Non-standard automation equipment reuse method and system based on instance reasoning

Abstract

The invention provides a non-standard automation equipment reuse method and system based on instance reasoning, and relates to the technical field of data processing, and the method comprises the steps: obtaining feature parameters and feature parameter values of non-standard automation equipment; non-standard automation equipment is decomposed into different non-standard modules according to functions, and the non-standard modules are stored in an instance library; obtaining a user demand non-standard module; establishing a similarity calculation model; calculating the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module by using a similarity calculation model in combination with the objective weight of each feature parameter of the non-standard module; retrieving the instance library according to the comprehensive similarity to obtain a non-standard module having the highest similarity with the required non-standard module; calculating a relative importance value among the components in the non-standard module with the highest similarity, establishing an interpretation structure model, and determining a component transformation sequence; modifying the non-standard module with the highest similarity; and reusing the modified non-standard module. And the reuse accuracy and the reuse design efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for reusing non-standard automation equipment based on case reasoning. Background Art

[0002] Non-standard automation equipment is usually a complex device integrated with multiple parts. During the design and manufacturing process, nearly 70% of the products are realized by variant design based on previous design results. In order to quickly and efficiently realize design changes and meet personalized needs, it is of great significance to analyze the change characteristics of each structure on the basis of the existing design, make full use of the company's existing resources, and improve the company's product design and development responsiveness.

[0003] With the development of the manufacturing industry, market competition is becoming increasingly fierce. Traditional design and production methods can no longer adapt to the rapid changes in market demand. The increase in personalized product manufacturing has led to the emergence of a large number of non-standard equipment in mechanical products. The so-called non-standard equipment refers to equipment that is not manufactured in accordance with unified national standards and general specifications, but is customized and manufactured according to actual needs. Because of the characteristics of special-purpose machines, multiple types, and small batches in the design process, there are problems such as long development, manufacturing and debugging cycles, low reuse rate of engineering drawings, and poor reproducibility. Today, the rapid iteration of products has put forward requirements for non-standard equipment with short construction cycles, low development difficulty, and fast line change speed, forcing manufacturing companies to seek more suitable design and development models.

[0004] The design reuse method based on case reasoning has begun to be applied to complex mechanical products such as non-standard automation equipment. However, in the process of design reuse, in the process of modifying similar modules to the expected modules, it relies heavily on manual experience, has a long design cycle, high limitations, poor reproducibility, and is prone to subjective errors. In addition, it is difficult to determine the best way to reuse and modify similar modules when the modules become more complex. Summary of the invention

[0005] In order to solve the technical problems in the prior art that, in the process of design reuse, in the process of modifying similar modules to expected modules, it is heavily dependent on manual experience, has a long design cycle, high limitations, poor reproducibility, is prone to subjective errors, and is difficult to determine the best reuse and modification method of similar modules when the modules become more complex, the present invention provides a non-standard automation equipment reuse method and system based on instance reasoning.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect

[0008] An embodiment of the present invention provides a method for reusing non-standard automation equipment based on case reasoning, comprising:

[0009] S1: Obtain characteristic parameters and characteristic parameter values ​​of non-standard automation equipment;

[0010] S2: Decompose the non-standard automation equipment into different non-standard modules according to their functions, and store the non-standard modules in the instance library;

[0011] S3: Obtain the user's expected characteristic parameters and expected characteristic parameter values ​​for the required non-standard module;

[0012] S4: Establishing a similarity calculation model between the required non-standard module and each non-standard module in the instance library, wherein the similarity calculation model includes a first function in which the first parameter and the second parameter are both numerical values, a second function in which the first parameter and the second parameter are both numerical intervals, and a third function in which the parameter values ​​of the first parameter and the second parameter are different in form, wherein the first parameter is a characteristic parameter value of the non-standard module, and the second parameter is an expected characteristic parameter value;

[0013] S5: combining the objective weights of each characteristic parameter of the non-standard module, and using the similarity calculation model to calculate the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module;

[0014] S6: Using the expected feature parameter and the expected feature parameter value as the search condition, searching the instance library according to the comprehensive similarity, and obtaining the non-standard module with the highest similarity to the required non-standard module;

[0015] S7: Calculate the relative importance values ​​of the components in the non-standard module with the highest similarity, and establish an explanation structure model based on the relative importance values ​​to determine the transformation order of each component;

[0016] S8: modifying the non-standard module with the highest similarity according to the transformation order until the requirements of the required non-standard module regarding the expected characteristic parameters and the expected characteristic parameter values ​​are met;

[0017] S9: Reuse the modified non-standard module with the highest similarity as the required non-standard module.

