity of each part is calculated by using the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended, and the target recommended part is determined according to the total similarity of each part; Acquire at least one piece of part feature information corresponding to the target recommended part, and determine at least one piece of target part feature information from each piece of part feature information; According to the feature information of each target part, the feature similarity of the process knowledge corresponding to the feature information of each target part is calculated by associating the feature information of each process knowledge in the process knowledge base; According to the process knowledge feature similarity, the target recommended process knowledge corresponding to each target part feature information is determined, and each target recommended process knowledge is fed back to the user.

2. The method according to claim 1, characterized in that: The total similarity of each part is calculated by using the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended, including: Calculate the similarity of basic part information and part geometric information by using the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended; The total similarity of each part is calculated based on the similarity of the basic information of the part and the similarity of the geometric information of the part.

3. The method according to claim 2, characterized in that The method of calculating the similarity of basic part information and geometrical part information by using the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended includes: According to the formula z1=∑ t n ＝1 ω t LD(a ti ,a tj ), to calculate the similarity z1 of the basic information of the parts; Wherein, n means that the part description information includes n part description fields; ω t Indicates the field weights corresponding to each part description field; t represents a variable, and the value of t is [1, n]; a ti Indicates the specific value of the part description field; a tj Indicates the specific value of the standard part description field in the standard part description information; LD(a ti ,a tj ) represents the edit distance between the specific values ​​of the part description field and the standard part description field; i represents the recommended part i; j represents the standard part j; According to the formula To calculate the geometric information Euclidean distance D(s i ,s j ); According to the formula To calculate the part geometry information similarity z2; Among them, s i Part geometry information representing part description information; j Part geometry information representing standard part description information; ix The longest dimension of the part that represents the part description information; s iy The widest dimension of the part shape that represents the part description information; s iz The thickest dimension of the part shape indicating the part description information; jx The longest dimension of the part that represents the description information of the standard part; s jy The widest dimension of the part shape that represents the standard part description information; jz The thickest dimension of the part outline that represents the standard part description information.

4. The method according to claim 3, characterized in that The calculating of the total similarity of each part according to the similarity of the basic information of the part and the similarity of the geometric information of the part comprises: According to the formula sim(i,j) = ω ′ z1+ω″z2, to calculate the total similarity sim(i,j) of each part; Among them, ω ′ represents the weight of the similarity of the basic information of the parts; ω″ represents the weight of the similarity of the geometric information of the parts.

5. The method according to claim 4, characterized in that The step of determining the target recommended parts according to the total similarities of the parts includes: The total similarities of the parts are sorted from large to small, and the standard part corresponding to the largest total part similarity is determined as the target recommended part.

6. The method according to claim 5, characterized in that The process of calculating the process knowledge feature similarity corresponding to each target part feature information respectively through each process knowledge associated feature information in the process knowledge base according to each target part feature information includes: Acquire characteristic information of a target part in sequence; According to the target part feature information, determine the target process knowledge associated feature information from each process knowledge associated feature information corresponding to the process knowledge base; Calculating the process knowledge feature similarity based on the target part feature information and the target process knowledge associated feature information; Return to execute the step of acquiring the feature information of each target part in turn until all the feature information of each target part is traversed.

7. The method according to claim 6, characterized in that The process knowledge feature similarity is calculated based on the target part feature information and the target process knowledge associated feature information, including: According to the formula To calculate the similarity of process knowledge features; Among them, p represents the target part feature information of the target recommended part; q represents the target process knowledge associated feature information; m represents that the target part feature information includes m part features; ω r represents the weight of each part feature; r represents a variable, and the value of r is [1,m]; b rp b) the specific value of the part feature representing the target part feature information; rq The specific value of the part feature that represents the target process knowledge associated feature information; LD(b rp ,b rq ) represents the specific value edit distance between the target part feature information and the target process knowledge associated feature information; r p Vectorized parameter representing the target part feature information; r q Vectorized parameters representing the associated feature information of target process knowledge.

8. A process knowledge recommendation device based on part process design, characterized in that: include: A parts description information acquisition module is used to acquire the parts description information of the parts to be recommended; A target recommended part determination module is used to calculate the total similarity of each part through the standard part description information corresponding to each standard part in the pre-built process knowledge base and the part description information of the part to be recommended, and determine the target recommended part according to the total similarity of each part; A target part feature information determination module, used to obtain at least one part feature information corresponding to the target recommended part, and determine at least one target part feature information from each of the part feature information; A process knowledge feature similarity calculation module is used to calculate the process knowledge feature similarity corresponding to each target part feature information respectively through each process knowledge associated feature information in the process knowledge base according to each target part feature information; The target recommended process knowledge feedback module is used to determine the target recommended process knowledge corresponding to each target part feature information according to the process knowledge feature similarity, and to feed back each target recommended process knowledge to the user.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the process knowledge recommendation method based on part process design as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the process knowledge recommendation method based on part process design as described in any one of claims 1 to 7 when executed.

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