[0018] Second aspect

[0019] An embodiment of the present invention provides a non-standard automation equipment reuse system based on case reasoning, including:

[0020] processor;

[0021] A memory stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the non-standard automation equipment reuse method based on case reasoning as in the first aspect is implemented.

[0022] The third aspect

[0023] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for reusing non-standard automation equipment based on case reasoning as described in the first aspect is implemented.

[0024] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0025] In the present invention, by constructing an instance library, a similarity calculation model covering qualitative parameters and quantitative parameters is established, and according to the similarity of expected characteristic parameters and expected characteristic parameter values ​​with the required non-standard modules in the instance library, combined with the objective weights of each characteristic parameter of the non-standard module, the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module is calculated using the similarity calculation model, and the most similar non-standard modules are automatically, quickly and accurately extracted according to the comprehensive similarity, which is more efficient and has a wider retrieval range. Then, the relative importance values ​​between each component in the non-standard module are used to establish an explanatory structure model, the transformation order of each component is determined, and similar non-standard modules are modified according to the transformation order to ensure that the expected requirements are met with minimal changes. While ensuring the coverage breadth and modification accuracy, the degree of automation and the reuse design efficiency of non-standard equipment are greatly increased, and the problems of long design cycle, high limitations, poor reproducibility, and easy subjective errors caused by excessive reliance on manual experience to select similar non-standard modules are avoided, thereby reducing the equipment cost of enterprises and increasing their competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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.

[0027] Figure 1 A flowchart of a method for reusing non-standard automation equipment based on case reasoning provided by an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of the structure of a non-standard automation equipment reuse system based on case reasoning provided by the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0031] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0032] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0033] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0034] Reference Manual Attached Figure 1 , shows a flow chart of a non-standard automation equipment reuse method based on case reasoning provided by an embodiment of the present invention.

[0035] The embodiment of the present invention provides a non-standard automation equipment reuse method based on case reasoning, which can be implemented by a non-standard automation equipment reuse device based on case reasoning, and the non-standard automation equipment reuse device based on case reasoning can be a terminal or a server. The processing flow of the non-standard automation equipment reuse method based on case reasoning can include the following steps:

[0036] S1: Obtain characteristic parameters and characteristic parameter values ​​of non-standard automation equipment.

[0037] Among them, the characteristic parameters are the parameters of each module in the non-standard automation equipment, such as motor power, degree of freedom, arm span radius, working range, maximum working range, position accuracy, communication interface, control system, maximum joint speed and running smoothness, etc. The characteristic parameter value is the specific value of the corresponding characteristic parameter. By collecting all relevant characteristic parameters and corresponding values ​​of existing non-standard automation equipment, these parameters may include size, performance indicators, energy consumption, material type, etc., these data provide a data basis for subsequent retrieval, increase the automation degree of reuse design and the coverage of reuse process retrieval.

[0038] S2: Decompose the non-standard automation equipment into different non-standard modules according to their functions, and store the non-standard modules in the instance library.

[0039] It should be noted that non-standard automation equipment is decomposed into multiple modules according to its different functions, and these modules are stored in the case library. The advantage of this process is that it enhances the modularity and flexibility of the design, making future design and manufacturing more efficient. Through modularization, the design process can be simplified and rapid assembly and customization can be achieved. In addition, storing these modules in the case library is also convenient for reuse and sharing, reducing development time and cost, and improving the convenience of production and maintenance. This approach also helps to standardize the production process and ensure product quality and reliability by reusing verified modules.

[0040] S3: Obtain the user's expected characteristic parameters and expected characteristic parameter values ​​for the required non-standard module.

[0041] Among them, demand non-standard modules refer to automation equipment modules customized according to specific user needs or specific application scenarios. These modules do not follow the general standardized product line, but are specially designed and manufactured to meet unique functional or performance requirements. The expected characteristic parameters are the specific attributes that users want the demand non-standard modules to have, such as size, weight, speed, energy consumption, capacity, etc. These parameters are directly related to the functionality and operating performance of the module. The expected characteristic parameter value is the specific required value of each expected characteristic parameter by the user. For example, if the expected characteristic parameter is speed, the expected characteristic parameter value may be "200 times per minute". If it is size, the value may be "500mm x 500mm x 600mm". By accurately obtaining the expected characteristic parameters and values ​​of the user for the demand non-standard module, it can be ensured that the design fully meets the user's needs, thereby improving the market adaptability and customer satisfaction of the product. In addition, a clear definition of requirements helps to optimize the design process, reduce unnecessary iterations and resource waste, and accelerate the product development cycle.

[0042] S4: Establish a similarity calculation model between the required non-standard modules and each non-standard module in the instance library.

[0043] Among them, the similarity calculation model includes a first function in which the first parameter and the second parameter are both numerical values, a second function in which the first parameter and the second parameter are both numerical intervals, and a third function in which the parameter values ​​of the first parameter and the second parameter are different in form, wherein the first parameter is the characteristic parameter value of the non-standard module, and the second parameter is the expected characteristic parameter value.

[0044] The different parameter value formats mean that the first parameter and the second parameter have different numerical types, that is, the first parameter is a numerical value and the second parameter is a numerical range, or the first parameter is a numerical range and the second parameter is a numerical value.

[0045] It should be noted that the versatility of the similarity calculation model allows it to handle different types of parameters (numerical values ​​and numerical intervals), making it flexible to adapt to various comparison needs. The advantage of this method is that it can accurately match the existing modules that are closest to user needs, greatly improving the efficiency and accuracy of the design. By using this meticulous similarity evaluation, it can be ensured that the selected modules meet specific needs to the greatest extent, while optimizing resource usage and reducing development costs. In addition, it also supports precise searches within a wide range of feature parameter spaces, ensuring that the best match is found from a large number of available options.

[0046] In a possible implementation, S4 specifically includes:

[0047] S401: performing normalization processing including quantization on the characteristic parameter values ​​and expected characteristic parameters of the non-standard modules.

[0048] In a possible implementation manner, S401 specifically includes:

[0049] When the parameter value is a qualitative feature, the parameter value is quantified by a fuzzy mathematical processing algorithm, otherwise, the parameter value is quantified by a normalization algorithm, wherein the parameter value includes a characteristic parameter value and an expected characteristic parameter value.

[0050] It should be noted that by processing qualitative features with fuzzy mathematics, fuzzy or difficult to directly quantify data can be converted into numerical forms that can be used for mathematical operations, so that these data can be effectively used for calculation and comparison. Normalization of quantitative data ensures that data of different scales and magnitudes can be compared under the same standard, eliminating the impact of measurement units and making data analysis more accurate and consistent. These two processing methods together improve the compatibility of data and the effectiveness of comparison, providing a data basis for subsequent similarity calculations and decision-making.

[0051] In a possible implementation, the fuzzy mathematical processing algorithm includes the Mamdani algorithm, the Sugeno method and the fuzzy C-means clustering algorithm, and the normalization algorithm includes the minimum-maximum normalization algorithm, the Z-score normalization algorithm and the logarithmic transformation algorithm.

[0052] S402: Establish a similarity calculation model based on the normalized feature parameters and the expected feature parameters:

[0053] S1(M′,M)=1-|m′-m|

[0054]

[0055] Among them, S1(M′,M), S2(M′,M) and S3(M′,M) represent the first function, the second function and the third function respectively, M′ represents the expected characteristic parameter, M represents the characteristic parameter of the non-standard module, m′ represents the expected characteristic parameter value, m represents the characteristic parameter value, [a,b] represents the expected characteristic parameter interval range, [c,d] represents the characteristic parameter interval range, and h represents the optimal value position of the characteristic parameter.

[0056] It should be noted that there are only three cases for the value of the characteristic parameter: the larger the better, the smaller the better, and it cannot be determined, which correspond to the three segments of the third function. For the optimal position h of the characteristic parameter, when the larger the better, the maximum value is taken, that is, h = b; when the smaller the better, the minimum value is taken, that is, h = a; when it cannot be determined, the median value is taken. Specifically, regarding uncertain situations, such as lubricant viscosity: If the viscosity of the lubricant is too high, it will increase friction resistance, and if the viscosity is too low, it may not form an effective lubricating film. It is necessary to select the appropriate viscosity according to the specific application. Feed speed: In machining, a feed speed that is too fast may lead to a decrease in machining quality, while a feed speed that is too slow may affect production efficiency, so a balance is required. Spindle speed: A high speed may improve machining efficiency, but it may also increase heat and wear. It is necessary to select an appropriate speed according to material and process requirements.

[0057] Wherein, a and b represent the lower limit and upper limit of the expected characteristic parameter interval range, and c and d represent the lower limit and upper limit of the characteristic parameter interval range, respectively.

[0058] It should be noted that step S4 involves normalizing the characteristic parameters of non-standard modules and expected characteristic parameters, and establishing a similarity calculation model based on this. This method includes a variety of functions to process different types of parameter comparisons (such as value to value, interval to interval). By normalizing the characteristic parameters, it is ensured that data from different sources or different metrics can be uniformly compared, so that the similarity calculation is more accurate. Different functions correspond to different types of parameter comparisons, such as comparisons between values ​​and values ​​or intervals and intervals, which enables the model to flexibly respond to various data types and enhances the scope of application and practicality of the model. Through the refined similarity function, non-standard modules can be more accurately matched with the specific needs of users, especially when dealing with complex or multivariate characteristic parameters. This meticulous matching process ensures that the selected module is closest to the user's expectations in terms of function and performance, thereby improving the satisfaction and effectiveness of the final product.

[0059] S5: Combined with the objective weights of each characteristic parameter of the non-standard module, the similarity calculation model is used to calculate the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module.

[0060] It should be noted that by combining the objective weights of the characteristic parameters of non-standard modules with the similarity calculation model, the calculation of the comprehensive similarity is more accurate and targeted. The advantage of this method is that it can comprehensively consider the importance of each characteristic parameter to ensure that the selected module is most closely matched with the required module in terms of the most critical attributes. This not only improves the effectiveness of module selection, but also optimizes the accuracy of the design and the probability of meeting user needs, thereby achieving optimization in terms of resource and time investment.

[0061] In a possible implementation, S5 specifically includes:

[0062] S501: Calculate the similarity between each feature parameter of the non-standard module in the instance library and the expected feature parameter using the similarity calculation model, and construct a difference matrix describing the similarity:

[0063]

[0064] Among them, C represents the difference matrix, S ij It represents the similarity between the jth characteristic parameter of the i-th non-standard module in the instance library and the corresponding expected characteristic parameter value, i=1,2,…,m, j=1,2,…,n, where m represents the total number of non-standard modules in the instance library and n represents the total number of characteristic parameters.

[0065] S502: Calculate the objective weight of each characteristic parameter of the non-standard module in the example library according to the difference matrix:

[0066]

[0067] Among them, w i represents the objective weight of the i-th feature parameter, σ j Represents the similarity standard deviation of the j-th feature parameter.

[0068] S503: Calculate the comprehensive similarity according to the feature parameter similarity and the similarity objective weight:

[0069]

[0070] Among them, S s Indicates the comprehensive similarity.

[0071] Among them, the difference matrix provides a detailed view showing the similarity between each feature parameter of each non-standard module and the user's expectations, which helps to fully understand the matching degree of each parameter. Secondly, the weights are determined by calculating the standard deviation of the feature parameters, ensuring that more attention is paid to features with large variability during the evaluation process, thereby reflecting the actual influence and difference of the features on performance. Finally, this method generates a comprehensive similarity score by comprehensively considering the similarity and importance of all feature parameters, providing a quantitative basis for selecting the most matching non-standard module, significantly improving the accuracy and efficiency of the selection. This systematic evaluation method not only optimizes the allocation of resources, but also enhances the reliability and accuracy of the design.

[0072] S6: Using the expected feature parameter and the expected feature parameter value as the search condition, searching the instance library according to the comprehensive similarity, and obtaining the non-standard module with the highest similarity to the required non-standard module.

[0073] It should be noted that by using the set expected feature parameters and values ​​as retrieval conditions and searching the instance library in combination with the comprehensive similarity, the module with the highest similarity to the required non-standard module can be determined. This can efficiently and accurately lock in those existing solutions that are closest to user needs, greatly reducing the time and cost of design and development. In addition, through precise matching, it ensures that the performance and functions of the new design meet expectations to the greatest extent possible, thereby improving the success rate of product development and the speed of market response.

[0074] S7: Calculate the relative importance values ​​between the components in the non-standard module with the highest similarity, and establish an explanation structure model based on the relative importance values ​​to determine the transformation order of each component.

[0075] Among them, the Explanatory Structural Model (ISM) is a system analysis method used to identify and organize the elements and their interrelationships in a complex system. In this way, the components in the system can be decomposed hierarchically to show their dependencies and mutual influences. The transformation order refers to the priority order of adjusting or changing the components when designing or modifying the system. This order is determined based on the importance of each component and their impact on the overall function of the system. By calculating the relative importance of each component in the non-standard module with the highest similarity, an explanatory structural model is established to determine the best transformation order. The advantage of this step is that it provides a systematic method to ensure that when modifying the module, the key components that have the greatest impact on the system are processed first, thereby reducing unnecessary design changes, optimizing resource allocation, and maximizing modification efficiency and accuracy. This orderly transformation order can effectively reduce conflicts and duplication of work in the design process, and ultimately ensure that the modified module can better meet the design requirements.

[0076] In a possible implementation, S7 specifically includes:

[0077] S701: Construct an adjacency matrix A describing relative importance values:

[0078]

[0079] Among them, a pq It represents the relative importance value. If the p-th component is more important than the q-th component, the relative importance value is assigned to 1, otherwise, it is assigned to 0. p,q=1,2,…,k, where k represents the total number of components in the non-standard module with the highest similarity.

[0080] S702: Calculate the overall evaluation matrix describing each component according to the adjacency matrix:

[0081] H=A+E

[0082] Among them, H represents the overall evaluation matrix, A represents the adjacency matrix, and E represents the unit matrix.

[0083] S703: Based on Boolean operation rules, calculate the reachability matrix of the overall evaluation matrix:

[0084] H k =H k-1

[0085] Among them, H k represents the reachability matrix.

[0086] S704: Calculate the reachable set R based on the reachable matrix i , look-ahead set A i and intersection X i ,

[0087] R p ={q|H pq =1}

[0088] A p ={q|H qp =1}

[0089] X p =R p ∩A p

[0090] Among them, R p represents the reachable set affected by the pth component, H pq represents the element value of the element in the pth row and qth column of the overall evaluation matrix, H qp A represents the value of the element in the qth row and pth column of the overall evaluation matrix. p represents the predecessor set that affects the pth component, X p represents the intersection of the reachable set and the predecessor set with respect to component p.

[0091] The intersection includes the components that are affected by part p and affect component p.

[0092] S705: Extract components whose reachable sets and intersection sets are the same, and retain the components whose reachable sets and intersection sets are the same to the highest level.

[0093] When R i =X i When , the determined highest level part elements are removed from the matrix, and the reachable sets, predecessor sets and intersection sets of the remaining part elements are recalculated.

[0094] S706: Remove the components belonging to the highest layer, reduce the number of the highest layer by 1, and obtain the updated highest layer.

[0095] S707: Repeat S701-S706 until all components are removed, and an interpretation structure model including all highest layers is obtained, wherein the highest layers in the interpretation structure model decrease in number.

[0096] S708: Determine the transformation order of each component according to the number of layers of each highest layer, wherein the priority of the component transformation order in the interpretation structure model is consistent with the number of layers of the highest layer.

[0097] Specifically, first, an adjacency matrix is ​​constructed to describe the importance of each component relative to other components. Then, the overall evaluation matrix is ​​generated by adding the identity matrix to include self-importance. Then, the reachability matrix of the overall evaluation matrix is ​​calculated using Boolean operation rules, which helps to determine the direct or indirect impact between components. Further, through the reachability matrix, the reachability set and precedent set of each component are calculated, and the intersection of simultaneously affected and affecting other components is found. Finally, this process is repeated until all components are assigned to different levels, and the transformation order of components is finally determined according to these levels. By interpreting the structural model, the importance of components can be systematically identified and ranked, which helps to optimize design modification and maintenance strategies, especially in complex systems involving multiple interdependent components. In addition, this method improves the efficiency of the design process because it makes it clear which components are critical and which components' modifications will affect the overall system, thereby ensuring that the components with the least impact on other components are prioritized. At the same time, this hierarchical approach also helps to reduce possible errors in the design and reconstruction process.

[0098] S8: Modify the non-standard module with the highest similarity in the transformation order until the requirements of the required non-standard module regarding the expected characteristic parameters and the expected characteristic parameter values ​​are met.

[0099] In a possible implementation manner, S8 specifically includes:

[0100] The non-standard module with the highest similarity is modified through various modification methods until the requirements of the required non-standard module regarding the expected characteristic parameters and the expected characteristic parameter values ​​are met, wherein the modification methods include replacement modification, addition and deletion modification, decomposition modification and expansion and contraction modification.

[0101] It should be noted that a variety of modification methods are used to adjust the non-standard modules that are most similar to user requirements to fully meet the specific expected characteristic parameters and values. Specific modification methods include replacement modification (replacing certain components), addition and deletion modification (adding or removing components), decomposition modification (decomposing components into smaller units), and expansion and contraction modification (changing the scale or size of components). These various modification methods provide great flexibility and precise control, allowing designers to fine-tune modules according to specific functional requirements and operating conditions. The advantage of this method is that it can ensure that the final product strictly meets the needs of users while maintaining most of the structure and functions of existing modules, thereby optimizing development costs and time efficiency, and reducing the risk and waste of resources of developing from scratch.

[0102] In a possible implementation process, the following is also included:

[0103] Store the modified non-standard module in the instance library.

[0104] It should be noted that storing the modified non-standard modules in the instance library improves the reuse of resources and reduces the development time and cost of future projects. By saving these adjusted and verified modules, it can provide immediately available solutions for subsequent design and manufacturing tasks.

[0105] S9: Reuse the modified non-standard module with the highest similarity as the required non-standard module.

[0106] In the actual application process, from obtaining the characteristic parameters of existing non-standard automation equipment, to functionally decomposing these equipment into reusable modules and storing them in the case library, to accurately capturing the specific needs of users for new equipment, an efficient non-standard automation equipment design and reuse process is demonstrated. This process establishes a complex similarity calculation model, which not only allows accurate matching of different types of parameters (such as values ​​and intervals), but also combines the objective weights of each characteristic parameter to calculate the comprehensive similarity, ensuring that the existing module that best matches the user's needs is selected. In this way, the module closest to the needs can be efficiently retrieved and determined from the case library.

[0107] In addition, by evaluating the relative importance of each component in the selected module and building an explanatory structural model based on it, this process not only optimizes the module modification strategy, but also ensures the logic and efficiency of the design modification. This orderly modification process reduces resource waste, speeds up the development cycle, and improves market response speed. Finally, the modified modules are reused, which not only saves costs, but also improves the sustainability of production, greatly improves the design and production efficiency of non-standard automation equipment, optimizes resource use, ensures that the design results can accurately meet the specific needs of users, and also strengthens the company's adaptability and competitiveness to market changes.

[0108] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0109] In the present invention, by constructing an instance library, a similarity calculation model covering qualitative parameters and quantitative parameters is established, and according to the similarity of expected characteristic parameters and expected characteristic parameter values ​​with the required non-standard modules in the instance library, combined with the objective weights of each characteristic parameter of the non-standard module, the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module is calculated using the similarity calculation model, and the most similar non-standard modules are automatically, quickly and accurately extracted according to the comprehensive similarity, which is more efficient and has a wider retrieval range. Then, the relative importance values ​​between each component in the non-standard module are used to establish an explanatory structure model, the transformation order of each component is determined, and similar non-standard modules are modified according to the transformation order to ensure that the expected requirements are met with minimal changes. While ensuring the coverage breadth and modification accuracy, the degree of automation and the reuse design efficiency of non-standard equipment are greatly increased, and the problems of long design cycle, high limitations, poor reproducibility, and easy subjective errors caused by excessive reliance on manual experience to select similar non-standard modules are avoided, thereby reducing the equipment cost of enterprises and increasing their competitiveness.

[0110] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a non-standard automation equipment reuse system based on case reasoning provided by the present invention.

[0111] The present invention further provides a non-standard automation equipment reuse system 20 based on case reasoning, which is applied to the above-mentioned non-standard automation equipment reuse method based on case reasoning, comprising:

[0112] Processor 201.

[0113] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201 , the non-standard automation equipment reuse method based on case reasoning in the method embodiment is implemented.

[0114] The non-standard automation equipment reuse system 20 based on case reasoning provided by the present invention can execute the above-mentioned non-standard automation equipment reuse method based on case reasoning and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0115] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0116] In the present invention, by constructing an instance library, a similarity calculation model covering qualitative parameters and quantitative parameters is established, and according to the similarity of the expected characteristic parameters and expected characteristic parameter values ​​of the required non-standard modules in the instance library, combined with the objective weights of each characteristic parameter of the non-standard module, the similarity calculation model is used to calculate the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module, and the most similar non-standard module is automatically, quickly and accurately extracted according to the comprehensive similarity, which is more efficient and has a wider retrieval range. Then, the relative importance values ​​between each component in the non-standard module are used to establish an explanatory structure model, the transformation order of each component is determined, and the similar non-standard modules are modified according to the transformation order to ensure that the expected requirements are met with minimal changes. While ensuring the coverage breadth and modification accuracy, the degree of automation and the reuse design efficiency of non-standard equipment are greatly increased, and the problems of long design cycle, high limitations, poor reproducibility, and easy subjective errors caused by excessive reliance on manual experience to select similar non-standard modules are avoided, thereby reducing the equipment cost of enterprises and increasing the competitiveness of enterprises.

[0117] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0118] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0119] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0120] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0121] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0122] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0123] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0125] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0128] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0129] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for reusing non-standard automation equipment based on case reasoning as described in the method embodiment is implemented.

[0130] A computer-readable storage medium provided by the present invention can implement the steps and effects of the non-standard automation equipment reuse method based on case reasoning in the above method embodiment. To avoid repetition, the present invention will not go into details.

[0131] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0132] In the present invention, by constructing an instance library, a similarity calculation model covering qualitative parameters and quantitative parameters is established, and according to the similarity of the expected characteristic parameters and expected characteristic parameter values ​​of the required non-standard modules in the instance library, combined with the objective weights of each characteristic parameter of the non-standard module, the similarity calculation model is used to calculate the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module, and the most similar non-standard module is automatically, quickly and accurately extracted according to the comprehensive similarity, which is more efficient and has a wider retrieval range. Then, the relative importance values ​​between each component in the non-standard module are used to establish an explanatory structure model, the transformation order of each component is determined, and the similar non-standard modules are modified according to the transformation order to ensure that the expected requirements are met with minimal changes. While ensuring the coverage breadth and modification accuracy, the degree of automation and the reuse design efficiency of non-standard equipment are greatly increased, and the problems of long design cycle, high limitations, poor reproducibility, and easy subjective errors caused by excessive reliance on manual experience to select similar non-standard modules are avoided, thereby reducing the equipment cost of enterprises and increasing the competitiveness of enterprises.

[0133] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0134] There are a few points to note:

[0135] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0136] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0137] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0138] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A non-standard automation equipment reuse method based on case reasoning, characterized in that: include: S1: Obtain characteristic parameters and characteristic parameter values ​​of the non-standard automation equipment; S2: Decomposing the non-standard automation equipment into different non-standard modules according to functions, and storing the non-standard modules in an instance library; S3: Obtain the user's expected characteristic parameters and expected characteristic parameter values ​​for the required non-standard module; S4: Establishing a similarity calculation model between the required non-standard module and each non-standard module in the instance library, wherein the similarity calculation model includes a first function in which both the first parameter and the second parameter are numerical values, a second function in which both the first parameter and the second parameter are numerical intervals, and a third function in which the parameter values ​​of the first parameter and the second parameter are different in form, wherein the first parameter is a characteristic parameter value of the non-standard module, and the second parameter is an expected characteristic parameter value; S5: combining the objective weights of the characteristic parameters of the non-standard modules, and using the similarity calculation model to calculate the comprehensive similarity between each non-standard module in the instance library and the expected non-standard module; S6: using the expected feature parameter and the expected feature parameter value as search conditions, searching the instance library according to the comprehensive similarity, and obtaining a non-standard module with the highest similarity to the required non-standard module; S7: Calculate the relative importance values ​​between the components in the non-standard module with the highest similarity, and establish an explanation structure model based on the relative importance values ​​to determine the transformation order of the components; S8: modifying the non-standard module with the highest similarity according to the transformation order until the requirements of the required non-standard module regarding the expected characteristic parameters and the expected characteristic parameter values ​​are met; S9: Reusing the modified non-standard module with the highest similarity as the required non-standard module.

2. The method for reusing non-standard automation equipment based on case reasoning according to claim 1 is characterized in that: The S4 specifically includes: S401: performing normalization processing including quantization on the characteristic parameter values ​​of the non-standard module and the expected characteristic parameters; S402: Establishing the similarity calculation model according to the normalized feature parameters and the expected feature parameters: S1(M′,M)=1-|m′-m| Among them, S1(M′,M), S2(M′,M) and S3(M′,M) represent the first function, the second function and the third function respectively, M′ represents the expected characteristic parameter, M represents the characteristic parameter of the non-standard module, m′ represents the expected characteristic parameter value, m represents the characteristic parameter value, [a,b] represents the expected characteristic parameter interval range, [c,d] represents the characteristic parameter interval range, and h represents the optimal value position of the characteristic parameter.

3. The method for reusing non-standard automation equipment based on case reasoning according to claim 2 is characterized in that: The S401 is specifically as follows: In the case where the parameter value is a qualitative feature, the parameter value is quantized by a fuzzy mathematical processing algorithm, otherwise, the parameter value is quantized by a normalization algorithm, wherein the parameter value includes the characteristic parameter value and the expected characteristic parameter value.

4. The method for reusing non-standard automation equipment based on case reasoning according to claim 3 is characterized in that: The fuzzy mathematical processing algorithms include Mamdani algorithm, Sugeno method and fuzzy C-means clustering algorithm, and the normalization algorithms include minimum-maximum normalization algorithm, Z-score normalization algorithm and logarithmic conversion algorithm.

5. The method for reusing non-standard automation equipment based on case reasoning according to claim 1, characterized in that: The S5 specifically includes: S501: Calculate the similarity between each characteristic parameter of the non-standard module in the example library and the expected characteristic parameter using the similarity calculation model, and construct a difference matrix describing the similarity: Among them, C represents the difference matrix, S ij Represents the similarity between the jth characteristic parameter of the i-th non-standard module in the instance library and the corresponding expected characteristic parameter value, i = 1, 2, ..., m, j = 1, 2, ..., n, where m represents the total number of non-standard modules in the instance library, and n represents the total number of characteristic parameters; S502: Calculate the objective weight of each characteristic parameter of the non-standard module in the example library according to the difference matrix: Among them, w i represents the objective weight of the i-th feature parameter, σ j Represents the similarity standard deviation of the jth feature parameter; S503: Calculate the comprehensive similarity according to the feature parameter similarity and the similarity objective weight: Among them, S s Indicates the comprehensive similarity.

6. The non-standard automation equipment reuse method based on case reasoning according to claim 1 is characterized in that: The S7 specifically includes: S701: Construct an adjacency matrix A describing the relative importance values: Among them, a pq Represents the relative importance value. If the pth component is more important than the qth component, the relative importance value is assigned to 1, otherwise, it is assigned to 0. p,q=1,2,…,k, where k represents the total number of components in the non-standard module with the highest similarity. S702: Calculate the overall evaluation matrix describing each component according to the adjacency matrix: H=A+E Among them, H represents the overall evaluation matrix, A represents the adjacency matrix, and E represents the identity matrix; S703: Based on Boolean operation rules, calculate the reachable matrix of the overall evaluation matrix: H k =H k-1 Among them, H k represents the reachability matrix; S704: Calculate the reachable set R according to the reachable matrix i , look-ahead set A i and intersection X i , R p ={q|H pq =1} A p ={q|H qp =1} X p =R p ∩A p Among them, R p represents the reachable set affected by the pth component, H pq represents the element value of the element in the pth row and qth column of the overall evaluation matrix, H qp A represents the value of the element in the qth row and pth column of the overall evaluation matrix. p represents the predecessor set that affects the pth component, X p represents the intersection of the reachable set and the predecessor set about component p; S705: extracting the same components of the reachable set and the intersection, and retaining the same components of the reachable set and the intersection to the highest level; When R i =X i When , the determined highest level part elements are removed from the matrix, and the reachable sets, predecessor sets and intersection sets of the remaining part elements are recalculated; S706: remove the components belonging to the highest layer, reduce the number of layers of the highest layer by 1, and obtain an updated highest layer; S707: Repeat S701-S706 until all components are removed, and obtain an interpretation structure model including all highest layers, wherein the highest layers in the interpretation structure model decrease in number of layers; S708: Determine the transformation order of each component according to the number of layers of each highest layer, wherein the priority of the component transformation order in the interpretation structure model is consistent with the number of layers of the highest layer.

7. The method for reusing non-standard automation equipment based on case reasoning according to claim 1 is characterized in that: The S8 is specifically: The non-standard module with the highest similarity is modified through a variety of modification methods until the requirements of the required non-standard module regarding the expected feature parameters and the expected feature parameter values ​​are met, wherein the modification methods include replacement modification, addition and deletion modification, decomposition modification and expansion and contraction modification.

8. The method for reusing non-standard automation equipment based on case reasoning according to claim 1, characterized in that: Also includes: The modified non-standard module is stored in the instance library.

9. A non-standard automation equipment reuse system based on case reasoning, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the non-standard automation equipment reuse method based on case reasoning as claimed in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the non-standard automation equipment reuse method based on case reasoning as described in any one of claims 1 to 8 is implemented.

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